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OCTOBER 2011

HPIMPACT SPECIALREPORT TECHNOLOGY

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Automation systemsimprove process control and increase profits

Refinery safetyin Europe

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OCTOBER 2011 • VOL. 90 NO. 10

SPECIAL REPORT: PROCESS CONTROL AND INFORMATION SYSTEMS

39 Intelligent severity optimization project pays off in two monthsMajor olefin producer uses new process control to fine-tune energy consumptionH. Wang, Z. Wang, W. Du, D. Wang, F. Qian and Z. Tang

45 Fine-tune diesel hydrotreating operations This refinery used a simulator to train operators effectivelyA. T. AlDaiel, M. M. AlMulla, M. A. AlNajrani, K. Chaitanya, A. Deshpande and V. Harismiadis

51 Process gas chromatography: Avoid the iceberg of hidden expensesTotal cost of ownership can quickly add up for field analytical equipmentM. Gaura, Emerson Process Management, Houston, Texas

57 Find benefits in automating boiler systemsDynamic models unravel potential problems in high-pressure steam production and consumptionA. Bourji, D. Ballow and M. Choroszy

65 Consider model-based inferential properties for reformersA European refiner opts to apply embedded multivariable predictive controllers as part of an advanced process control systemS. Birdi, A. Autuori, S. Lodolo and C. Beautyman

Cover Operators in a state-of-the-art control room use Emerson’s DeltaV operator interface to monitor and control a refinery process. The interface uses a standard PC mouse and keyboard and Microsoft Windows display graphics. The system architecture is designed to be scalable, economical for small unit operations and capable of handling refinery wide applications.

HPIMPACT17 European refiners’ safety performance in 2010

19 Libya at a glance

21 EU Parliament seeks stricter greenhouse gas rules

COLUMNS9 HPIN RELIABILITY

Suction specific speed choices have consequences

11 HPIN EUROPEEurope warily prepares to enter a newly globalized age of biofuels

15 HPINTEGRATION STRATEGIESAutomation-related lessons learned from March 11 disasters in Japan

90 HPIN CONTROLReshaping process control: A corporate prerogative

DEPARTMENTS 7 HPIN BRIEF • 23 HPIN INNOVATIONS • 29 HPIN CONSTRUCTION 36 HPI CONSTRUCTION BOXSCORE UPDATE 86 HPI MARKETPLACE • 89 ADVERTISER INDEX

REFINING DEVELOPMENTS

71 Refinery configurations: Designs for heavy oilConceptualization and economic evaluations considered all possible scenarios to process clean gasoline and diesel from domestic feedstock S. Kumar, S. M. Nanoti, Y. K. Sharma and M. O. Garg

INSTRUMENTATION AND MEASUREMENT

77 Improve material balance by using proper flowmeter corrections Here are guidelines to increase accuracy for flow measurementsS. Peramanu and J. C. Wah

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HYDROCARBON PROCESSING OCTOBER 2011 I 7

The real-time process optimization and training (RPO) market is expect-ed to grow at a compound annual growth rate (CAGR) of over 9% a year over the next five years, according to a new study from ARC Advisory Group. The market was slightly more than $1 billion in 2008, but dropped during the global recession to slightly over $950 million in 2010, the study says. The market is expected to reach more than $1.5 billion in 2015. The RPO market has rebounded from the lows of 2009, and is expected to return to pre-2008 growth as the global process industries’ need for safer, more effi-cient operations continues.

“The global economy has still not returned to its pre-2008 optimism,” said Dick Hill, a co-author of the study. “The economic slowdown adversely affected growth but the market will rebound as many of the issues fac-ing manufacturers, like reducing costs, still require solutions such as those offered by RPO suppliers.”

The RPO market consists of three unique types of applications: advanced process control, online opti-mization, and training simulation and control validation software. Advanced process control includes model-based software to direct and control process operations. Online optimization con-tinually monitors the state of the pro-cess and through a reference model predicts an optimum operation path. Meanwhile, training simulation and control system validation are real-time dynamic simulators designed to train process operators and verify control system functionality.

The recession that began in 2008 affected all corners of the globe and is still the single biggest influencing factor on growth of the RPO market. Much of the industrial world was forced to curtail capital project expen-ditures and RPO investments in the short-term.

However, cautious optimism is now returning. Global growth in the industry is being driven by developing regions of the world. HP

Sunoco plans to exit the refining business and has begun a process to sell its refineries located in Philadelphia and Marcus Hook, Pennsylvania. Sunoco also announced that it is conducting a company-wide comprehensive strategic review. Suisse Securities (USA) LLC has been retained to assist in the review process. Sunoco will pur-sue all options to sell its refineries, but if a suitable transaction cannot be implemented, the company intends to idle the main processing units at the facilities in July 2012.

“We have made progress in increasing the efficiency of our refineries over the last several years, but, given the unacceptable financial performance of these assets, it is clear that it is in the best interests of shareholders to exit this business and focus on our profitable retail and logistics businesses which have higher returns, growth potential and provide steady, ratable cash flow,” said Lynn L. Elsenhans, Sunoco’s chairman.

Together with the separation of SunCoke Energy and the sale of the chemicals busi-ness, Sunoco’s decision to exit refining marks a fundamental shift away from manufac-turing that will reposition the company.

BASF has formed a new startup business with Alberta, Canada-based manufacturing technology firm Quantiam Technologies, seeking to commercialize advanced catalytic surface coatings for steam-cracker furnace tubes. The business is named BASF Qtech. Quantiam had previously developed the coatings for use in the global petrochemical industry. Manufacturing, R&D and technical services support for the new business entity will be provided by the Quantiam team in Edmonton, while marketing and sales support will be led by BASF’s catalysts division, headquartered in Iselin, New Jersey. The catalytic surface coatings developed by Quantiam are applied on the internal surfaces of steam-cracker furnace tubes and coils, enabling the catalytically-assisted manufacture of olefins. The coatings are designed to improve operational profit-ability of petrochemical furnaces by reducing carbon formation, increasing online pro-duction time and cutting maintenance times, energy expenditures and CO2 emissions.

Shell has agreed to sell its interests in natural gas transport infrastructure joint venture Gassled to Infragas Norge for about $730 million, based on current exchange rates. Gassled is Norway’s integrated gas transportation system and processing facility which transports most of the gas production on the Norwegian Continental Shelf to consumers on the European continent and in the United Kingdom. The agreement with Infragas Norge AS relates to Shell’s 5.0% interest in Gassled JV and associated interests of 3.3% in the Dunkerque terminal and 2.5% in the Zeepipe terminal. Gassled is a joint venture established in 2003. It provides trans-portation services on an open access basis to producers on the Norwegian Continental Shelf. The parties’ intention is to close in the fourth quarter of 2011.

Murphy Oil has agreed to sell its 125,000-bpd refinery and related assets in Meraux, Louisiana, to Valero for $325 million in cash plus the value of its hydrocarbon inventory, putting the overall sale value near $625 million. The hydrocar-bon inventory will be valued based on market prices at closing. Currently, that invento-ry is valued at around $300 million. The sale is subject to customary regulatory approv-als and conditions and is expected to close in the fourth quarter of 2011. Following the sale, Murphy plans to focus on completing the sale of its assets in the UK.

BP has completed its acquisition of a 30% stake in 21 oil and gas production sharing contracts (PSCs) that Reliance Industries operates in India. This significant step will commence the planned alliance which will operate across the gas value chain in India, from exploration and production to distribution and marketing, the companies said. This should accelerate the creation of infrastructure for receiving, transporting and marketing natural gas in India. BP will pay Reliance an aggregate consideration of $7.2 billion. HP

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HPIN RELIABILITY

[email protected]


A refinery engineer was in a quandary over requests from a project group. High volume/pressure/temperature pumps in hydrocarbon service were involved and the refinery’s standards had, in 2007, been changed so as to avoid purchasing pumps that might not operate well in the lower flow region. The engi-neer asked if it was really practical to insist on accepting only pumps with an Nsss (meaning “suction specific speed”) below 9,000, although decades ago his company had allowed pumps with Nsss values up to 12,000. But first, a greatly simplified introduction to Nsss and its importance.

Note: The pump suction specific speed (Nss or Nsss ) differs from the pump specific speed parameter, Ns. Suction specific speed is calculated by the straightforward mathematical expression:

(1)

N ss =(r/min)[(gal/min)/eye]

12

(NPSHr )34

In Eq. 1, both the flowrate and net positive suction head required (NPSHr ) pertain to conditions observed at 100% of design flow—at the best efficiency point (BEP)—on the maxi-mum available impeller diameter for that particular pump.

The higher the design suction specific speed or Nsss, the closer the point for troublesome internal flow recirculation to BEP. Similarly, the closer the internal recirculation capac-ity is to BEP, the higher the hydraulic efficiency. Pump sys-tem designers are tempted to aim for highest possible effi-ciency—thus, high suction specific speed. However, such designs might result in systems with restricted pump operat-ing range. If operated inside the restricted (high recirculation) range, then disappointing reliability and frequent failures will be experienced.

Although more precise calculations are available, trend curves for probable NPSHr for minimum recirculation and zero cavitation-erosion in water (Fig. 1) are sufficiently accurate to warrant our attention.1 The NPSHr needed for zero damage to impellers and other pump components may be many times that published in the manufacturer’s literature. The manufacturer’s NPSHr plot (lowermost curve in Fig. 1) is based on observing a 3% drop in discharge head or pressure; at Q=100%, we note NPSHr = 100% of the manufacturer’s claims. Unfortunately, whenever this 3% fluctuation occurs, some damage may already be in progress. Assume the true NPSHr is as shown in Fig. 1 and aim to provide a net positive suction head available (NPSHa) in excess of this true NPSHr.1

Irving Taylor compiled his general observations and alerted pump specialists to this fact.1 Taylor cautioned against con-sidering his curves as totally accurate and mentioned that the demarcation line between low- and high-suction specific speeds was somewhere between 8,000 and 12,000. Many data points were taken after 1980 and point to 8,500 or 9,000 as

numbers for concern. If pumps with Nss numbers higher than 9,000 are being operated at flows much higher or lower than the BEP, then their life expectancy or repair-free operating time will be reduced.

In the decades after Taylor’s presentation, controlled testing was done in many industrialized countries. The various findings have been reduced to relatively accurate calculations that were later published by the Hydraulic Institute.2 Relevant summaries can also be found in Ref. 3. Calculations based on Refs. 2 and 3 determine minimum allowable flow as a percentage of BEP.

Note: Again, recirculation differs from cavitation—a term that describes vapor bubbles that collapse. Cavitation damage is often caused by low NPSHa. Such cavitation-related damage starts on the low-pressure side and proceeds to the high-pressure side. An impeller requires a certain NPSH; this NPSHr is simply the pressure needed at the impeller inlet (or eye) for relatively vapor-free flow.

Suction specific speed choices have consequences

00 20 40 60

Q, %

Trend of probable NPSHr for zero cavitation-erosionVarious pumps (high head) = 650 ft (~200 m), first stage

High head high suct. specific speedHigh head low SSSLow-moderate head high SSSLow-moderate head low SSSNPSHr for 3% head drop

H, %

NPSH

r, %

80 100 120

25

50

75

100

0

100

200

300

400

H-Q

Pump manufacturers usually plot only the NPSHr trend associated with the lower most curve. At that time, a head drop or pressure fluctuation of 3% exists and cavitation damage is often experienced.

FIG. 1

HPIn Reliability continued on page 88

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TIM LLOYD WRIGHT, EUROPEAN EDITOR

HPIN EUROPE

[email protected]


Biofuels are growing up in Europe. From an exotic outlet for European Union (EU) agricultural product in the 1990s, to a boutique fuel for the green consumer in the 2000s, now today’s industry has filled out and bulked up. In 2011, biofuels are a globally traded business; this industry has transformed some of its pioneering suppliers into household names. But many more European startups lie bloodied by the wayside or limping along the sidewalk.

Manufacturers, suppliers, consumers, some non-govern-mental organizations (NGOs) and even pure-play fossil fuels suppliers have had their share of growing pains during the rapid rise of the market. Many biodiesel and bioethanol manufactur-ers in Europe have exited the industry or have gone bankrupt.

Growing pains. Now, the European industry stands at a milestone, warily eyeing billion-dollar investments in equatorial regions, while nursing the bruises of a less competitive domestic industry. “The industry suffers from over-investment,” says Andrew Owens, chairman and co-founder of the UK’s Green-ergy, a prime example of a biofuels distributor that has suc-ceeded in the “green” industry. “During the mid-2000s, credit was easy and too much was built,” he says. “The industry still has a hangover from that time.”

Dieter Bockey, spokesperson for the Union for the Promo-tion of Oilseeds and Protein Plants in Germany, identifies credit conditions as part of the cause of the rapid growth of the indus-try. “In 2006, everyone could finance a plant,” he says. “But now in Germany, several hundred thousand tons of production have been idle for several years now.”

Legislation. Another pillar of the industry’s growth is that phenomenon without which Greenergy might never have made its astonishing journey from being the “new kid” in the 1990s, to its position today as the UK’s third largest private company, the tax incentive. When the consulting group Arthur D. Little reported, in the early stages of the development of the 1997 Fuel Quality Directive, that when Sweden had used taxation policy to steer its refiners into profitable, clean fuels production, the detaxation of greener products was taken up with relish across large parts of Europe. Article 16 of the European Energy Taxation Directive allowed governments to exempt fuels, and the stimulus this provided led many to conclude that there were sound reasons to turn more of Europe’s agricultural product into biofuels. That phase, which effectively afforded manufacturers a 10-year trade wind, is now drawing to a close. “The rules are clear, and the general policy is to move from promoting biofuels with detaxa-tion to mandating their use with quotas,” says Bockey.

Two pieces of European rule-making are responsible for this. The Renewable Energy Directive is a major policy initiative

currently being transposed into national laws in the member states. In the UK, where it should enter fully into law by year end, it calls for 15% of UK energy all energy, but specifically 10% of transport fuels, to be supplied from renewable sources by 2020. Compared to initial ambitions for 20% biofuels in 2020, this goal has effectively been halved, and some of the more ambitious biofuels champions have seen slower growth as they realign their trajectory to a 10% share of the renewable energy in the transport sector. The target includes second-generation biofuels that can be double counted in the quota sys-tem because they are derived from used oils, waste or residues. Conventional biofuel producers perceive this next generation of sources as further reducing the demand for rapeseed and soy oils and sugar-derived ethanol.

Alongside the renewables directive are the greenhouse gas (GHG) provisions of the Fuel Quality Directive. These require fuel suppliers to reduce the lifecycle GHG lifecycle emissions of products that they supply by 6% by 2020. In July 2011, the European biofuels information initiative, EurObserv’ER, reported that biofuels sales grew by 1.7 mil-lion tons/yr (1.7 MMtpy) to 13.9 MMtpy between 2009 and 2010. Of this total, 10.7 MMtpy is biodiesel and 2.9 MMtpy is bioethanol.

Biodiesel. The European standard for biodiesel, EN590, lim-its the blending of biodiesel to 7 vol%. Against a total market for 209 MMtpy within the EU, that suggests a potential market of 14 MMtpy. The Union zur Förderung von Oel- und Pro-teinpflanzen e.V. (UFOP) reports the stark fact that European production capacity, at 22.4 MMtpy, exceeds that by almost 10 MMtpy. The association is calling for B100 or B30 blends to be made available for sale as a way to boost the European industry, something that would also help fuel suppliers to hit their own quotas.

Ethanol. In the ethanol market, a failure to meet even the existing quotas, means that, this year, oil companies in Europe will likely pay hundreds of millions of Euros in fines for failing to blend sufficient biofuels into their products.

As I reported in May, a bungled introduction of E10 gaso-line into the large German gasoline market means that the large players will be paying the German government some €620 for every 1,000 l when they are below their quota commitment. Likewise, there are growing pains for German consumers due to a lack of persuasive information on the suitability of high-ethanol blends. Result: Many German drivers have persistently avoided the blended fuels. But for German oil companies to meet their quotas, they really need to attain 80%–90% of the total marketshare as E10.

Europe warily prepares to enter a newly globalized age of biofuels

HPIN EUROPE

12

Other problems. Aside from the bottom line hit that the German companies face this year, there have also been reputa-tional issues to contend with. Consider Neste, the export-ori-ented Finnish refinery. This refiner focused on reacting nimbly to the need for on-spec bioblending components, as it once did to US West Coast reformulated gasoline demand. But its attempts to control its supply chain through involvement with Indonesian palm oil producers have left it exposed to constant criticism from environmentalists.

Despite its technical and commercial leadership in hydroge-nated vegetable oil production, and its rapidly growing boiler-plate capacity in Europe and Singapore, it is dogged by claims that its oils resources are destructive to rainforest.

Greenpeace members wearing orangutan suits who leafleted on the steps of the Rotterdam World Biofuels Markets confer-ence this year may be tolerable for a fuels manufacturer at an industry conference. But how easy it is for some of Europe’s major consumer brands, including the airlines involved in the fledgling biojet fuel market, to deal with environmentalist criticism of what they see as their green initiatives remains to be seen.

Article 17 of the Renewable Energy Directive requires the whole supply chain of compliant fuels to be certified. Unsus-tainable biofuels will simply not generate the tradable certifi-cates that must be surrendered to avoid fines under the scheme. Fuels that do not offer a 35% GHG gas saving compared to gasoline or diesel will not count in the early phase of the legis-lation. In 2017, this threshold will rise to 50% and, in 2018, to 60% for new plants that come onstream after Jan. 1, 2017.

Faced with the difficulty of sourcing biofuels that meet sustainability requirements, it’s understandable that large oil companies should seek relationships with Brazilian companies. Sugarcane ethanol from Brazil has lifecycle GHG emissions that are hard to beat in an energy-dense liquid.

This year Shell announced a joint venture with Cosan, the world’s largest manufacturer, which the companies value at some $12 billion. Cosan represents the best entry to sustainable biofu-els in the market—the best entry of scale,” Mark Williams, Shell’s director of downstream operations, told the Financial Times, adding, “we will take the lowest-carbon, least-impact form of ethanol and leverage that into a worldwide opportunity.”

It’s understandably difficult for some European manufactur-ers to accept that their markets, stimulated by the European tax-payer, will be supplied from outside of the EU. Get over it, says Owens. “Trade bodies are looking to be protectionist and close the door and I think that’s absolutely the wrong way to. European producers need simply to ask, ‘who are my custom-ers, what’s my customers, and how do I meet my customers’ needs,” he says.

Globalization. But as Shell spends its billions in the tropics and Owens feeds UK cars on US cooking oil and Brazilian etha-nol, the apparent winners in the European biofuels market will need to contend with a political risk that could yet upset their plans. Globalization is not a philosophy that has emerged com-pletely unscathed from the restructuring of European economies post-2008 crash. “German and European politicians are no lon-ger accessible like they once were to the biofuels industry. They don’t reply to letters,” says Bockey. “Their answer to requests to support the European biofuels market with tax exemptions is to ask why they should spend European money to line the pockets of manufacturers in the US or South America.”

European politicians don’t necessarily have to put up further tariffs to deter biofuel imports. There are less dense biofuels, uneconomic to transport across the planet, than that are pro-duced on small farms and at local council facilities from Sweden in the North to Naples in the south.

Other options. Biogas—methane derived from biomass, human, animal and household waste—has lifecycle GHG cre-dentials that Brazilian biofuels can only dream of. Stimulating the market leads to investments in the local neighborhood, not the Atlantic Basin. Vehicles are becoming more efficient, and the product is interchangeable with natural gas. Already, European politicians have seen to it that there are some 7,000 sites in Germany manufacturing biomethane, and the Swedish market grew 40% last year. “It’s becoming more important for German agriculture than liquid biofuels,” says Bockey. That may overstate the importance of a market that is largely restricted to a strip of Europe from Sweden, through Germany and the Alps, to Italy in the south.

But manufacturers will be wise to remember that policy mak-ers created the biofuels markets, and their influence, alongside the power of the free market, will continue to shape it. HP

The author is HP’s European Editor and is also a specialist in European distillate markets. He has been active as a reporter and conference chair in the European downstream industry since 1997, before which he was a feature writer and reporter for the UK broadsheet press and BBC radio. Mr. Wright lives in Sweden and is the founder of a local climate and sustainability initiative.


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HPINTEGRATION STRATEGIES


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SHIN KAI, CONTRIBUTING EDITOR

On July 22, four months after the unprecedented earthquake and tsunami struck the Pacific Coast side of eastern Japan, Japanese engineers from various process and other industries gathered in Tokyo to participate in a series of panel discussions entitled, “What automation should learn from the 3/11 disas-ters.” The panels, held as a session at ARC Advisory Group’s 2011 Japan Forum in Tokyo, were jointly organized by ARC and the Society of Instrument and Control Engineers (SICE). The goal of the participating engineers was to review their own notions of safety and control systems in an objective manner. Not surprisingly, much of the discussion focused on the inad-equacies of the process control and protective systems installed at the Fukushima nuclear power plant. However, in most cases, the same lessons learned can also be applied to critical opera-tions in hydrocarbon processing industry (HPI) plants.

‘Reinvent ourselves from scratch.’ Among the 200 attendees at the ARC Tokyo Forum were end-user engineers, integrators and contractors, automation suppliers, consultants and researchers. Many of the plant-level engineers would not have been able to attend if ARC had held the event one month earlier.

Akira Nagashima, co-chairman of the SICE 50th anniversary project steering committee and moderator of the panel, opened the discussion by summarizing its purpose: “I think there is a serious task we engineers must address before we think about how to rebuild Japan. Yes, the triggering event of this crisis was a 9.0-scale super earthquake; but we must admit that we engineers had underestimated the power of Mother Nature, and thereby allowed a runaway chain reaction of accidents. The vulnerability of the artifacts and technologies we ourselves introduced made this crisis worse ... All engineers, whether involved in addressing this crisis or not, must stop and rethink what we have taken for granted. I believe this is a rare oppor-tunity to review our own mindset and behaviors and reinvent ourselves from scratch.”

Protective control meets human beings. The first panelist, Toshiaki Itoh, formerly of Mitsubishi Chemical and current SICE Fellow, took the approach of discussing the entire plant system operations. He analyzed the causes of the troubles in the Fukushima nuclear power plant from the viewpoint of instrument control engineering. Then, he pointed to irregu-larities of the accident by showing that fundamental protective control could not be enabled by ordinary steps or procedures. Because the tsunami washed out auxiliary power supply units and cooling systems abruptly, the risk level had not increased sequentially in Fukushima. “By its nature, current protective control is not enough to cope with such unpredictable events,” he said.

From homogeneous to heterogeneous. Presenting the control system suppliers’ viewpoint, the second panelist, Chiaki Itoh, Yokogawa Electric, started his presentation with the prem-ise that “science, or technology, is not almighty.” He explained the evolution of control systems since the introduction of digital controllers in the late 1970s. The need to allocate computing resources flexibly and avoid the risk of system downtime spurred the growth of system decentralization in the early 1980s. At the same time, the need for nonstop control system operations led to the profusion of redundant systems through the ’80s. System suppliers have continued to develop redundancy architectures, from duplex systems with redundant communications to the highly advanced controller architectures in which redundant CPU modules monitor each other continuously.

In addition, the industry nurtured a hierarchical safety sys-tem that stops plant operation in an orderly manner to minimize damage in an emergency. The safety system operates indepen-dent of the control system, which is designed to operate a plant in a stable manner.

But, according to Mr. Itoh, the limitations of both current redundant architectures and safety systems have been revealed. “We all saw the limitations of redundant architecture in an open system, in the troubles at the Fukushima nuclear power plant. We also faced the limitations of safety systems, because stopping the system is complicated and not safe, as was shown in the case of the nuclear plant.”

Itoh turned his remarks toward the common engineering of redundancy. “We must note that most redundancy technologies, including the ones used in heavy process industries, are more or less the same in nature.” He continued, “A typical plant control system is installed in an enclosure that has redundant power sup-plies sitting side by side. The prevalence of this design approach indicates that the safety mechanisms we have in mind will be effective only to the extent that they prevent accidents caused by the potential failure of the engineered product themselves.” He suggested that, “We now need to pursue a structural switch in redundant architecture, from homogeneous to heterogeneous, and need to add diversified technologies such as wireless com-munication systems and various kinds of sensors to measure open systems.”

These panel discussions were the first of their kind following the March 11 earthquake and tsunami. Attendees agreed that, while natural disasters cannot be avoided, it would be a shame if we can’t learn and gain important insights from them. HP

The author, Director of Research at ARC Advisory Group Japan, has over 25 years of experience writing about and covering the industry for leading publications in Japan including Control Engineering, Asia Electronics Industry and others. He was based in New York during most of 1990s covering the electronics industry for Dempa Publications. Mr. Kai has BA and MA degrees from Sophia University, Tokyo.

Automation-related lessons learned from March 11 disasters in Japan

The Emerson logo is a trademark and a service mark of Emerson Electric Co. © 2011 Emerson Electric Co. D351992X012 MX11 (H:)

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HPIMPACTBILLY THINNES, TECHNICAL EDITOR

[email protected]


European refiners’ safety performance in 2010

European oil company association CONCAWE started compiling statistical data on safety performance for the Euro-pean downstream oil industry 17 years ago. The group recently released its safety report for 2010, featuring data from 34 CONCAWE member companies. These companies when combined account for approximately 93% of the refining capac-ity of the EU-27 countries, plus Norway, Switzerland and Croatia.

The results are reported mainly in the form of key performance indicators that have been adopted by the majority of oil companies operating in Europe, as well as by other industry sectors.

Accident frequencies in the European downstream oil industry are generally at low levels and the 2010 performance con-tinues this trend. Standing at 1.9, the lost work incident frequency (LWIF) indicator for 2010 is less than 2.0, as has been the case since 2007 (this figure is calculated from the number of lost workday injuries (LWIs) divided by the number of hours worked expressed in millions).

For the second consecutive year, CON-CAWE members were asked to provide process safety performance indicator (PSPI) data which describe the number of process safety events (PSEs) expressed as unintended loss of primary contain-ment (LOPC). Twenty-four companies provided data in 2010 which represented a significant increase from the 18 com-panies that responded in 2009. From these responses, a process safety event rate (PSER) indicator of 2.3 for all PSEs was recorded. This is a notable reduction versus the 4.1 recorded in 2009, caused mainly by a significant increase in the working hours of those companies report-ing PSE data.

Focus on fatalities. A total of 14 fatali-ties were reported for 2010 that were the consequence of 14 independent incidents. Following a steady downward trend dur-ing the 1990s, fatality numbers started to increase in 2000. Fatalities reached an alarming peak of 22 in 2003 before sub-

stantially trending downward from 2004–2006. Fatalities were recorded at 11 in 2008 and in 2009.

This year manufacturing contractors appeared to be the most vulnerable work group, experiencing 13 fatalities. Clearly, this is of concern and all companies should ensure that the contractor work-force is fully integrated into the compa-nies’ safety monitoring systems. The fatal accident rate (FAR) of 2.68 continues to be at a level similar to that observed in the late 1990s.

The report notes that road traffic acci-dents clearly decreased compared to earlier years with the rate reaching a plateau from 1999. There was a small reduction in the road accident rate (RAR) in 2010. These accidents essentially occur in the market-ing activity where the bulk of the driving takes place.

One point of particular interest is the “safety triangle,” which is the relationship between the total number of recordable incidents or the number of LWIs and the number of fatalities. This diagram is illus-trative but not to scale, as shown in Fig. 1. Also shown is a graph of LWI and all recordable incidents (AI) per fatality.

Fig. 1 illustrates the declining num-ber of fatalities until 1999 whereas the total number of incidents remained fairly constant. The period from 2000–2003 saw a steady increase in fatalities while both AI and LWI were still on a decreas-ing trend, resulting in a decrease of the ratios. The lower number of fatalities from 2004–2009 reversed the trend resulting in relatively steady ratios with a small positive spike in 2006 when there were only seven

050

100150200250300350400450500

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

LWI per fatalityAI per fatality

Fatalities

LWI

AI

979

2609

14

2010

The European refining industry’s safety triangle, which is the relationship between the total number of recordable incidents or the number of lost workday injuries (LWIs) and the number of fatalities. Also shown is a graph of LWI and all recordable incidents (AI) per fatality from 1992–2010.

FIG. 1

0

1

2

3

4Road accident

Fall

Construction/maintenance

Burn/electrical

Confinedspace

Other

ManufacturingMarketing

Causes of fatalities in 2010.FIG. 2

Road accidents

Maintenance/construction

Burn/fire/explosion

Others

60%

40%

20%

0%

Third partyaction

1998–20102006–2010

Causes of fatalities from 2006–2010 and from 1998–2010.

FIG. 3

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HPIMPACT

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fatalities. Despite an increase in fatalities in 2010 the ratios were only slightly reduced. These observations led to the conclusion that the overall improvement in the level of lower severity safety indicators is not neces-sarily leading to the prevention of the more severe incidents that result in fatalities.

Fig. 2 details the causes of the 14 fatali-ties recorded in 2010 and Fig. 3 shows the percentage of the main causes over the last five years and for all years since this infor-mation was first collected in 1998.

The FAR (2.68 per 100 million hours worked) and the total number of fatalities (14) in 2010 were somewhat higher than in 2009, which is of concern. Thirteen of the 14 fatalities were associated with con-tractors: five (~36%) were caused by burn or electrical incidents, three (~21%) were a result of confined space entry incidents, two (14%) were caused by road accidents, two (~14%) resulted from construction and maintenance, one (~7%) resulted from a fall from height, and one (~7%) was classified as “other.”

For the last five-year period, construc-tion/maintenance/operations activities and road accidents remain the principal causes of fatalities.

Libya at a glance Wood Mackenzie recently undertook

an analysis of Libya, attempting to discern how long it could take for a recovery of oil and gas production. One of the key issues in this respect is how quickly the National Transitional Council (NTC) can stabilize the security situation across the country. Regardless, it is too early to expect a material recovery in Libya’s oil and gas production.

“Once a resolution is reached, we believe it will take around 36 months for oil production to recover to the pre-conflict level of 1.6 million bpd,” said Ross Cassidy, a research analyst for Wood Mackenzie. “It may be possible, however, for up to 600,000 bpd to be restored within three months assuming a swift end to hostilities, and an early focus by the NTC and inter-national community on stability and infra-structure repair.”

Wood Mackenzie’s global gas research shows that gas production could take less time to recover. Eight billion cubic meters of gas per year is contracted from Libya to Italy, with Eni as the primary off-taker sell-ing to customers in Italy. The Greenstream gas pipeline routes gas from Eni-operated fields in Libya to Italy.

“The Italian market is presently over-supplied with gas and Eni has had to delay off-take obligations from other suppliers because insufficient market is available,” said Massimo Di-Odoardo, a European gas analyst for Wood Mackenzie. “During the Greenstream outage, Eni increased off-take of Russian pipe gas supplies therefore, resumption of Greenstream will add gas to an already oversupplied Italian market with implications for downside price risk

and reduced flows of pipe gas from other suppliers, notably Russia. It could take as little as three months to restart Green-stream supply and reach pre-crisis produc-tion levels, however, the time to resume supply will depend on local security and the state of infrastructure.”

Wood Mackenzie estimates that it will take around 36 months for Libya to recover its full production capacity, from when-ever the current crisis reaches a resolution.

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This depends on the scale of damage to oil infrastructure being limited, swift removal of international sanctions and the timely return of international oil companies and foreign workers. The Libya state-owned NOC and the international industry will have to work in partnership to repair facili-ties, restart production and ramp-up to pre-crisis rates. Production recovery is likely to vary by basin. It will take longer in the mature and complex Sirte Basin, in eastern Libya, which is the foundation of Libyan production, than in the more modern and less complex fields of the Murzuk and Pela-gian Shelf basins, of western Libya.

Substantial oil volumes could be back in the market by late 2012, if a resolution is achieved by the end of 2011. But the recovery period will extend if production remains shut-in for longer, as infrastructure continues to deteriorate. There is unlikely to be any increase in production or restart of exports, while Libya’s oil infrastructure is open to sabotage by either side.

In the longer-term, the production outlook will be largely dependent on the nature of the outcome to the conflict and its political fallout. Libya has the poten-tial to produce up to 3 million bpd of oil and become a major gas exporter through partnering with the international industry, which will bring finance, skills and tech-nology to existing fields. But, for now, this brighter future remains on hold until mili-tary operations are concluded.

EU Parliament seeks stricter greenhouse gas rules

The European Parliament is calling for fast action to reduce non-CO2 cli-mate forcers including black carbon soot, hydrofluorocarbons (HFCs), methane and ground-level ozone. The Parliament’s call for action came in a resolution passed this week by an overwhelming majority (578 to 51 with 22 abstentions).

The resolution calls for a comprehensive climate policy and “stresses that in addition to considering CO2 emission reductions, it should place emphasis on strategies that can produce the fastest climate response,” spe-cifically strategies to cut black carbon soot, HFCs, methane and ground-level ozone.

Because these climate forcers are short-lived, reducing them produces a fast cli-mate response, the Parliament said.

This is in contrast to long-lived CO2, where a significant portion remains in the

atmosphere for thousands of years. Even cutting CO2 emissions to zero today will not produce cooling for a thousand years, officials said.

“Cutting just two of the short-lived climate forcers (black carbon soot and ground-level ozone) can cut the rate of global warming in half and by two-thirds in the Arctic for the next 30 to 60 years, assuming we also make progress on CO2,” said Durwood Zaelke, president of the

Institute for Governance and Sustainable Development.

Emissions of black carbon and other short-lived climate forcers can be reduced quickly using existing technologies and existing laws, according to a recent assess-ment by the UN Environment Program and World Meteorological Organization. The EU resolution follows the first-ever ministe-rial meeting on short-lived climate forcers held September 12 in Mexico City. HP


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HPINNOVATIONS


SELECTED BY HYDROCARBON PROCESSING EDITORS

[email protected]

Mercaptan oxidation in aqueous waste

Tert butyl mercaptan (TBM) and dimethyl sulfide (DMS) are oxidized to destruction in an aqueous-waste solution containing methanol (MeOH), mono-ethylene glycol (MEG), ammonia and hydrocarbon sources using advanced oxi-dation process (ozone, hydrogen peroxide and uV). The aqueous solution in the trial mimics the expected waste stream from a gas-transport pipeline.

Background to the problem. PSE Kinsale Energy required a process to dispose of an aqueous waste stream in a gas-storage project. Injection gas consti-tutes odorized natural gas containing 4.7 ppmv and 1.3 ppmv of TBM and DMS respectively. This is injected into an off-shore reservoir during summer for winter withdrawal. Withdrawn gas is expected to be water saturated, and hydrate inhibi-tors, MEG and MeOH, are injected. The aqueous waste generated from the onshore separator contains MEG/MeOH, TBM/DMS and trace amounts of native petro-leum species (alkanes, cyclics, phenol, etc.).

Initial treatment options. Due to the uncertainty of produced water flowrates (from 10 m3/d to 100 m3/d) and the relatively low absolute value of flows, disposal is best achieved by third-party offsite disposal. Third-party water-treat-ment plants cannot accept a waste with mercaptan (thiol).

Pilot-trial results. Phase 1 of trials by PSE Kinsale Energy was to establish back-ground rates of MEG/MeOH destruction. If the process achieved significant MEG/MeOH destruction, disposal could be implemented within the site and transport infrastructure could be avoided. Batches ran up to two hours. Achieved destruction for samples of different chemical oxygen demand (COD) concentrations, respec-tively 98 mg/l and 35 mg/l, were 45% and 12%. This did not yield a viable disposal process and was unexpected.

Phase 2 dosed mercaptan and petroleum species into MEG/MeOH solutions. The trials were located in a remote area due to

odor potency. Vials were opened under a liquid surface to prevent gas escape, and equipment was rinsed with hypochlorite to destroy mercaptan odor. The trial equip-ment was placed under a fume-hood with an extract fan fitted with a KOH/KI-impregnated activated-carbon filter.

In tests, mercaptan odor was not evident after 30 minutes. Subsequent trials with varying solution strengths confirmed this. Increasing the background COD from 700 mg/l to 2,400 mg/l equivalent did not sig-nificantly affect the mercaptan destruction rate as detected by the trial operators, as shown in Fig 1.

Attempts to identify a rate of reaction were not possible; the reaction was quicker than one simple residence time (50 liters circulated at 1m3/h).

In Phase 3, two batches of a fully simu-lated waste with petroleum species were processed for 120 minutes. The analysis to confirm odor destruction was three-fold:

• Liquid samples were taken at time intervals and analyzed for mercaptan.

• After 120 minutes, the batch was transferred to a barrel for headspace analysis using graphite adsorption tubes.

• Liquid samples were taken at time intervals and subsequently sampled by an odor panel. Mercaptan destruction was confirmed within 60 minutes.

Conclusion. The advanced oxidation process using ozone/uV rapidly and selec-tively destroyed mercaptan in an aque-ous waste containing MeOH, MEG and petroleum species. Competitive behavior was negligible despite the higher concentra-tions of the potentially competing species. Despite the inference in published research, the oxidation process was not capable of destroying MeOH or MEG in a time suit-able for process implementation.Select 1 at www.HydrocarbonProcessing.com/RS

Wastewater treatment exceeds standards

The result of a four-year development effort, GE’s next-generation membrane bioreactor (MBR) wastewater-treatment technology, LEAPmbr, is claimed to offer the lowest life-cycle costs available from any MBR technology, while also being cost-

competitive with conventional treatment. These cost savings, along with operational simplicity and a compact footprint, derive from innovations to the popular GE Zee-Weed 500 MBR product line. Cost and efficiency savings include:

• A minimum 30% reduction in energy costs

• A 15% improvement in productivity (greater water-treatment capacity)

• A 50% reduction in membrane aera-tion equipment and controls, leading to a simpler design with lower construction, installation and maintenance costs

• A 20% reduction in physical footprint, leading to further reduced construction and installation costs, as well as lower ongoing consumption of cleaning chemicals.

MBR technology consists of a sus-pended-growth biological reactor inte-grated with GE’s high-performance, rug-ged ZeeWeed hollow-fiber ultra-filtration membranes. ZeeWeed membranes are immersed in a membrane tank, in direct contact with the water to be treated, which is known as mixed liquor. Through a permeate pump, a vacuum is applied to a header connected to the membranes. The vacuum draws the water through the ZeeWeed membranes, filtering out solids,

As HP editors, we hear about new products, patents, software, processes, services, etc., that are true industry innovations—a cut above the typical product offerings. This section enables us to highlight these significant developments. For more information from these companies, please go to our website at www.HydrocarbonProcessing.com/rs and select the reader service number.

TBMDMSCOD

0.0 04008001,2001,6002,0002,4002,800

0 10 20 30Time, min

40 50 60

1.0

2.0

3.0

Odor

ant c

onc.

, mg/

l

COD,

mg/

l4.0

5.0

6.0

Mercaptan destruction rate. FIG. 1

We don’t have a department dedicated to quality. We have a company dedicated to it.

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HPINNOVATIONS

25

along with bacteria and viruses. The filtered water, or permeate, can then be further treated, reused or discharged as needed.Select 2 at www.HydrocarbonProcessing.com/RS

Alliance brings together production technology

UOP LLC, a Honeywell company, has formed a licensing alliance with ExxonMobil Research & Engineering Co. (EMRE) to offer integrated solutions for producing lubricant oils and high-quality fuels. The agreement between Honeywell UOP and EMRE will reportedly provide a one-stop solution for refiners to maximize lubricant oil and diesel fuel production levels. The alliance harmonizes EMRE technology, used to produce lube base oils for use in motor oil, with UOP hydropro-cessing solutions that produce the high-quality feedstocks needed for lubricant production.

Users will also have access to integrated process design solutions for EMRE fuel-dewaxing technologies and UOP hydro-processing solutions to produce high-cetane, ultra-clean diesel for cold climates in a single engineering package. “By bring-ing together these two well-established portfolios, we are maximizing solutions for our customers to produce more and better products from each barrel of crude,” said Pete Piotrowski, vice president and general manager of Process Technology and Equip-ment for Honeywell’s UOP.Select 3 at www.HydrocarbonProcessing.com/RS

Redesigned analyzer for H2S in crude oil

The OMA-300 hydrogen sulfide (H2S) analyzer crude oil edition from Applied Analytics, Inc. (AAI) is a specialized con-figuration of the OMA-300 H2S system. Equipped with a headspace sample-con-ditioning system, it monitors an opaque liquid process. When a sample is too dark or dirty to transmit a light signal, the head-space system is said to produce a represen-

tative vapor-phase sample that can be easily monitored via ultraviolet-visible absor-bance spectroscopy and correlated to the chemical composition of the liquid process.

“AAI has always offered a highly effec-tive solution for measuring H2S in opaque liquids, but the current demand for crude analysis has given us cause to rethink our offering,” said Dan Murphy, senior mechanical engineer. “The process resulted in modifications to the crude oil edition

Applied Analytics’ headspace system.

FIG. 2

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HPINNOVATIONS

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of the OMA-300 H2S. The refined design puts everything in one enclosure and adds the capability to monitor multiple crude streams at once using multiple headspace columns running in parallel.”Select 4 at www.HydrocarbonProcessing.com/RS

Renewable fuel options for condensing hydronic boiler

Fulton’s Vantage boiler, which has been available since 2003 as an ultra-high-effi-

ciency condensing hydronic boiler, is said to be drawing attention for its ability to use B100 biodiesel and ultra-low-sulfur (under 15 ppm) heating oils for full con-densing operation. “As a result of com-prehensive testing at the independent Brookhaven National Laboratory, it has been proven that the Vantage can meet or exceed the thermal efficiencies attainable with natural gas,” said Erin Sperry, Fulton’s commercial heating product manager.

The biodiesels used in the Brookhaven testing facility included biodiesels pro-duced from both soybeans and recycled tallow. According to findings, ignition on B100 biodiesel, even from a cold start, was identical to traditional No. 2 heating oil. Testing also discovered that carbon-monoxide emissions and smoke-number readings were essentially maintained at zero during steady-state operation and at a normal excess-air level of 25%. Fol-lowing test runs, burner head inspections found no significant coke deposits and measurable reductions for NOx, SO2 and soot were observed. Predicted corrosion rates were in the acceptable range for the application. Boiler-jacket loss—moni-tored using the standards of the Ameri-can Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Standard 103—was found to be 0.2% of steady-state input, a very low value.

At Brookhaven, boiler efficiency was measured using both an indirect flue-loss method and a direct input/output method. As typically observed with hydronic boilers, efficiency and conden-sate collection rate are impacted by the return-water temperature. At high fire with a return-water temperature of 122°F, efficiency was found to be 88%. At low fire with a return-water temperature of 90°F, efficiency was 93%. Under BTS-2000 test conditions of 80°F, return-water temperature and 180°F supply-water temperature, the rated efficiency was 98% at high fire.Select 5 at www.HydrocarbonProcessing.com/RS

Fulton’s Vantage boiler. FIG. 3

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[email protected]

North AmericaDominion is proceeding with its next

major project in the Marcellus and Utica Shale regions, the construction of a large natural gas processing and fractionation plant along the Ohio River in Natrium, West Virginia.

The first phase of construction includes facilities that can process 200 million cfd of natural gas and fractionate 36,000 bpd of natural gas liquids (NGL). This phase of the project is more than 90% contracted and is expected to be in service by Decem-ber 2012.

US Senator John Hoeven has announced that the US Environmental Protection Agency (EPA) has approved a key permit for the proposed petroleum refinery on the Fort Berthold Indian Reservation near Makoti, North Dakota. The Mandan, Hidatsa, Arikara (MHA) Nation Clean Fuels Refinery is expected to be one of the few oil refineries to be constructed in the US in the last 30 years, and be capable of refining 15,000 bpd of oil.

During his tenure as governor, and now as a member of the US Senate Indian Affairs Committee, Senator Hoeven has worked to support the project and see that it achieved federal approval. In anticipa-tion of the refinery’s approval, the Fort Berthold Community College created a two-year training program to educate stu-dents interested in energy-related careers, including refinery operation.

Enbridge Energy Partners, L.P. will construct a 150 million cfd cryogenic natu-ral gas processing plant on its Anadarko gas-gathering system near Wheeler, Texas. The $230 million Ajax plant—strategically located to serve the rapidly growing Granite Wash play—will add much-needed natural gas processing capacity to the partnership’s Anadarko system and is expected to be in service by early 2013.

Black & Veatch has begun front-end engineering design (FEED) for a new, liq-uefied natural gas (LNG) export facility that will be constructed on a barge and transported to the Douglas Channel near Kitimat Village, British Columbia, Canada.

The unique facility will feature the com-pany’s patented PRICO process to liquefy natural gas for transport to Asian markets.

The project is owned by HN DC LNG Ltd. Partnership (Haisla Nation), LNG Partners, LLC, and Douglas Channel Gas Services Ltd. It will reportedly be the first barge-mounted export facility serving the Pacific Basin, as well as the first for export-ing Canadian natural gas

Black & Veatch’s FEED work for the facility, which will produce more than 800,000 tpy of LNG, will be completed in January 2012. The FEED will provide a definitive estimate in finalizing a lump-sum, turnkey contract between the parties for the facility’s engineering, procurement, construction, testing and commissioning.

Golden Valley Electric Association and Flint Hills Resources Alaska have com-menced engineering on a natural gas liq-uefaction (NGL) facility on Alaska’s North Slope. The two companies have signed a memorandum of understanding to exclu-sively negotiate agreements to construct and operate a facility that would enable liquefied natural gas (LNG) to be trucked to the Interior by the first quarter of 2014.

GVEA would use the gas to power its newest turbine at the North Pole Power Plant. Flint Hills would use the gas as a supply fuel for the refining process at its North Pole refinery.

The deal would deliver gas “at cost” to each company. Lower costs mean lower rates to GVEA members. Flint Hills would reportedly become more competitive and efficient by burning LNG instead of refined crude oil in its refinery. Engineering for the project is underway. The objective is to have LNG available in the North Pole by the first quarter of 2014.

UOP LLC, a Honeywell company, has

begun construction of a biofuels demon-stration unit in Hawaii that will convert forest residuals, algae and other cellulosic biomass into green transportation fuels. Backed by a $25 million US Department of Energy (DOE) award, the Honeywell UOP integrated biorefinery will upgrade biomass into high-quality renewable gaso-line, diesel and jet fuel. The project will also

support the Hawaii Clean Energy Initiative goal to achieve 70% clean energy by the year 2030.

Located at the Tesoro Corp. refinery in Kapolei, the biorefinery will demonstrate the technology’s viability, test the fuels pro-duced and evaluate the environmental foot-print of the fuels and process technology. The project is scheduled to begin initial production in 2012, and is expected to be fully operational by 2014.

The demonstration unit will utilize the rapid thermal processing (RTP) technology to rapidly convert biomass into a pourable, liquid biofuel. This liquid biofuel will then be upgraded to green transportation fuels, using hydroprocessing technology from Honeywell’s UOP.

South AmericaFoster Wheeler’s Global Engineering

and Construction Group has been released to perform the second phase scope for an existing contract with Ecopetrol S.A. for modernization of the refinery in Barran-cabermeja (PMRB), Colombia. This release includes additional project management consultancy (PMC) and front-end engineer-ing design (FEED), detailed engineering for the crude unit revamps, assisting Eco-petrol in the selection process for engineer-ing, procurement and construction (EPC)

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contracts, as well as control and supervision of the EPC and construction contractors.

The second phase of the PMRB will add heavy-crude processing capability to take advantage of the available domestic heavy sour crudes, and it will provide a process-ing configuration to meet the Colombian clean-fuels product specifications. This refinery upgrade will eliminate fuel-oil pro-duction and increase overall profitability.

BASF will invest in a world-scale pro-duction site for acrylic acid, butyl acrylate and superabsorbent polymers (SAPs) in Camaçari, Bahia, Brazil. It will reportedly be the first acrylic acid and superabsorbents plant in South America. With an invest-ment volume of more than €500 million, it is the largest investment in BASF’s century-long history in South America.

In addition, BASF will start to produce 2-ethyl-hexyl acrylate in its existing chemi-cal complex in Guaratinguetá, São Paulo. This will be the first plant for this product in South America.

Construction of the new acrylic-acid complex will start in 2011, with produc-tion expected to begin in the fourth quarter of 2014. Production for 2-ethyl-hexyl acry-late in Guaratinguetá is expected to start in 2015 on the basis of acrylic acid produced in Camaçari.

EuropeA subsidiary of Foster Wheeler AG’s

Global Engineering and Construction Group has a contract with Aibel AS to pro-vide detailed design engineering services to support Aibel, which has been appointed by Statoil as the main engineering, pro-curement and construction (EPC) contrac-tor for the Kårstø Development Project (KDP) 2013 for the Kårstø and Kollsnes oil and gas processing plants in Norway.

Foster Wheeler’s scope of work includes detailed design engineering services for modifications at the Kårstø plant to enable it to process light oil from the Gudrun offshore oil field and condensate from the Sleipner field. Its scope of work also covers the upgrading of eight compressors: three at Kårstø and five at Kollsnes. There are two additional scopes of work included as options under the KDP 2013 contract: a boiler-upgrade project and a hydrogen-sul-fide removal project at Kårstø. This work has not yet been released and is dependent on Statoil choosing to exercise the options.

Statoil is acting on behalf of Gassco. Gassco is the operator for the Kårstø and

Kollsnes terminals, which are owned by the Gassled joint venture, with Statoil as the technical service provider.

Lummus Technology, a CB&I com-pany, has a contract from Lukoil for the license and basic engineering of a grassroots delayed coking unit. The coker will be designed to process 6,100 tpd of heavy feedstocks and will be located at Lukoil’s refinery in Perm, Russia. Lummus Tech-nology’s proprietary delayed coking tech-nology will enable Lukoil to economically produce two grades of specialty coke from vacuum resid and other heavy feedstocks to meet the region’s coke-quality requirements and light product needs.

BP has agreements with JBF RAK LLC

under which JBF RAK is to build a new 390,000-tpy polyethylene terephthalate (PET) production unit in Geel, Belgium, subject to required approvals.

The agreements provide JBF RAK with the rights to build and operate this PET unit on BP’s existing petrochemicals com-plex in Geel, adjacent to BP’s world-class purified terephthalic acid (PTA) facility. BP

will, in return, supply PTA directly to this new PET manufacturing unit. Unit startup is scheduled in 2014.

A subsidiary of Foster Wheeler AG’s Global Engineering and Construction Group has a contract for the basic engi-neering design of a new hydrogen produc-tion unit, based on Foster Wheeler Terrace-Wall steam-reforming technology, to be built at the Atyrau refinery, a member of JS National Co. “KazMunayGas” group, the leading national oil and gas company of Kazakhstan.

The contract was awarded to Foster Wheeler by OJSC Omskneftekhimpro-ekt, the project engineering contractor for the Deep Oil Conversion Complex, a major revamp and modernization of the Atyrau refinery. The project’s main purpose is to increase the oil conversion rate and produc-tion of all types of motor fuels to meet Euro IV and Euro V standards. The refinery will, as a result of this modernization, require additional volumes of high-purity hydrogen.

The new hydrogen unit, which will use high-olefinic liquefied petroleum gas (LPG) as the main feedstock and natural gas as the


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alternate feedstock, will be designed to pro-duce 24,000 Nm3/h of pure hydrogen. The basic design package is scheduled for com-pletion during the third quarter of 2011.

AfricaKBR has a contract with Anadarko

Mozambique Area 1, Ltd., to perform a pre-front-end engineering and design (pre-FEED) study for a prospective liquefied natural gas (LNG) plant in Mozambique, Africa. The contract award follows the recent natural gas discoveries by Anadarko and its partners in the Rovuma Basin, off-shore Mozambique.

The pre-FEED study is designed to help Anadarko further assess the viability of developing an LNG facility to export natural gas from the region. Joint-venture partners include Anadarko, ENH, Mitsui, BPRL, Videocon and Cove.

A subsidiary of Foster Wheeler AG’s Global Engineering and Construction Group has been awarded a contract by Engen Petro-leum Ltd. for engineering, procurement and construction management (EPCm) services for Engen’s new multiproducts pipeline

feeder-lines and infrastructure project at Engen’s Durban refinery in South Africa.

The project objective is to develop the pipeline network from Engen’s depots to tie in to the Transnet pipeline to enhance Engen’s access to key refined-product mar-kets in the South African interior. Transnet is the principal operator of South Africa’s fuel pipeline system.

Middle EastFluor Corp. has been awarded an engi-

neering, procurement and construction (EPC) contract by SAPCo, a joint-venture company being formulated between Saudi Acrylic Acid Co. and Evonik Industries, for its Super Absorbent Polymer (SAP) Project in Jubail, Saudi Arabia.

The SAP project work is underway and is expected to be completed and commis-sioned by the fourth quarter of 2013.

Jacobs Engineering Group Inc. has been awarded the second phase of the Ras Tanura Refinery Clean Fuels and Aromat-ics Project. This award is under the Saudi Aramco General Engineering and Project Management Services contract. Jacobs is

executing the project from its office in Al-Khobar, Saudi Arabia.

The project’s scope of services includes front-end engineering design (FEED) ser-vices for both inside battery limits (ISBL) and outside battery limits (OSBL). In addi-tion, the project includes refinery modi-fications to comply with expected future environmental regulations. The Ras Tanura refinery is located in the Eastern Province of Saudi Arabia.

UOP LLC, a Honeywell company, has announced that Oman Oil Refineries and Petroleum Industries Co. (Orpic) has selected UOP/Foster Wheeler technol-ogy to help process heavy oil and expand fuel and petrochemicals production at a refinery in Sohar, Oman. Orpic will use the UOP/Foster Wheeler Solvent Deasphalting (SDA) process technology to convert heavy crude to low-contaminant deasphalted oil, a product that is further processed in refineries to produce liquefied petroleum gas (LPG), gasoline, jet fuel, diesel and propylene. The new unit will process 2.5 million metric tpy of heavy crude to signifi-cantly increase the refinery’s production of valuable petroleum products.

In collaboration, Honeywell’s UOP and Foster Wheeler will provide the technology license and basic engineering package for the new unit, which is expected to start up in 2015.

Wood Group GTS has been awarded an agreement to supply parts for 14 Gen-eral Electric (GE) Frame 6B gas turbines located at various sites in Oman. Under this 12-year contract, all GE Frame 6B new components will be exclusively sourced through Wood Group GTS. To support this contract, Wood Group GTS will rely on its advanced parts manufacturing (APM) program.

In addition, Wood Group GTS will assist its customer to model the overall new parts demand for its fleet, allowing optimization of parts usage based on sev-eral factors. Under the agreement, there is an option to also purchase Dry Low NOx components. All these gas turbines are used in the process of efficiently extracting natu-ral gas from established gas fields.

The Qatargas-operated Laffan Refin-ery Co. Ltd. has awarded a lump-sum front-end engineering design (FEED) con-tract for the refinery’s Phase 2 expansion to Technip. Phase 2 will increase the current


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refinery capacity to enable Qatar to meet all of its naphtha, diesel, liquefied petroleum gas (LPG) and kerojet requirements. The FEED study is scheduled to be completed by the first quarter of 2012, and it will be executed by Technip’s engineering center in Paris, France. The engineering, procure-ment and construction (EPC) contract is expected to be awarded by the third quarter of 2012.

Asia-PacificGladstone Liquefied Natural Gas

(GLNG) in Queensland, Australia, has selected Meridium, Inc. as the platform for its enterprise-wide APM initiative. The GLNG plant, which began construction earlier this year, is a joint venture between Santos, Petronas, Total and KOGAS.

GLNG involves exploration and pro-duction of coal-seam gas, a 420-km gas pipeline from the gas fields to Gladstone and a gas liquefaction and export facility on Curtis Island. The Meridium software implementation project at GLNG kicked off in May. GLNG has chosen to imple-ment almost the entire suite of Meridium technology including Risk-Based Inspec-tion, Reliability-Centered Maintenance, and Asset Strategy Management and Imple-mentation.

Lummus Technology, a CB&I com-pany, has been awarded a contract by Ningbo Haiyue New Material Co. Ltd., for the license and engineering design of a grassroots propane dehydrogenation unit to be built in Ningbo City, Zhejiang Prov-ince, China. The unit will use the CATO-FIN dehydrogenation process to produce 600,000 metric tpy of propylene, and it is expected to start up in 2014.

CB&I has been awarded, through its joint venture with Chiyoda Corp. and Saipem S.p.A., the preparation and sup-ply of the project-specification contract for the Arrow Liquefied Natural Gas (LNG) Plant Project in Australia. Arrow Energy Pty Ltd., the project operator, is a 50/50 joint venture partnered by Royal Dutch Shell and PetroChina.

The project, which will be designed with a production capacity of 8 million tpy (4 million tpy x 2 trains), is planned to be constructed on Curtis Island, off the coast of Gladstone, on the east coast of Queensland, Australia. The project plans to expand its capacity up to 16 million tpy in the future. The LNG plant will be sup-

plied with coal-seam gas from the Surat and Bowen basins in Queensland, and will process, treat and liquefy the gas for export.

Alfa Laval has an order to supply Alfa Laval Packinox heat exchangers to a pet-rochemical plant in Singapore. The order value is about SEK 110 million, and deliv-ery is scheduled for 2012. The Alfa Laval Packinox heat exchangers will be used in a catalytic processing section for production of mixed xylenes.

Qingdao Haijing Chemical (Group) Co. Ltd. has selected INEOS Technolo-gies’ vinyl chloride monomer (VCM) and suspension polyvinyl chloride (S-PVC) technologies for a project at its new site located at Dongjia-kou Pro Port Indus-trial Zone, Qingdao, People’s Republic of China. The 400 kiloton/yr VCM plant features production of VCM by pyrolysis of ethylene dichloride (EDC). The EDC will be produced using high- and low-temper-ature chlorination and INEOS Technolo-gies’ unique two-stage fixed-bed oxychlo-rination process.

The 300 kiloton/yr S-PVC plant will produce a full range of suspension PVC grades from VCM using INEOS Technolo-gies’ suspension process, including the use of INEOS Technologies’ proprietary PVC additives and recipes.

The two plants, forming part of a broader petrochemical complex, are sched-uled to start up in 2013.

China Petroleum & Chemical Corp. (Sinopec) and Syntroleum Corp. have announced the grand opening of the Sino-pec/Syntroleum Demonstration Facility (SDF) located in Zhenhai, China. The SDF is an 80-bpd facility utilizing the Syntroleum-Sinopec Fischer Tropsch tech-nology for converting coal, asphalt and petroleum coke into high-value synthetic-petrochemical feedstocks.

Sinopec and Syntroleum entered into a technology-transfer agreement in 2009. As part of the agreement, Sinopec relocated Syntroleum’s natural-gas-fed Catoosa dem-onstration facility to the Zhenhai Refining and Petrochemical Complex in Ningbo City, Zhejiang Province, China, for joint technology demonstration and develop-ment. Upon successful completion of the Zhenhai program, Sinopec intends to build commercial-scale coal and petroleum coke-based Fischer Tropsch facilities using the Syntroleum-Sinopec technology.



HPIN CONSTRUCTIONThe Qinghai Salt Lake Industry

Co. has selected UNIPOL polypropyl-ene process technology from The Dow Chemical Co. for its new 160-kiloton/yr polypropylene unit. The unit will pro-vide polypropylene as part of Qinghai’s integrated magnesium metal project to produce homopolymers, random copoly-mers and impact copolymers. As part of the magnesium metal project, it will uti-lize coal as feedstock to produce ethylene and propylene through coal gasification, and then use the ethylene and propylene as feedstock for the polypropylene unit.

Installation at Qinghai Salt Lake Indus-try Co. is scheduled to start in 2012, with startup expected in the second half of 2013.

Air Liquide’s Engineering and Construc-tion division has signed a contract with the Shenhua Ningxia Coal Industry Group(SNCG) to build a 500,000-tpy methanol-to-propylene (MTP) plant, following the successful commissioning of the first indus-trial-scale unit built with the same client.

The contract comprises the basic engi-neering, license and supply of proprietary equipment, as well as services for procure-

ment and technical advisory services at the site. This will be the third large-scale MTP plant licensed by Lurgi. SNCG, in close cooperation with the Lurgi team, played an important and constructive role in the commissioning and startup phases of the reported MTP-1 first-of-a-kind plant, thereby contributing to proving the success of Lurgi MTP technology at the industrial scale.

The unit to be built in Ningdong, in the Chinese province of Ningxia, will have the capacity to produce around 500,000 tpy of propylene from coal. The engineering phase for the contract is to be completed within about eight months.

Bathinda refinery, the producer of 180,000 bpd, started crude oil processing in trial runs on August 29th, a source with direct knowledge of the plant told Reuters. The refinery is owned by Hindustan Mit-tal Energy Ltd (HMEL), a joint venture between state-run Hindustan Petroleum and Mittal Energy.

Project consultant, Engineers India Ltd. (EIL), had said last month that the plant would start crude runs in August

and be fully operational by November. The land-locked refinery is in the north-

ern Punjab state. It adds to India’s current refining capacity of close to 4 million bpd. The last refinery commissioned in India was earlier this year, when Bharat Oman Refin-ery Ltd. (BORL), a joint venture of Bharat Petroleum and Oman Oil Co., started its 120,000 bpd Bina plant in central India.

KBR has a contract with Jaiprakash Associates Ltd. (JAL)—a Jaypee Group company—to provide license and engi-neering-design services for JAL’s brown-field 2,200-metric tpd ammonia plant in Kanpur, India.

KBR is licensing its Purifier technol-ogy. Its design will reportedly enable JAL to build the ammonia unit with lower energy consumption and reduced capital costs. The new unit will be set up in an existing plant at the Kanpur site, which was recently acquired by JAL. The award follows KBR’s execution of a revamp study for JAL’s exist-ing three identical 450-metric tpd ammo-nia trains completed in late 2010. HP

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HPI CONSTRUCTION BOXSCORE UPDATE Company City Project Ex Capacity Unit Cost Status Yr Cmpl Licensor Engineering Constructor

AFRICA Kenya Kenya Petroleum Refineries Mombasa Refinery RE None 899 F 2016 Niger CNPC Zinder Reformer, Cat 2 bpd 600 U 2012 Lanzhou Petrochemical Nigeria Brass LNG Ltd Brass Island LNG 5 MMmtpy H 2013 ConocoPhillips Bechtel Bechtel

ASIA/PACIFIC Australia United Petroleum Pty Ltd Dalby Ethanol EX 160 MMl/y S China BP Zhuhai Chemical Co Zhuhai PTA (3) EX 1.25 tpy P 2014India Indian Oil Corp Ltd Barauni Gasoline Desulfurization 403 Mm-tpy U 2012 Axens Toyo India|Punj Lloyd Ltd Punj Lloyd LtdIndia HMEL Bathinda Hydrogen Generation (1) 180 bpd C 2011 Haldor Topsøe L&T India BPCL/Bharat Oman Refineries Ltd. JV Bina, Madhya Pradesh Refinery EX 120 bpd 446 P 2017 India Indian Farmers Fert Coop Nellore Urea 2330 Mtpd P 2014 India Indian Oil Corp Ltd Paradip Cracker, FCC EX 85 Mbpd U 2012 Lummus Technology India ONGC Ltd Tatipaka Crude Unit 1500 bpd C 2011 Ventech PDIL|Ventech Nicco India Rashtriya Chemicals Thal Alibagh Carbon Dioxide Removal TO 43 MMcfd H 2011 GV Haldor Topsøe India HPCL Vizag Distillation, Crude (4) 300 bpd P 2016 Japan Inpex Joetsu LNG Receiving Termin 160 Mm3 E 2014 Chiyoda ChiyodaSouth Korea JX Nippon Oil/SK Innovation Ulsan Paraxylene 1 m-tpy 1000 P 2014

EUROPE Belgium BP/JBF RAK LLC Geel Polyethylene Terephthalate (PET) 390 tpy P 2014 France Total Gonfreville Lube Hydroprocessing 8 Mbpd H 2012 CLG CLG Romania Petrom Ploesti Coker, Delayed EX 36 Mbpd 1332 E 2014 Lummus Technology CB&I Lummus|FW Russian Federation Alco-Naphtha Kaliningrad PET 240 Mtpy C 2011 Uhde Inventa-Fischer Uhde Inventa-Fischer Serbia Gazprom Pancevo Cracker, Catalytic RE 22 bbl 20 M 2012 Spain Enagas Gijon LNG Terminal 800 Msm3/hr U 2012 Fluor FluorUzbekistan Sasol/Petronas/Uzbekneftegaz Ustyurt GTL 1.3 MMtpy 817 E 2014 Samsung Eng

LATIN AMERICA Colombia Ecopetrol Barrancabermeja Crude Unit RE 100 Mbpd E 2013 FW FWPeru PlusPetrol Peru Undisclosed Diesel Production 4 Mbpd C 2011 Ventech Ventech VentechSurinam Staatsolie Paramaribo Visbreaker (VBU) 3 Mbpd 800 U 2013 Lummus Technology CB&I Lummus|Aker Saipem Shell Solutions|Saipem

MIDDLE EAST Kuwait KNPC Mina Al Ahmadi Diesel RE 60 m-bpd E 2013 Haldor Topsøe Oman Oman Oil Co Sohar Refinery 187 bpd 40 E 2015 FW|Honeywell CLG|UOP|CB&I|JGC UOP|CLG|JGCQatar Qatargas Ras Laffan Refinery EX 292 bpsd F 2012 Technip Saudi Arabia Saudi Aramco Ras Tanura Refinery (2) None 80 H 2012 WorleyParsons WorleyParsons WorleyParsons Saudi Arabia Saudi Aramco Shedgum Sulfur Recovery Unit RE 550 t/a C 2011 Jacobs Nederland BV Saudi Arabia IBN RUSHD Yanbu Xylene, Para 460 kty E 2014 UOP CTCI CTCITurkey Petkim/SOCAR/Turcas JV Aliaga Hydrogen 160 MNm3/h 5000 U 2015 FW

UNITED STATES Louisiana ExxonMobil Baton Rouge Desalter RE 103 m-bpd E 2012 Cameron Cameron CameronNorth Dakota NDAREC Fort Berthold Indian Reservation Refinery 15 bpd 400 U 2013 Mustang Corval GroupTexas Enbridge Wheeler Co Cryogenic Gas Plant 150 MMcfd 230 P 2013 Washington BP West Coast Cherry Point Distillate, HDS 20 Mbpd U 2011 Cameron Cameron Cameron

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PROCESS CONTROL AND INFORMATION SYSTEMS SPECIALREPORT


Intelligent severity optimization project pays off in two monthsMajor olefin producer uses new process control to fine-tune energy consumption

H. WANG, Z. WANG, W. DU, D. WANG and F. QIAN, East China University of Science and Technology, Shanghai, China; and Z. TANG, Sinopec Yangzi Petrochemical Co. Ltd., Nanjing, China

T he Sinopec Yangzi Petrochemical Corp. (SYPC) wanted to increase ethylene production at the Nanjing facility while decreasing energy consumption. The ethylene producer

elected to use a real-time optimization (RTO) program to meet its production and energy consumption goals. The SYPC ethylene plant at Nanjing, China, increased higher-value product yield by 1.3% and decreased energy consumption by 2.5%. The authors discuss the thinking and techniques applied to achieve both goals.

Background. As one of the pioneers in the development and application of novel technologies in Sinopec, SYPC has been working closely with East China University of Science and Tech-nology (ECUST) for technical research to improve operation efficiency, reduce energy consumption and increase benefits. On the way to total automation, the “step-by-step” principle was observed. The first target for the ethylene plant should be the ethylene cracker, since it is not only the most important device but also the largest energy consumer.

Simple advanced controls and product quality controls have been successfully implemented in the plant and have continuously produced benefits.1,2 The benefits achieved in SYPC were soon extended to other ethylene plants.

Although a more consistent furnace yield slate was produced by using severity control in place of coil outlet temperature (COT) control, determining severity setpoint remained an issue. The setpoint was set by plant management and nearly always around the design value. This conservative approach inevitably incurs profit loss, due to aging of the furnace. Also, decision-making could not keep up with the frequent feed composition changes.

In response to the frequent market prices and feed changes, SYPC decided to implement an RTO system for the ethylene crackers. This is the first RTO project for the ethylene crackers in China. It began in 2007 and was completed in 2009. In this project, the severity was defined to be only independent variables. The feedrate and steam-to-hydrocarbon ratio would be fine tuned in the plant optimization project.

This decision was made based on the plant instrument condi-tions and the “step-by-step” principle. From another viewpoint, it also reflected the uncertainty of management on the potential benefits. This uncertainty was quickly eliminated after the system performance test.

System overview. Fig. 1 shows the optimization and con-trol structure for the SYPC ethylene cracker. The RTO system is on top of the severity control, regular controls (including the COT control) and other plant provided basic controls. These well-tuned control systems and reliable instruments promise (to keep) stabilizing plant operation and to maintain the optimum operating conditions found by the RTO system.

The RTO system comprises six processes and includes yield prediction, data collection and validation, status monitoring, model updating, optimization and results output. The system provides the engineer with a choice of four objective functions or operating modes, including: maximum overall profit, maximum

PHD

Online GCsValves and measurements

Data collectionRTO

Data validation

Process monitor Model updating

Optimization

Results output

Operators interface Setpoint check

Severity control

Regular controlsDCS

Process

Serial interface

Ethylene cracker severity optimization and control architecture.

FIG. 1

PROCESS CONTROL AND INFORMATION SYSTEMSSPECIALREPORT


ethylene yield, maximum propylene yield and maximum olefins yield. The operators’ interfaces for optimization are embedded in the distributed control system (DCS). Plant engineers and operators can write feed composition data and enable/disable optimization commands through the user interface. The data are continuously uploaded to the process history database (PHD).

The RTO system takes the process data, feed composition data and operators command flags from the PHD. After calcula-tion, the optimization results are written to the DCS, via a serial interface (SI) card. The results are implemented as setpoints for the existing controllers after validity check in the DCS.

Time interval varies in different parts of the optimization system. The data collection runs every 5 minutes, and the yield prediction also runs every 5 minutes. The execution cycle for optimization process can be 30 minutes or 1 hour, depending on the set boundaries of the step size for setpoints downloaded. If any process or analytical abnormity is detected, the timer will be cleared and will restart only when the process returns to normal.

For the project, a dedicated workstation (also called an “opti-mization server” in the plant) was added. The optimization server was placed in the engineering station room. A hardware firewall was also installed to assure safe data flowing to the server from the plant website.

Different access levels were assigned to different user classes. Plant engineers and managers can update the product market prices and select the optimization objectives. The software design-ers and maintainers can do system performance monitoring and necessary parameter debugging. The RTO system can be divided into four components, including the yield prediction model, an optimizer, custom programs and user interfaces.

Yield model. SYPC has access to a rigorous model for predict-ing effluent compositions. This model is normally used for feed selection and COT optimization in an offline mode. The rigorous model requires a large amount of measured information including the detailed feed compositions or feed characteristics. However, there is no online measurement for the feed detailed composi-tion in the plant, and the routine feed analysis cannot suffice the required information, which makes the rigorous model difficult to be incorporated in the RTO system.

Neural-network composite models comprising several sub-models were developed to predict the cracked product yield for the furnaces. Different sub-models feature feeds with distinct pyrolysis characteristics, thus different product spectrum.

The model uses several feed characteristics and key operat-ing variables to predict product yields. The feed characteristics used include the PIONA values and density that can be acquired by routine feed analysis. The operating variables include COT, feedrate, steam-to-hydrocarbon ratio, and other temperature and pressure variables.

The training data were based on the simulation results from the rigorous model over a wide range of operating conditions, with special emphasis on the normal operating range. A feedstock historical database collected over more than five years was used, and those representative feed samples were chosen for the simula-tion and to produce the training data. The developed model was first tested against the rigorous model. Until the relative error for yields prediction fell below a predefined value, the model moved to the online test stage. During online testing, the model was fur-ther evaluated against analysis results from online gas chromato-graphs (GCs). Before the test, the GCs were calibrated and also tested by several repeated laboratory analyses to ensure reliability.

Fig. 2 compares the model prediction of propylene-ethylene ratio (PER) with GC analysis results. The data are randomly scattered around the x-axis, and the relative errors are below 3%. Since the yield prediction model uses steady-state approximations to estimate the dynamic furnace operations, the model will not match the process exactly. But the model mismatches are taken into account in the optimization results to update the optimizer.

Optimizer. The optimizer is the engine of the RTO system. The primary function is to solve the yield model and execute the online optimization. Meanwhile, it can do offline optimization studies. As stated earlier, four optimization goals, or objective functions, are available, including: maximum overall profit, maxi-mum ethylene yield, maximum propylene yield and maximum olefins yield. The profit function is defined as:

Profit = (Product values) – (Feedstock costs) + (Utilities values) – (Utilities costs)For the furnace, utilities values include super high-pressure

steam (SHPS) generation, and utilities costs comprise fuel con-sumption, dilution steam and boiler feedwater (BFW) used. These alternatives help plant managers or engineers to choose the “best”

−5

−4

−3

−2

–1

0

1

2

3

4

5

One month

Rela

tive

erro

r, %

Yield model accuracy compared with online GC data.FIG. 2

Engineer’s interface to the server. FIG. 3


way to operate the furnaces, based on the political policy, market situation and feedstock availability.

An optimization toolkit was developed and can be used to solve both single-objective optimization problems and multi-objective optimization problems. Deterministic algorithms including a sequential quadratic programming (SQP) technique, golden-sec-tion search, a simplex method and hybrid methods with stochastic algorithms can be selected according to the problem characteristics.

The optimization toolkit was designed not only for this sever-ity optimization project, but also for plant optimization that involves several independent variables in the future.

The downloaded optimization variables are furnace severities. The setpoints downloaded are not the optimal severity solved by the optimizer. The optimal severity is updated based on the model prediction error to account for model inconsistency. The optimizer would take a bounded step from the current setpoint toward the updated severity value. This updating strategy allows the controllers to have enough time to achieve the setpoints pre-dicted by the optimizer, thus ensuring smooth furnace operation.

Custom programs. The custom programs were developed to facilitate model predictions and to optimize the process while addressing possible abnormal situations and maintaining all com-munications involved and process data download as safe as pos-sible. These functions were implemented in both the optimization server and the DCS.

Server programs. The programs in the optimization server were mainly designed to do data collection and validation, to monitor the process based on data analysis, to update the system

with new input variables, to send the final results to the DCS, to execute self-diagnosis and to log all important changes or even system breakdown.

Several status flags are displayed in the server user interface, including the communication status from PHD to server, the optimization on/off command from operators, the status of fur-nace and key instruments, and process data validity.

These statuses determine the sequence of events defined in the RTO system. For example, if the process data was confirmed

98

99

100

101

102

103

96 hr

Yiel

d, %

Before RTO After RTO

RTO increased the yield of high-value olefins production.FIG. 4

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questionable, the RTO system will get into the “initializing” state. The system will continuously collect data and validate them unless the process data return to normal. Meanwhile, the optimization results from the previous cycle will be used as the downloaded set points, and a corresponding flag will be sent to the DCS screen to inform the operators of possible countermeasures. For security consideration, any changes to the key parameters, such as the market prices for each product, and corresponding username will be logged in a recording text file.

DCS programs. The programs in the DCS were designed to examine, restrict and implement the setpoints downloaded from the server. If the communication faults or abnormal signal from the server were detected, the programs will hold the current setpoint and inform the operators through the dedicated user interface in DCS.

User interfaces. The user interface is the “window” to the RTO system.3 Two sets of user interfaces were developed in the server and DCS, respectively.

Server interfaces. The interfaces in the server were designed for plant engineers and RTO system maintainers. There are seven pages each with different functions. They are used to select the optimiza-tion goals, to update the market prices and model parameters, to monitor the process variables and optimization results, to perform process analysis using the historical trends, to predict product yield for given inputs and to carry out system test and parameter adjust-ments. Fig. 3 shows the fourth page for variables historical trending.

DCS interfaces. The interfaces in the DCS were designed for the console operators. There are four pages each with different functions. They are used to write the feed characteristic data and boundaries for the severity setpoints, to enable/disable the RTO system, to display the system status, and to monitor the key operat-ing variables and analytical system. The interfaces were designed to be consistent with the previous severity control user interfaces, which made the operator more familiar with the system.

Performance. Performance tests were conducted to verify the effectiveness of the RTO system. The optimization base was to keep the feedrate and steam-to-hydrocarbon ratio invariant during the test. The representative base-line data and new base-line data were collected to form a quantitative basis to evaluate olefin yield increases and to reduce energy use.

The estimated tangible benefits indicate a project payback period of less than two months. The achieved benefits include 95% from the desirable products increase and 5% from the utili-ties saving. The average increase of ethylene plus propylene yields is 1.4%. The average decrease of the fuel-gas consumption is 3.5%.

The fuel gas, SHPS and BFW all contributed to the utili-ties. When the fuel gas decreased, the SHPS generation also decreased. This leverage makes the utilities saving occupy only a small share (5%) in the benefits, despite a considerable fuel gas saving observed. Fig. 4 shows the yields increase before and after the performance test. Fig. 5 shows fuel gas decrease before and after the performance test.

The benefits clearly show the capability of the RTO system to push the cracker severity to the profitable constraints based on the latest economic data. During the test, the daily tube metal temperatures (TMTs) were also measured and recorded before and after performance test. The maximum TMT of the week before the test was 1,080°C and 1,078°C after the test. This result indi-cates that the RTO system can potentially prolong the furnace run length. This can be considered as one of the intangible benefits from the RTO system.

Other intangible benefits also include the offline use of the RTO system. The system can be used to predict effluent yields, to evaluate different operating conditions or to do sensitivity analyses based on feed properties and operating conditions. Another intan-gible benefit is that the system provided the plant management with a better understanding of economic sensitivities of different operating modes. The performance test showed that increas-ing some olefin losses can increase the total profitability, which helped plant management gain a much clearer understanding of the tradeoff between the product yield and energy consumption.

Key to success. As the first successful RTO project on ethylene crackers in China, the management of the SYPC and ECUST project team were both awarded the “National Science and Tech-nology Progress Award” in 2009. Several features leading to this successful project are:

• Transparency to users. The system was designed, to the greatest extent, to make the plant personnel understand what the system is doing and why.

• Safety first principle. Comprehensive abnormal situation management can assure the safety of the optimization control system, in spite of any abnormality in yield model prediction, process data, system program and even the server breakdown.

• Flexibility. The system provides several possible operating modes and optimization algorithms. The system can be used in online or offline mode.

• Friendly interfaces. The standard user interfaces in DCS interface facilitate interpretations and operations of the optimiza-tion control system.

• Easy to maintain. All possible adjustable parameters have been summarized and displayed in a specific interface for mainte-nance. This feature has great value in view of frequent manpower flow and the movement of the plant engineers.

• Software portability. The generic and modularized codes behind the software make it easy to integrate with planning system in higher levels and easy to redevelop for furnaces in other plants.

In addition to the on-the-job training plus the special training session for operators, the commitment of plant management, and the seamless cooperation between the user and provider all contribute to the success of the project.

91

93

97

95

101

99

103

96 hr

Fuel

gas

con

sum

ptio

n, %

Before RTO After RTO

The RTO decreased energy consumption, as shown in the before and after performance testing.

FIG. 5



Future plans. The SYPC ethylene plant is continuing to look for ways to further improve the RTO system performance. The dilution steam and the feedrate have been discussed to incorpo-rate into the RTO system, which requires the revamp or replace-ment of the steam instrument and also the development of a total plant model.

The implementation of this RTO system in ethylene plants of Sinopec Qilu and Shanghai is in preparation. HP

ACKNOWLEDGMENTThis work was supported by the National Natural Science Funds for Distin-

guished Young Scholar, National High-Tech Research and Development Program of China and the Shanghai Key Technologies R&D Program. A special thanks is extended to Ling Zejing, Huang Xianping, Hu Tiansheng and Zha Xingqi from SYPC for their contributions.

LITERATURE CITEDComplete literature cited available online at HydrocarbonProcessing.com.

Honggang Wang is currently a PhD student at the East China University of Science and Technology, Shanghai, China. He directly began his doctoral study after receiving his BS degree from the China University of Petroleum (Beijing) in 2005. His research field includes first-principle modeling and optimization of ethylene crackers. His experience includes design, configuration and commissioning of APC and RTO projects. He has published 8 papers and holds 1 patent.

Zhenlei Wang is a full professor at the East China University of Science and Technology. He specializes in the field of advanced control. He has served as the lead control engineer on several advanced process control projects, such as ethylene crackers, ethylene distillation and propylene distillation. Dr. Wang has published 15 papers and holds 2 patents in process control area. He was awarded the “National

Wenli Du is a full professor and also the deputy director of the Institute of Auto-mation in East China University of Science and Technology. She has more than 8 years of experience in modeling and optimization of chemical plants. Dr. Du has published several papers and holds patents in this area. She has been awarded the “National Science and Technology Progress Award” in 2002, 2005 and 2009. Dr. Du holds a PhD in control theory and control engineering from the East China University of Sci-ence and Technology, Shanghai, China.

Dahai Wang is a final-year postgraduate student at the East China University of Sci-ence and Technology, Shanghai, China. His areas of interest are multiple model adap-tive control and user interface development. He has published three research papers.

Feng Qian is a full professor and vice president of the East China University of Science and Technology. He has extensive project experience in the design and implementation of DCS-resident APC, inferential control and optimization strategies as applied to petrochemical processes (olefins, aromatics, specialty chemicals). He has published more than 250 papers and holds 22 patents. He has been awarded the “National Science and Technology Progress Award” in 2002, 2005 and 2009. Dr. Qian received his BS degree in 1988 and his PhD in 1995 both in industrial automation from the East China University of Science and Technology, Shanghai, China.

Zhiwu Tang is a section chief in the Science and Technology Office, SINOPEC Yangzi Petrochemical Co., Ltd., Nanjing, China. He has more than 20 years of profes-sional experience in the petroleum oil refining, petrochemicals and polymer industries. He is responsible for simulation, APC and RTO project management in the plants. Mr. Tang was awarded the “Shanghai Science and Technology Progress Award” in 2004 and “The Ninth China Patent Award of Excellence” in 2005. He received his BS degree in applied chemistry from Huazhong University of Science and Technol-ogy in 1987, and received his MS degree in chemical engineering from the Nanjing University of Technology in 2005.

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Fine-tune diesel hydrotreating operations This refinery used a simulator to train operators effectively

A. T. ALDAIEL, M. M. ALMULLA and M. A. ALNAJRANI, Saudi Aramco, Dhahran, Saudi Arabia; and K. CHAITANYA, A. DESHPANDE and V. HARISMIADIS, Hyperion Systems Engineering, Modeling and Simulation, Pune, India

Modern plants are heat integrated and are increasingly more auto-mated. Smooth processing con-

ditions dull the operators’ skills in handling abnormal events. Likewise, fewer young people are entering refinery operations as senior personnel retire. To remedy the expe-rience gap, a Middle East refinery opted to use an operator training simulator for a new diesel hydrotreater. This program focused on training and fine-tuning the needed skill set for the hydrotreater opera-tions’ employees.

BACKGROUNDLong production runs and few major

upsets within the process diminish the oper-ators’ skills in handling infrequent or unfore-seen situations. In practice, this is seen with operating losses through poorly managed disturbances, delays in achieving maximum throughput, inability to follow customer demands or exploiting market opportuni-ties and unrealized profits. In addition, it is becoming increasingly more difficult to recruit and train new operators effectively. The situation is exacerbated by the market competition for local skilled resources.1

An effective method that can support improved plant operator skills and main-tain process understanding and engineering analysis is a dynamic process simulator.2 This simulator can be either:

• Generic, standard or customized—Providing only a typical representation of the actual process unit.

• Custom-tailored and detailed—Accurately representing the end-user’s actual unit, by closely following the process and instrumentation diagrams.

There are arguments in favor of using a generic, standard or customized simulator

as a training tool (see Table 1). They are based on its immediate delivery and low cost. However the return value for these systems can be low; experienced operators will not see these simulators as relevant to actual plant operations.

A custom-tailored simulator is more effec-tive in training. It costs a fraction of a percent of a modern process unit, and the payback time can be significantly less than two years, depending on how it is applied.

The value to the corporation using such a simulator can be huge due to avoiding pro-duction losses or major plant incidents. Simi-larly, significant improvements are seen in safety and environmental compliance. Due to the greater understanding of the value that simulators can bring in improved operational

performance, operator training simulators are becoming a requirement for grassroots plants. If they are used before plant commis-sioning and for a detailed troubleshooting of the control system, a simulator can pay for itself before the plant is even operational and provide continuous value to the plant.3

This case history investigates a new oper-ator training simulator for a diesel hydro-treater plant in the Middle East.

DIESEL HYDROTREATING PROCESSHydrotreating reactions take place in a

fixed-bed reactor at elevated temperatures (300°C–400°C) and pressures (30–130 atmospheres), in the presence of a catalyst consisting of an alumina base impreg-nated with cobalt and molybdenum.4,5

Feedpump

Plantfeed

Heater

Reactor

Hotseparator

Strippercolumn

ColdseparatorRich amine

HP amineabsorber

Recycle gascompressor

Makeup gas

Purge gas

Lean amine

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Productpump

Hydrotreateddiesel

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Sour gasGas to amine treater

for H2S removal

A schematic process flow diagram for a diesel hydrotreater.FIG. 1



Fig. 1 is a schematic of the equipment and process flow streams for a typical diesel hydrotreating (DHT) unit. Liquid feed (at the bottom left hand side in the diagram) is pressurized, preheated and mixed with hydrogen-rich recycle gas. The resulting mixture flows through a fired heater where

it is totally vaporized before entering the reactor. The hydrotreating reactions, i.e., desulfurization, denitrogenation, satura-tion and cracking, occur on a fixed-bed catalyst. The reaction effluent is partially cooled and flashed at the hot separator ves-sel. The vapor flow is further cooled. The

resulting mixture of liquid and gas enters the cold-separator vessel at about 40°C and 45 atmospheres.

Most of the hydrogen-rich vapor from the cold-separator vessel is recycled to the reactor, passing through an amine contac-tor to remove hydrogen sulfide (H2S). Any excess gas from the cold-gas separator vessel joins the sour gas from the stripping of the reaction liquid product.

The bottoms product from the stripper is the final product from the hydrotreating unit. The overhead gas from the stripper is flared off or recycled. The amine solution to and from the recycle gas contactor comes from and is returned to the refinery’s main amine gas treating unit.

MODELING HIGHLIGHTSA commercial off-the-shelf dynamic

process simulator software package was used to model in high fidelity and from first principles all plant equipment. A typical screenshot for a model flowsheet is shown in Fig. 2. Process modeling included:

• Oil assay. An oil assay with a series of pseudo-components was configured to represent the oil properties. About 35 com-ponents were used to describe the simulator material streams.

• DHT reaction kinetics. All reac-tion types (desulfurization, denitrogena-tion, aromatics saturation, hydrocracking, etc.) were modeled. Reactions were tuned to match the heat and mass balance pro-vided by the process licensor. The model-ing accounted for impurities, like CO and H2O that could act as catalyst poison.

• Recycle gas compressor. Anti-surge control and compressor startup sequence were incorporated into the model. Thus, the operator could be trained in following the exact compressor startup procedures, either in distributed control-system (DCS)mode or in local mode.

• Hydrogen makeup reciprocal com-pressors. The manual loading and unload-ing provisions for compressor was modeled. The operator can change the compressor load from the DCS only, exactly as in the actual unit.

• Gas-amine contactor columns. The H2S absorption in amine was modeled using a reaction in the column. Foaming, including liquid entrainment, was simu-lated when conditions were appropriate.

• Diesel quality. The final hydrotreated diesel product quality was matched to licensor data, i.e., ASTM D86 curve.

• All control loops were tuned, so that the reaction to disturbances is quick

Process model flowsheet in simulator.FIG. 2

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TABLE 1. Types of training simulators available

Simulator type Definition Characteristics

Standard A typical unit Not accurate representation of the end user’s plant or control system. operation

Generic A typical plant Not accurate representation of the end user’s plant or control system.

Customized Modified standard Seems closer to the end user’s unit operation or plant. Often, this or generic models similarity is limited to equipment and instrument tag-names. Not accurate representations of the end-user’s plant or control system.

Custom P&ID based Accurate simulation of end user’s plant. Modeling is based on P&IDs and a copy or accurate emulation of the control system.



but stable. A number of complex control loops were tuned, using the lambda tuning method.6 The controller tuning parameters obtained can be utilized as the initial values in the actual DCS to expedite commis-sioning.

• Emergency shutdown system (ESD) was modeled in the simulator, using a series of simple logic blocks. To facilitate trouble-shooting and monitoring of the shutdown system during abnormal conditions, the logic diagrams were used as backgrounds, and red/green colored feedback for true or false respectively, was used for signals going from the ESD to the DCS or to the field. A typical screenshot of the ESD model is shown in Fig. 3.

• “Fast action” buttons have been provided for the draining and filling of hold-ups. This was done to ensure that the operator does not wait for long times dur-ing start-up and shutdown for a vessel to empty or fill-up.

System architecture. A typical opera-tor training simulator (Fig. 4) is composed of three main elements:

• Instructor station• Calculation engines• Operator stations (i.e., DCS consoles).The master simulator environment

provides the necessary tools to prepare and seamlessly integrate the aforementioned elements:

• Instructor station. In this case, it was built in the native simulator system. It contains graphics that are based on the DCS system and includes the field-opera-tor duties. It provides all the necessary tools for the operator to navigate through the plant, generate equipment malfunctions, create training scenarios, and review the performance of the operators. Typically, there is one PC used by the instructor.

• Calculation engines. The process model calculations are done in a series of “engines.” The overall process model is divided over a number of smaller models. These individual models are interconnected by multi-component streams, using physi-cal property information obtained directly from the simulator’s physical property and thermodynamic database. Each one of these models is running on a single CPU-core. A number of PCs may be used to run a process model.

• DCS engineering station. It per-forms all DCS functions (controller calcu-lations, alarm managing, etc.) and allows a control engineer to modify the database. It is connected to the operator stations, as

in the real plant. The connection between the simulator master environment and the DCS engineering station is through a spe-cial link designed to allow it to function as an integral part of the dynamic simulation system, e.g., simulator starting/stopping, loading/saving initial conditions.

• Cross-reference tables in the simula-tor link process model variables, instruc-tor functions, etc. with the DCS and ESD input and output points.

In this particular project, one instructor station PC and three simulator worksta-tions were used. This was done to ensure that the simulator system can achieve at least 2 x real time (i.e., simulate the plant phenomena in half the time that would be needed in reality) and in all operating conditions. The workstations were con-nected to a DCS system—consisting of four dual-screen operator stations, one engineering station and two FCS servers, performing the control calculations. The

architecture of the delivered system can be seen in Fig. 5.

Malfunction and scenarios. A mal-function can be defined as an unexpected, abnormal occurrence, e.g., a valve that does not operate as commanded or expected. The introduction of malfunctions is one of the most important aspects of simula-tor based training. They are used by the instructor to test a trainee-operator’s abil-ity to analyze, and to correctly respond to, similar challenges in the physical plant. Without a malfunctions capability, simu-lator-trained operators would be capable of handling a plant only under normal operat-ing conditions. Malfunctions for operator training simulators can be classified as:

• Standard malfunctions. These are malfunctions that are automatically pro-vided by the simulator system and are accessible through the instructor station (e.g., valve failure to its safe mode, global

Simulator masterenvironment

Calculation Engine 1

Calculation Engine 2

Calculation Engine N

...

Instructor station DCS Engineering station

Operator station(DCS console 2)

...

Operator station(DCS console M)

Operator station(DCS console 1)

Base simulator architecture.FIG. 4

Simulation network

Instructor station Color laserprinter

Switch

Simulator PCSimulator PC

ENG PC

Operatorstation (HIS)

Operatorstation (HIS) FCS0101

SwitchControl network

Operatorstation (HIS)

Operatorstation (HIS) FCS0102

Delivered simulator system architecture.FIG. 5



power failure, etc.) Note: These standard malfunctions depend on the simulation software used; although similar between vendors, the standard malfunctions are not necessarily the same.

• Custom malfunctions. This refers to the special equipment malfunctions that the simulator vendor must configure, especially for the delivered simulator (e.g. heat exchanger tube rupture, catalyst poi-soning, etc.)

• Generic custom malfunctions. These are malfunctions that must be custom-designed and implemented in the simulator for all similar equipment, e.g., blockage for all filters, fouling for all heat exchangers, etc.

Custom malfunctions. In the simula-tor delivered, the main/starting instructor

station page contains a series of navigation buttons, leading to different categories of custom malfunctions. A part of the page can be shown in Fig. 6. Each page contains detailed schematics that can be used by the instructor for easier control of the malfunc-tions. These schematics include custom-ized trends, transmitter values, etc., and they support the instructor in training the operator. Fig. 7 is a typical implementation.

Training scenarios. Scenarios enable the plant instructors to record and replay predefined sequences of events. Such events may be simple, i.e., closing a valve or trip-ping a pump or executing a malfunction; but they are quite complex as a plant nor-mal shutdown. All operator actions can be recorded and replayed by the instructor. These simulator features contribute to

training operators on emergency situations and the plant operation procedures. Now, operators can receive hands-on experience for troubleshooting rare and complex situ-ations, and the instructors can closely track the operators’ activities.

For the delivered operator training sim-ulator modeling system, a number of pre-configured scenarios were incorporated into the model (Fig. 8). Some are listed here:

• Loss of feed• Loss of recycle gas compressor• Loss of amine feed to the low pressure

amine absorber• Fuel/pilot gas to furnace failure• Steam failure• Less feed to unit• Loss of product pumps.

Trainee evaluation methodology. The trainee operator evaluation is based on their ability to maintain the plant at its nominal steady state. In this event, steady state is characterized by a series of variables, their acceptable operational limits (e.g., HH/LL alarms or ESD triggering points) and their relevant importance. When a malfunction is triggered, the operator is expected to:

• Recognize the plant area to respond• Understand what is not functioning

correctly• Mitigate any adverse effects.The simulator system keeps track of

the variables specified and reports the time that the variables in question are above or below the acceptable limits. The smaller the areas above/below these limits, the better the skills of our operator. A weighted aver-age of the response provides the “score” for each operator. This is shown schematically in Fig. 9.

Project milestones. The main project milestones are listed in Table 2. The typical work-breakdown structure for an opera-tor training simulator is shown in Table 3. Comprehensive tests of the model are required and include:

Process battery limitand

power/instrument airfailure malfunctions

Fouling/tube rupturemalfunctions

Reactormalfunctions

Amine malfunctionsand flow blockage

malfunctions

Part of the main navigation page, leading to a series of custom malfunctions.FIG. 6

A detailed schematic for heat exchanger fouling and tube rupture malfunctions .FIG. 7

A typical screenshot showing the training scenario summary window. The “loss of recycle gas compressor” scenario is selected and ready to run.

FIG. 8

TABLE 2. Project milestones

Milestone Week into the project

Kick-off meeting 0

Preliminary design review meeting 6

Critical design review meeting 11

Model acceptance test 25

Factory acceptance test 50

Site acceptance test 54

Training simulator “ready for use” 56



• During model acceptance tests, local model stability tests, training scenario and custom malfunctions detailed review, plant shutdown and start-up should be done. The earliest that modeling challenges are identified, the better. Accordingly, the simulator vendor will have enough time to review modeling without impact on the delivery schedule.

• Factory acceptance test for the train-ing simulator should be done as a dry run of the actual plant startup, and actual startup procedures and manuals should be used. Commitment to this approach should be obtained from all involved par-ties, including proponent, EPC contractor and simulator vendor.

• A simulator is a multi-purpose tool. It should be used to identify early any DCS modifications or design changes required that would normally be uncovered during commissioning. A simple statistical analy-sis of the factory acceptance test punch-list items for the current training simulator proj-ect revealed that over 53% of the observa-tions were related to the design of the DCS or the ESD. Details are shown in Fig. 10.

Benefits. The main benefits from this grassroots plant operator training simula-tor are:

• Troubleshooting and validating the plant process control system:

o It was verified that the control sys-tem operates as intended, without generat-ing spurious equipment trips.

o The DCS graphics and controls were reviewed prior to plant commission-ing and updates were suggested; a number of observations were reported. The actual DCS was updated to incorporate these changes. For example missing graphics or graphics elements were identified; associa-tions of graphics with the DCS controls were updated; incorrect DCS connections were fixed.

o Design changes were proposed, such as adding air or power backup to cer-tain critical valves or motors.

o Reasonable estimates for the con-troller tuning parameters were provided; these are realistic starting points for the commissioning of the actual plant.

• Reviewing the emergency shutdown logic:

o Cause and effect logic was rigor-ously tested on the simulator.

o Improvements were suggested and verified prior to plant commissioning. For example, delay timers were changed, and trip limits were reviewed.

• Verification of the plant startup and shutdown procedures. The factory accep-tance test was conducted as a dry-run of the actual plant startup and shutdown, using the detailed procedures provided by the EPC contractor. This allowed the par-ticipants a) to test the simulator system, ensuring that all objectives are met, and b) validate, clarify and improve the facil-ity operating manuals to be used for the actual startup.

• Facilitating the operator training: o Simulator provided a series of cus-

tom malfunctions and training scenarios o Continuous personnel training and

operator certification achieved o Since the simulator was available

six months prior to plant commissioning, the operators were fully trained on the new control systems functions, graphics and project configurations.

After model troubleshooting and deliv-ery of the simulator system, experienced operators commented favorably on the similarity between the dynamic response of the simulator and existing plants using the same technology. These factors are expected to play a significant role for the real plant, resulting in:

• A faster and smoother startup and achievement of the nominal steady state

• Increased process understanding for both engineers and operators

• Safer and more efficient operation under transients and abnormal conditions

• Longer plant runs and prolonged catalyst life, since extreme or adverse opera-tional conditions are avoided.

Status and future work. The DHT simulator was successfully delivered to the site six months before the actual plant start

635

640

645

650

0 10 20 30 40Time, minutes

Heat

er e

xit t

empe

ratu

re, °

F

Time belowlow limit is 9 min

Area low

High limitAreahigh

Low limit

Time abovelow limit is 7 min.

A typical example of an operator evaluation.FIG. 9

Model observations 39.7%Other 7.1%ESD 5.8%DCS affectingmodel 9.5%

DCS observations 38.9%

Factory acceptance test observations for current project categorized. FIG. 10

TABLE 3. Work breakdown structure for the operator training simulator

Activity Effort, %

Model building 45

Project management, quality, 15documentation

Instructor station 5

Integration and testing 12.5

Reviews, acceptance tests (factory, site) 10

Updates (plant data alignment) 7.5

Training and documentations 5

Total 100


50

up. Since delivery time, the operators used the simulator extensively, obtaining confi-dence, learning about the plant behavior, and getting hands-on training on opera-tional procedures and in mitigating the effects of equipment malfunctions.

After the plant commissioning, startup and attaining the nominal steady state, the simulator was successfully updated to reflect the realities in the plant. Thus, it will continue to offer a) valuable insight to engineers for verification of plant design

updates, controllability and de-bottle-necking studies, b) continuous training and certification to plant operators, and c) increased value for the shareholders for the years to come. HP

LITERATURE CITED 1 Resnik, C., “Better Operator Ergonomics Increase

Plant KPIs,” Automation World, December 2009. 2 Pankoff, J. A. Sr., Use a Competency-Based

Approach to Develop High-Performance Workers, Hydrocarbon Processing, August 1999.

3 Harismiadis, V. I., “Earn two million dollars a year.

Dynamic Process Simulation: DCS Integration, Quality Assurance, and Operator training,” 3rd Pan-Hellenic Chemical Engineering Conference, 31 May–2 June 2001.

4 http://en.wikipedia.org/wiki/Hydrodesulfurization 5 Hydrocarbon Processing, “Refining Processes

Handbook;” “Advanced Process Control and Information Systems Handbook,” 2005.

6 Olsen, T. and B. Bialkowski, ”Lambda Tuning as a Promising Controller Tuning Method for the Refinery,” AIChE Spring National Meeting, March 2002.

Amr AlDaiel is a project engineer at Saudi Aramco for Northern Area Projects Department. He has five years of experience in project management in the area of Instrumentation, pro-

cess control and automation. Mr. AlDaiel holds a BS degree in electrical engineering, cum laude, from the University of Colorado.

Maan AlMulla is a senior Project Engineer at Saudi Aramco for North-ern Area Projects Department. He has 13 years of experience in project man-agement and has managed from the

client side two OTS Projects. Mr. Maan holds a bachelor degree in Systems Engineer—Automation and Control from KFUPM University since 1998.

Mansour AlNajrani is a senior process engineer at Saudi Aramco for the Ras Tanura Refinery Opera-tion Department. He has 14 years of experience in operation and was rep-

resented the refinery for the OTS Project. Mr. AlNajrani holds a bachelor degree in chemical engineering from KFUPM University since 1995.

Krishna Chaitanya is a pro-cess simulation engineer at Hyperion Systems Engineering, Pune, India. He has two years of experience in refinery FCCU process operations and three

years in dynamic process modeling and operator train-ing simulators. Mr. Chaitanya holds an MS degree in refining and petrochemical engineering.

Amol Deshpande is a team leader at Hyperion Systems Engineer-ing, Pune, India. He has nine years of experience in the process industry: three years in process design and

detailed engineering and six years in dynamic process modeling. Mr. Deshpande holds a BE degree in chemical engineering from Pune University, India.

Vassilis Harismiadis is the real-time process optimization and train-ing manager at Hyperion Systems Engineering. He has over 13 years experience in the oil and gas indus-

try with particular emphasis on using dynamic process modeling to improve plant effectiveness. Dr. Harismiadis holds a PhD from NTU, Athens, Greece in the thermo-dynamic modeling of complex systems.

Scott Rollman Strategic Business Manager:

Global Engineering

[email protected]

www.vega-americas.com

VEGA Americas’ VEGAPULS 62 through-air

radar sensor produces a safety high level

signal, protecting against overfill situations.

With self-diagnostics and self-calibration,

the unit provides the user with measurement

accuracy and reliability.

The VEGAPULS 62 through-air radar supplies the following benefits:

Mountable on a ball valve assembly for service and access

Integral self-monitoring significantly reduces maintenance costs

Operation verification without process interruption and system downtime

SIL approved

Overfill Protection with

Through-Air Radar Technology




Process gas chromatography: Avoid the iceberg of hidden expensesTotal cost of ownership can quickly add up for field analytical equipment

M. GAURA, Emerson Process Management, Houston, Texas

P rocess gas chromatographs (GCs) are the most common multi-component, online chemical analyzer used in modern hydrocarbon processing industry (HPI) facilities—refineries,

petrochemical plants and natural gas sites. GCs are proven and can provide data to control processes, supervise product quality and monitor facility emissions. Historically, GCs were placed in shel-ters that provided a stable temperature environment and a clean work area for operation and maintenance periods.

To ensure optimal performance and reduce sample lag time—the time required for a sample to travel from the sample tap to the GC—the shelters were often located in areas classified as hazardous by industry codes and standards. Also, the space for the GC and shelter was not always available in close proximity to the process line being sampled.

In both situations, significant costs were added to these proj-ects that operations or environmental personnel had not consid-ered in the project budget. The primary focus would have been on purchasing a GC that could provide a reliable and repeatable analysis of a sample stream at the best price. Unfortunately, the cost for the GC often represents a very low percentage of the total project cost, about 5% to 20%. Parties responsible for profit and loss (P/L) of operating units, plants and pipelines are keenly aware of the total cost impact of adding a traditional GC to perform a stream analysis. Accounting systems are now more open, and in-house engineering and installation resources have been replaced with contractors and consultants. So where are the “hidden” costs for field-mountable GCs, and what can be done to greatly reduce the total costs to install and operate a GC?

The iceberg. Selecting, installing, operating and maintaining a traditional GC can, and does, involve costs that exceed the capital cost for the GC. Think of the total cost structure as being similar to that of an iceberg in the ocean. A typical iceberg has about 10% to 15% of its volume above the water with the remainder hidden below the water’s surface. When deciding to install a GC, think of the price for the GC (only) as the portion of an iceberg visible above the water. This example shows that significant additional costs may not be considered when selecting the GC. Those costs can include: a protective shelter, in-house or contracted engineer-ing, installation charges, instrument air, heated sample lines, training, and startup and check out.

Shelter. If one assumes that the shelter will need to control temperature in both directions—heating during winter and cool-

ing during summer—and be compliant to hazardous ratings of the location, it will represent at least 40% of the total project cost. One cannot simply consider the cost of four walls, a door, a roof and a floor for a typical installation. The additional costs of the heating, ventilation and air conditioning (HVAC) unit also involve a purge system, area monitors and alarms, lighting, com-munication and electrical distribution, as well as instrument air, plumbing and vent headers; all must be considered.

Engineering. The cost for both in-house and contract engi-neering are significant for any project. They involve bringing a physical structure onto an industrial plant. These costs are some-where between 15% to 18% of the total project cost.

At the onset, equipment requirements need to be understood and discussed by those wanting the new GC and those that are responsible for the processing unit that will house the GC. Speci-fications must be developed and distributed to internal teams and potential suppliers that address the installation area including

Iceberg of hidden costs.FIG. 1



hazards present, available footprint, environmental conditions, proximity to sample point and utilities, etc. After hundreds of hours of reviewing proposals, clarifying concerns and ultimately making a purchasing decision, site-preparation engineering must begin. Foundation requirements need to be detailed, plumbing and conduit runs drawn up, communication and power inter-connections finalized. All actions require significant time and, therefore, add cost to the GC addition project.

Installation. Once the planning and designing are detailed, the shelter must be physically installed. Installation charges will make up 15% to 20% of the total GC project costs. The material and labor costs to install a secure base (concrete pad) represent a portion of the installation costs, but not all of it. If the shelter is large, the installation may require renting a large crane to place the shelter in the final mounting location—a significant additional expense. Even if the shelter can be placed at the selected loca-tion utilizing available plant equipment, labor costs will still be incurred as the structure is secured, communication and power interconnects are made, and tubing and piping are connected to existing points in the plant.

Instrument air. Although instrument air is often readily avail-able in a plant, lines must be installed to the shelter, thus adding more costs to the project. These expenses are composed of not only materials, but also the labor required to install and con-nect the hardware. The operational cost of air use should also be considered. Using a cost of $0.80/1,000 scf for plant air, an air-bath-heated GC can add over $2,000 to the operational costs. If the GC and shelter also require purge system, the operating costs to escalate.

Heated sample line(s) and probe(s). For samples necessitat-ing extended sample line runs (assume 200 ft = “extended”) and a heated probe, it is reasonable to assume an additional cost of $7,000–$15,000 to total project expenses. Sample lines that are unheated and uninsulated can save several thousand dollars in costs, but one needs to closely evaluate the typical stream com-position and its possible dew point and compare them to the known environmental conditions before foregoing heated sample lines. Failure to do so may result in multi-phase samples entering the sample-handling system or the GC, resulting in inaccurate analysis values.

These “hidden” costs account for 70% to 80% of the total installation cost of a new GC. They are referred to as “hidden”

costs, as they are often not considered when the initial decision is made to add a control or required measurement in a plant. Obvi-ously, they must be.

How can you reduce ‘hidden’ costs? Many users of GCs are convinced that they (GCs) are complex pieces of equipment and must be housed in environmentally controlled shelter. A GC can be intimidating. Why? These devices have an electronic section similar to that of a personal computer (CPU, communication interfaces and video displays); and they have an analytical oven that can consist of shut-off, vaporizing inject, column and back-flush valves, and, more important, such ana-lytical units include multiple detector technologies such as ther-mal conductivity, flame ionization and flame photometric, and a variety of possible separation media, e.g., columns. Couple this mindset with the critical nature of the results being gener-ated, and it is easy to understand why process GCs are often placed in shelters. Of course, some users have experienced per-formance issues with their GCs when atmospheric temperatures and/or pressures swing; they also contribute to the preference of placing a GC in a shelter.

What if the GC did not need to be placed in a shelter, or did not require the shelter to be temperature controlled? Yes, to properly analyze a sample stream, GCs require stable tempera-ture, pressure and flowrate of the sample as it travels through the analytical oven. Variations in temperature can result in drifting baselines, peak shifts and even multi-phasing of the sample. Pres-sure and flowrate changes can also impact sample values if they are not controlled.

Minimizing analytical errors can be accomplished by using a properly designed sample handling system, appropriate transport tubing and an application-defined probe assembly. At no point in this extraction, transport or conditioning of the sample gas is a shelter required. The items listed here can be electrically or steam-heated and mounted outside in nearly any environment without compromising performance or safety. The costs associated with a shelter—including the shelter itself as well as the engineering and installation costs—have already been discussed. They are significant, but can they be removed or reduced?

Recently, field-mountable process GCs have been introduced to the industry and are gaining increasing acceptance. Field-mountable GCs are generally smaller and typically have more limited application capability compared to traditional air-bath oven GCs. But the initial costs to house, install, operate and maintain these field-mountable process GCs are less than larger “conventional” GCs.

Issues of environmental impact, hazardous area classifica-tion, utility consumption, application capability, availability and maintainability are discussed and compared and costs assigned where possible. The total cost of installing, main-taining and operating process GCs will be examined over a hypothetical installation and 10-year period and compared to a traditional air-bath oven analyzer design and field-mountable transmitter designs.

Environmental. For a conventional GC, the instrument is designed for installation in an analyzer house. It is not recom-mended to be installed in the field without additional climate-control protection because of repeatability issues. It cannot withstand rain and is sensitive to high humidity. Normally, con-ventional GCs need some ambient temperature control to ensure

Field-mountable process GC.FIG. 2



oven temperature stability, particularly in low-temperature envi-ronments and those with widely varying temperature cycles.

For the new field-mounted GC, the instrument is designed to be installed directly in the field without any additional protection. Field-mountable GCs are designed to withstand rain, high humid-ity and a wide ambient temperature range—typically –20°C to 60°C (–4°F to 140°F)—without impact on their analytical per-formance. Housings are typically IP 56 or higher. A rain shield or three-sided rack can be included for those times when technicians must do maintenance on the equipment, but it is not required.

Hazardous area classification. For a conventional process GC, the area classification GC depends upon the manufacturer, but is normally Class 1 Division 2 Groups B, C and D, utilizing an appropriate purge mechanism. Some manufacturers offer Class 1 Division 1 Groups B, C and D. None of these instruments are explosion proof. Fig. 2 is a typical example of a conventional process GC.

The field-mountable GC derives its protection for flammable hazardous areas from its enclosure (Fig. 3). The instrument hous-ing is explosion proof, and there is no need for an air purge to ensure rating. The typical area classification is Class 1, Zone 1, AEx d IIB+H2, T4, Enclosure Type 4—various agency approvals, such as ATEX, CSA and IEC-Ex are often obtained.

Utilities. For the conventional GC, normal electrical require-ments for the oven are from 1,140 W to 1,200 W during initial startup and 400 W to 500 W during normal service. Instrument air is required for cooling and purging of the electronics (Class 1 Division 1), as well as for the oven temperature air-bath heater. Some manufacturers also recommend purging electronic sections with “dry” instrument air to prevent the buildup of dust or mois-ture in them. Failure to do so can result in the premature failure of electronic board assemblies or components housed in this section of the process GC.

For field-mounted GCs, the instrument consumes less elec-trical power during initial startup and during normal use—often less than 150 W. The field-mountable GC does not require instru-ment air for any functions because of oven and housing design. Therefore, continuous heating of “plant air” is not required, reducing the power requirements. Pneumatic valves can be actu-ated safely by carrier gas. Electric sample shut-off valves, solenoids and sample stream switching valves can be utilized. Pneumatic sample valves and column valves are also utilized. Carrier gas fol-lows a flow path from reference detector to oven columns/valves, and then to the measurement detector to minimize carrier gas consumption. The field-mountable GC provides significant utility cost savings over the useful life of the instrument.

Oven design capability. For a conventional GC, the oven heat is provided by a heating tube and heater coil. Because the oven space is large, air must circulate to ensure adequate tem-perature distribution and control. This arrangement is known as an air-bath oven. Almost all process GCs use air-bath ovens. Tight proportional, integral, derivative (PID) control of the oven temperature is normally used. (Some older process GCs have only proportional integral control.) This provides adequate tempera-ture control; however, the large thermal mass of the oven makes it slow to heat up and cool down. Temperature stability upon initial power-up or after oven maintenance will take at least one hour to attain. Maximum oven temperatures vary depending upon the manufacturer. Typical upper limits are 180°C to 200°C. The larger internal space allows multiple detectors and valves to be housed. Sub-oven assemblies can also be installed, allowing for

temperature programming that is required for applications like simulated distillation.

For the field-mounted GC, a central core is used for the oven. This is heated by block wrap-around heaters. The thermal mass of the oven assembly maintains temperature stability and transmits heat to the detectors mounted to the oven assembly. Tight PID control can be maintained because the oven’s thermal response time is fast. The columns are near the detectors and heaters, allowing stable heating throughout the analysis. The entire oven assembly is enclosed in an insulation packing. This ensures the ambient temperature rating of the field-mountable GC—typically –20°C to 60°C (–4°F to 140°F).

A maximum of four sample/column switching valves (6-port or 10-port) can fit into the oven, based on the manufacturer. The oven can house up to two thermal conductivity detector (TCD) sets: TCD/TCD; or a flame ionization detector (FID) and TCD in a TCD/FID detector set. This is also dependent on the manufacturer.

Given the compact design and the heating method of the analyzer, the maximum oven temperature is lower than that of a traditional GC, but can still be up to 150°C. The lower number of possible valves, reduced space for columns and lower tempera-ture capabilities can all limit the number of applications capable by the field-mountable GC; in some instances, it can limit which high carbon number compounds can be analyzed. Programmed temperature type applications, such as simulated distillation, cannot be done.

$60,000 $60,000 $60,000

$5,000 $5,000 $10,000 $7,500 $60,000

$125,000

$3,300

$24,750

$56,824

$50,000

$50,000

$50,000

0

50,000

100,000

150,000

200,000

Anal

yzer

inst

alla

tion

cost

, $

250,000

300,000

350,000

Sun shields 3-Sided shelter Analyzer house

Engineering Installation and training Shelter/enclosure Heated sample line cost GCs and SHS

$125,800

$199,750

$301,824

Installation cost comparison of field-mountable and conventional process GC (1 GC).

FIG. 3

2,722 5,610 143 6,600 2,900

19,400 6,800

20,400

20,736

50,681

0

20,000

40,000

60,000

80,000

Tota

l ana

lyze

r ope

ratin

g, $

100,000

120,000

140,000

Airless oven Air-bath oven

HVAC electricity Utility air Carrier gas Spares Calibration gas Electricity

Total OPEX Savings over10 years = $105,086

Ten-year cost comparison of field-mountable and conventional process GC (1 GC).

FIG. 4



Cycle times. Column configurations and oven temperatures for both the field-mountable GCs and conventional GCs do not differ significantly for most applications. Accordingly, the cycle times are relatively equivalent. A complete analysis of natural gas up to and including C6+ hydrocarbon components,

giving a measurement within 0.1 Btu in 1,000 Btus, for exam-ple, is accomplished in a field-mountable GC in four minutes.

Sample transport lag times. Conventional process GCs are usually installed in analyzer houses, which are often five times more expensive than the cost of a single GC. Therefore, an opti-mization of the analyzer house is done to place as many analyzers as possible into a single house. This rationalizes the cost of the house between various GCs. Location of the house is determined by plant geography (space available as opposed to the geographic distribution of the sample taps). This rarely allows for the opti-mizing transport lag. A conventional GC typically has longer lag times when compared to a field-mountable GC. A 200-foot ¼-in. sample line will introduce a two-minute transport lag into a control loop. The effect of this lag depends upon the control strategy, process dynamics and analysis cycle time. Additionally, compromises and costs in sample/speed loop disposal to a flare may have to be made to obtain fast sample transport times.

The field-mountable GC can be installed close to the sample tap to reduce the sample transport lag and to help optimize con-trol response. A closely coupled field-mountable GC (30 ft to 40 ft) will typically have a 20-second sample lag time.

Installation considerations. For a conventional GC, typical extra expenses include engineering time and costs, additional labor associated with a shelter installation, safety systems for the enclosed space, pouring an installation pad and bringing plant instrument air to the conventional process GC. For a field-mounted GC, the design allows it to be mounted closer to the sample takeoff; so the sample line itself can be shorter. The installed cost is lower, and that can be significant when heat-traced sample lines are used. Fewer problems will be encountered obtaining a representative sample due to the sample characteristics

TABLE 1. Basis for cost estimates

Utility and calibration costs

Electricity cost $0.05 $/kW-hr

Instrument air $ for 1000 scf $0.80 $/1,000 SCF

Carrier gas cost $170.00 $/cylinder

Calibration cost $330.00 $/cylinder

GC data Air bath oven Airless oven

Utility air CFM 5 0

Electricity (W) 630 33

Calibration gas bottles/year 2 1.7

Carrier gas bottles/year 12 4

Annual spares and replacement parts $1,940 $290

Shelter

HVAC unit power 6.6 KW

HVAC description CSA Certified Class I Div

2 Groups B, C & D,

includes freestanding 25 ft

fresh air stack, certified

to 130 mph wind

Replacement A/C after 10 years $21,620

TABLE 2. Installation cost comparison: Field-mountable GC vs. traditional GC

1 Transmitter GC 1 Transmitter GC 1 Conventional GC in sun shield in 3-sided shelter in analyzer house

Number of GCs/enclosed house 1

Number of GCs/3-sided shelter 1

Number of GCs/sun shield 1

Number of 3-sided shelters 1

Number of enclosed shelters 1

Gas chromatograph cost $45,000 $45,000 $45,000

Sample system cost $15,000 $15,000 $15,000

Sample line cost per ft-insulated Installed $5,000 $5,000 $10,000

Engineering costs $50,000 $50,000 $50,000

Enclosed house cost C1 D2 10 ft x 14 ft $125,000

3-sided shelter cost C1 D2 6 ft x 6 ft $60,000

Sun shield for single GC cost $7,500

Enclosed shelter installation at site cost $49,725

3-sided shelter installation at site cost $20,800

Sun shield installation at site cost $650

Analyzer installation cost $650 $650

Shelter startup and check-out cost $1,300 $2,600

Training cost $2,000 $2,000 $2,000

Instrument air piping (300 ft) cost $2,499

One-year cost estimate

Required capital $125,800 $199,750 $301,824

Savings $176,024 $102,074



changing during transport. Problems with clogging, two-phase flow and condensation are also reduced. The field-mountable GC can be freestanding, mounted to a pipe, or mounted in a simple three-sided shelter. The field-mountable GC will occupy less space in the plant than an analyzer house, and will simplify transporta-tion of the analyzer to the final site.

Availability. Preventative maintenance needed by both the field-mountable GC and conventional GC is similar as is the time required to do this maintenance. Should oven substitutions be uti-lized, some field-mountable GC ovens can be completely replaced in approximately 20 minutes. Mean time to repair (MTTR) will depend upon the particular defect in question. Both the field-mountable GC and conventional process GC will require about 10 minutes of cool-down before components can be handled, plus the time to substitute any required components. It will take from 1 to 4 hours to re-establish temperature stability.

So how can you avoid or reduce the hidden costs? When considering a new GC for your site, one should be able to determine whether a field-mountable process GC can be utilized for a specific plant need. If possible, using a field-mountable GC can significantly lower the costs associated with adding a process GC measurement. Tables 1–3 outline not only the upfront cost savings if a fully enclosed shelter can be eliminated, but also the expected cost savings over the life of the equipment. In this example, 10 years was selected. Tables 1–3 detail the differences between field-mountable and conventional process GC installa-tions and life-cycle costs, and Figs. 4 and 5 add graphical repre-sentations of the comparative costs.

A decision to add a process GC in a plant is often the result of extensive research and planning, and the benefits or required needs are well understood. However, many hidden costs are often not considered in the planning and budgeting phase(s). Selecting the appropriate GC and enclosure type can significantly reduce both capital and operational expenses. In appropriate applica-tions, field-mountable GCs offer significant savings and greatly reduce total cost of ownership compared to conventional process GCs by reducing costs with climate controlled shelters, instru-ment air, electrical power, carrier gas consumption, installation costs, and also by reducing sample lag times. Typical installation cost savings, given the estimates and assumptions can be as much as $175,000, and the 10-year operational cost savings can be as much as $105,000. HP

TABLE 3. Cost comparison field-mountable GC vs. traditional GC over 10 years

GC operation cost Air bath oven Airless oven Savings

Electric cost $2,722 $143 $2,579

Utility air cost $20,736 $0 $20,736

Calibration gas $6,600 $5,610 $990

Carrier gas $20,400 $6,800 $13,600

Spares and replacement parts $19,400 $2,900 $16,500

GC total operation costs $69,858 $15,453 $54,405

Total shelter and HVAC $50,681

Total OPEX savings $105,086

Total OPEX savings over 10 years $105,086


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Find benefits in automating boiler systemsDynamic models unravel potential problems in high-pressure steam production and consumption

A. BOURJI, D. BALLOW and M. CHOROSZY, WorleyParsons, Houston, Texas

Steam-load shedding is a series of automated actions imple-mented to prevent or to minimize the impact to a refinery or petrochemical complex due to a shortage of steam supply

shortage or in drop steam-system pressure. Steam load is eliminated by shutting down or “shedding” expendable steam users. Without an appropriate strategy, safety systems will engage possibly shutting down critical equipment to prevent catastrophic consequences, and production will be lost. It is important to note that steam load shedding is done to improve the reliability of the steam system; it is not a safety system. Steam load shedding will replace or eliminate the need for a properly designed emergency shutdown system.

Design stability. The stability of high-pressure (HP) steam boiler(s) is critical for hydrocarbon processing industry (HPI) facilities. There are several stability definitions in steam delivery systems. Process and mechanical engineers generally think of sta-bility in terms of the equipment’s ability to function as intended. The steam system is unstable when a boiler cannot produce the requested amount of steam due to insufficient fuel or heat-transfer surface limitation. Control engineers, however, think of stability in terms of loop stability. An automatic controller is deemed stable if the process variable tracks the setpoint in an acceptable manner. The third stability type is combustion stability. A well-anchored flame is produced when combustion is stable. Unstable flames are produced when local upsets in the air-to-fuel ratio occurs. Local upsets in air-to-fuel ratios are sometimes caused by oil mixtures that contain excessive amounts of light volatile hydrocarbons.

Realizing the potentially varied viewpoints of all stakeholders, the goal of stability control is to steer the steam system toward operating conditions that meet all of these expectations. To that end, a master controller is often applied.

Case study. To better illustrate the concept of steam load shed-ding and boiler stability, the following case study investigates the expansion of an ethylene petrochemical complex. The complex consists of three facilities (A, B and C), and it was built in three phases at different times over 15 years. Each phase of the complex was provided with its own separate steam system. At the end of the third phase, one design objective was to integrate the steam system throughout the three phases to optimize cost and provide a reliable, robust and stable steam supply for the entire complex.

The integrated steam system consists of seven boilers and various users from the three processing facilities. Each boiler’s

control philosophy is similar, consisting of feedback control using local steam header pressure to adjust boiler loading. Individu-ally, this control strategy works well, maintaining the required steam header pressure at the local source. When used in a large, integrated facility with boilers separated by more than 1,000 m of piping as in this case study, the total system stability and required response time becomes critical.

Defining upsets. In the system, an initial condition must first be established. This initial condition can be viewed as the normal operating backdrop against which the dynamic analysis will intro-duce upsets. For this case study, a base scenario of normal operation was set at running all boilers in automatic pressure cascade control. Alignment with the operating plan is important at this stage, since the dynamic results will be influenced by the initial conditions.

To properly mitigate declining steam availability, the likely causes for steam shortages must be defined. These cases are best agreed upon by consulting with the process engineering and oper-ations personnel. For this case study, these upsets are proposed:

• Loss of one hydrocarbon feed source to the ethylene furnaces, resulting in the shutdown of the furnace-wall burners on 3 of the 10 cracking furnaces.

• Loss of super-high pressure (SHP) boiler feedwater to the ethylene furnace convection section steam system or fuel gas, result-ing in a shutdown of all furnace burners on all cracking furnaces.

• Boiler trip, including a series of boilers going out of service: Single boiler tripping Two boilers tripping Three boilers tripping. These scenarios are analyzed using a dynamic simulation to

determine their impact on the steam system.

Dynamic modeling. To achieve a reliable, robust and stable steam supply throughout the complex, the fully integrated steam system must be analyzed in a dynamic state to understand the probable interactions between the shedding logic programs. Oper-ating facilities are generally not able to risk a major shutdown to test system responses from upsets. The next best option is to model the system dynamically. The dynamic model is a testing platform on which the logic can be proved out and adjusted, if necessary.

A steam balance is first developed using a steady-state model. These models are “based on the assumption that feed streams and specifications are constant, with no holdup, delay or transient



condition in the process being modeled. Dynamic simulators, in contrast, do account for holdup, delay and some transient condi-tion.”1 Fig. 1 is a dynamic model of the steam system, and it is built using these cases:

• Steam suppliers and users are modeled as control valves with their performance characteristics programmed into the valve actions. This allows for reduced complexity and stable model solutions while still giving the user control over rates of change at the supplier/user.

• Let down valves are modeled using valve data.• HP steam header piping configuration is determined from

isometrics when available and plot plans in the absence of iso-metrics.

• Control information: Boiler performance per manufacturer and/or plant data

Maximum boiler ramp rate Boiler capacity limitations Turndown restrictions. • Compressor turbines’ steam use is based on performance curves

• Ethylene furnace trip action narratives and cause/effect matrices.

As shown in Fig. 1, the model is designed as a simplified system using a source and sink construction. This is an appro-priate model construction when the boiler characteristics are well understood and the primary interest is the piping system’s dynamic characteristics. The model is focused on studying the changes in header pressure during upsets; a simplified source-sink structure is used. Boiler ramping characteristics can be programmed into the emulating controller to closely mimic the boilers observed or predicted behavior.

Dynamic modeling limits. As with any engineering exercise, it is important to understand the limitations of the chosen analyti-cal method. For this case study, the dynamic model is ideal but has limitations. Noise, lag and process variability will force the real controllers to work at maintaining their setpoints. The idealized model presented here does not account for this behavior. How-ever, dynamic simulation software generally has the capability to emulate this non-ideal behavior. It is up to the user to determine the appropriate level of complexity and to recognize that increased complexity will result in slower, less stable simulation runs. Addi-tionally, the model simulates the steam-distribution network. But, it does not include the other two sides of the boiler stability triangle—fire side and water side. This will be important when analyzing the results for boiler stability.

Shedding strategy. After identifying the upset cases and setting the major parameters and assumptions for the dynamic model, a shedding strategy must be developed (see Table 1). Major steam users must “shed” to recover from the upset scenario. The input of experienced operations personnel is essential in develop-ing a ranking of the major steam users for the “shedding process.” This ranking will allow developing steam-shed actions resulting from steam-header pressure loss.

For the case study, System A was constructed first. Its steam shedding was initially developed on a stand-alone basis. Each sub-

sequent addition, systems B and C, were also initially configured on a stand-alone basis. The total facility must be evaluated for inter-actions. If required, setpoint adjustment or other mitigation strategies may develop from the integrated case check. For example, if steam header pressure sag causes simultane-ous actions in all three major areas that shed too much load, some actions may be shifted to a different trigger point in the logic.

Automated steam-shed control system. Each of the three case study pro-cess facilities have steam-shed control sys-tems implemented by logic programmed within their respective distributed con-trol systems (DCSs). Note: For this case study, three separate and distinct DCSs are involved due to the timing and execution strategy applied when building facilities A, B and C.

Each process area’s steam-shed control system monitors the pressure of the HP steam header within its process area. When

Ethylene unitcompressorturbine

Boiler 1Boiler 2

Turbineuser

Condensinguser 1

Condensinguser 2 Let down

station

FCxxx

FCxxx

PCxxx

FCxxx

FCxxx

FCxxx

FCxxx

FCxxx

FCxxx

Secondaryproducers

Secondaryconsumers

Include sufficientpipe operations tosimulate holdup

volume

Pipe 1 Pipe 4 Pipe 6 Pipe 7

Pipe

2

Pipe

3

Pipe

5

Pipe

8Pi

pe 9

Direct manipulation of theboiler source stream flow

Sample model flowsheet.FIG. 1

TABLE 1. Basic principles of steam-load shedding

Steam-load shedding is done through an automated program in the DCS.

The steam-load shedding program is configurable at an engineering level to allow the facility to make setpoint adjustments as required in the future.

The steam-load shedding program will have an automatic (or active) mode and a manual mode.

Proactive actions—Simultaneous shed actions that are triggered upon receipt of trip signal such as a boiler-master fuel trip.

Reactive actions—Shed actions as a result of a steam header pressure sag.

Shedding a turbine driver and activating a motor backup must include steps to ensure that the load is taken by the electric driver prior to shedding the steam turbine. This happens above the turbine auto trip point, since the turbine con-troller would automatically switch the driver to electric if steam pressure is lost.

Shedding strategy includes the impact of header pressure sag on the large turbo-machinery equipment. This equipment often must be protected from high pressure differentials that can cause damage.


the steam-header pressure within a process area drops to a prede-termined pressure, the area’s steam-shed control system automati-cally shuts down the expendable steam users within its process area, in a predefined “shed group” determined according to the shedding strategy. If the steam-header pressure continues to drop, the steam-shed control system automatically shuts down addi-tional steam user shed groups within its process area. As a shed group is shut down, the demand on the steam system is reduced, assisting the boilers in restoring header pressure.

Steam shed groups are predefined groups of steam users. Each group consists of steam users that have been selected to be shut-down or “shed” at a specific HP steam header pressure. There can be several shed groups within a single facility. The shed groups within each process facility are grouped by their importance to plant operations. The first shed group contains users that are least important, followed by the second, third and fourth shed groups in order of increasing importance.

For the case study, the steam-shed control systems within each of the three process facilities are programmed to shed only steam users within its process area. The steam-shed control system for one process area does not shut down steam users in another process area.

Once a proposed shedding logic has been added to the model, analysis of the system response to the defined upsets can begin. For the case study, the analysis of the dynamic simulation model shows that the existing steam-shed control system within each process facility properly mitigates the simulated steam system failure scenarios and prevents an uncontrolled collapse of the integrated HP-steam system pressure.

Results. For each upset scenario, a dynamic model is run with and without steam-load shedding actions. The upset begins 30

seconds into the simulation so that the steady-state normal opera-tion prior to the upset is visible. A representative sample of the dynamic modeling results is presented for Scenario 1—loss of one hydrocarbon feed source to the ethylene furnaces resulting in a shutdown of the furnace wall burners on three cracking furnaces.

The initial state for Scenario 1 is normal operation with nine ethylene unit furnaces online and the tenth in hot steam standby (HSSB). The upset occurs when loss of a single hydrocarbon feed to the system results in the activation of the furnace emergency shutdown system. The resulting safety interlock activation will force three of the eight operating furnaces from normal opera-tion to HSSB.

For the case study facility, SHP steam is produced in the eth-ylene unit cracking furnaces and is let down to the HP level. The ethylene units are connected on the HP header level to balance

050,000

100,000150,000200,000250,000300,000350,000400,000450,000500,000

0 5 10 15 20 25 30 35 40 45 50 55 60Time, minutes

Flow

, kg/

h

Ethylene unit HP steam production. The initial steady-state operation of the HP steam production is interrupted after a partial furnace trip. The loss of furnace heat input results in a ramp down of HP steam production.

FIG. 2

Since 1968Select 72 at www.HydrocarbonProcessing.com/RS



the facility. An upset in SHP steam production in an ethylene unit cascades to reduce available HP steam for the rest of the facility. After a partial trip, each tripped furnace produces steam equiva-lent to HSSB rates. Simultaneously, the cracked-gas compressor in the ethylene unit (due to reduced feed) will go to turndown operation. The net effect on the HP steam header will be a ramp down of HP steam extraction from the cracked-gas compressor as shown in Fig. 2.

The pressure controller for each boiler area automatically senses the drop in HP steam header pressure and ramps up steam production to compensate. Fig. 3 shows a typical boiler ramp up for each area. The ramp rate is limited by the emulating flow con-troller based on known boiler characteristics. Once steam header pressure nears a recovery point, the header pressure controller seeks its setpoint according to its tuning parameters, as evident by the slight oscillations shown starting at 10 minutes.

The boilers are not able to ramp up production fast enough to prevent a rapid drop in header pressure. The header pressure response is shown in Fig. 4. The steam-load shedding logic begins switching off expendable users at 38 kg/cm2g, thus facilitating a more rapid pressure recovery.

Fig. 5 is an example on the impact of steam load shedding. Once Facility A header pressure drops to 38 kg/cm2g, the load shedding logic stops an expendable user within the Facility A utility area. A typical user is a large turbine driver that has an

available electric motor backup. As shown in Fig. 5, Facility A has a turbine user in its utility area that is included in the shed-ding logic. For this example, when the header pressure reaches the shedding trigger point, the logic engages the backup electric driver and disengages the turbine driver, thus reducing the steam requirement for the Facility A utility area more quickly than a boiler can ramp up steam production.

Integrated system stability control. As previously dis-cussed, steam-load shedding logic prevents or lessens the impact from upsets on the steam system. During operation, some vari-ability will occur within the normal operating range. The facility-wide steam system must be capable of responding to these normal variations without relying on steam-load shedding logic. For a complex site with multiple boiler installations, such as the case study, a supervisory stability controller or plant master controller can be used to ensure appropriate responses by different boiler areas. The objective of the stability controller is to prevent a boiler trip or steam-header pressure sag scenario from causing steam loss, resulting in an uncontrolled crash of multiple process units.

What is a plant-master controller? Without a plant master, there is a potential that although the individual control system responses to a perturbation are correct, the sum total of the individual actions can cause the total system to become unstable, and all boilers to be knocked offline. However, a plant master cannot function effectively unless each boiler’s individual boiler master is fully functional.

For all boilers to be effectively integrated and function as a single central steam plant, each boiler must be fully functional and capable of being put into automatic control. When each boiler is capable of functioning in automatic, a plant-master control can be used to orchestrate the response of individual boilers to system

TABLE 2. Potential issues causing boiler instability

Process issues

Effects of produced fuel gas composition swings on boiler stability

Variability of oil delivery temperature, pressure and composition

Lack of system blow off

Environmental constraints

Boiler mechanical issues

Inability to produce name-plate steam rate

Cold end corrosion

De-superheat circuit reliability

Position of swirler in burner

Position of oil gun (proper insertion)

Vibration associated with oil burning rates

Vibration associated with excess FGR rates

Limitations of the burners

De-superheat circuit reliability

Control function issues

Inaccurate/unstable air measurement

Possible inaccurate gravity feed bias to air-to-fuel ratio adjustment

Damper characterization

Repeatability of valve and damper positioning on demand change

Environmental constraints

126,250136,250146,250156,250166,250176,250186,250196,250206,250216,250226,250

0 10 20 30 40 50 60Time, minutes

Flow

, kg/

h

Boiler steam production. Boilers outside of the ethylene unit will respond as header pressure falls, ramping up their steam production rate. The ramp rate is limited by the emulating flow controller based on known boiler characteristics.

FIG. 3

30

32

34

36

38

40

42

44

46

0 5 10 15 20 25 30 35 40 45 50 55 60Time, minutes

Pres

sure

, kg/

cm2 g

B A C

Steam header pressures. The header pressures begin declining immediately due to the upset. Boiler ramp rates are insufficient to stop the decline until the first shedding logic trigger point is reached at 38 kg/cm2g. The system rebounds quickly after the shedding logic engages.

FIG. 4


perturbations. The plant master is essentially instructions that tell each boiler what to do when the system is perturbed. The plant master tells the individual boiler’s boiler master what to do.

Options. A plant master can be designed with various goals. For the case study facility, the plant-master control can be imple-mented in several ways:

As is. This option basically does the best with what the plants already have in terms of hardware. It will run the seven boilers in automatic under a limited number of conditions and will flip the system into manual when it sees certain conditions.

When a predetermined number of signals are unavailable or deemed out of range, the plant master puts the system in manual just as the individual boiler master puts a control loop into manual when it loses a primary signal. For example, for an individual boiler, if the signal from the flue-gas recirculation flow transmitter becomes unavailable to the controls of the boiler, this condition is alarmed, and the associated FGR control station is automatically switched to manual mode. To understand what it means to configure a plant master in this fashion, one must understand the limitations of the existing equipment, fuels and individual system dynamics.

The boilers operate under many constraints. All boilers must work within the constraints set up by their individual burner management systems (BMSs). A BMS can be thought of as a set of go/no-go instructions. The BMS does not control boiler modulation; it simply allows the boilers to modulate. The “rules” or setpoints within the BMS are fixed and cannot be altered dur-ing operation.

It is not uncommon for boilers to operate on a wide range of fuel gas gravity and composition. This fact causes an inherent control difficulty for all of the boilers. The control systems of all boilers can only function if gravity and heat release are consistently

related. Fig. 6 represents the programmed relationship between gas specific gravity (sg) and Btu content, when the sg of the gas is 0.5, then the fuel value must be approximately 1,000 Btu/scf and when the sg is 0.2, the fuel value must be approximately 500 Btu/scf. The system cannot function if the heat value of the gas is ever 500 Btu/scf or when the sg is 0.5, as represented by the red dot in Fig. 6.

The relationship between gravity and air demand must also be similar. Any changes in air demand for a given gravity will cause problems for the burner. For example, in combustion controls, it is not uncommon to program gas gravity compensa-tion into the control system if a variable fuel gas composition is expected. The potential downside of this strategy is that gas’s gravity must be consistently related to specific gravity. If com-positions can exist that create a gas sg with a heating value that is not in line with the programmed relationship, then combustion instability can occur.

Facility A otherprocess users

0

20

40

60

80

100

120

0 10 20 30 40 50 60Time, minutes

Flow

, tho

usan

d kg

/h

Facility A utility area

User shedding example.FIG. 5

Since 1968Select 72 at www.HydrocarbonProcessing.com/RS



Each BMS has a set of rules that determines the allowable fuel gas pressure to each burner. If the gas pressure is too high, or if the gas pressure falls too low, the burner is no longer “safe,” and the BMS will take action to shut the burner down. Since a burner is essentially a fixed orifice, the gas pressure at the burner for a given heat release will be much higher on fuel gas compared to natural gas. The problem can be that the high-high fuel gas pressure trip was set to natural gas. When the boiler fires produced fuel gas, its output is constrained. It may not be able to make its nameplate rating for steam because the BMS shuts the burner down when it gets to that high-high gas pressure setting. The plant master and boiler master must understand the constraints and tell the boilers to act accordingly. This information can be incorporated into the steam-shed dynamic model, introducing new constraints to the model and altering conclusions when compared to more ideal boiler performance constraints.

The essence of the “as is” strategy is to define how to live with the physical and dynamic constraints of the system. This strategy will need to identify when and how the current equipment can be controlled in automatic. Some of the issues with the “as is” scenario are:

• Difficulties in accurately measuring combustion air• Difficulties in achieving NOx compliance under all load

scenarios• Effect of demanded low load on the metallurgy• Ability to maintain boiler operation on single burner MFT• Repeatability of FGR valve positioning on load change• Mechanical vibration associated with oil burning• Thermo-acoustic vibration on fans.Further dynamic modeling of the system “as is” will help

increase the understanding of the limitations and constraints that exist in the current boiler systems. The modeling will need to incorporate field measurements of various process parameters, and to be tested against historical data trends from known upsets.

Most of the time. This strategy addresses any issues experienced by existing boilers. It will require that all existing boilers be tuned and that operational priorities and strategies for waste disposal be addressed. After studying the existing system, hardware and philo-sophical changes can be made to stabilize operations. Once indi-vidual boiler stability is demonstrated, a plant master can be config-ured to reinforce stability when rapid and coordinated responses are required from the three steam plants. The plant master can decide which plant reacts first, second and third, taking into account the constraints of the individual systems, as shown in Table 2.

Dynamic modeling will be useful in this scenario as a testing platform for proposed hardware or control changes. After more precisely emulating current boiler behavior, the model can be updated to predict the benefits of the proposed modifications.

All of the time. This is the perfect world scenario. All boilers would run totally in automatic. This is probably not realistic for many boiler installations, especially those firing fuels with varying compositions such as waste oils. It would require that all of the fuels (gas and liquid) being fired in the boilers be consistent in terms of composition and delivery pressure.

Field verification and stability. Input from the field is essential to the success of developing a plant master. Current problems in automatic and manual control must be understood and mitigated where possible.

The robustness of a plant master depends on the robustness of the individual boiler masters. It is necessary to discuss operating practices and challenges with the operators to ensure that all issues are addressed and factored into the design of the plant master.

System stability depends on the ability of each individual boiler to do requested actions consistently without affecting the other boilers within the system. Individual boiler stability depends on the ability of that boiler to perform the action requested without knocking itself offline.

The dynamic model can be easily tailored to mimic current operation and then used to predict future response and guide decisions. This tailoring can only be accomplished through close consultation with operators who have hands-on experience with the existing boilers under a variety of operating condi-tions. Once tailored, the model is tested for acceptance during an organized meeting by a team comprising of engineering and operations representatives. This is commonly known as a “model acceptance test.”

Options. With the five boilers of Facilities A and B in opera-tion, the plant operators are able to maintain header pressure by manually modulating the five boilers. At any given time, they could make as many as 120 (5 factorial) wrong decisions. When Facility C comes online, with two additional boilers, if the system is in total manual, they could make 5,040 (7 factorial) wrong deci-sions in response to a system perturbation, a major increase in the probability of system wide destabilization.

The dynamic load shed analysis has shown that the capacity and layout of the system are essentially functional but do not address system stability. Further model development could incor-porate additional information from the field and provide insight to stability issues.

If the multiple decentralized steam plants are to be run in automatic for any length of time, as in the case study, there must be plant-master controller. The plant-master controller will tell each boiler how to respond to perturbations in the system. A plant-master controller can work only if each boiler is capable of responding to the request being made. To configure a plant mas-ter in the most economic and effective manner, the difficulties encountered by each individual boiler must be acknowledged and understood. Problems that can be mitigated through boiler tun-ing should be resolved. Problems requiring changes in hardware should be evaluated relative to cost and benefit. HP

0

500

1,000

1,500

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Gas specific gravity

Gas

Btu

cont

ent,

Btu/

scf

Heating value as a function of specific gravity.FIG. 6

Complete literature cited and author biographies and photos available online at HydrocarbonProcessing.com.

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Consider model-based inferential properties for reformersA European refiner opts to apply embedded multivariable predictive controllers as part of an advanced process control system

S. BIRDI and A. AUTUORI, Eni, Italy; S. LODOLO, Aspen Technology, Italy; and C. BEAUTYMAN, Aspen Technology, UK

Eni settled on using inferred properties models as part of a control and optimization application for two refineries on reformer units. Using improved rigorous models in opera-

tions would help achieving a more accurate and reliable control for the reformers. One solution identified was applying an open-loop advisory systems area and closed loop control and optimization area. In the present low-gasoline market, reformers must improve hydrogen (H2) supply and consumption. European refiners, often, operate reforms to balance gasoline or hydrogen production.

Reformers can be operated in many different ways depend-ing mainly on process type (continuous or semi-regenerative), market scenario and specific refinery or aromatics complex setup. Research octane number (RON) always plays an important role. RON can be minimized, to ensure proper gasoline blending, maximize hydrogen production or operated as a trade off with catalyst life in semi-regenerative units.

It is rare that a proper RON online analyzer is available in reformers, and it almost never happens that information related to other properties, such as coke laydown rate, is made avail-able in real time to operations. Such information is normally reported by very infrequent lab analysis. Important constraints like skin temperatures are often unreliable or not available after some run time.

Some process licensors provide tables and correlations on their manuals and occasionally proprietary code is deployed online to estimate some reformer constraints like RON. This code is a black box, difficult to maintain, tuned on a specific catalyst and not based on a commercial and open simulation tool. This code becomes unusable after changes in the process, like a secondary air added in the regenerator tower, or catalyst vendor changes. This area is also non-core business for process licensors, and these black box applications tend to be poorly maintained. eni decided to develop the model for one refinery and with good results apply it to eni’s Livorno refinery reformer.

Process operations and constraints. The eni Sannazzaro refinery’s continuous catalytic reformer (CCR) is equipped with a multivariable predictive controller that, in current scenario, pushes the unit against constraints (basically furnaces metallurgy) maximizing feed while guaranteeing a minimum RON in the reformate, i.e., in the stabilizer bottom stream. Most active con-straints in the multivariable predictive controller application are:

1. Reformate naphtha octane number (RON)2. Coke deposition on catalyst3. Maximum tube skin temperatures4. Hydrogen to hydrocarbon ratio in reactors. This refinery is quite complex with many units that influence

H2 and the gasoline pools. A less than maximum reformer feed or a too low or too high RON would result in extra costs or reduced profit coming from more expensive H2 used in conversion and desulfurization units—a suboptimal solution. It would incur extra MTBE consumption if a too low RON or simply because of a dif-ferent-than-planned blending receipt in case of a too high RON.

Solution description. Inferentials, based on rigorous models have been developed for:

• Reformate naphtha octane number (RON), WAIT, WABT• Coke laydown rate (kg/h)• Coke on catalyst (wt%)• Coke profile in reactors• Duties of furnaces• Skin temperatures• Minimum hydrogen to hydrocarbon (H2/HC) ratio.The solution development major steps are:• Calibrate the reformer rigorous model offline using unit

test run data• Run case studies (4,000+ cases) to exercise model over com-

plete operating range• Analyze model responses and build quadratic inferential

models to be deployed online and used as controlled variables within a multivariable predictive controller application

• Periodically (e.g., annually) recalibrate model to represent long-term catalyst activity decline and any other process changes that may be implemented.

The rigorous model is not directly deployed online. Quadratic correlations are derived instead and deployed proprietary software is used to build and deploy online inferential properties. The rigorous model could be deployed online, and this is actually an area being investigated to assess additional benefits that could be obtained. The presented solution is based on standard and configurable field proven tools as it was the better fit for the eni Sannazzaro refinery.

The rigorous model online deployment would open the way to a truly adaptive inferential, given the fact that the recalibration



of the model could be automated. Fig. 1 shows the overall archi-tecture deployed online.

Rigorous model. The rigorous reformer model is based on fundamental kinetics and it’s integrated with fractionation/heat exchange models providing a full flowsheet model that is repre-sented in the Fig. 2. Both continuous (CCR) and semi-regenera-tive (SRR) reformers can be modeled. Some of the main features of the model are:

• Detailed feed characterization o separate responses have been analyzed for feed naphthenes

and aromatics content and not simply N+2A content• Rigorous reactor model o Response against average bed temperature (or WABT)

have been analyzed and not simply inlet temperature (or WAIT), obtaining more representative responses

• Rigorous heat balance o “Heat sink” effect of recycle gas is modeled giving realistic

Hydrogen/Hydrocarbon ratio response.

Inferentials development includes:WAIT and WABT. Weight average inlet temperature (WAIT)

is calculated as:

WAIT =

TIN(R1)×CW (R1) +TIN

(R 2)×CW (R 2) +TIN(R 3)×CW (R3)

CW (R1) +CW (R 2) +CW (R3)

where TIN (for R1, R2, R3) represent the inlet temperatures of the three reactors and CW the catalyst weights.

The weight average bed temperature (WABT) based on the reactors average bed temperatures (ABT(Ri) ) is calculated as:

WABT =

ABT (R1)× CW (R1) + ABT (R 2)×

CW (R 2) + ABT (R3)× CW (R3)

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟⎟

CW (R1) + CW (R 2) + CW (R 3)

RON is inferred based on these process measurements:• Rxj inlet temperature• Rxj outlet temperature• Feed rate• H/HC ratio• Separator pressure• Catalyst circulation rate• Feed naphthenes• Feed aromatics• Rvp specification.The average bed temperature for each reactor is calculated

using the Rxj measures, and RON is then inferred based on a qua-dratic correlation and using these average bed temperatures and all other measures reported here. The correlation actually uses all measures both linear and quadratic terms.

Coke. Both the coke laydown rate (kg/h) and its amount on catalyst (wt%) have been calculated based on both the reactor and the regeneration operative conditions. A coke profile in the reactor has been also calculated depending on the occasional event of not completely burning the coke in the regenerator and thus not completely regenerating the catalyst. This profile is used to evaluate the maximum amount of coke deposition in the reactor to be used to set the minimum hydrogen-to-hydrocarbon ratio. The minimum H2/HC ratio is calculated to avoid excessive coke on catalyst entering the regenerator.

It is well known that a full catalyst cycle reactors-regenerator is as long as 8 or 10 days but the regenerator section (i.e., CCR sec-tion of the reformer) can stop for a long time while the reformer keeps running. These events have then to be properly managed.

The current coke laydown rate (i.e. the rate in kg/h at which coke is laying down on the catalyst) and the resulting equilibrium coke on catalyst (wt%) are inferred based on the same process measurements used for RON (obviously apart Rvp) plus naph-tha feed boiling range information and using a similar linear and quadratic correlation.

The current coke laydown rate (kg/h) inferred represents the spot coke laydown rate. If the same feed and operating conditions are maintained for a complete catalyst cycle, then this would represent the equilibrium coke on catalyst (wt%) entering the regenerator. The regenerator entry point is where the catalyst samples are taken and then analyzed.

The catalyst cycle through all reactors in the eni Sannazzaro refinery is approxi-mately eight days and during this period feed quality and operating conditions nor-mally change many times. Using laboratory results to update this inferential would be incorrect. Nevertheless the coke on catalyst can be calculated as:

Flowsheet model of reformer.FIG. 2

Diagram of inferred property model for the reformer.FIG. 1



Cokewt % =

Cokekg /h

CatalystCirculationRatekg /h

×100

We could call the above properties equilibrium coke laydown rate and coke on the catalyst. The actual coke laydown rate to the regenerator (kg/h) and the current spot (wt%) coke on catalyst is calculated directly from the air and oxygen content process mea-surements. The calculation is based on a total mass and oxygen balance around the regenerator. The combustion reaction, assum-ing a chemical formula for coke equal to CHx is:

4×CH x + 4 + x( )×O2 ⇔ 4×CO2 + 2x×H2O

With the air flow measures, it is possible to calculate the oxy-gen amount that is consumed during the burning process and the burned coke (kg/h) corresponding to the amount of consumed oxygen. Once the coke laydown is obtained, the coke on catalyst (wt%) can then be calculated.

H2/HC ratio. The minimum H2/HC ratio, that guarantees to maintain the coke deposition over catalyst within limits, must also be calculated. This is actually the minimum H2/HC ratio that maximizes reformate yield while respecting the coke on catalyst constraint in regenerator capacity.

Both the reactor and regenerator operations affect the mini-mum H2/HC ratio because the coke deposited over the catalyst depends on the operative conditions of the reactor but also on the coke that passes through the regeneration section without burning. It’s very important to consider the event of the coke not burning in the regenerator due to a CCR failure and passing through and thus underestimating the minimum H2/HC ratio.

Fig. 3 reports three different cases that explain what happens when the regenerator tower shuts down:

• In normal operations (A,) the coke deposited in the reac-tor is totally burned in the regeneration section; thus the coke on catalyst depends only on the reactor operative conditions. A uniform deposition of coke along the reactor can be assumed in this case.

• If the regeneration tower fails to burn completely, then the coke (B), a slice of catalyst not completely regenerated enters the reactor.

• When the regeneration tower is again operative (C), the coke is completely burned and the catalyst is fully regenerated again. However the slice of catalyst covered by coke is still in the reactor, slowly moving and generating a “coke profile”.

The minimum H2/HC ratio calculation considers the presence of the moving coke slice to avoid underestimating the H2/HC ratio. Without this accurate calculation, when the coke slice exits the reactors section, the operator has to reduce the catalyst circu-lation rate to avoid an overload of the regeneration tower. This would happen days after the CCR failure event. The coke profile is described and monitored within the solution and accurate minimum H2/HC ratio is supplied to the multivariable predictive controller application.

Duties and skins temperatures. Duty is evaluated on the process side with the classical heat transfer equation taking into consideration the mass flow, feed composition, H2/HC ratio and the delta in I/O temperatures.

Skin temperatures are inferred by adopting the Standard API-530 method, where the radial component of the heat flow is cal-culated together with the transfer heat coefficients in bulk, fouling

and across the tube describing this way the temperature profile from the inner part to the skin of the tube itself. This certified methodology is not straightforward to implement and requires a detailed knowledge of furnace geometry and metallurgy and also products thermodynamic properties, but it permits safe use of inferred skin temperatures as closed-loop controlled variables.

The products affecting heat exchange are naphtha and hydro-gen, in a vapor-phase mixture, and also coke deposited along the walls of the tubes. The thermodynamic properties that are required for naphtha and hydrogen are specific heat, viscosity and thermal conductivity; while for coke the only needed property is thermal conductivity. All of these properties have been calculated using the rigorous model and then building accurate correlations, function of pressure and temperature, to be deployed online. Heat transfer coefficients have been calculated according to API-530 as function of Reynolds (Re) and Prandtl (Pr) numbers:

h = f Re,Pr, Tb

Tw, μbμw

⎛

⎝⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

where: Tb, μb represent respectively the bulk temperature (coil outlet temperature) and bulk viscosity

3) tube wall

2) coke layer

1) fluidBulk temperature

Maximum skin temperature

Temperature profile across tube: naphtha mixture, coke layer and tube wall.

FIG. 4

Rege

nera

tor

Rege

nera

tor

Rege

nera

tor

Reactor Reactor Reactor

Cokeprofile

a) b) c)

Coke profile of the CCR reformer.FIG. 3

■ Incorporating advanced process

control can provide users with accurate,

dependable and timely process

information on variables that are not

easy to measure directly.



Tw and μw represent respectively the temperature of the fluid that is in direct contact with the coke layer and its viscosity

Tw is unknown and must be found through an iterative process that has been easily implemented on line.

The temperature profile across tubes is defined by the three layers as shown in Fig. 4:

1. Fluid as mixture of naphtha and hydrogen2. Coke layer3. Tube wall. The duty generated in the furnace must be decomposed in an

axial duty flow and radiant duty flow; therefore, the global gener-ated duty is multiplied by corrective factors: FC a factor account-ing for circumferential heat flux variations; FL for longitudinal heat flux variations; FT to take into consideration the effect of tube metal temperature on the radial heat flux.

QRAD = DUTY ×FC ×FT ×FL

Once the radial duty is calculated using API-530 method to obtain the three correction factors, then the Δ T across the three different layers can be derived as:

ΔTf =Q RAD

h×

DeDi−2×tc⎡

⎣⎢⎢

⎤

⎦⎥⎥ ΔT across fluid

ΔTc =Q RAD×tc

kc×

DeDi− tc⎡

⎣⎢⎢

⎤

⎦⎥⎥ ΔT across coke

ΔTm =Q RAD×ta

kw×

DeDe− ta⎡

⎣⎢⎢

⎤

⎦⎥⎥ ΔT across metal

where:De and Di are external and internal diameter of the tubestc is the coke layer thicknessta is the tube thicknesskc and kw are the thermal conductivities of coke and metal

respectivelyThe maximum skin temperature can then be calculated adding

the above ΔTs to bulk temperature.

Results. Some obtained results are reported:RON results:• 1 year data (2008, Fig. 5)• 160 lab analysis,• 11 outliers (plant shutdown-startup),• 149 used samples.

where in blue the inferential, in red the infrequent lab. The pro-cess licensor correlation was based mainly on the WAIT measure and the N+2A feed analysis was required to update the mea-sure (this analysis is done once per week) to account for feed quality changes. The blue line RON inferred measure takes into account the feed quality changes, by including in the calculation the WABT, which is a measure of the reaction progress. Fig. 6 provides an idea of how well the lab analysis data are reproduced: 99% of the data falls within ± 0.45 range, and the official lab analysis ASTM reproducibility is ± 0.70.

99.006,660 6,720 6,780 6,840 6,900 6,960 7,020 7,080

98.5099.1798.6799.3398.8399.5099.0099.6799.1799.8399.33

100.0099.50

100.1799.67

RON_ATRON_LAB

RON values predicted and measured by the lab.FIG. 5

0-3.00 -2.00 -1.00 0.00

RON (LAB-INF)

DataGauss

1.00 2.00 3.00

5

10

15

20

25

Scatter of lab vs. model predicted RON.FIG. 6

Coke LabCoke INF

4.000

27/0

2/08

05/0

3/08

14/0

3/08

11/0

3/08

20/0

3/08

17/0

3/08

23/0

3/08

08/0

3/08

02/0

3/08

4.750

5.500

6.250

7.000

4.000

8.500

Coke make values predicted and measured by the lab.FIG. 7

■ APC can maximize profitable operations

while respecting both constraints that can

be measured directly and those variables

that can be accurately inferred to maintain

safe and reliable operation.



Coke results:• 1 year data (2008, Fig. 7)• 97 lab analyses• 6 outliers (regeneration tower OUT)• 86 used samples.

where in blue the inferential and in red the infrequent lab analy-sis, Fig. 8 provides an idea of how well the lab analysis is data is reproduced.

Skins results as shown in Fig. 9:1. Green lines: installed thermocouples (low reliability, par-

ticularly with furnace in end-of-run conditions); red line: inferred skin temperature

2. Change in process operative condition: inferred measure was responding properly, following the most reliable thermo-couples indications

3. Plant shutdown: during startup the inferred measure fol-lowed the thermocouples signal

Similar impressive results have been obtained for H2/HC ratio, Duties and other estimated properties.

Advantages. The solution described in the previous sections and deployed online to be used by the closed-loop control, is by far superior to any other available solution and also to any online analyzer. It can provide not only RON but also coke on catalyst, minimum H2/HC ratio, Skins and other accurate information that analyzers can’t supply.

Some of the advantages for this solution are:1. Use of standard software: open architecture; no “hidden

code” nor black boxes; and use of configurable, integrated and user friendly tools.

2. Inferred measures (RON, coke laydown, skins, etc.) are calculated by regressing a reformer rigorous model that can be tuned and calibrated easily. No simple correlations nor “simpli-fied” models.

3. Detailed feed characterization in the model (not simply N+2A).

4. Accuracy in constraints calculations makes it possible rid-ing actual constraints as defined by an LP or blending model, i.e., obtain the true potential from the unit.

5. The application can be easily maintained, recalibrated and even customized by changing a few parameters in the configura-tion section.

6. The deployment online is made through a standard and field-proven tool that provides validation for both input and output signals to guarantee a safe DCS interfacing.

7. The operator interface used for online deployment is stan-dard, web based, auto-configurable, i.e., does not need any effort to be maintained and modified in case of application changes: it reads the configuration files and updates automatically.

8. Inferred measures updates with lab analyses or analyzers to correct bias are embedded in the web based application.

9. Automatic links with APC platform and inferred proper-ties are made available via standard architecture that allows only validated values to be used for closed loop control.

10. Availability of a rigorous reactor model for offline what-if analysis, test different naphtha feeds and catalyst deactivation monitoring.

11. Model can be also used for planning (LP) models accuracy improvements and online KPI targets calculation and perfor-mance monitoring.

12. Catalyst and even process vendor independent solution

model can be tailored on specific process configuration and if catalyst is changed and even if process is revamped/modified, the investment is preserved.

Benefits. The benefits related to more accurate, reliable and real-time information on quality depend on the use of informa-tion. Certainly using RON, skins, H2/HC, coke laydown as controlled variables within a control application that is designed to continuously push the unit, permits to make the best use of such information. Obtainable benefits obviously depend on market scenario, the way reformer is operated and specific refinery constraints.

At present, H2 costs are well above 1,000 €/ton and a less than maximum H2 production from a reformer due to constraints such as skins or coke, that are not truly represented, can result in huge profit losses from hydrocracking or desulfurization units. Just 200 kg/h of H2 not available for refinery conversion and desulfurization units could mean 2 million (MM) €/y loss.

The gasoline pool plays a role even in current diesel oriented market, because gasoline is one of the crude cuts and must be sold. Being able to run closer to a RON target, minimizes give-away, and permits avoiding downgrading too much gasoline to LPG. Conversely, a too low RON leads to more expensive blending receipts and higher MTBE consumption. Typical sav-ings, even if strongly depending on specific refinery layout and blending pool, could range from 0.5 up to 1.5 MM€/y for each reformate RON point.

0

Δ Coke (LAB-INF)

5

10

15

20

25

-3.00 -2.00 -1.00 0.00 1.00 2.00 3.00

DataGauss

Scatter of lab vs. model predicted coke make.FIG. 8

Skin temperatures values predicted and measured by the thermocouples.

FIG. 9

70

PROCESS CONTROL

It has been verified that efficient reformer inferentials embed-ded in a closed-loop application led to a feed increase of 3.7% in the eni Sannazzaro refinery. These came with:

• Increased H2 production being able to push truly repre-sented constraints

• Reduced RON giveaway• Reduced MTBE consumptions• Better CCR temperature profile.In a semi regenerative reformer, where catalyst life cycle is

driving operations having accurate RON and coke, i.e., catalyst life, estimates is even more important and benefits can be much higher particularly in a pro-aromatics reformer. This solution has also been successfully applied to a semi-regenerative reformer.

Options. The proposed application for CCR and SRR infer-entials, developed with a rigorous model and deployed online through a field proven tool, is by far superior to any other solution currently available. It’s based on open architecture and is accurate, reliable, easy to configure and maintain. The investment is always preserved also in case of process or catalyst changes.

Using such inferentials in closed-loop-control applications to obtain additional benefits, if compared to poor estimates, may repay the investment in just a few months. The availability of a rigorous reformer model can be used for other purposes like “what-if ” analysis and to test different feed types, maximizing this way the investment. The next step is deploy online directly the rigorous model and move to a truly adaptive inferential. HP

Stefano Lodolo is a senior advisor and industry consultant with Aspen Technology in Italy. He has nearly 25 years of field experience in advanced process control in refining, chemical and petrochemical industries. Mr. Lodolo has successfully implemented dozens of MPC and other automation projects on a wide variety of process units. He

holds an MS degree in chemical engineering for Bologna University, Italy.

Dr. Clive Beautyman is a senior advisor with AspenTech and is based in the UK. After earning a BSc in chemical engineer-ing from UMIST and a PhD from Imperial College, he joined BP in 1984 working on early refinery RTO and inferential projects. Dr. Beautyman has subsequently worked for a number of refinery

services companies including Profimatics, Honeywell and KBC Process Technology. In 2001, he joined AspenTech. He specializes in refinery reactor modeling across a range of planning, operational and process engineering applications. He has worked on projects with numerous refining clients throughout the world. Dr Beautyman is a Fellow of the Institution of Chemical Engineers and is a Chartered Engineer.

Santo Biroli is with the process control dept. responsible at the eni R&M Sannazzaro Refinery. He has a broad range of experience in process control, modeling and optimization. Over the past 15 years, he has been actively involved in developing, implementing and commissioning of APC algorithms, inferential sensors, multi-

variable predictive control and optimization projects carried out on numerous refinery processes. He holds a BS degree in electronic engineering for the Pavia University, Italy.

Augusto Autuori is responsible for APC project coordination of eni refineries. After a degree in chemical engineering from University of Salerno, in 2002, he joined eni as an APC engineer. Between 2002 and 2006, he participated on several APC projects (DMCplus and inferential implementation) at some refinery plants. In 2006, he

moved to eni R&M HQ technology department and is responsible for APC project coor-dination, oil movement systems implementation on eni primary logistics hubs a long with innovative systems implementation for plants monitoring and operator training.

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A challenge for existing refineries is how to process heavy crudes and handle the technical constraints

associated with such feedstocks. A new heavy crude was discovered at the Mangala field in the Thar Desert of Rajasthan, India, in January 2004. The crude resources went into production in late August 2008. The Indian Institute of Petroleum (IIP) con-ducted a detailed analysis of this crude for product yields and characteristics. Lower distillate yield (23 wt%) and difficulties associated with its transportation through pipeline due to a higher pore point (39+ °C) clearly indicate that neat processing of the new crude by existing refineries may not be feasible.

One solution was to design a grassroots refinery designed specifically for this chal-lenging heavy crude oil located near the Mangala field. Eight grassroots refinery configurations capable of processing the Mangala crude were conceptualized and evaluated economically with regard to finish

products meeting Euro IV specifications. Results from the study indicated that indi-vidual product and combined distillate yield (gasoline + kerosine + diesel) are configura-tion dependent, and they are governed by the combination of secondary conversion processes as part of the processing scheme included in the configuration.

Need for more oil. Reduced avail-ability of lighter conventional crudes and growing global demand for energy drive efforts to find and produce new crude

resources. India is actively seeking new offshore and onshore crude sources. Like-wise, heavy crude oil reserves are increas-ing in availability. For example, the heavy crude reserves at the Mangala field in the Thar Desert of Rajasthan, India are esti-mated at 3.6 billion barrels (570 billion m3) oil of which 1 billion barrels (160 bil-lion m3) are recoverable. Cairn India is the current operator of the field, a subsidiary of Cairn Energy. At present, 125,000 bpd (125 Mbpd) of crude oil is pumped out from wells in Rajasthan by Cairn India,

Refinery configurations: Designs for heavy oilConceptualization and economic evaluations considered all possible scenarios to process clean gasoline and diesel from domestic feedstock

S. KUMAR, S. M. NANOTI, Y. K. SHARMA and M. O. GARG, Indian Institute of Petroleum, Dehradun, India

Amine treating

Treaters

Treaters

Hydrotreater

Gas processing

SR naphtha

SR gasoil

Kero

Naphtha

Other units gas

Gas

Gas

Gas

VGO

Vacu

umdi

still

atio

nDe

laye

d co

ker

LCOFCCunit HN NHT NSPL

Reformer

LN

LNCoker LN

Vacuumresiduum

Long

resi

due

Coker HN

LCGOHCGO

H2

H2

H2

Crude ADU

Refinery gas

Gasoline

Diesel

Claus sulfur plant

Kero/jet fuel

Sulfur

LPG

Slurry oil

Light naphtha

Coke

Configuration 1—CDU + DCU + FCC + Reformer + HDT.FIG. 1

TABLE 1. Mangala crude characteristics

Characteristics Value

Density, 15°C, kg/liter 0.8804

Gravity, °API at 60°F 29.13

Pour point, °C +39

Total sulfur, wt% 0.08

Total acid value, mg KOH/g 0.25

Wax content, wt% 20.60

Watson characterization factor, KUOP 12.47

LPG potential (C3 + C4), wt% 0.01

Naphtha (IBP–140°C), wt% 1.10

Distillate (IBP–370°C), wt% 23.30



and plans are in effect to to produce 150 Mbpd in the near term.1,2

Reliance Industries, Essar Oil and Indian Oil Corp. Ltd. (IOCL) and Man-galore Refinery have shown interest in pro-cessing a blending stock to conventional crude. With an increasing production

rate, lower distillate yield (23 wt%) and difficulties associated pipeline transport issues associated with the Mangala crude, existing refineries are not designed to han-dle this very heavy crude oil. A grassroots refinery located near the Mangala field is the best option.

Mangala crude characterization. Detailed analysis of Mangala crude was carried out at IIP. Table 1 lists the major characteristics of the crude oil. With a spe-cific gravity value of 0.881 (API: 29.1), the Mangala crude is neither heavy nor light. However, its distillate (from IBP–370°C) and naphtha (from IBP–140°C) fraction yield values of approximately 23 and 1.1 wt % of crude are significantly lower in comparison to corresponding values of approximately 50 and 12 wt% for conventional crude. This crude oil can be considered part of the heavier crude category. Watson characterization factor value of 12.47 clearly indicates that it is paraffinic in nature. Also, the higher pore-point value of 39+°C poses the challenges in transpiration via pipelines.

Refinery configurations. Present day data indicate that there is a continuous shift to middle and light distillates at the expense of heavy ends and to ever increas-ing higher quality standards.

In view of constraints associated with Mangala crude and its present explora-tion rate, eight refinery configurations for a 5 million metric tpy (5 metric MMtpy or 100,000 bpd (100 Mbpd)) crude processing capacity were conceptu-alized and analyzed. Table 2 summarizes possible processes and configurations. In each configuration, diesel and gasoline pool streams from different processes units are blended to produce Euro IV diesel and gasoline.

TABLE 2. Mangala refinery processing schemes and configurations

Configuration-1 CDU + DCU + FCC + Reformer + HDT

Configuration-2 ADU + FCC* + SHDS + PRU + DHDT + HGU

Configuration-3 ADU + DCU + FCC* + SHDS + PRU + HDT + HGU

Configuration-4 CDU + DCU + HDK + HDT + HGU

Configuration-5 CDU + DCU + HDK (60% conversion) + FCC + HDT + Reformer + HGU

Configuration-6 CDU + SDA + FCC + Reformer + HDT

Configuration-7 CDU + SDA + HDK + HDT + HGU

Configuration-8 CDU + SDA + HDK (60% conversion) + FCC + HDT + Reformer + HGU

Amine treating

Treaters

Treater

Hydrotreater

Gas processing

SR naphtha

SR gasoil

Kero

Naphtha

Other units gas

Gas

NHT NSPL

HGU

PRU

HN

LN

H2

H2

H2

H2

Crude

Long

resi

due

ADU

SHDS

Refinery gas

Gasoline

Diesel

Diesel

Claus sulfur plant

Kero/jet fuel

Sulfur

Propylene

LPG

Slurry oil

Gas + LPGLCO

LSHS

FCC*

Configuration 2—ADU + FCC* + SHDS + PRU + HDT + HGU.FIG. 2

Amine treating

Treaters

SHDS

Treater

Hydrotreater

Gas processing

SR naphtha

SR gasoil

Kero

VGO

Naphtha

Gas + LPG

Other units gas

Gas

NSPL

HGU

PRU

HN

LN

H2

H2

H2

Crude

Long

resi

due

ADU

Refinery gas

Slurry oil

Diesel

Claus sulfur plant

Kero/jet fuel

Sulfur

Gasoline

Propylene

LPG

Coke

Vacu

umdi

still

atio

n

Vacuumresiduum Hy

drot

reat

er

Gas

Coker LN

Coker HN

LCGOHCGODe

laye

d co

ker

NHT

Configuration 3—CDU + DCU + FCC* (50% LR) + SHDS + PRU + HDT + HGU.FIG. 3



These configurations were developed using technologies and processes that are already commercially proven and well established in refineries. Figs. 1–8 are flow diagrams for the proposed processing con-figurations. Based on technical and eco-nomic ranking criteria, six configurations (1–7) are shown here (Fig. 8 is available online at Hydrocarbonprocessing.com). In configurations 1 and 6, the hydrogen generation unit (HGU) is not included, as hydrogen (H2) demand can be met by recovering the H2 from the gasoline reformer unit.

Product yields and properties. In all cases, product streams generated in each process unit were blended to obtain the final products with desired quality speci-fications such as Euro IV for gasoline and diesel.3 A commercially available software was used in the optimization and planning of plant operations in the refineries; in-house developed correlations and a knowl-edge data base available at IIP were used to calculate the yields and properties of dif-ferent products obtained from each process unit.4–9 Product yields obtained for each refinery configuration are listed in Table 3, along with the distillate yield, which is the summation of kerosine, gasoline and diesel yields.

Study results indicate that the individ-ual product and combined distillate yield (gasoline + kerosine + diesel) are configura-tion dependent and governed by the com-bination of secondary conversion processes included in the configuration. Accordingly, the configurations can be categorized in these classes based on configuration selec-tivity toward specific types of product man-ufacturing potential.

• Gasoline and diesel-oriented con-figurations (1, 5, 6 and 8). Euro IV gaso-line and diesel can be manufactured.

• Diesel-oriented configurations (4 and 7). Only Euro IV diesel can be pro-duced. However, these processing configu-rations do not have gasoline production potential.

• Propylene-oriented configurations (2 and 3). These processing configurations have propylene manufacturing potential that the other options do not have due to FCC*/propylene recovery unit inclusion in these configurations.

From Table 3, it is clear that in Con-figurations 1 and 6, there is surplus light naphtha whereas in Configuration 7, about 52,000 metric tpy of light naph-tha procurement is needed to meet H2

demand in this configuration. Distillate yield value (gasoline + kerosine + diesel) follows configuration numbers in the order of 4>5>7>1>8>3>6>2. However, including

LPG yield in the distillate yield changes the former trend to 4>5>1>7>3>8>2>6. These trends suggest that including a hydrocracker will yield more distillates.

Amine treating

Treaters

Hydrotreater

Gas processing

TreaterKero

SR naphtha

Naph

tha

SR gasoil

VGO

Gas

Other units gas

Gas

NSPL

NHT

LNH2

H2Crude

Long

resi

due

ADU

Refinery gas

DieselDiesel

Claus sulfur plant

Kero/jet fuel

Sulfur

LPG

Coke

Vacu

umdi

still

atio

n

Vacuumresiduum Hy

droc

rack

er

Gas

Coker LN

Coker HN

LCGO

LN naphthaHN naphtha

Diesel

Kero

HCGODela

yed

coke

rHGU

Configuration 4—CDU + DCU + HDK + HDT + HGU.FIG. 4

Amine treating

Treaters

Hydrotreater

Gas processing

TreaterKero

SR naphtha

Naph

tha

SR gasoil

VGO

Gas Gas LCO

HN

HN

LN

LN

Other units gas

Gas

NHT

HGU Ref

H2

H2

H2Crude

Long

resi

due

ADU

Refinery gas

Diesel

Slurry oil

Diesel

Claus sulfur plant

Kero/jet fuel

Sulfur

LPG

Coke

Gasoline

Vacu

umdi

still

atio

n

Vacuumresiduum

Hydr

ocra

cker

FCC

unit

Gas

Coker LN

Coker HN

LCGO

Naphtha

Distillate

HT gasoil

HCGODela

yed

coke

r

NSPL

Configuration 5—CDU + DCU + HDK (60%) + FCC + Reformer + HDT + HGU.FIG. 5



The configurations (6, 7 and 8) with the solvent deasphalting (SDA) unit give a lesser combined distillate yield value cor-responding to the configurations (1, 4, and 5) with the delayed coking unit (DCU) in place of the SDA.

From the crude vacuum resid (VR) fraction physico-chemical characteriza-tion, it is clear that the VR has a low sulfur and vanadium content but has a high nickle (Ni) content. Thus, only fuel-grade coke can be produced from the

DCU using VR as a feedstock due to Ni content. However, if the VR’s Ni metal content can be reduced by pretreatment, then premium-grade anode coke can be produced due to the very low sulfur and vanadium content in the VR. Lowering the sulfur content (<1%) of the fuel oil provides opportunities to sell it at a higher price than the refinery-fuel grade.

Economic evaluation. The economic analysis for these configurations was car-ried out for 5 metric MMtpy (100,000 bpd) crude processing capacity. The study was done during second quarter (2Q) of 2010. Crude and product prices were taken from the database available on Inter-net, in public sector oil refineries and IIP database.1, 9, 10 Capital costs of processing units were also taken from data available in technical journals, Internet and informa-tion provided from oil refineries; units cap-ital cost were corrected for the base price corresponding to 2Q 2010, using the Mar-shall & Swift equipment cost index.10–12

To calculate payback for each con-figuration, a straight-line depreciation method was used assuming a plant life of 15 years. Corporate tax was considered at the rate of 30% of gross profit. Manpower charges of $22.2 million, and insurance, maintenance and miscellaneous costs at the rate of 0.5%, 4.5% and 0.15% of plant cost, respectively, were considered under the working capital head along with the crude’s cost. These configura-tions were compared with respect to prod-uct sales value realization, the investment required to set up the grassroots refinery, utility cost, gross profit and the payback period. Table 4 lists the details of the eco-nomic evaluation.

The results from Table 4 indicate that gross profit follows the configura-tion number trend: 2>4>7>3>1>6>5>8. Although, products sale values for Con-figuration 2 and 4 are comparable but payback period values are significantly different due to higher capital investment and utilities cost requirements for Con-figuration 4. Furthermore, Configuration 7 (CDU + SDA + HDK + HDT + HGU) has comparable gross profit and payback period value with Configuration 2, but a significant amount of pitch is generated that can pose a serious demand and dis-posal problems, and pushes this configura-tion as less attractive than 2 and 4.

Options. These preliminary refinery configurations conceptualization and

Amine treating

Treaters

Treater

Hydrotreater

Gas processing

SR naphtha

SR gasoil

Gas

Kero

Naphtha

Diesel

Kero

HN

LN

Other units gas

Gas

H2

H2

Crude

Long

resi

due

ADU

Hydr

ocra

cker

Refinery gas

Diesel

DAO

Claus sulfur plant

Kero/jet fuel

Sulfur

LPG

Pitch

Vacuumresidium

HCGO

SDA

unit

VGO

Vacu

umdi

still

atio

n

NSPL

NHT

LNNaphtha

Purchased L napahtha

HGU

Configuration 7—CDU + SDA + HDK + HDT + HGU.FIG. 7

Amine treating

Treaters

Treater

Hydrotreater

Gas processing

SR naphtha

SR gasoil

Gas

Kero

Naphtha

Other units gas

Gas

NHT NSPL

Reformer

H2

H2

LN

LN

H2

Crude

Long

resi

due

ADU

FCCunit

Refinery gas

Gasoline

Light naphtha

Slurry oil

Diesel

DAO

Claus sulfur plant

Kero/jet fuel

Sulfur

LPG

Pitch

Vacuumresidium

HCGO

LCO

SDA

unit

VGO

Vacu

umdi

still

atio

n

Configuration 6—CDU + SDA + FCC + Reformer + HDT.FIG. 6



their economic evaluation analysis results indicate that Configuration- 2 (ADU + FCC* + SHDS + PRU + HDT + HGU) tops the gross profit and payout period ranking list. Maximum gasoline yield is obtained in Configuration-1 (CDU + DCU + FCC + Reformer + HDT), but it occupied 5th place in gross profit pay-back period ranking. However, Configu-ration 4 (CDU + DCU + HDK + HDT + HGU), which ranked just below Configu-ration-2 from profit and payback points of view, but provides the maximum distil-late (4,305 metric tpy diesel) manufactur-ing potential against the distillate yield (2,856 metric tpy gasoline and diesel) for Configuration 2.Therefore, in view of current diesel driven economy, Configu-ration 4 may be proved the best over the long term. HP

* The INDMAX technology maximizes the conver-sion of heavy oils to highly olefinic LPG through a fluidized catalytic cracking (FCC) process.

NOMENCLATUREADU Atmospheric distillation unitVDU Vacuum distillation unitCDU Crude distillation unit

(ADU + VDU)DCU Delayed cocker unitFCC Fluidized catalytic cracking unitSHDS Selective hydrodesulfurization unitPRU Propylene recovery unitHDK Hydrocracker unitSDA Solvent deasphalting unitHDT Hydrotreating unitDHDT Diesel hydrotreating unitNHT Naphtha hydrotreating unitNSPL Naphtha splitterHGU Hydrogen generation unitINDMAX FCC/propylene recovery unitLN Light naphthaHN Heavy naphthaLCGO Light coker gasoilHCGO Heavy coker gasoilLCO Light cycle oilVGO Vacuum gasoil

LITERATURE CITEDComplete literature cited avaiable at HydrocarbonProcessing.com

TABLE. 3. Material balance and product yields for all refinery configurations

Configuration number, thousand tpy 1 2 3 4 5 6 7 8

Feed

Crude 5,000 5,000 5,000 5,000 5,000 5,000 5,000 5,000

Hydrogen 0 11 14 74 63 0 66 48

Total 5,000 5,011 5,014 5,074 5,063 5,000 5,066 5,048

Product yields

Fuel gas 176 283 276 91 225 87 11 128

Sulfur 2 1 2 2 3 1 1 1

LPG 501 623 538 102 250 416 48 261

HGU Naphtha 0 40 51 222 189 0 198 143

Surplus/procured naphtha 123 0 0 0 0 52 –73 0

Gasoline 1,409 1,327 868 0 650 1,005 0 0

Kerosine 225 225 225 399 225 225 417 225

Diesel 1,908 1,304 1,993 3,906 3,047 1,738 3,410 ,037

Slurry oil 188 134 88 0 71 201 0 120

Coke 347 0 348 347 347 0 0 0

Pitch 0 0 0 0 0 1,143 1,048 1,048

FCC coke 131 302 194 0 36 128 0 79

Propylene 0 762 416 0 0

Total 5,009 5,000 4,998 5,069 5,043 4,996 ,061 5,043

Distillate 3,542 2,856 3,086 4,305 3,922 2,968 3,827 3,262

Dr. M. O. Garg is the director of Indian Institute of Petroleum, Deh-radun, a constituent laboratory of Council of Scientific and Industrial Research. Dr. Garg has 33 years of

experience in the refining industry. He started his career after graduating from IIT-Kanpur in the Research and Development Division of Engineers India Ltd. in 1976. He earned a PhD at University of Melbourne. In 1994, he joined the process system services division of KTI-Technip India Ltd. and joined Indian Institute of Petroleum in 1998. Dr. Garg has developed and commercialized sev-eral technologies and has received two CSIR Technology Award . Dr. Garg has published over 207 papers and holds 26 patents . He has been elected Fellow of Indian National Academy of Engineering. Dr. Garg specializes in the area of liquid-liquid extraction, simulation and modelling, process integration, advance control, and process conceptualization. He is acknowledged as an expert in petroleum refining and petrochemicals.

Shrikant Nanoti is head of sep-aration processes division at Indian Institute of Petroleum, Dehradun, India. He received a chemical engi-neering degree from Laxminaryan

Institute of Technology, Nagpur and a PhD from the Indian Institute of Technology. Dr. Nanoti has over 26 years of experience in the development and scale-up of separation-based technologies, process design, process integration and pinch analysis for the petroleum refining and petrochemical industries. He has published more than 35 research papers in national and international journals and holds eight patents.

Yogendra Kumar Sharma has 30 years of experience in analyti-cal, research and development work and presently heads the crude oil eval-uation laboratory at Indian Institute

of Petroleum, Dehradun. Dr. Sharma was awarded the INSA/DFG fellowship to work on mechanism of degrada-tion of middle distillate fuels at Engler Bunte Institut der universitat Karlsruhe, Germany and has submitted the D.Sc theses at B.R Ambedakar University of Agra. He is a

Sunil Kumar received an MS degree in chemical engineering from the Indian Institute of Kanpur, India in 2009. He has been awarded with Cer-tificate of Merit for Academic Excel-lence in the Master of Technology Pro-

gramme in chemical engineering at IIT Kanpur and also honored with Ambuja’s Youngh Researchers Award. He started his career in modeling and simulation group, as a scientist, at Indian Institute of Petroleum (CSIR), Dehra-dun, India, in 2009. He has completed several projects in the area of petroleum refinery separation and conversion processes using the advanced state-of art tools.

TABLE 4. Economic evaluation for various grassroots refineries

Configuration number 1 2 3 4 5 6 7 8

Product sales value, billion $ 3.148 3.482 3.24 3.417 3.288 3.022 3.322 3.15

Refinery cost, billion $ 1.751 1.613 1.926 2.01 2.16 1.368 1.663 1.722

Utility cost, million $ 109 126 133 200 266 90 205 220

Working capital, billion $ 2.685 2.678 2.694 2.699 2.706 2.666 2.681 2.684

Gross profit, million $ 237 570 284 384 172 176 326 131

Payback period, years 6.2 3.2 5.9 5.0 8.2 6.4 4.9 8.3

NABL technical assessor and has significantly contributed to the evaluation of various indigenous and imported crude oils, natural gas liquids, condensate and petroleum products. Dr. Sharma has published 12 research papers in international journals and has filed seven patents.

GulfPub.com/IRPC

Hydrocarbon Processing’s 3rd Annual International Refi ning and Petrochemical Conference will be held in Milan, Italy on June 12–14 2012.Hydrocarbon Processing’s International Refi ning and Petrochemical Conference (IRPC) is a market-leading technical conference, providing an elite forum for industry leaders to come together to share knowledge and ideas relating to the refi ning and petrochemical industries. IRPC emphasizes the latest technological and operational advances from both a local and global perspective and is attended by project engineers, process engineers and hydrocarbon processing industry management offi cials from around the world.

In today’s increasingly competitive global HPI, managers and engineers are actively seeking information and solutions to make their company or organization more effi cient and profi table. This is your chance to take part in the discussion. IRPC off ers an intimate, thought-provoking working environment to meet and network with industry leaders and key decision makers as they explore how technological and operating advances can benefi t their organization or plant.

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Exhibit or Sponsor: Contact your localHydrocarbon Processing sales representative



Improve material balance by using proper flowmeter corrections Here are guidelines to increase accuracy for flow measurements

S. PERAMANU and J. C. WAH, Canadian Natural Resources Ltd., Calgary, Canada

P rocess plants frequently encounter mass imbalances. These can be attributed to various factors, but often they lead back to inappropriate measuring devices, improper calibration,

incorrect installation or incorrect interpretation of the measured flows. There are well-established guidelines available to ensure appropriate flowmeter selection based on the process conditions and control requirements.

Instrument vendors follow the industry standards and approved procedures for flowmeter calibration, calculating the calibration factors based on the data provided in the flowmeter specification datasheets. Engineering and construction service contractors often follow vendor guidelines and standard prac-tices to correctly install the flowmeters. This means that most flowmeter installations, therefore, meet accepted project stan-dards and specifications.

What’s the flowrate? However, measured flow interpreta-tion, normally a process or production engineer’s responsibility, is often done without proper directions or guidelines. Although it appears straightforward that the flowmeter measures the flowrate and the flowrate value is read from the display, significant error can be introduced if the flow measurement conditions are not understood and appropriate correction factors are not applied. Accurate stream flow interpretation and critical mass balance reconciliation require understanding flowmeter characteristics and their associated measurement uncertainties. This is of particular importance where mass balances may be used for highly sensitive process control operations, production accounting or government reporting on royalties and emissions.

This article provides a background on the importance of accurate measurements, a description of measurement errors and the role of uncertainties in mass balance and reconciliation. Flowmeter correction equations are derived for differential pres-sure flow, volumetric flow and mass flowmeters, and flow cor-rection factors are provided for various units of measurements (UOM). Flowmeter uncertainty equations are derived for differ-ential pressure flow, volumetric flow and mass flowmeters.

METERING APPLICATIONSTo achieve the most accurate flow measurement (minimum

uncertainty), proper flow system operation and maintenance must be practiced so that meter accuracy capabilities are realized. Periodic maintenance, testing and recalibration are essential because the cali-bration will shift over time due to wear, damage or contamination.

The maintenance may be only a secondary-equipment calibra-tion, a complete system mechanical inspection, an actual through-put test against some agreed-upon standards or any combination of these. The equipment used to test the meter, such as thermom-eters, dead-weight tests, pressure gauges, differential-pressure gauges, chromatographs and provers (used for throughput tests), must have accuracy certification and should be approved and agreed upon by the interested parties. Having operators who have had experience with similar metering systems also increases the calibration and test procedure confidence levels. Test equipment itself should be recertified periodically by the agency or manufac-turer that originally certified the equipment.

Custody transfer operations. In custody-transfer measure-ment, the measurement furnishes quantity and quality informa-tion that can be used as the basis for a change in ownership and/or a change in responsibility for materials.

Custody-transfer measurement is distinct from other measure-ment types because of the contractual nature of the meter. Cus-tody-transfer metering may require accuracy of ± 0.1% or better, whereas control measurement may be accepted at a ± 2% accuracy and operational measurement may require a ± 5% accuracy. A high-integrity custody-transfer measurement system is a result of careful design based on the specific application requirements comprising fluid control, conditioning, metering, computation and a means of traceable site data validation.1, 2

Custody-transfer management involves the entire chain from the custody-transfer metering conceptualization to the final pro-duction or sale data reporting. For example, in the upstream oil and gas sector, measurement includes all intermediate steps such as measurement and sampling guidelines, operational procedures, data processing, data transmission and reconciliation, allocation or custody-transfer procedures. To solve the flow measurement equation, it is imperative that every equation parameter be well understood and represented.

A primary custody-transfer measurement consideration is to minimize flow variations by maintaining better flow control. For situations where this may not be possible, a meter with a wide-ranging flow capacity is needed. If a single meter with the required flow capacity to cover the intended operating range with mini-mum uncertainty does not exist, using multiple meters with some type of meter-switching control is required. Most meters operate with a specified uncertainty within the stated flow capacity limits that is typically from 25% to 95% of the flowmeter maximum



capacity. For custody-transfer metering and critical control mea-surement, it is important to maintain the meter operation within the stated flow capacity limits.

Errors. Other than some operating problems and poor mainte-nance that may affect the measurement, the main cause of error is the fluid characteristics and errors in fluid density calculation. For gases, mixtures are more accurately measured if the stream has rel-atively constant composition. This allows specific PVT tests to be run, or data may be available for common mixtures from previous work. If the mixture is changing rapidly, a densitometer or a mass meter may be required to determine an accurate measurement.

During times when a custody-transfer meter is out of service or registering inaccurately, a procedure must be in place for mea-suring or estimating deliveries. This procedure may need to be in accordance with regulatory standards if the meter flow is used for regulatory reporting. An example of this is the recent USA EPA Greenhouse Gas Mandatory Reporting Rule issued Sept. 22, 2009.

A typical accuracy limit from ± 0.5% to ± 2% may be used, but may be set closer or wider depending on the specific meter costs and measurement ability.

For custody-transfer meters, a prover system or master meter should be used for throughput testing and recalibration. The best throughput test can be run directly in series with a prover. The prover can come in many forms, but essentially it involves a basic volume that has been certified by a government or industrial group. Since most meters are not totally linear, tests may have to be run over the meter’s operating range to calculate the calibration factors dependent on the flow capacity.

Commercial mass balance software. The characteristics and strengths of commercial mass-balance software may include:

• Graphically aided input that is user friendly and intuitive• Interactive diagnostics and feedback on input errors• Flexibility to select the measurement units desired by the user• Flexibility for the user to select start and end times to per-

form the reconciliation• Facility to construct mass-balance units based on plant con-

figuration user input• Reconciliation processes perform linear, nonlinear and

inequality constraints on the measurement data to produce rec-onciled measurements and unmeasured flow estimates

• Algorithm for efficient iteration and fast convergence to the solution. Some algorithms may include the Monte Carlo method to generate sets of random values for measurement errors within a prescribed range (±%uncertainty) that are solved and iterated in the reconciliation algorithm

• Algorithm to ensure numerical robustness and prevent numerical runaway

M4Stream 1, M1

Stream 2, M2 Stream 3, M3

Tank

Flow reconciliation example for a storage tank.FIG. 1

TABLE 1. Flow correction factors for various flowmeters

Differential-pressure meters, Volumetric flowmeters, Coriolis with orifice, venturi, nozzle, vortex, turbine, ultrasonic Mass flowmeters, integrated densityPhase, UOM wedge, pitot and annubar and magnetic Coriolis and thermal measurement

Liquid flow, act. m3/h 1 1

ρD

ρM

ρD

ρM

Liquid flow, std. m3/h

ρM

ρD

×ρD _ Std

ρM _ Std

ρM

ρD

×ρD _ Std

ρM _ Std

ρD _ Std

ρM _ Std

ρD _ Std

ρM _ Std

Liquid flow, kg/h 1 1

ρM

ρD

ρM

ρD

Gas flow, act. m3/h 1 1

(MD PD / ZDTD )(MM PM / ZMTM )

MD PD ZDTD

MM PM ZMTM

Gas flow, std. m3/h*

(MD ZDTD / PD )(MM ZMTM / PM )

ZDTD PD( )ZM TM PM( )

MD

MM

MD

MM

Gas flow, kg/h 1 1

(MM PM / ZMTM )(MD PD / ZDTD )

(MM PM / ZMTM )(MD PD / ZDTD )

*For gas flow, std. m3/h, the design and measured compressibility factors at standard conditions (101.325 KPaa; 15°C) are assumed to be the same compressibilty factors for most gases is close to unity at standard conditions.



• A reconciled mass balance at the processing unit level or group of processing units, progressing all the way up to the plant level

• Ability to reconcile total mass and selected component frac-tions at the same time

• Reporting tool to verify the present error in balance for each node and generate the customized reports.

Mass balance and reconciliation. Data reconciliation3,4

improves process data accuracy by adjusting the measured values so that they satisfy the process constraints. The amount of adjustment made to the measurements is minimized since the random errors are expected to be small. Data reconciliation can be formulated by the following constrained weighted least-squares optimization problem:

Minimize the function (known as objective function):

ΔMi( )2 σ i

2

i=1

n

∑Where n is the number of measurements, ΔM is the difference between the reconciled and measured values of measurements, i and �i is the measurement i standard deviation. The value 1/�2

which is an inverse of variance (square of standard deviation) is the weight factor representing the accuracy of the respective measure-ments. Since a higher value of standard deviation implies that the measurement is less accurate, the above choice gives larger weights to more accurate measurements.

The above objective function minimization is subject to con-straints:

fj (Mi � �Mi ) = 0 j = 1,….m

Where f is the balance equation for the measurements (i = 1,….n ) and m is the number of balance equations.

During the reconciliation process, the measurements contain-ing systematic bias or gross errors are detected by comparing the difference between the reconciled and measured values to the measurement uncertainties.

If Abs(�Mi / Mi ) > Uncertainty, measurement i has gross error.The measurements containing gross errors are either eliminated

or appropriately compensated for data reconciliation to be effec-tive, as shown in Fig. 1. In this example, the reconciliation involves:

M1 = stream 1 cumulative measured mass flow over a period (a day, for example)

M2 = stream 2 cumulative measured mass flow over a periodM3 = stream 3 cumulative measured mass flow over a periodM4 = measured mass inventory gain or depletion over the

same periodIt is evident that because of measurement errors these measured quantities do not balance, namely:

M1 � M2 � M3 � M4

Therefore, reconciliation is required; adjustments to each of the measured quantities need to be made to obtain a mass balance:

(M1 � �M1 ) � (M2 � �M2 ) = (M3 � �M3 ) � (M4 � �M4 )

where ΔMi is an adjustment (+ve or –ve) for the measured quan-tity Mi .

It is evident that there are infinite sets of ΔMi, each of which will give the desired mass balance, i.e., they will satisfy the equa-tion above. Of the infinite sets of ΔMi , the one particular set that corresponds to the least amount of total adjustment is required. This suggests that the problem to find this particular set of ΔMi can be formulated as one that entails minimizing a function (usu-ally referred to as objective function) subject to some constraints (mass-balance equations). For the simple example here, the prob-lem of finding that particular set of ΔMi can be formulated as:

Minimize the objective function:

ΔM1( )2

σ12 +

ΔM2( )2

σ 22 +

ΔM3( )2

σ 32 +

ΔM 4( )2

σ 42

Subject to the constraint:

(M1 � �M1 ) � (M2 � �M2 ) –

(M3 � �M3 ) – (M4 � �M4 ) = 0Where �i is the uncertainty associated with the instrument that gives the measured quantity, Mi .

The use of uncertainties for reconciliation can be explained with this example. Assume that a flowmeter M1 with percent uncer-tainty at 95% confidence level (%U95) as ±2% is reading 300 kg

True value Average

Biased, not precise

BiasTrue value Average

Biased, precise

Bias

Not biased, not precise Not biased, preciseFr

eque

ncy

True value and averageTrue value and averageFr

eque

ncy

Freq

uenc

y Fr

eque

ncy

Measured value Measured value

Measured value Measured value

Bias and precision errors.FIG. 2

TABLE 2. Variable uncertainty

Sensitivity coefficient Sensitivity value, S Uncertainty, U95 (S U95 )2

CD Discharge coefficient 1 1 0.45 0.2025

d Orifice diameter 2/(1 – �4) 2.298 0.05 0.0132

D Pipe diameter –2�4/(1 – �4) –0.298 0.25 0.0056

�P Differential pressure 1⁄2 0.5 0.5 0.0625

� Actual density 1⁄2 0.5 0.45 0.0506

�Std Standard density –1 –1 0.5 0.25

Sum of squares ∑(S U95 )2 = 0.5844

Square root of sum of squares √∑(S U95 )2 = 0.7644



during a certain time period. Since the 95% confidence level cor-responds to two standard deviations, 2�1, the standard deviation error, �1, for this measurement can be calculated as ±3 kg as:

2σ1 =%U95

100×M1 =

±2100×300 = ±6 kg

(95% confidence error)Therefore, the weight factor, (1/�1

2), for measurement M1 in the objective function above is 1/9. Suppose if the reconciled flowrate for M1 is 307 kg, then the reconciled error (difference between the reconciled value and the measured value) is 7 kg. This value is greater than the 6 kg error (95% confidence) calculated. This means that measurement M1 has a gross error and should be eliminated or properly compensated for effective reconciliation.

FLOW CORRECTIONSProcess industry flowmeters can be classified into three broad

categories that include differential-pressure meters, actual volu-metric flowmeters and mass flowmeters. The differential-pressure meters include orifice, venturi, nozzle, wedge, pitot tube and annubar; volumetric flowmeters include vortex, turbine, ultrasonic and magnetic; and mass flowmeters include Coriolis and thermal meters. The meter operating principles and flow equations are provided in Appendix A.

For any of these flowmeters, the vendor should make sure that the flowmeters measured outputs are in the UOM requested in the flowmeter specification datasheet. For this, the vendor calculates the conversion factor by using the design density data (or pressure, temperature and molecular weight data for gases) specified on the datasheet to output measured values in the desired UOM.

During process operation, the measured density (or P, T, MW and z for gases) values may not be the same as the values on the datasheet. Therefore, the measured flowrates should be corrected to account for the measured process conditions. The correction factors for various flowmeters using different UOM are provided in Table 1. The details of the flowmeter correction calculation are available in Appendix B.

Flow uncertainty equations. Uncertainty, U95, is a statisti-cal statement of measurement accuracy that is useful in:

• Defining tolerances for reconciling measurements with concurrent gross-error detection and elimination

• Estimating accuracies when reporting to government on measurements that impact royalties and emissions

• Evaluating custody-transfer metering performance. Uncertainty is a measurement process characteristic. It provides

an estimate of the error band within which the true value for that measurement process must fall with high probability.5 It is based on the probability of 95% that is twice the standard deviation, 2�. The 95% confidence level for the estimated flowmeter uncertainty is in accordance with prudent statistical and engineering practice.

Flowmeter uncertainty is actually a function of both bias (sys-tematic or gross error) and precision (random error), as shown in Fig. 2. Flowmeter part manufacturers follow rigorous testing and calibration to remove or randomize the measurement biases. In Canada, they follow the standards by Measurements Canada, and in the US, the test method follows the National Bureau of Standards (National Institute of Standards and Technology). The values used for the precision may be obtained from manufacturer’s specifications for the respective equipment provided that the values are adjusted to reflect operating conditions.

To calculate the uncertainty values, the significance of each variable (parameter) in the flow calculation equation is examined and is related to flow measurement. It is assumed that the meter has been properly installed, operated and maintained. It is also assumed that the systematic equipment biases are randomized within the database, which means that variations in the equip-ment and laboratories will not impose any bias in the equations’ ability to represent reality.

For practical considerations, the pertinent variables are assumed to be independent to enable simpler uncertainty cal-culations. It was noted that the simplified uncertainty equations would provide very good uncertainty estimates.6 The mathemati-cal relationships among the variables establish the sensitivity of the metered quantities to each of these variables. Each variable that influences the flow measurement uncertainty has a specific sensitivity coefficient. The uncertainty for a general equation Q = f (x1, x2 ,.....xN ) can be derived analytically by partial differen-tiation based on propagation of uncertainty by the Taylor series.

Refer to Appendix C for derivation using the Taylor series. The uncertainty in Q can be given as:

δQQ

=

x1

Q∂Q∂x1

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2δ x1

x1

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

+x2

Q∂Q∂x2

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2δ x2

x2

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

+.... +xN

Q∂Q∂xN

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2δ xN

xN

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

⎡

⎣

⎢⎢⎢⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥⎥⎥⎥

1 2

This can be represented in a simpler form as:

δQQ

=Sx1

( )2U x1

( )2+ Sx2

( )2U x2

( )2+ ............ +

SxN( )2

U xN( )2

⎡

⎣

⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥

1 2

where �Q/Q is the uncertainty in Q, Sx is the sensitivity coefficient associated with the variable and Ux is the variable uncertainty. The uncertainty equations are derived for differential pressure, volumetric and mass flowmeters in Appendix D using the flow equations representing the basic operating principle.

FLOWMETER UNCERTAINTYUncertainty for orifice, venture or nozzle meter measuring in

standard flow is given by:

δQStdVol

QStdVol

=

1( )2 δCd

Cd

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

+2

1−β 4

⎛

⎝⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2δdd⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2

+−2β 4

1−β 4

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2δDD⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2

+12⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2 δΔPΔP⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

2

+12⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2 δρAct

ρAct

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

+ −1( )2 δρStd

ρStd

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

⎡

⎣

⎢⎢⎢⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥⎥⎥⎥

1/2

The same equation above can be used for a wedge meter; however, the deviation in the equivalent diameter, d, for a wedge meter is calculated by using:

δdd

=Hd

1π 1/2G1/2 2n1/2 +

1

1−m2( )1/2 −12

m2

n1/2

⎧⎨⎪⎪⎪

⎩⎪⎪⎪

⎫⎬⎪⎪⎪

⎭⎪⎪⎪

⎡

⎣

⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥

δHH⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

+Dd

1π 1/2G1/2 G− H

D1

1−m2( )1/2 + 2n1/2 −12

m2

n1/2

⎛

⎝

⎜⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟⎟

⎧⎨⎪⎪⎪

⎩⎪⎪⎪

⎫⎬⎪⎪⎪

⎭⎪⎪⎪

⎡

⎣

⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥

δDD⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟



where:

d D1/2 G1/2 ; G Cos 1m 2mn1/2 ;

m 1 2 HD

; n HD

HD

2

Uncertainity equations for vortex, turbinem ultrasonic and coriolis flowmeters are in Appendix D.

Measured flowrate correction. An orifice meter is used in a refinery to measure the flowrate of liquid hydrocarbons and it is calibrated to indicate (readout) flowrate in standard volumetric flows. For example, design stream conditions indicated on the flowmeter specification datasheet are:

TD = Design temperature = 300°CPD = Design pressure = 1,500 kPaa�D_Std = Design standard density = 950 kg/m3 (normally

obtained from process simulation)�D = Design actual density = 750 kg/m3 (normally obtained

from process simulation).During actual operation, the measured conditions are: TM = Measured temperature = 310°CPM = Measured pressure = 1,500 kPaa�M_Std = Measured standard density = 960 kg/m3 (measured in

the laboratory using the sample) �M = Measured actual density = 740 kg/m3 (measured, or

calculated using an appropriate correlation).For liquid flows, if the measured densities are not available at

the actual operating conditions, the established correlations can be used. It should be noted that these correlations may result in some error in the density predictions.

Actual liquid hydrocarbon stream density can be estimated using the equation by Yawas:7

SGm = SGr( )2 −0.0011×(Tm −Tr )For C20 and heavier alkanes, the densities can be obtained

using the method by Fisher:8

SGm = 1.29 +

SGr −1.29Tr + 737

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

Tm + 737( )

where SGm is specific gravity at measured temperatureSGr is the specific gravity at reference temperature Tm is measured temperature in Kelvin Tr is reference temperature in °C.A method for calculating actual density using liquid critical

properties is given by Noor:9

ρm =M

VC 3.964−1.957TmTC

( )where �m = Density at measured temperature in kg/m3, M = Molecular weight, VC = Critical volume in m3/kg Tm = Measured temperature in KelvinTC = Critical temperature in Kelvin.From Table 1, the correction factor for the orifice meter with

indicated (readout) liquid flowrate at standard conditions is given by:

Correction Factor =

ρM

ρD

×ρD _Std

ρM _Std

H D

Wedge flowmeter

P1

PS

Pt

ΔP

P2 P1 P2 P1 P2

D d

Orifice flowmeter

Flow

Pitot tube flowmeter

D

D d

Venturi flowmeter

Flow D d

Nozzle flowmeter

Flow

Flow Flow Flow

Flow Flow Flow

D w

Interval(frequency)

Vortex flowmeter

D

Angular velocity measurement

Turbine flowmeter

D L Ɵ

Upstream transducer

Downstream transducer

Ultrasonic flowmeter

D

Magnetic flowmeter

Voltage (E)

Magnetic field (B)

L

Examples of various flowmeters used by industry.FIG. 3



and the corrected flowrate at standard conditions is given by:

QStdVol_Corr = QStdVol_Meas � Correction Factor

If the flowmeter indicated (readout) flow is 600 std. m3/d, then the corrected flowrate at standard condition is:

= = 589.8 Std m3/d ~ 590 std. m3/d

600× 740750×

950960

Uncertainty calculation. A 3-in. orifice meter run with a � ratio of 0.6 is selected for the previous liquid hydrocarbon flow measurement example at a static pressure of 1,500 kPaa and flow-ing temperature of 310°C. Differential pressure recorded for the flow is 25 kPa and the flowrate is 590 std. m3/h.

The variable sensitivity coefficients can be calculated using the orifice uncertainty equation:

δQStdVol

QStdVol

=

1( )2 δCd

Cd

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

+2

1−β 4

⎛

⎝⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2δdd⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2

+

−2β 4

1−β 4

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2δDD⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2

+12⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2 δΔPΔP⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

2

+

12⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2 δρAct

ρAct

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

+ −1( )2 δρStd

ρStd

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2

⎡

⎣

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎤

⎦

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

1/2

The uncertainty values for the variables �x/x at 95% confi-dence level, U95, can be obtained from industry standards and procedures (AGA, API, ASME, ASTM) and/or manufactures’ specifications for the equipment or parts. For each variable, the uncertainty listed in Table 2 represents random errors only, which are obtained from AGA RP-3-1.

Based on the calculations, the standard volumetric flow mea-surement uncertainty at 95% confidence level is ± 0.76%. For mass flow measurement uncertainty, the standard density variable, �Std, in the above equation is excluded, which gives the % U95 value of ± 0.58%.

APPENDIX A The operating principles and flowmeter equations are listed in

this appendix for the flowmeters as shown in Fig. 3.

Differential pressure flowmeters. The flowmeters that measure differential pressure to calculate the flowrates can be clas-sified as differential pressure flowmeters.

Orifice, venturi and nozzle flowmeters. For fluid flow in an orifice, venturi or nozzle flowmeter, the actual volumetric flowrate can be given as:10

QActVol = Cd EuY

π4

d 2⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

2 P1−P2( )ρ1

= Cd EuYπ4

d 2⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

2ΔPρ1

where d is the orifice diameter for an orifice meter or throat diam-eter for venturi and nozzle meters,

P1 = Pressure at the upstream pressure tap, P2 = Pressure at the downstream pressure tap �1 = Density at P1 pressure condition.Cd = Discharge coefficient to account for frictional losses

(kinetic energy into heat) due to viscosity and turbulence effects. Eu is the velocity approach factor that relates the flowing fluid

velocity in the meter approach section (upstream meter tube) to the orifice/throat fluid velocity:

Eu =

11−β 4

where � = d / D is the orifice bore (or throat for the venturi and nozzle) to pipe inner-diameter ratio.

Y is the expansion factor to account for the gas compressibility that is given by:

Y = P2 P1( )2

k kk−1⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

1− P2 P1( )k−1( )

k

1− P2 P1( )

⎛

⎝

⎜⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟⎟⎟

1−β 4

1−β 4 P2 P1( )2

k

⎛

⎝

⎜⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟⎟⎟

where k is specific heat ratio CP /CV. For � less than 0.25, �4 value approaches zero in the equation.

Pitot tube or annubar flowmeters (for velocity less than 30% of sonic velocity). For fluid flow in a Pitot tube flowmeter, the actual volumetric flowrate can be given as:

QActVol = K π

4D2⎛

⎝⎜⎜⎜

⎞⎠⎟⎟⎟

2 Pt −Ps( )ρAct

= K π4

D2⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

2ΔPρAct

Where: K = Instrument coefficient that is usually determined through calibration,

D = Pipe inside diameterΔP = Pressure drop measured by the Pitot tube, which is the

difference between the total (stagnation) pressure, Pt, and the static pressure, Ps.

Wedge flowmeter (used for liquid flows only). For liquid flow in a wedge flowmeter, actual volumetric flowrate can be calculated using the orifice equation:

QActVol = Cd Eu

π4

d 2⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

2ΔPρAct

where d is equivalent orifice diameter that is calculated using equivalent beta ratio:

β =dD

=1

π 1/2 cos−1 1−2HD

⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟− 2 1−2

HD

⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

HD−

HD⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

2⎧⎨⎪⎪

⎩⎪⎪

⎫⎬⎪⎪

⎭⎪⎪

1/2⎡

⎣

⎢⎢⎢

⎤

⎦

⎥⎥⎥

where H = wedge segment opening height, D = Pipe inside diameter, ΔP = Pressure drop across the orifice �Act = Liquid density at actual temperature and pressure condi-

tions, T, P. Cd is the wedge meter discharge coefficient to account for

frictional losses (kinetic energy into heat) due to viscosity and turbulence effects.

Eu is the velocity approach factor that relates the flowing fluid velocity in the wedge meter approach section (upstream meter tube) to the fluid velocity in the wedge section.

Eu =

11−β 4

where � is d/D which is equivalent orifice to pipe inner diameter ratio.

Volumetric flowmeters. The flowmeters that directly inter-pret the actual volumetric flow from other measured parameters are called volumetric flowmeters. To interpret the velocity, vortex



meters use vortex shedding frequency; ultrasonic meters use sound transit time; and magnetic meters use voltage induced in the fluid (conductive) flowing through an imposed magnetic field.

Vortex flowmeter. A vortex flowmeter measures the volumet-ric flowrate by using the vortex shedding frequency caused by a flow barrier.11

Strouhal number, S, is related to vortex shedding frequency by S = fw / u where f = Vortex shedding frequency that depends on flow veloc-ity, fluid viscosity and flow barrier dimensions (bluff, which is either a cylinder or a square column) used to create vortex

w = Flow barrier width (bluff ) u = Fluid velocity in the bluff section. Actual volumetric flowrate can be given by:

QActVol = Au =

π4

D2B fwS⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟

where D is the pipe inner diameterand B is the blockage factor that is defined as the pipe bore

area less the bluff body blockage area, divided by the pipe full bore area:

B =

π4

D2 −KwD

π4

D2= 1−

4Kπ

wD

where K factor is used to compensate for the pipe flow nonuni-form profile in industrial applications. Combining the above equations the actual flowrate is given as:

QActVol =

fπD3

4SwD⎛⎝⎜⎜⎜⎞⎠⎟⎟⎟ 1− 4K

πwD

⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

The Strouhal number, S, is about constant across a wide Reyn-olds number range of (102–107). The S value depends on the bluff width to the pipe inner diameter ratio. S = 0.18 for w/D = 0.1; S = 0.26 for w/D = 0.3; and S = 0.44 for w/D = 0.5.

Turbine flowmeter. A turbine flowmeter measures the volu-metric flow by counting the rotor revolutions (rotor angular velocity) that turns in proportion to the flow velocity.12–14 The equation for a turbine meter can be given as:

utan� = Kr �

where u = Incoming flow velocity, � = Angle between the pipe axis (incoming flow direction)

and the turbine blades, r = Root-mean-square value of the blade inner and outer radii

to represent the average radius, K = Instrument factor to compensate velocity loss (nonideali-

ties) due to rotor blade design and � is the rotor angular velocity.

r =

ro2 + ri

2

2where ro = Blade radius outer edge and ri = Radius blade root. Actual volumetric flowrate can be given by:

QActVol = Au =

π4

D2⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

Krωtanθ

Ultrasonic flowmeter. An ultrasonic flowmeter measures the volumetric flow by using sound pulse transit time in the flow medium caused by doplar effect.15–17 A typical ultrasonic flowmeter (transit-time flow measurement) system utilizes two

ultrasonic transducers that function as both transmitter and receiver. The flowmeter operates by alternately transmitting and receiving a sound energy burst between the two transducers and measuring the transit time that it takes for sound to travel between the two transducers. The difference in the transit time measured is directly and related to the liquid velocity in the pipe.

If tD is the sound pulse transit-time (or time-of-flight) traveling from the upstream transducer to the downstream transducer, and tU is the transit-time from the opposite direction, the equations can be given as:

tDLinear distance between transducers (L)Net sound velocity along flow direction

(D / sin )c u cos

tULinear distance between transducers (L)

Net sound velocity opposite flow direction(D / sin )c u cos

where � = Angle between the transducer axis to the flow direction, c = Sound speed in the liquid, D = Pipe inside diameter u = Flow velocity averaged over the sound path. Solving the

above equations leads to:

tU − tD

tU tD

= u 2 sinθ cosθD

= u sin 2θD

u =KD

sin 2θtU − tD

tU tD

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

=KD

sin 2θt f

where tf = (tU – tD) / (tU tD) is the transit-time function and K is the instrument factor determined through calibration. There-fore, by accurately measuring the upstream and downstream transit-times, tU and tD , the flow velocity, u, can be obtained.

Actual volumetric flowrate is calculated as:

QActVol = Au =

π4

D2⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

KDsin 2θ

t f

where A is the pipe inner cross-section area.Magnetic flowmeter. Magnetic flowmeter operation is based

on Faraday’s Law that states that the voltage induced across any conductor as it moves at right angles through a magnetic field is proportional to the conductor velocity.18 To apply this principle the fluid being measured must be electrically conductive.

The voltage, E, generated in a conductor is given by: E � BLuwhere:E = Voltage generated in a conductorB = Magnetic field strength perpendicular to the flow directionL = Distance between the electrodes (usually equal to pipe

inside diameter in most construction)u = Conductor velocity.The fluid velocity can be given by: u K E

BL=

where K is the instrument coefficient that is usually deter-mined through calibration.

Subsequently, the actual volumetric flow rate is calculated as:

QActVol = Au =

π4

D2⎛⎝⎜⎜⎜

⎞⎠⎟⎟⎟

KEBL

where A is the pipe inner cross-section area and D is the pipe inside diameter.



Mass flowmeters. A coriolis flowmeter directly measures the mass flow based on the inertial forces exerted on the tube vibrations.19–21 When an oscillating excitation force is applied to the tube, causing it to vibrate, the fluid flowing through the tube will induce a twist (or rotation) to the tube because of the Coriolis acceleration acting in opposite directions on either side of the applied force.

In a U-tube coriolis meter, the flow is guided into the U shaped tube that is vibrated using an actuator. The vibration is commonly introduced by electric coils and measured by magnetic sensors. When the tube is moving upward during the first half of a cycle, the fluid flowing into the meter resists being forced up by pushing down on the tube. On the opposite side, the liquid flow-ing out of the meter resists having its vertical motion decreased by pushing up on the tube. This action causes the tube to twist. When the tube is moving downward during the second half of the vibration cycle, it twists in the opposite direction. The two vibrations are shifted in phase (time lag) with respect to each other, and the degree of phase-shift is directly affected by the mass passing through the tube.

A U-shaped Coriolis flowmeter mass flow is given as:

QMass =

K u − Iuω2( ) τ

2KL2

where Ku = Tube stiffness, K = A shape-dependent factor L = Width, � = Time lag, � = Vibration frequency Iu = Tube inertia that includes the tube fluid mass. The expres-

sion can be simplified as:

QMass =

K uτ 1− ωωu

⎛

⎝⎜⎜⎜⎜

⎞

⎠⎟⎟⎟⎟

2⎡

⎣

⎢⎢⎢

⎤

⎦

⎥⎥⎥

2KL2

where:

ωu =

K u

Iu

is the natural frequency of the U-shaped tube system.

Thermal flowmeter. A thermal flowmeter measures the mass flow based on heat absorption. As molecules of a moving fluid come into contact with a heat source, they absorb heat and cool the source. At increased flowrates, more molecules come into contact with the heat source absorbing even more heat. The heat dissipated from the source in this manner is proportional to the number of molecules of a particular gas (its mass), the gas thermal characteristics, and its flow characteristics. The mass flow of a thermal mass flowmeter can be given as:

QMass = K � �H

where K is the instrument coefficient which is usually determined through calibration, and ΔH is the amount of heat dissipated from the heat source. HP

ACKNOWLEDGMENTSThe authors thank their colleague Ken Fernie, P.Eng., for review and valu-

able comments on custody transfer metering, and Andrew Nelson, Production management manager from Matrikon Inc., his for review and valuable input on flow meter uncertainties.

LITERATURE CITED 1 Spitzer, D. W., Flow Measurement: Practical Guides for Measurement and

Control, 2nd Edition, Research Triangle Park, NC: ISA, 2001. 2 Upp, E. L. and P. J. LaNasa, Fluid Flow Measurement: A Practical Guide to

Accurate Flow Measurement, Gulf Professional Publishing, 2nd Edition, 2002. 3 Romagnoli, J. A. and M. C. Sanchez, “Data Processing and Reconciliation for

Chemical Process Operations,” Process Systems Engineering, Vol. 2, Academic Press, 1st Edition, 1999.

4 Ozyurt, D. B. and R. W. Pike, “Theory and Practices of Simultaneous Data Reconciliation and Gross Error Detection for Chemical Processes,” Computers and Chemical Engineering, 28, pp. 381–402, 2004.

5 ASME MFC-2M, Measurement Uncertainty for Fluid Flow in Closed Conduits, American National Standard, 1983 (Revised 2006).

6 AGA RP-3-1, Orifice Metering of Natural Gas and Other Related Hydrocarbon Fluids Part 1—General Equations and Uncertainty Guidelines, American Gas Association, June 2003. (API MPMS 14.3-1; ANSI/API 2530-91 Part 1; Gas Processors Association GPA 8185 Part 1).

7 Yawas, C. L., et al, “Equation for Liquid Density,” Hydrocarbon Processing, Vol. 70, No 1, January 1991, pp. 103–106.

8 Fisher, C. H., “How to Predict n-Alkane Densities,” Chemical Engineering, Vol. 96, No 10, pp. 195, October 1989.

9 Noor, A., “Quick Estimate of Liquid Densities,” Chemical Engineering, Vol. 88, No. 7, pp. 111, 6th April 1981.

10 ASME MFC-3M, Measurement of Fluid Flow in Pipes Using Orifice, Nozzle and Venturi, American National Standard, 2004.

11 ASME MFC-6M, Measurement of Fluid Flow in Pipes using Vortex Flowmeters, American National Standard, 1998 (Revised 2005).

12 AGA RP-7, Measurement of Natural Gas by Turbine Meters, American Gas Association, February 2006.

13 API MPMS-5.3, Measurement of Liquid Hydrocarbons by Turbine Meters, American Petroleum Institute, September 2000.

14 ASME MFC-4M, Measurement of Gas Flow by Turbine Meters, American National Standard, 1986 (Revised 2008).

15 AGA RP-9, Measurement of Gas by Multipath Ultrasonic Meters, American Gas Association, April 2007.

16 API MPMS-5.8, Measurement of Liquid Hydrocarbons by Ultrasonic Flow Meters Using Transit Time Technology, American Petroleum Institute, February 2005.

17 ASME MFC-5M, Measurement of Liquid Flow in Closed Conduits Using Transit-Time Ultrasonic Flowmeters, American National Standard, 1985 (Revised 2006).

18 ASME MFC-16M, Measurement of Liquid Flow in Closed Conduits with Electromagnetic Flowmeters, American National Standard, 1995 (Revised 2006).

19 AGA RP-11, Measurement of Natural Gas by Coriolis Meter, American Gas Association, January 2003.

20 API MPMS-5.6, Measurement of Liquid Hydrocarbons by Coriolis Meters, American Petroleum Institute, October 2002.

21 ASME MFC-11M, Measurement of Fluid Flow by Means of Coriolis Mass Flowmeters, American National Standard, 1989 (Revised 2003).

Appendices B–D can be found at HydrocarbonProcssing.com.

Subodhsen Peramanu has more than 15 years of experi-ence in conceptual, front-end design and detailed engineering of upgrading and refining processes. He has authored papers on topics including hydrogen separation and economics, bitumen character-ization, and asphaltene solubility and reversibility. Dr. Peramanu was

involved in commissioning and start-up of CNRL Horizon Upgrader and is working with CNRL Thermal Team as a senior engineering specialist on in-situ oil recovery. He holds a BChemEng degree in chemical engineering from Institute of Chemical Technology (formerly UDCT), Mumbai, MTech degree from Indian Institute Technology, Kanpur and PhD from University of Calgary.

Juon Wah’s career in process engineering spans more than 30 years and covers conceptual design, FEED, EPC and detailed pro-cess and equipment design of major projects in refining, bitumen upgrading and oil and gas production facilities. At present, Mr. Wah is a consultant on process design and plant operations. At the

time of writing, he was working on an expansion project for the Horizon Upgrading complex of CNRL. Mr. Wah holds a BSc degree in chemical engineering from the University of Birmingham, UK, and a Diplôme d’Ingénieur in chemical engineering and petroleum refining from the IFP, France.

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HPIN RELIABILITY


What guideline to use? As a general rule, adhering to the lowered Nsss requirement (not exceeding 9,000) will prob-ably force industry to require purchase of 1,800 rpm multistage pumps instead of the obviously less expensive (albeit somewhat higher efficiency) 3,600 rpm single-stage pumps that have made inroads over the past few decades.

Sticking with the questionable practice of buying the higher speed (higher NSSS, or > 9,000) pumps should require a written waiver by the parties accepting the procurement of high Nsss pumps. Issuing such a waiver should (hopefully) force the various “parties to such agreements” into understanding, describing and clearly communicating the associated risks.

There is no easy choice. However, at low flows, the less expensive pumps cannot operate safely for long periods. In the end, these are judgment issues and reliability professionals must advise the organization on the issues. Again, I believe reliability professionals will have to caution and properly advise operating and management personnel of the obvious risk of allowing high Nsss pumps to operate at low flows.4

The ultimate decision as to what to buy and how safe of a plant to have is management’s. Management, and operating personnel, must be apprised of the need to stay away from certain flows. One alternative to just cautioning others verbally is to budget costly instrumentation that would have to be procured and installed. Such instrumentation might mitigate the risk of operating at low flow by automated means. But automated means may shift the life-

cycle cost calculation into numbers that were not previously antici-pated. A second alternative would be to have only experienced and thoroughly trained operating personnel—people who will simply not allow these (high Nsss) pumps to run at low flows. A third alter-native would be to accept the need of more frequent monitoring and to do repairs more often on high Nsss pumps. What is saved in capital will soon be spent as maintenance-related money.

The risks of purchasing pumps from the lowest bidder, or buying process pumps without invoking experience-based specifications, or employing an untrained labor force also deserve to be assessed. US oil refineries have widely differing pump failure frequencies, and paying close attention to NPSH matters is but one of the many issues that require the attention of responsible personnel. HP

LITERATURE CITED 1 Taylor, I., “The most persistent pump-application problems for petroleum

and power engineers,” ASME Publication 77-Pet-5, Energy Technology Conference and Exhibit, Houston, Texas, Sept. 18–22, 1977.

2 “Allowable Operating Region,” ANSI/HI9.6.3-1997, Hydraulic Institute, Parsippany, New Jersey.

3 Bloch, H. P., and A. Budris, Pump User’s Handbook, 3rd Ed., Fairmont Press, Lilburn, Georgia, 2010.

4 Bloch, H. P., Pump Wisdom, John Wiley & Sons, Hoboken, New Jersey, 2011.

The author is Hydrocarbon Processing’s Reliability/Equipment Editor. A practicing consulting engineer with close to 50 years of applicable experience, he advises pro-cess plants worldwide on failure analysis, reliability improvement and maintenance cost avoidance topics. He has authored or co-authored 18 textbooks on machinery reliability improvement and over 490 papers or articles dealing with related subjects. For more, read his Pump Wisdom: Problem Solving for Operators and Specialists, John Wiley & Sons, Hoboken, New Jersey, April 2011.

HPIn Reliability continued from page 9

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HPIN CONTROL

The author is a principal consultant in advanced process control and online optimization with Petrocontrol. He specializes in the use of first-principles models for inferential process control and has developed a number of distillation and reactor models. Dr. Friedman’s experience spans over 30 years in the hydrocarbon industry, working with Exxon Research and Engineering, KBC Advanced Technology and since 1992 with Petrocontrol. He holds a BS degree from the Israel Institute of Technology (Technion) and a PhD degree from Purdue University.

[email protected]

ALLAN KERN, GUEST COLUMNIST

Prior to the 1980s, process control was in the dark ages. Our main “technology” was the single-loop controller, despite that it operated obliviously to the many interactions and non-first order responses that characterize most processes. Due to cumbersome capabilities, usually involving hardware and wires, if not tubing and pneumatics, any enhancement, such as feedforward, override or adaptive gain, was undertaken only for the most demanding or profitable loops.

Then along came computers and a solution that most peo-ple had not even dreamed of. Multivariable model-based pre-dictive control (MPC) provided a solution that could manage all the loops and all the constraints as one. Moreover, it used actual process models and predictive control algorithms that promised to render the limitations of feedback control, dif-ficult dynamics and process interactions—all problems of the past. The vision of MPC “dynamically” controlling processes, while “safely pushing” multiple constraints, seemed poised to become a reality.

After three decades, this vision has become widespread, through its collective pursuit and enthusiasm, but has the real-ity become widespread? Industry has been slow to recognize the limitations of MPC in practice and to adapt its strategy.

Minimize, don’t maximize, CVs. A theme of MPC has always been, the bigger the matrix, the bigger the “success.” In matrix design, this means including many secondary control vari-ables (CVs), sometimes literally by the dozen. But in operation, this results in unwanted control, not improved control, when a secondary CV takes over. CV priorities are relative to each other, but they all have a claim to the manipulated variable (MV).

Operators handle this by opening the CV’s limits, so that it might as well not be there in the first place. Or, to prevent yet another unwanted override, by “clamping” the MV, so that the whole MPC might as well not be there. This defeats the big matrix MPCs, usually in days or weeks, not months or years. Such efforts produced 20 x 50 matrices with typically 10% of the variables actually in service.

Minimizing, not maximizing, the number of CVs assures that only the essential CVs drive the MVs, and that the MVs are available (not clamped) when needed. An appropriate matrix size is indicated by the number of variables operators actually keep active, or by the variables involved in the advanced regulatory schemes before MPC ever came along—that is, much nearer to 2 x 5 than 20 x 50—for most processes.

The vast majority of secondary CVs are better “managed” than “controlled,” as they historically have been. This means they alarm only, allowing the operating team to weigh the best response, not the MPC controller, which lacks the “big picture” information, such as planning, equipment health and current priorities, etc.

Model-based control is not always the solution. And, like all feedforward, it is best used sparingly. Model-based control does not overcome fundamental dependence on accurate gains. Carefully conducted plant tests are supposed to address this, but gains change, not just over time, but hourly and daily, with charge rates, feedstock qualities, product slates and valve posi-tions. In practice, this means that an average gain must suffice, and that adaptive gain, where demanded by critical loops, is more easily and reliably implemented in traditional base-layer control algorithms.

As experienced control engineers know, feedforward (and model-based control can be thought of as feedforward across the board, just as big matrix control can be thought of as overrides across the board) can do more harm than good if the model gain and dynamics are not accurate. In this regard, it is not surprising that, although MPC in theory can provide optimum control for ideally behaved systems, in practice it is nearly always charac-terized by classic control “cycling” and is subject to traditional “detuning,” once again defeating its perceived advantage.

Industry has been slow to learn these and many other les-sons. Matrix size has perhaps finally begun to shrink, but special interests counter this important trend, such as licensing on a per variable basis and surreal economic numbers on both the cost and benefit sides of the equation.

Needed improvements aside, three decades of an MPC-cen-tric strategy are enough to apprehend its limitations. MPC can make life more difficult at the plant level. A common sentiment among management now is that process control seems to be part of the problem, not the solution. That is sorely disappointing, because smart-base layer controls—including field devices, safety systems and DCS controls—are the solution to process safety, reliability and performance. This idea, rather than MPC, should be at the center of industry’s process control vision and strategy going forward.

Reshaping the process control landscape—from one of big-matrix MPCs and neglected base-layers, to one of high-per-formance base-layers with automation intelligence embedded throughout—probably needs to begin with reassignment of cor-porate MPC budgets to a smart base-layer orientation. If that sounds expensive or like starting over, the good news is that a base-layer-centric strategy is inexpensive and mainly leverages existing control system capabilities and in-house human resources, investing high-value knowledge and experience into smart control designs. Consequently, it is a strategy that soon fuels itself. HP

Reshaping process control: A corporate prerogative

The author has 30 years of process control experience and has authored numer-ous papers on advanced process control, decision support systems, inferentials, and distillation control, with emphasis on operation and practical process control effec-tiveness. Mr. Kern is a professional engineer, a senior member of ISA, and a graduate of the University of Wyoming.


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