Chemical Plant Flare Minimization via Plantwide Dynamic Simulation

Qiang Xu,* Xiongtao Yang, Chaowei Liu, Kuyen Li, Helen H. Lou, and John L. Gossage

Department of Chemical Engineering, Lamar UniVersity, Beaumont, Texas 77710

Flaring is crucial to chemical plant safety. However, excessive flaring, especially the intensive flaring duringthe chemical plant start-up operation, emits huge amounts of volatile organic compounds (VOCs) and highlyreactive VOCs, which meanwhile results in tremendous industrial material and energy loss. Thus, the flareemission should be minimized if at all possible. This paper presents a general methodology on flare minimizationfor chemical plant start-up operations via plantwide dynamic simulation. The methodology starts with setupand validation of plantwide steady-state and dynamic simulation models. The validated dynamic model isthen systematically transformed to the initial state of start-up and thereafter virtually run to check the plantstart-up procedures. Any infeasible or risky scenarios will be fed back to plant engineers for operationimprovement. The plantwide dynamic simulation provides an insight into process dynamic behaviors, whichis crucial for the plant to minimize the flaring while maintaining operational feasibility and safety. The efficacyof the developed methodology has been demonstrated by a real start-up test.

Introduction

Chemical plant start-up operations can be considered asplantwide dynamic operations, by which the whole plantoperating status is transferred from one steady state to another.Apparently, the start-up operation is a highly nonlinear, complexoperation that usually involves discontinuous and/or paralleloperating procedures, as well as a wide change of manycontrollers’ set points. Such complex operations would inevi-tably generate huge amounts of off-spec product streams thathave to be sent to flaring systems for destruction, if the plantstart-up could not be completed promptly. Flaring can protectchemical plant personnel and equipments, as well as protectthe local environment from direct emission pollutions. Thus,flaring is crucial to the chemical process industry (CPI).

Excessive flaring, however, will also cause negative envi-ronmental and social impacts and result in tremendous materialand energy losses.1 The flaring emissions during chemical plantstart-up operations generate huge amounts of CO, CO2, NOx,volatile organic compounds (VOCs), highly reactive VOC(HRVOCs) (defined in Texas air quality regulation as ethylene,propylene, isomers of butene, and 1,3-butadiene), and partiallyoxygenated hydrocarbons (such as formaldehyde). It has beenestimated that an ethylene plant with a capacity of 1.2 billionpounds of ethylene production per year will easily flare about5.0 million pounds of ethylene during one single start-up.2 Giventhe 98% flaring efficiency (destruction efficiency), the resultantair emission will include at least 40.0 klb CO, 7.5 klb NOx,15.1 klb hydrocarbon, and 100.0 klb HRVOCs.

The flaring emission will cause highly localized and transientair pollution events, which are harmful to people’s health. Forinstance, the industrial flare emission of HRVOCs mixed withNOx has been identified with high concentrations of ozoneobserved in the Houston/Galveston area of Texas, which violatesthe National Ambient Air Quality Standards (NAAQS) forozone.3-7 Note that the flaring emission not only causesdangerous environmental pollution but also results in tremendousraw material and energy loss that could generate much neededproducts from the industry. As a result of the increasingly strictenvironmental regulations and economic competition, flare

minimization has become one of the major concerns for thechemical process industry.

Current practice of flare minimization in CPI plants is notstandardized due to the complexity of the different process andoperating procedures. This causes flare minimization to dependalmost exclusively on the experienced and well trained operators,engineers, and administrators.8,9 The focal point for flareminimization is to reduce the number of instances when theplant has to flare and the quantity of the materials to be flared.However, the industrial-experience based methods are oftenlimited, when they have to confront complex plantwide dynamicoperations (e.g., start-up) with critical control and safety issues.10

Consequently, virtual models are employed to study the start-up behaviors. Some studies have tried steady-state simulation(SS) for start-up operation, based on a set of predicted steady-state operating points to project the system dynamic response.11

The methods have inherent deficiency because they could notreveal the real dynamic behaviors between two adjacent steadystates and thus lack the capability to guide critical processcontrol and operation.12

To provide more accurate dynamic information, plantwidedynamic simulation (DS) methods have recently become popu-lar.13 It is used to virtually test plant start-up operationsaccording to the operating strategies that will be undertaken bythe plant operating personnel.14-17 It examines critically thepotential process operational risks and infeasibilities. Based onthe DS results, the feedback will help the plant improve thestart-up operating strategies and thus reduce flare emissions.The methodology is cost-effective and proved very successfulin real application. Based on the previous studies, this paperfor the first time generalizes a systematic methodology for flareminimization during CPI plant start-up operations via plantwidedynamic simulation. It covers the modeling of recent practicefor start-up operations with total recycles. Meanwhile, modelingexperience and required industrial data are also summarized. Afield test for flare minimization during an ethylene plant start-up is presented to demonstrate the efficacy of the methodology.

Scope of the Dynamic Simulation Model

The amount of flaring emission during a plant start-upnormally increases with the start-up duration. To shorten the

* To whom correspondence should be addressed. Telephone: 409-880-7818. Fax: 409-880-2297. E-mail: Qiang.xu@lamar.edu.

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10.1021/ie8016219 CCC: $40.75 2009 American Chemical SocietyPublished on Web 02/27/2009

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start-up time, the important plant units are usually commissionedat some designated steady states before initiating the start-up,which is called the initial state for start-up. For instance,distillation columns need to run with total reflux, and reactorsneed to be controlled at certain temperature levels. Recent flareminimization practices have shown the total-recycle start-up maysignificantly reduce the flare emission. This suggests that theplant should start with full-recycled streams from downstreamprocess as system input, which can capture, recycle, and reuselarge amounts of off-spec products that would otherwise be sentto the flare system as waste streams. To model the total-recyclestart-up without losing the generality, the proposed methodologywill address the dynamic simulation of the process systemdefined in Figure 1.

For comparison, the plantwide dynamic simulation model atnormal operations is shown in Figure 1a, where normal feedmeans the raw material input at normal operating conditions.Note that Figure 1a includes all the internal recycles at normalworking conditions. Figure 1b shows the initial state of thedynamic simulation model for start-ups. Based on a general case,the DS model contains all the designated outer and inner loopsof recycled streams, which are only used for plant start-up. Here,a recycled stream is called an outer-loop stream because itrecycles the off-spec materials to the very beginning of thesystem input; otherwise, it is called an inner-loop stream. Withthe help of Figure 1, the DS model for a plant start-up can bedefined as simulating the transient behaviors from the initialcondition of start-up (Figure 1b) to the normal working condition(Figure 1a). Note that the topology of the simulation model

changes when full-recycle start-ups are considered, which makesthe simulation task extremely complex.

General Methodology for Plantwide Dynamic Simulation

To conduct the plantwide dynamic simulation for total-recyclestart-ups, a systematic modeling methodology has been devel-oped in this paper, which considers more general and complexsituations than previous methods. The plantwide dynamicsimulation is performed based on the integration of rigorousprocess models, plant design data, P&ID, DCS historian, andindustrial expertise. Generally, the developed methodology isthe integration of modeling activities among three interactivestages as shown in Figure 2.

Steady-State Modeling and Validation. In the first stage,model development starts with the setup of the SS model forthe plant process system that needs to be investigated duringthe start-up. The sequential modular approach is used in thismethodology, by which each subsystem will be modeled andsolved independently. The reasons for selecting sequentialmodular approach instead of the equation oriented approach aremainly because the whole plantwide simulation task is moreconvenient to be decomposed into small subsystems for valida-tion and troubleshooting. Although the modeling process willbe slower, it does not influence too much as the start-upsimulation task is conducted offline. The developed SS modelis usually validated by plant design data first, which are collectedfrom plant design documents. Then the model will be furthervalidated by normal steady-state operating conditions, whereDCS (distributed control system) historian will be used.Sometimes, the real plant data are not in mass or energy balancedue to unpredicted reasons such as sensor drifting, malfunction,or the negligence of some input/output streams. Under such

Figure 1. General sketches of normal operation and the initial status forstart-ups.

Figure 2. General methodology framework.

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conditions, data verification, data reconciliation, and the supportfrom industrial expertise will be extensively involved. Typicalmodel turning parameters in this stage include column trayefficiency, heat transfer efficiency, reaction kinetic parameters,etc.

Dynamic Modeling and Validation. If the plantwide SS issatisfied, the validated SS model is then transferred to the DSmodel. Three types of information must be involved to supportsuch a model transition. First, equipment dimension data (suchas initial equipment hold-up level, vessel type and geometry,hydraulics, and tray geometry and details) from updatedmechanical drawings is required, which provides the processcapacity information. Second, control strategy and controllerparameters from current plant P&ID (piping and instrumentdiagram) are needed, which provides process control informa-tion. Third, process and equipment heat-transfer methods shouldbe given for the dynamic simulation model, which provides thethermodynamic information.

When the plantwide DS model has been built, the plant DCShistorian of some process upset scenarios will be used againfor further model validation. This is a nontrivial task, becauselots of data preprocessing and postprocessing, as well asmodeling troubleshooting activities, are induced. Meanwhile,it is also unrealistic to expect the DS results will exactly matchthe real measurements even if all possible modeling efforts havebeen made. The success of the DS validation depends onwhether or not the timing and amplitude of the dynamicresponses of each subsystem will match the real DCS historian.Sometimes, the plant may lack some transient concentration,temperature, or pressure information, which really providesdifficulties for DS model validation. Therefore, it is always betterto collaborate with experienced plant engineers to judge thevalidation results, so as to improve the modeling quality. Notethat this is the last chance to validate the plantwide DS model.

Identification of Start-up Initial Status. Before the validatedplantwide DS model is applied for start-up simulation, the wholemodel should be adjusted to run at the initial state of start-up.This step actually highlights the most important differencebetween normal DS and start-up DS. The initial state of start-up usually involves low-load running equipment, zero inflowand outflow rate, and full reflux of distillation columns, as wellas temporary multiple recycles and auxiliary streams at the

beginning of start-up. Meanwhile, the transfer procedure fromthe normal status to its initial start-up status oftens lackssupporting information, such as real plant data, parameterestimations, and operating guidance. Thus, the activities ofmodel status adjustment present the most challenging step forplantwide start-up simulation, where sufficient care is neededto prevent the divergence of the modeling process. Figure 3shows a general model system to illustrate the transformation,where the initial feed for start-up represents the feed used forcommissioning the system to its initial state for start-up. Notethat the transition of model status requires not only system modelinput changes but also process topology changes (recycle andauxiliary streams) and operating status changes (e.g., temper-ature, pressure, concentration, and control parameters changes).A general algorithm to accomplish the status-adjusting task,which was completely ignored in previous studies, is presentedbelow.

Step 1. Make the plantwide DS model run at the normalsteady state. At this step, the flow rates of the entire inner andouter loop streams for start-up are zero. The normal feed (streamA in Figure 3) will be exactly equal to system input (stream Cin Figure 3), while the flow rates of initial feed for start-up(stream B in Figure 3), system recycle (stream D in Figure 3),total recycle (stream E in Figure 3), and purge (stream F inFigure 3) are all zero.

Step 2. According to the plant start-up procedure, addadditional dynamic models into the plantwide DS model. Theadditional models include mixers, splitters, and outer and innerloop streams (heat exchanger models may also be needed).

Step 3. Identify the operation conditions for the key units atthe initial status for start-up, such as the composition of streamB, controller set points, reflux ratios, and heating and coolingduties.

Step 4. Gradually reduce the flow rate of A and meanwhilegradually increase the flow rate of B, which will make thesystem input of C gradually transform from A to B. During theinput transform, the flow rates of the entire inner and outer loopstreams for start-up should be gradually increased from zero tothe designated value. The controller set points for all the keyunits should be gradually adjusted accordingly. At the end ofthis step, the flow rate of A will be zero; the flow rate of B will

Figure 3. General model for plant start-ups.

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reach the desired value, which will make the system run closeto the initial status for start-up.

Step 5. Gradually increase the splitter ratio of D and makeits flow rate increase. Meanwhile gradually decrease the flowrate of B. At the end of this step, the flow rates of B and purgestream F will be zero. Thus, the total recycle flow rate of Ewill exactly equal the system recycle flow rate of D, which alsoequals the system input flow rate of C.

Step 6. Check the entire DS model to see if every unit isrunning at the expected initial state of start-up. If yes, the modelsystem has reached the initial state of start-up. Otherwise, themodel system needs further fine-tuning. First, make sure all thecontroller set points are fixed at the specified values in step 2.Then increase the flow rate of B from zero to some small value,and meanwhile reduce the flow rate of D by the same value byreducing its split ratio, and go to step 5.

Note that in steps 4 and 5, the system input transformationneeds sufficient care to avoid computational divergence, becausesuch transformation influences the dynamic simulation resultof every unit of the model system. Except the small changes atevery input adjustment, the most important is to make theplantwide system reach the steady state at some intermediatetransformation points (e.g., 50%, 70%, and 90% completion offull recycles), and then continue the model tuning. Thus, thesystem input transformation in steps 4 and 5 actually suggeststhe acquisition of a series of steady states. Because of that, theidentification of start-up initial status is considered as the mostcritical and time-consuming task during the dynamic simulation.Also note that this general algorithm is a hypothetical modelingprocedure without any real implementation assumption. Itpresents a systematic way to obtain the initial state of theplantwide DS model for the start-up simulation.

Dynamic Simulation for Flare Minimization. After theinitial state of a start-up is obtained, the DS model is ready tovirtually test the start-up operating procedures provided by theplant. The final modeling activity is to schedule all the dynamicoperating procedures as the input of the DS model and “let itrun”. Note that a chemical plant start-up undergoes a numberof stages. At each start-up stage, specific objectives andconstraints must be obtained, which usually involves processinputs, temperature, pressure, flow rate, and stream componentsof each unit changing nonlinearly.18-20 Thus the controller setpoints and even control strategy should be changed accordingly.To model the dynamic process inputs and dynamic start-upoperation procedure, a set of program scripts, or so-called start-up “tasks”, have to be developed and embedded into the DS.21

Then, the DS model is ready to run, while the simulation resultswill be fully repeatable.

Supposedly, the dynamic simulation results will identifyunexpected or unsafe operation conditions. This will be fed backto the plant operation group to recheck their previous operatingprocedures. The modified operating procedures will be virtually

tested again by rerunning the DS model with modified input.Similarly and iteratively, the DS model will help the plantidentify viable or even optimal operating strategies for its start-up operations. Note that when simulation meets an infeasibleproblem, two possibilities may exist: either the model itself hassome problems, which are not detected in previous modelvalidation, or the plant operating procedures do have uncontrol-lable operations. Thus, troubleshooting should be conducted withthe help of both theoretical analysis and industrial expertise.Also note that all the developed methodology is based on first-principles models. Therefore, commercial simulators such asAspen Plus, Aspen Dynamics, PRO/II, Dynsim, or Hysys arerecommended. The developed methodology is applicable ingeneral and is not limited to one particular plant or process. Itis also a cost-effective approach for flare minimization.

Table 1 gives a summary about the major supportinginformation needed from a real plant. It is worth noting thatalthough the focal point of the developed methodology isdynamic DS, SS is absolutely necessary. This is not onlybecause DS models are generated from the SS models but alsobecause SS can identify the controller set point information,which determines the settling point of a dynamic response.Therefore, the controller set points at the end of start-upoperations should be identified through SS before the DSvalidation and application.

Due to the inherent complexity of plantwide dynamicsimulation and the possible data incompleteness, on-site indus-trial expertise is required at every stage. It helps validate thesimulation result and facilitate troubleshooting. Two groups,operation group and simulation group, usually work togetherfor the DS based flare minimization project. The operation groupfrom the plant includes experienced operators and engineers.They provide alternative start-up procedures based on theirplanning and experience. Then the simulation group virtuallytests the proposed procedures with the plantwide DS model tocheck the operational feasibility, reliability, and safety issues.The plantwide DS model will be validated or modified basedon the simulation results and joint discussions. The time costgenerally increases as the modeling activity goes to the nextstage. Based on the experience, if time expense for steady-statemodeling is normalized as one unit, dynamic modeling activitywill cost two to three units. The final start-up simulation forflare minimization will take three to four units, which is becauseconsiderable troubleshooting and improvement efforts will beinvolved at this stage.

Case Study

Ethylene plant start-ups emit huge amounts of VOCs andHRVOCs that may cause highly localized and transient airpollution events and also result in tremendous raw material andenergy loss. Thus, one flare minimization project based on the

Table 1. Major Information for Plantwide Dynamic Simulation

modeling level SS validation DS validation DS for start-up operation

targets model capability check model capability check feasibility testset point identification safety check

start-up procedure improvementmajor supporting data from plant plant PFD and P&ID equipment capacity turnaround operating data

plant design data control strategy and parameters industrial expertisenominal operating data process upset dataindustrial expertise industrial expertise

typical simulators Aspen Plus Aspen Dynamics Aspen DynamicsHysis Dynsim DynsimPro II Hysis Hysis

normalized time expense 1 2∼3 3∼4

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developed methodology has been conducted. A general ethyleneplant starts with thermal cracking of raw feedstock in furnaces.The effluent cracked charge gas is cooled and partiallycondensed and then sent to a charge gas compression system.Charge gas from the final stage compression is dried prior tothe chilling train. After the majority of hydrogen is removedfrom the chilling train, the cold charge gas is then fed to thefractionation section, which consists of a demethanizer (DeC1),deethanizer (DeC2), depropanizer (DeC3), and debutanizer(DeC4). In the demethanizer, methane and hydrogen areseparated from the charge gas mixture as overhead vapor. Thebottom stream with the enriched mixture of C2+ (ethylene,ethane, propane, and heavier hydrocarbons) is then sent to therecovery section for further separation. The recovery sectionemploys a deethanizer, depropanizer, and debutanizer to separatethe products of C2s, C3s, C4s, and C5s plus heavier components,respectively. All the products have to meet the productspecifications or purity at normal operation conditions.

To reduce flare emission, the start-up with total recycle hasbeen implemented. Figure 4 presents a sketch of the entiremodeling system for the case study, where some subsystemsare simplified for easy illustration. It should be noted that sincethe furnace operations are not within the total-recycled start-upprocess, the charge gas flow rate and component concentrationsfrom furnaces only functioned as the simulation inputs. Mean-while, because the charge gas concentrations from a specificfurnace are fixed (for either heavy or light naphtha cracking),in this case study only the furnace flow rate is changeddynamically according to increased number of activated fur-naces. Thus, the furnace simulation is unnecessary to bedynamic. To reduce the computational load without the sacrificeof simulation accuracy, the furnace simulation is conductedindependently and is not included in the dynamic simulation ofthe case study.

Note that four major outer recycles are considered duringstart-up, which include the recycles of H2 from the chilling train:H2 and methane from the top of DeC1, C2s from the top ofDeC2, and C3s from the top of DeC3. A major inner recycle isthe stream of C4s from the top of DeC4 fed back to the bottomof DeC2. With these recycles, the start-up emissions areexpected to be greatly reduced due to two reasons: one is thatthe huge amounts of components from H2 though C4 will bereused instead of being flared during start-up; the other is thatwith the help of these recycled streams, the key units of DeC1

through DeC4 can gear toward their normal operation conditionsquickly, which means the start-up flaring time will be reducedcompared with the start-up without recycles.

This daring start-up indeed presents considerable challengesfor modeling and dynamic simulation. With the developedmethodology, the plantwide dynamic simulation has successfullyhelped the plant validate the conceptual start-up procedure. Inaccordance with the developed methodology, the simulationresults at different modeling stages are shown below.

Model Development and Validation. After the SS modelhas been developed, model validation is needed. If the SS testis not satisfied, it needs to be calibrated with plant data byadjusting different model parameters to match the steady-statemodel results with plant data measurements. For instance, Figure5 shows the comparison between SS model prediction and realmeasurements for DeC3 tray temperature profile, which are keyfactors for evaluating separation performance of these distillationcolumns. It is clear that the simulation results match well withthe real measurements.

As examples of dynamic model validation, Figures 6-8show the flow rate dynamic response for DeC2, DeC3, andDeC4 product streams under a recorded disturbance from theDCS historian. The disturbance was caused by a crackingfurnace shutdown for decoking, which caused upstreamprocess upset. As a result, the disturbance was propagatedto the downstream process. The response time and trend

Figure 4. Sketch of the entire modeling system for the case study.

Figure 5. Depropanizer temperature profile at normal steady state.

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predicted by the DS model match the DCS historian quitewell, except for some system biases. Through troubleshooting,it is identified that this is because the DCS data does notsatisfy mass balance while the dynamic model does. Itactually reminded the plant that the flow rate sensors mighthave problems during that period. The simulation results and

analysis are also acknowledged by the plant engineers basedon their industrial expertise.

DS for Flare Minimization. Once the steady-state anddynamic model validation has been completed, the plantwideDS model needs to be transformed to its initial state for start-up. At this initial state, the whole process system runs stablywith the total recycle. A light hydrocarbon mixture containinghydrogen, methane, ethane, ethylene, and propylene with thecomposition ratio of 1.5:15:1.5:73:9. in mole percent is circu-lated in the system. There are no system inputs and outputs atthe initial state of start-up, but only the inner and outer recyclesare fully open. Based on the plant operation strategy, the start-up is made up of two different feeds: heavy naphtha and lightnaphtha. One studied start-up procedure in terms of feed inputis shown in Figure 9, which is also the input of the DS model.It shows the charge gas feed from heavy naphtha crackingfurnaces (the first two furnaces) is incremented in two rampsfor each of the first two furnaces with a half-hour duration inbetween. The feeds from light naphtha cracking furnace (thethird through the seventh furances) are also incremented in tworamps for each furnace but without any idle waiting betweentwo furnaces.

Based on this start-up procedure, the dynamic simulation forproduct streams of DeC1, DeC2, DeC3, and DeC4 are shownin Figures 10-13. The overall dynamic responses show the start-up will take about 14 h to reach the normal steady-stateoperation, during which all the product streams are withinspecification. Historically, the plant start-up took about at leastone day. Based on the simulation prediction, the new start-upprocedure will help the plant save more than 44% of the start-up time. The predicted start-up time has been really ac-

Figure 6. Dynamic response for the deethanizer.

Figure 7. Dynamic response for the depropanizer.

Figure 8. Dynamic response for the debutanizer.

Figure 9. Start-up procedure for the case study.

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complished on site with the help of the developed plantwideDS model. The dynamic simulation provided an insight intoprocess dynamic behaviors, which really helped the plantbeforehand start-up material preparations and process operation,and meanwhile increased the plant engineers’ confidence level.

As an example, Figure 10 shows that during the start-up DeC1top stream temperature will decrease continuously, while flowrate will generally decrease first and then increase. This isbecause the temperature of DeC1 feed from the chilling trainwill continuously decrease during the start-up, which makes thetop temperature also decrease from -150 to -250 °F. Becauseof that, the top flow rate of C1 will decrease initially. However,

as the system feed from cracking furnace increases, the totalamount of methane in DeC1 feed increases with each furnacefeeding in. The combined effects result in the obtained flowrate response. It helps operators avoid potential inappropriatecontrol to increase the top flow rate, as the flow rate will comeback automatically to the expected level after some time. Asanother example, Figure 13 shows that the DeC4 bottom flowrate will be zero during the first hour of start-up. The reason isthat at the initial start-up state, C5s or heavier components donot exist in the system. As the charge gas is fed in the system,the C5s or heavier components will gradually accumulate inthe sump of DeC4. The simulation predicts that the bottom flowrate will increase after 1 h of sump accumulation. Thisinformation helps the plant prepare the downstream unitsoperation for start-up.

With the plantwide dynamic simulation, the flare emissiondata is summarized in Table 2. For simplification, the flaredraw materials are aggregated and classified as C1 (methane),C2 (ethane and ethylene), C3 (propane, propylene), and C4+(butane, butylene, butadiene, etc.). The flaring emissions areaggregated as NOx and hydrocarbons, the calculation for whichis based on 98% flaring efficiency and the U.S. EPA FlareEfficiency Study in 1983.22 For comparison, the emission datafor the plant best start-up in the past are also calculated andshown in Table 2. The simulation shows C2 are the major flaringmaterials for both cases. The DS assisted start-up can save58.0%-66.8% of flared raw materials under different categories.As a result, the total NOx and hydrocarbons emissions of theDS assisted start-up are estimated as 7.4 and 101.2 klb,receptively. These are significant reduction by 62.1% and 62.6%,respectively, compared with 19.5 and 270.6 klb of emissionsfrom the historical best start-up.

It should be indicated that the flaring sources and thusemissions in the case study have been significantly reducedbecause of the novel start-up procedure with total recycles (see

Figure 10. Dynamic response of demethanizer top products during the start-up.

Figure 11. Dynamic response of deethanizer top products.

Figure 12. Dynamic response of depropanizer top products.

Figure 13. Dynamic response of debutanizer bottom products.

Table 2. Flare Minimization Results for the Case Study

amount offlared raw

materials (klb)

major emittedpollutantsa

(klb)start-upduration

(h) C1 C2 C3 C4+ NOx HC

shortest start-upin the pastb

25 2163 5569 3017 2782 19.5 270.6

DS assisted start-up 14 904 2237 1001 918 7.4 101.2saved percentage (%) 44 58.0 59.8 66.8 67.0 62.1 62.6

a Assuming 98% flaring efficiency and based on U.S. EPA FlareEfficiency Study, 1983.22 b The amounts of flared raw materials areestimated based on the plant start-up time and feedstock flow rate.

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Figure 4) and operation strategies (see Figure 9). The DS playsthe important role to virtually verify the applicability of the start-up procedures and the operation conditions, which helps reduceflaring during the plant start-up. The DS shows, due to variousrecycles during a plant start-up, that the majority of off-specproduct streams (recycled H2/C1, C2, C3, and C4 streams inFigure 4) from DeC1 through DeC4 have been reused insteadof flared. Meanwhile, start-up time is significantly reduced (11h less compared with the shortest start-up in the past). Notethat the reduction of flaring time means the increase of normalmanufacturing time. It also means the saving of raw materialsfrom flaring for future production. Furthermore, the salientenvironmental and societal benefits from flare minimization arepriceless. The predictions of the start-up time and dynamicresponses are in good agreement with reality.

Conclusions

Plant-wide dynamic simulation serves as a powerful tool inaiding flare minimization for CPI plants today. This paperdeveloped a general methodology on flare minimization forchemical plant start-up operations via plantwide dynamicsimulation. The plantwide dynamic simulation is performedbased on the integration of rigorous process models, plant designdata, P&ID, DCS historian, and industrial expertise. It canpredict accurately the dynamic behavior of a process prior toany real plant changes, which gives insight into the processbehavior that is not apparent through SS or operationalexperience. It helps test plant operating procedures and supportdecision making during plant start-up. It is also a cost-effectiveapproach for industrial emission source reduction. The benefitsfrom this study are not only for environment and society butalso for the economics and sustainability for the chemicalprocess industry.

Acknowledgment

This work was in part supported by the Texas Commissionon Environmental Quality (TCEQ), Texas Air Research Center,and Texas Hazardous Waste Research Center.

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ReceiVed for reView October 24, 2008ReVised manuscript receiVed January 19, 2009

Accepted January 22, 2009

IE8016219

3512 Ind. Eng. Chem. Res., Vol. 48, No. 7, 2009

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