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JUNE 2016 Issue 900

NEWS AND VIEWS FROM THE PROCESS INDUSTRIES, BROUGHT TO YOU BY THE INSTITUTION OF CHEMICAL ENGINEERS

The Chemical Engineer

HEALTH & SAFETY

MYTHBUSTINGAn interview with

Dame Judith Hackitt

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CCS

GOING NOWHERELord Oxburgh says CCS is in suspended animation

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TECH TRIAL

SOLAR CHALLENGE Engineers team up and battle to hack the Sun

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ENGINEERING ADVANCES

FUTURE GAZINGThe convergence that could change industry

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feature simulation & optimisation

JUNE 2016 | The Chemical Engineer | page 29

Testing TimesA review of the merits of the equivalent reactor network approach

BILL KULPLEAD PRODUCT MARKETING MANAGER, FLUIDS, ANSYS

A GLOBAL manufacturer of gas turbines was facing the next round of international emissions standards that required a drastic reduction in pollutants

including carbon monoxide and nitrogen oxides. The company turned to conventional computational fluid dynamics (CFD) but was limited by the very long run times needed to model detailed combustion chemistry. And when it tried to simplify and approximate the chemistry to get shorter run times, it no longer had the ability to accurately predict emissions. At that point, it could have resorted to physical testing, but real-world limitations in instrumentation, test stand availability and prototype hardware would have driven up the cost and resulted in significant delays to the schedule. Here, I discuss

how reduced order modelling saved the day by solving the problem more quickly without compromising accuracy.

Turbine, boiler and furnace equipment designers are facing new challenges and new opportunities in today’s market that are driving them to seek more accurate and cost effec-tive design methods. Most manufacturers realise they need to account for alternative fuels, fuel flexibility and greater fuel and emissions efficiency in combustion-equipment designs in order to address new markets where traditional fuel sources are either scarce or unreliable. To date, adding fuel flexibility has involved considerable cost and extensive experimental testing. Reducing these costly and time-consuming experimental testing cycles needed to validate combustion performance is crucial to driving

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development costs down. Minimising the risk of design mistakes identified after the sale reduces the cost of fixing them in the field.

Successful clean-combustion design is tied directly to the developer’s ability to integrate alternative fuels and fuel flexibility into the design; reduce emissions to stay ahead of environmental regulations; develop innovative technologies that increase efficiency; reduce the resources (cost and time) required to develop new designs; and improve the accuracy of emissions predictions that support performance guarantees.

Reducing these costly and time-consuming experimental testing cycles needed

to validate combustion performance is crucial to driving development costs down

All of these factors drive combustion designers toward new design methods that allow them to achieve a substantial impact on fuel flexibility and efficiency, while reducing the resources and time required to design and test these new combustion systems. Using accurate simulation effectively is a key element that can help clean-combustion designers achieve these objectives.

The gas turbine industry’s ability to effectively use alternative fuels will have a large impact on future growth. To illustrate, consider a simple fuel such as natural gas. Natural gas composition is highly variable across geographic locations (see Table 1). In this era of tightening emissions regulations, tried-and-true simulation methodologies like CFD don’t provide enough accuracy to ensure a confdent, thorough under-standing of the effects of composition variations of natural gas. To help manufacturers assess the effects of these variations, combustion simulation must provide seamless, integrated methods of linking familiar methodologies like CFD and the powerful, highly-accurate capabilities of detailed chemistry. The variations in gas composition elicit significantly different

combustion performance in terms of stability and emissions. Figure 1 illustrates how nitrogen oxides vary with fuel for large gas-turbine engines used for power production. The investment is staggering when you consider that each data point in this figure represents a full combustion test that costs tens of thousands of dollars and weeks of develop-ment resources. Moving this test cycle into a highly accurate virtual test environment provides a faster, more cost-effective means of ensuring fuel flexibility and lower emissions in new combustion systems.

Current Combustion Simulation CapabilitiesCombustion system designers have made great strides in recent years in developing low-emissions systems in the power and transportation markets, with minimal performance tradeoffs. In order to bring these new designs to market, combustion designers needed to move away from traditional build-and-test design processes. Experimental tests cost from tens to hundreds of thousands of dollars apiece and take months to complete – driving the need for new design methods. The development of low-NOx systems, such as dry low-NOx (DLN), lean premixed prevaporised (LPP) and rich burn-quick quench-lean burn (RQL), depended upon simulated combustion. Some of these new approaches often require the combustor or burner to operate closer to stability limits than in conventional combustion designs. So, developing fuel-flexible, advanced low-NOx designs still represents a significant challenge to the combustion designer.

Typically, combustion is modelled in CFD as the process of burning a hydrocarbon fuel in air to produce CO2 and H2O, represented by one, or a few, global reaction step(s). In fact, the process of combustion involves hundreds of short-lived species that participate in thousands of reactions while forming the products of combustion. These detailed chemical reactions are responsible for combustion stability and the formation of pollutants.

Table 1: Typical composition of natural gas1

Methane CH4 70–90%

Ethane C2H6

0–20%Propane C3H8

Butane C4H10

Carbon dioxide CO2 0–8%

Oxygen O2 0–0.2%

Nitrogen N2 0–5%

Hydrogen sulphide H2S 0–5%

Rare gases A, He, Ne, Xe Trace

Figure 1: Influence of gas constituents on NOx emissions2

Coal gas (air blown)

Biomass gas

Gob gas

Natural gas (raw)

Methane

Natural gas (pipeline)

Coal gas (oxygen blown)

Town gas

Ethane

Propane

Refinery waste gas

Hydrogen

Carbon monoxide

0 0.5 1.0 1.5 2.0 2.5

RATIO OF GAS CONSTITUENT NOX TO NOX FROM METHANE



Faster computers and better knowledge of fluid dynamics and combustion chemistry have allowed CFD to become integrated into the combustor design process and to provide valuable design assistance. However, CFD simulation run times are not fast enough to incorporate the detailed combustion chemistry of the fuel combustion. This is where reduced-order chemical reactor modelling approaches come into play. By simplifying the model, more detailed chemistry can be incorporated. Tools such as ANSYS’ Chemkin-Pro can quickly simulate the details of the combustion process using a fully accurate mechanism for the fuel oxidation with hundreds or thousands of species and reactions while simplifying the spatial geometry. The trick is to represent a complex 3D geometry and flow field as a series of 0D and 1D ‘reactors’ that allow you to apply accurate fuel chemistry mechanisms. This greatly simplifies the computations while preserving accuracy.

Faster computers and better knowledge of fluid dynamics and combustion chemistry have

allowed CFD to become integrated into the combustor design process

To accurately and cost-effectively incorporate detailed chemistry into combustion simulation, a method must bring together the best attributes of CFD and reactor modelling, as suggested in Table 2. This is done by carefully mapping regions in the combustor CFD solution, in which the cells have chemical similarity to discrete reactors and then accounting for all of the mass flow across region boundaries (see Figure 2) in a flow- connected reactor network.

The Equivalent Reactor Network ApproachApplying detailed fuel-combustion and emission formation chemistry in combustion simulation requires a simplification of the geometry. Equivalent reactors represent the combus-tor as a series of idealised reactors to allow use of detailed

chemistry within an acceptable amount of computational time. Complex combustor geometry and flow can be converted effec-tively into an equivalent reactor network (ERN) that links the reactors together. Once the ERN is created through a careful processing of the combustor flow field, a fully detailed reaction mechanism can be used to provide the most accurate understanding of combustion behaviour and performance. For this approach to be successful, however, it is critical that the ERN is a true representation of the actual combustor flow field.

The successful use of ERNs to accurately predict emissions has been demonstrated in many studies for a wide variety of combustion systemS

The successful use of ERNs to accurately predict emissions has been demonstrated in many studies for a wide variety of combustion systems3,4. However, systematic ways of generating reliable networks have been elusive. One major drawback of applying this approach is that an expert in detailed chemistry typically takes several weeks to several months to create ERNs manually. When you consider the fact that there are now dozens of fuel types that equipment must accommodate in order to be successful, the need for a streamlined manner of using ERNs with accurate CFD for emissions predictions is obvious. The alternative of conducting experiments to evaluate the performance of so many fuels costs too much in money and time and, as previously mentioned, CFD cannot handle the detailed chemistry needed for accurate results.

ANSYS has developed the Energico simulation, unique in that it automatically creates ERNs from reacting-flow CFD solutions. It uses a series of filters or user-defined variables

Table 2: Comparison between CFD and CHEMKIN-PRO reactor modeling

Computational fluid dynamics Chemkin-Pro

Detailed spatial geometry.

3D flow representation.

Accurate prediction of mass flows.

Accurate heat transfer.

Simplified combustion chemistry.

Full combustion chemistry details.

Accurate prediction of trace chemical species including

pollutants and soot pre-cursors.

Accurate ignition chemistry.

Simplified spatial geometry and flow field.

Figure 2: An equivalent reactor network (ERN) built on top of CFD results



that are applied to the CFD to generate the ERN for accurate prediction of combustion performance, including exit emissions. An algorithm is used to divide the combustor flow field into zones that will form the basis of the ERN. Once this ERN is created, Chemkin-Pro is used to apply the detailed fuel mechanism and solve the ERN with accurate chemistry to simulate the emissions of trace species such as NOx, CO and unburned hydrocarbons. The ERN can also be employed in a para-metric variation of operating conditions and fuel composition to determine how such variations would affect performance.

ERN ALGORITHM DEVELOPMENTAn algorithm to create the ERN from the CFD solution applies a series of ‘filters’ to different CFD variables such as tempera-ture, oxygen, and fuel. Once contiguous cells are grouped into zones based on these filters, Energico calculates the mass flow connecting each zone to each other zone, as well as the mass flow entering each zone from external gas inlets. Automating this approach creates significant advantages; rapid appli-cation of ERN algorithms allows the user to quickly evaluate the impact of algorithm variables and identify the optimum algorithm for the emissions target of interest. Furthermore, automatically applying the ERN algorithm ensures that the design is free from manual errors introduced that can affect the reactor-zone determination.

ALTERNATIVE FUELS ANALYSISThe ability to predict emissions accurately from flow-field- derived ERNs has a significant impact on the ability to assess the impact of real and alternative fuels. Alternative fuels can be modelled using the same approach that was described above. A CFD solution can be developed with global or dramatically- reduced reaction mechanisms that approximate the thermo-dynamics of the alternative fuel of interest.

From this solution, an ERN can be derived where a fully- detailed reaction mechanism for the fuel, or a reasonable fuel surrogate, can be applied in a computationally efficient manner.

ASSESSING LEAN BLOW OFF Low NOx-emission combustors utilise reduced peak-flame temperatures within the combustor to limit the forma-tion of thermal NOx, through staging strategies such as lean, premixed combustion. As combustion temperatures are decreased in low-NOx applications, several other undesirable combustion phenomena become more prevalent and must be addressed. The low-NOx limit is often bounded by the onset of combustion instability in the form of lean blow off (LBO). LBO occurs when the thermal energy generated by the burning fuel/air mixture is no longer sufficient to heat the incoming fuel to the ignition point.

Accurate Damköhler numbers are important to characterise whether diffusion rates or

reaction rates are more important for defining the chemistry throughout the reaction chamber

Energico uses an innovative method to address the issue of defining Damköhler numbers (which relate the reaction rate to the transport phenomena rate occurring in a system) for a combustor, that takes advantage of the local flow and thermochemical properties extracted from a CFD solution and the kinetics available in the detailed chemistry mechanism. Accurate Damköhler numbers are important to characterise whether diffusion rates or reaction rates are more important for defining the chemistry throughout the reaction chamber. Rather than trying to define a pair of ‘global’ chemical and flow residence times for a whole combustor, the software’s LBO analysis tool defines both chemical and residence times locally to account for the spatial variation of mean flow, turbulence, and

Figure 3: Flame captured for Lean Blow Off analysis

Figure 4: Damköhler Number results for a stable combustor



gas-mixture properties. The LBO analysis verifies the integrity of the flame locally and provides an indication of the overall soundness of the flame zone visually, expressed as contours of the local Damköhler number. The Damköhler number distribution exposes the location and the size of the stable flame core in the combustor. By examining the structure and topology of the flame core and the integrity of the flame, the likeliness of blow-off can be determined. The adequacy of the CFD mesh for flame predictions in that area can also be assessed. An example of LBO results for a wall-jet can combus-tor is shown in Figure 4, where regions with a Damköhler number of greater than unity (blue regions) indicate regions where the flame is unstable.

SOME SAMPLE RESULTSFigure 5 shows a comparison of Energico emissions results with experimental measurements for a single fuel injector from a low-NOx, industrial, gas-turbine engine. The injector is a well understood design and has significant experimental data that have been validated by the manufacturer. The experimen-tal data are from single-nozzle combustion tests in an isolated rig and include the effects of fuel/air variations on exit-NOx emissions. As can be seen in the figure, the tool predicted the NOx emissions at the baseline point. Moreover, parametric variation of the ERN inputs to increase fuel/air ratio (ie increase combustor exit temperature) yielded NOx predictions that are in excellent agreement with the experimental results. The simulation was conducted in a couple of hours and its results can be used to replace a physical experiment that costs upwards of US$100,000 to perform.

HOW REDUCED-ORDER MODELlING SOLVED THE GAS TURBINE EMISSIONS CHALLENGE

Remember the gas turbine manufacturer? It decided to adopt a reduced-order modelling solution. Now it can run simulations very quickly, with the full, uncompromised

Figure 5: ERN models accurately predict NOx emission variations with combustor exit temperatures.

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chemistry, and it can now obtain accurate predictions of carbon monoxide and nitrogen oxides, allowing it to reduce experimental testing.

REFERENCES1. www.NaturalGas.org (bit.ly/1za4itn), accessed 16 March 16,

2016.2. Carson, C, Fuel Flexibility and Alternative Fuels for

Aeroderivative Gas Turbines, in NRC and IAGT, 2008: Ottawa.3. Novosselov, IV et al, “Chemical Reactor Network Application

to Emissions Prediction for Industrial DLE Gas Turbine”, paper GT2006-90282, in ASME International Gas Turbine and Aeroengine Congress, 2006, ASME: Barcelona, Spain.

4. Novosselov, IV and Malte, PC, “Development and Application of an Eight-Step Global Mechanism for CFD and CRN Simulations of Lean-Premixed Combustors”, paper GT2007-2799, in Proceedings of GT2007, Montreal, Canada: ASME.

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The Chemical Engineer | PAGE 26

FEATURE process safety

Buncefield: a Decade OnLessons learned and risk management implications

by marc josephRISK ENGINEER AT MARSH

Picture the scene, early on a Winter Sunday morning. Calm and serene, you and your family are tucked up in bed at home. Suddenly, you’re awakened abruptly by

an ear-splitting noise followed by your room shaking around you. Your home and life as you know it has been shattered, and so the nightmare begins. This was the scenario for many residents of Hemel Hempstead, UK, on 11 Dec 2005. A shock wave, measuring 2.4 on the Richter scale, ripped through their homes. This was due to an explosion at the Buncefield complex, which BBC News reported as “the biggest peacetime explosion ever recorded”. Over 2,000 people were evacuated, over 630 businesses were affected, motorways were closed, and buildings as far as 8 km away from the complex were damaged as a result. Remarkably, no one died in the blast, although over 40 people were injured.

What happened at Buncefield?The Buncefield complex, located and operating in Hemel Hempstead since 1968, was a large tank farm occupied by Hert-fordshire Oil Storage Limited (HOSL), UK Oil Pipelines (UKOP) and BP Oil UK.

On 10 December 2005, Tank 912 and Tank 915 were being filled with petrol. Both tanks were fitted with an automatic tank gauging (ATG) system which measured the tank level. These levels could be displayed on a single screen in the control room along with all other tanks in the facility. Each tank was also fitted with an independent high-level switch (IHLS), which was the final layer of overfill protection to stop the filling process and also to activate an audible alarm.

On 11 December at 03:05, Tank 912’s level measurement failed and the level reading in the control room ‘flatlined’. This

buncefield: the smoke plume above hemel hempstead

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How to S IZE L INES

The fundamentals and best practices behind line sizing for a single phase fluid (liquid or gas)

BY vikram sharmaPROCESS DESIGN ENGINEER AT PETRONAS GROUP TECHNICAL SOLUTIONS

IntroductionMany young engineers rely on readily-available software to compute line sizes. But what are the fundamentals behind the software?

When we talk about chemical plants, we talk about process equipment such as reactors, compressors, pumps, vessels and much more, which all need to be connected by pipes which transport fluid from source to destination. While a pipe system’s physical appear-ance and functionality may be simple, its designer needs to have a good understanding of the concepts of fluid flow and line sizing to ensure piping is designed to specifications.

Bernoulli ’s equationBernoulli’s equation forms the basis of the line sizing equation that is used to calculate pressure drop across a pipe. But what is Bernoulli’s equation?

Let’s go back to the basics of line sizing. The first law of thermo-dynamics states that energy can neither be created nor destroyed, but it can change from one form to another. This law forms the basis of the interactions of energies that act on a system – also known as the conservation of energy principle(1).

The conservation of energy principle, in fluid flow, focuses on the conservation of the sum of six important energies – namely pressure, kinetic, potential, heat, work, and frictional energies – all of which are expressed in one simplified form called the mechanical energy balance (MEB). The expression of mechanical energy balance is provided in equations 1–3(2).

Eq 1

Eq 2

Eq 3

Bernoulli’s equation represents a steady-state fluid flow (see Eq 3). It was derived based on the assumptions that the fluid is incompressible

size linesHOW to

(ρ = constant), inviscid (μ = 0), isothermal (q = 0), there is no energy loss due to friction between the pipe inner wall and fluid, and no work done (w = 0) on the system. The assumptions made for this equation are impossible to achieve at any point in time by a fluid. However, it can be approximately achieved in a real situation. For example, the assumption on incompressibility is applied for both liquid and gas but in reality, gas is a compressible fluid. Therefore, this makes the assumption invalid. If the pressure change results in a density change of no more than 30%, the assumption on incompressibility is valid(3).

The MEB equation consists of two parts that are point function and path function. The first three terms on the right hand side of Eq 2 represent point functions where these terms are dependent on the inlet and outlet conditions. Parameters such as ef and w are known as path functions, where these terms are based on condition variations between the inlet and outlet points. The above equations also assume that the fluid velocity at a given point in a system is consistent throughout a cross section of a flow stream. In other words, fluid velocity at point 1 is the same as at point 2. This is not true for real fluids as the velocity can vary across the cross section of flow due to friction. Therefore, the kinetic correction factor (α) is incorporated to account for the non-ideal conditions. The fluid flow regime dictates the value of α. If the fluid flow is laminar, α = 2. In the case of highly turbulent flow, α is approximately 1.06. For practical applications, α = 1, is used as most line sizing scenarios are based on turbulent flow(3).

Pipe friction factorAny fluid transported in a pipe is accompanied by friction. This friction offers resistance to fluid motion and contributes to the overall pressure drop across a pipe system. In Bernoulli’s equation, the frictional loss term, ef, as seen in Eqs 1–3, is due to loss of mechanical energy from pipe fittings, length of pipe, and pipe entrances and exits(2).

To determine the pipe friction factor, we need to know the type of fluid flow regime in a pipe. The fluid flow regime is determined from a dimensionless parameter called Reynolds number (NRe). The Reynolds number is a function of pipe inner diameter, fluid density,

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feature NUCLEAR REACTORS


feature NUCLEAR REACTORS

There are a few technical features that need evaluating and testing, for example the internal control rod drive mechanism which some designs use in preference to the traditional external drive. Most developers have already tested these new features in their test facilities, but they will of course require approval.

Also the small size of the reactors is expected to require devel-opment of new inspection methods and technologies.

The smr market right nowThe SMR sector has been very active recently, with many designs proposed, though some are more developed than others. Currently leading the race to market are NuScale (US), KLT 40 (Russia), CAREM (Argentina), ACP100 (China), and SMART (Korea). Only time will tell how many designs will win through.

We’ve already seen one design fall out of favour during the push for SMR development by the US Department of Energy. mPower and NuScale won funding from the DOE in 2012 and 2013; however, mPower hasn’t since secured the industry funding required for the deal. As a result, NuScale is the leading SMR project moving forward in the US.

NuScale originated from research in the early 2000s by NuS-cale co-founder Jose Reyes. Industry backup came from Fluor in 2011 and Rolls Royce in 2013. The first deployment in the US is

scheduled for 2024 through a multi-state collaboration known as the Western Initiative for Nuclear. NuScale has announced that its first priority is to submit the design certification application to the Nuclear Regulatory Commission (NRC) in 2016.

Another reactor set for deployment is the SMART reactor that has already been licensed in its home country of Korea. Its first application, however, is planned for Saudi Arabia where it will be used to power water desalination.

An SMR developed in China is also set for market. The ACP100 is set to be built in Hainan Island in China. The project schedule is very aggressive and operations are planned to start as early as the end of 2021. There is also a plan to develop the next version of the reactor (ACP100+) for export in a bid to become a competitive SMR design for use in Europe.

more exotic applications include floating designs from developers in russia and china

The SMR concept has also led some to suggest more exotic appli-cations for nuclear reactors, including floating designs from developers in Russia and China.

These transportable reactors might be the best opportunity for newcomer countries, which don’t have strong grids. One approach would be to transport the floating reactor to the coast of a country that needs power, sell it the electricity, and once the reactor has reached the end of its life, transport it back to its country of origin. This kind of service is new in the nuclear area and it would need big changes in current regulations. We are discussing this kind of approach in an IAEA working group, and these discussions are proving very interesting.

Another interesting design is a subsea plant out of France where reactor modules would be positioned on the seabed and operated remotely from a control room onshore.

Clearly, these exotic designs face the biggest challenges when

SMART: KAERI, South Korea

Capacity: 100 MWe Design: System simplification, component modularisation, reduction of construction time and high plant availability. Reactor pressure vessel contains all of the primary com-ponents such as core structures, steam generators (SGs), and the reactor coolant pumps are mounted horizontally into the reactor vessel. Applicable for electricity production suitable for small or isolated grids and heat districts as well as processing heat for desalination.Safety: Features a reactor shutdown system, a safety injection system, a passive residual heat removal system (PRHRS), a shutdown cooling system, and a containment spray system. Additional engineered safety systems include a reactor overpressure protection system and a severe acci-dent mitigation system.

ACP100: NPIC/CNNC, China

Capacity: 100 MWeDesign: Uses natural convection reactor cooldown. Can deploy 1–8 modules to attain larger plant output. Designed for electricity production, heating, steam pro-duction or seawater desalination; suitable for remote areas.Safety: No large-bore primary coolant piping, so it eliminates large-break loss of coolant acci-dents (LOCAs). Reactor building and spent fuel pool are below ground level.

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AThe Chemical Engineer | PAGE 34

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