Flare minimization for an olefin plant shutdown via plant-wide dynamic simulation

Journal Pre-proof Flare Minimization for An Olefin Plant Shutdown via Plant-wide Dynamic Simulation

Yiling Xu, Ha Dinh, Qiang Xu, Fadwa T. Eljack, Mahmoud M. El-Halwagi PII:

S0959-6526(20)30176-1

DOI:

https://doi.org/10.1016/j.jclepro.2020.120129

Reference:

JCLP 120129

To appear in:

Journal of Cleaner Production

Received Date:

11 June 2019

Accepted Date:

11 January 2020

Please cite this article as: Yiling Xu, Ha Dinh, Qiang Xu, Fadwa T. Eljack, Mahmoud M. El-Halwagi, Flare Minimization for An Olefin Plant Shutdown via Plant-wide Dynamic Simulation, Journal of Cleaner Production (2020), https://doi.org/10.1016/j.jclepro.2020.120129

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Flare Minimization for An Olefin Plant Shutdown via Plant-wide Dynamic Simulation† Yiling Xu1, Ha Dinh1, Qiang Xu1*, Fadwa T. Eljack2, Mahmoud M. El-Halwagi3 1Dan

F. Smith Department of Chemical Engineering Lamar University, Beaumont, TX 77710, USA 2Department

of Chemical Engineering Qatar University, Doha, Qatar

3Artie

Mcferrin Department of Chemical Engineering Texas A&M University, TX 77843, USA Abstract

During shutdown operations of chemical plants, significant amounts of raw materials, intermediates, and products may be flared leading to economic losses and emissions such as carbon dioxide, nitrogen oxides, and volatile organic compounds. Thus, flare minimization during plant shutdown is a desirable goal towards economic benefit and environmental sustainability. In this paper, a systematic flare minimization methodology for an olefin plant shutdown operation has been developed. It includes three iterative stages: (i) steady-state modeling and validation to build the foundation of the dynamic modeling; (ii) dynamic modeling and validation to enable plantwide simulations under designated plant control strategies; and (iii) plant shutdown simulation and optimization to iteratively examine, validate, quantify various flare minimization opportunities so as to identify the improved shutdown strategy. Particularly, dynamic performances of the critical equipment (e.g., the cracked gas compressor) has been thoroughly investigated to ensure the operating safety associated with the developed new shutdown strategy. Compared with the conventional plant shutdown strategy, the case study has shown that the new development can significantly reduce flared raw materials and emissions by 90.23%, which result in estimated economic savings by 91.03% and the social cost of carbon saving by 90.37%. Keywords: Flare minimization, plant shutdown, dynamic simulation, cracked gas compressor ________________________________________________________________________________________________________

† *

For publication in Journal of Cleaner Production All correspondence should be addressed to Prof. Qiang Xu (Phone: 409-880-7818; Fax: 409880-2197; E-mail: [email protected]).

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1. Introduction

Olefins are among the most important industrial commodities. Their use spans a broad range of products such as plastics, fiber, rubber, solvents, cosmetics, paints, and packaging materials. Ethylene and propylene are the simplest but most produced and consumed olefins. For instance, the annual demand for ethylene is already over 140 million tons with a yearly growth rate of 3.5% (TechnipFMC, 2017), mainly due to the rapid growth of the chemical and plastic productions in Asia in the last decade from 180 to 400 MMT/yr (million metric tons per year). This fast increasing trend could be continuous because of the shale gas booming in North America, which projects that the global capacity of ethylene will reach to 200 MMT/yr by 2020 (Eramo, 2017). Note that most olefins are still produced by traditional olefin plants through the thermal cracking process. With the rapid increase of the global olefin productivity, many new olefin plants will be built and many expansion projects for existing olefin plants will be conducted as well. Accordingly, the air-quality impact from the increased olefin plant emissions become more significant, and thus the development and implementation of new emission reduction techniques becomes more important. Flaring is a major emission concern for olefin plants. It may occur due to gas seal, pressure relief, off-spec product disposal, equipment maintenance, planned startup and shutdown operation, or safety involved emergency shutdown. Although flaring may help protect plant personnel and equipment under normal and abnormal operating situations, it burns out lots of raw materials that should be utilized to produce valuable products. Meanwhile, flaring also produces a large amount of CO2 and air pollutants such as CO, NOx, volatile organic compounds (VOCs), highly reactive VOCs (HRVOCs), which could further lead to localized and transient high ozone concentrations

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(Nam et al., 2006). Thus, both flaring instances and flaring amounts need to be reduced as much as possible if at any possibilities. Obviously, flare minimization for olefin plants will be a doublewin effort, which could benefit both environment and industrial sustainability. Over the past two decades, there has been a growing interest in flare minimization studies and practices. Loring and Smith (1995) endeavored to develop smoke-less startups by eliminating or extensively mitigating the flaring load. Shaikh and Lee (1995) used natural gas during the plant commissioning operation to reduce off-spec materials. Chellino (2001) introduced a recycling method to reduce the start-up flaring at Westlake Petrochemicals Carlyss plant In the same year. Williamson and Dennehy (2001) reported their improved operating procedures to reduce flaring during olefin plant start-up and shutdown at Nova Chemicals Joffre site. Later on, Dow Chemical Freeport site adopted Six Sigma methodology to minimize flaring during plant upsets (Krientenstein, 2005). Meanwhile, a parking mode was implemented to reduce feed rates when unexpected flaring happened in Shell Chemical’s Deer Park OP-III olefins unit (Kagay et al., 2005). Also, Lyondell Chemicals practiced their emission reductions by recovering products from flaring material at its olefins sites (Chenevert et al., 2005; Cullen and Saionz, 2006). Kamrava et al. (2015) studied the utilization of gas flares for power generation. Tovar-Facio et al. (2016) evaluated the use of flaring and venting resources for cogeneration while accounting for stream uncertainties. Kazi et al. (2016) assessed the use of flare gas in water treatment and combined heat and power. In addition to the above examples of experience-based industrial practice, rigorous modeling and simulations based methods have also been extensively reported on the area of flaring minimization, such as the investigation of unit-based flare minimization opportunities during olefin plant startup and shutdown (Patel et al., 2007; Wang et al., 2007), employing plant-wide dynamic simulations for flare minimization during plant abnormal situations (Bernard, 2007; Dinh et al.,

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2016; Dinh et al., 2014; Gore, 2005; Li et al., 2007; Rutherford et al., 2003; Wang et al., 2014; Wei et al., 2014; Xu and Li, 2008; Xu et al., 2009; Yang et al., 2011; Yang et al., 2009). To the best of the authors’ knowledge, it should be noted that there is still a lack of systematic studies on flaring minimization for planned shutdown operations in olefin plants. Although Wang et al. (2014) estimated the flare emission amount during an olefin plant shutdown, however, they did not introduce any improved operating strategies to minimize the plant flaring. The planned shutdown operation of an olefin plant is one of the most intensive emission events, which could produce dozens or even hundreds of tons of emissions per hour. At least three (CO, NOx, O3) of six national controlled air pollutants are related to olefin plant shutdown flaring (Wang et al., 2016). Certainly, to perform flare minimization for an olefin plant is very challenging, because the plant-wide dynamic behaviors during its shutdown have to be clearly investigated and understood prior to the implementation of any appropriate flare minimization solutions. Meanwhile, if a solution candidate of flare minimization has been developed, it has to be virtually examined and tested via plant-wide dynamic simulations. Facing this challenge, a systematic flare minimization methodology for an olefin plant shutdown operation has been developed in this paper. It includes three stages iterative works: (i) steady-state modeling and validation to build the foundation of the dynamic modeling; (ii) dynamic modeling and validation to enable plant-wide simulations under designated plant control strategies; and (iii) plant shutdown simulation and optimization to iteratively examine, validate, quantify various flare minimization opportunities so as to identify the improved shutdown strategy. Particularly, dynamic performances of the critical equipment (e.g., the cracked gas compressor, CGC) need to be thoroughly investigated to ensure the operating safety associated with the developed new shutdown strategy. The developed methodology has been applied to a virtual case

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study of flare minimization for an olefin plant shutdown operation.

2. Methodology Framework

The developed methodological framework is shown in Figure 1, which includes three main stages of modeling and simulation stages. In the first stage, a steady-state (SS) model for the entire olefin plant will be developed and validated based on a PFD (process flow diagram), plant design data, and plant SS operating data. Certainly, appropriate model simplifications might be needed for certain process operations if modeling impacts from such simplifications are negligible. Validations in this stage are generally employed to tuning the SS model to ensure each unit operation output matching the real plant SS operating data. After the SS model has been validated, the plant-wide dynamic model will be developed in the second stage. In this stage, more modeling information will be included, such as equipment size, geometries data, controller parameters, and control loops. Also, the developed dynamic model will be further validated and refined according to the plant DCS (distributed control system) data, which should contain notable dynamic transitions of real plant operations. The dynamic model validation will examine if dynamic model responses will match the real plant data in terms of moving trends and magnitudes. Multiple validation runs might be performed in this stage to ensure the robustness and reliability of the developed dynamic models.

Figure 1. General methodological framework.

In the third stage, plant shutdown simulation and optimization will be performed. First, the plant-wide dynamic model will be adjusted from the normal operating condition to the ready5

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to-shutdown status, where only the minimum number of cracking furnaces will be still running to balance the downstream operation and other cracking furnaces will be cut off; meanwhile, plant liquid inventories (i.e., liquid levels in various drums and distillation columns) will be set at the minimum controllable level to reduce the potential of flaring. Note that running the plant from the normal operating condition to the ready-to-shutdown status should be a smooth operating for plant shutdown, which will not supposedly generate flaring. The shutdown flare minimization study in this work has also covered the modeling this transitional step. Once the plant-wide dynamic model is adjusted to the ready-to-shutdown status, which will be the initial state of the entire plant shutdown operation according to a designated shutdown strategy. Based on the obtained initial state, different shutdown case studies with detailed shutdown operation strategies will be conducted with the developed plant-wide dynamic simulation model. As the base case, the normal olefin plant shutdown procedure will be programmed and automated into the plant-wide dynamic model. With this base case simulation, the dynamic flowrate and species information of the flaring source with time can be obtained. Obviously, flare minimization opportunities based on the based case analysis, plant expertise and/or successful experience from other plants can be examined and reprogrammed into the plant-wide dynamic model to try different new shutdown cases, so that the improved shutdown strategy could be identified to help the plant shutdown flare minimization. Note that once an improved shutdown case has been identified, it will be examined via plant-wide dynamic simulations to check the operating safety and operability. Particularly, CGC is a crucial equipment of an olefin plant, which needs sufficient cares to ensure its safe operation during the plant shutdown. If an improved shutdown case is satisfied, its flare accounting and analysis will be further conducted to compare with the base case. Iteratively, such a flare minimization exploration will continue until it

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eventually leads to the final optimal shutdown strategy.

3. Flare Minimization Strategy Development for An Olefin Plant Shutdown

3.1 Introduction to an olefin plant In this paper, the studied olefin plant produces the polymer-grade ethylene, high-purity hydrogen, and liquid fuels. It is a front-end de-ethanizer process, which will split C2 and lighter components first rather than C1 and lighter components after the cracked gas compression. At the beginning of the process, the raw material of sour naphtha containing 2-6% H2S and 17% CO2, will be fed to the acid gas removal and sulfur recovery unit (AGRU-SRU) to remove the most of its sour gas. After the recovery of the by-product liquid sulfur, the sweetened gas feed will be sent to pyrolysis furnaces and mixed with steams for cracking. There is a total of five identical furnaces working parallelly. The effluent of the cracked gas mainly contains ethylene, hydrogen, methane, C3s, C4s, and other heavier components. After reactions in cracking furnaces, the hot cracked gas and steam will enter the quench tower for cooling. After the quench processing, heavy fuels will be collected at the bottom while the left cracked gas will be sent for compression in the CGC section. The CGC section has four stages in total. The cracked gas will firstly go through the first three stages to reach the pressure of 14.3 bar. Next, the cracked gas comes out of the 3rd stage will be directed to a caustic wash tower and a gas dryer to further remove the trace amount of H2S, CO2, and water. After that, the effluent will be sent to DeC2 (de-ethanizer) to split C2 and lighter components. After separation, the bottom stream from DeC2 will enter DeC3 (de-propanizer), which will recover C3 from its top stream and recycle it back to the furnace section for the secondary cracking. The bottom stream

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of DeC3, containing C4/C5 and heavier components, will be sent out of the plant as fuels. The top of DeC2, containing C2, hydrogen, and methane, will be further compressed by the 4th stage of CGC to reach the pressure of 30 bar. To obtain more ethylene product, the compressed stream will go through a series of heat exchangers and the acetylene reactor to convert the acetylene component to ethylene. After the acetylene reactor, the effluent will go through two stages of flash drums prior to entering DeC1 (de-methanizer). The overhead stream of the second stage drum (DEC1SEP2), which mainly contains C1 and hydrogen, will enter the chilling train for further separation. The two bottom streams from flash drums are collected and directed to DeC1 to separate C2 components from methane. The methane and a small amount of H2 and C2 from the top of DeC1 will be recycled through the chilling train and directed to the suction drum of the 4th stage of CGC. The bottom stream of DeC1 contains ethylene and ethane, which will be sent to the C2 splitter to separate. The top stream of C2 splitter is the high purity (≥ 99.5 mol%) of ethylene product. The bottom stream of C2 splitter is ethane, which will be recycled to the furnace section for the secondary cracking.

Figure 2. Process flow diagram of the studied front-end de-ethanizer olefin plant.

3.2 Normal plant shutdown strategy Wang et al. (2014) summarized the four-stage normal operations of an olefin plant shutdown: (1) de-inventory and feed reduction; (2) facility shutdown; (3) unit decommissioning involving liquid discharge and vapor discharge; (4) nitrogen purge of each unit. In the first stage, the operation brings a plant to the ready-to-shutdown operating status, for the studied olefin plant, two of five cracking furnaces will be shut down within half an hour in this stage, which suggests the cracked gas flow rate to the quench tower will be the 60% percent of its normal value. 8

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Meanwhile, the liquid level of each column and major storage tanks will be reduced to its minimal controllable value to de-inventory. Note that at this moment, the CGC is still running and the ethylene product quality is still on-spec. In the second stage, the CGC section will be shut down, and thus all other plant facilities will lose the driving force and have to shut down. During this shutdown stage, three working furnaces will be shut down gradually and the cracked gas coming to the quench tower will be flared via relief valves. Next, in the third stage, all plant facilities will be isolated to sections by sections for decommissioning, i.e., conducting the liquid discharge and vapor discharge sequentially. During this stage, all the discharged vapors and liquids are supposed to be sent to the flare system. Finally, all leftover hydrocarbons each facility will be purged out to the flare system by nitrogen in the fourth stage. This operating stage will also generate flare. However, the flaring amount will be smaller than that from the former stage. After the nitrogen purge, the entire plant shutdown operation will be completed. In the normal shutdown process, the major flaring comes from the furnace shutdown operation as well as the facility liquid and vapor discharge.

3.3 A new plant shutdown strategy Conceivably, there is a great amount of flaring induced by the normal olefin shutdown procedure. The key points are: (i) when the CGC is shut down, the driving force for the process throughput will be lost, so that all left vapor and liquid inventories in all columns and drums have to be flared instead of being reused or recycled; and (ii) the cracked gas during the shutdown of three remaining cracking furnaces in the second stage cannot be effectively utilized. To overcome these two problems causing significant flaring, a new operating strategy for the olefin plant shutdown operation has been developed. The main idea of this new strategy is to keep the CGC

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running as long as possible during the second and third stages to continue sucking in the cracked gas during the furnace shutdown; meanwhile, keep all columns running recycle their overhead gas effluents back to the CGC suction drum and direct all recycled hydrocarbons as the fuel gas Based on the above idea, two vapor-recycle lines will be built as shown the dotted blue lines in Figure 2: one is from the C2 splitter top and the other is from the DeC3 overhead. Both recycle lines will be directed to the suction drum of the first-stage CGC. The purpose is to recycle hydrocarbons as much as possible during the plant shutdown operations. The new shutdown strategy is summarized as below: (1) The entire plant will go through de-inventory and feed reduction, which is the same operation as the first step of the normal shutdown procedure. (2) Gradually shut down the remaining three cracking furnaces within half an hour, and correspondingly reduce the flowrate of the fresh feed to zero. Note that the CGC will continue running at this stage even the fresh feed has been shut down. The CGC intake are from two newly developed vapor lines. Meanwhile, open and control anti-surge valves from the stages 3-to-1 recycle line and from the stages 4-to-4 recycle line (as shown by green lines in Figure 2) of CGC to keeps a sufficient volume feed rates to each stage of the compressor, to ensure the CGC working in a safe condition during the shutdown procedure. (3) Keep the CGC running smoothly and recycle the downstream liquid and vapor inventories as much as possible. As the CGC continues running, the driving force is still available to push the upstream materials to the downstream. With the throughput continuing, keep running reboilers of all towers but stop running their condensers; meanwhile close cooling functions of online exchangers in the downstream of CGC.

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Such that, the leftover liquid holdups in column and drums will be evaporated and recycled to the CGC inlet. All recycled hydrocarbon gases will be finally directed to the fuel gas pipeline (as shown in the red lines in Figure 2), which will be out of the process to be treated as fuel gas. (4) When the mass flowrate of the recovered vapor to GCG is very low, which means the majority of liquid inventory and a certain amount of vapor inventory have already been recovered, the CGC can be shut down. After the CGC shutdown, each facility will go through unit decommissioning and nitrogen purge operations, which are the same operations of Steps 3 and 4 as those from the normal shutdown procedure.

4. Crucial Safety Considerations

The newly developed shutdown strategy has changed the normal shutdown operations mainly in Steps 2 through 3, which will inevitably draw safety concerns from the olefin plant. Substantial efforts should be spent on the related safety review. One of the key safety concerns for this shutdown strategy is: whether the CGC will have some safety problems or not ? The CGC system is a crucially important, extremely expensive, but also very fragile facility in an olefin plant. instantly improper operations may cause significant damages to this equipment. The most concerned risk for the CGC operation is the surge problem. Usually, surging happens when the flowrate through a compressor is below a safety minimum level. When this occurs, the compressor head is periodically insufficient to overcome the pressure at the discharge side of the compressor, which will result in the backflow correspondingly. After a backflow has re-established enough inlet flowrate, the compressor will create a short-time higher head to push out the compressed gas.

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After that, the inlet flowrate reduces back to an insufficient value and cause the overall backflow again. Such a phenomenon swiftly repeats. As a result, the compressor will have strong vibration and periodically roar. This abnormal condition will easily damage internal bearing, wheel, impeller, and other important parts, quickly leading to mechanical failures to a compressor. It will cause a tremendous economic loss to an olefin plant due to long-time equipment repairing and production downtime. In contrast to the possible low inlet flowrate that may cause the CGC surge problem, when the inlet volume flowrate is too high, the CGC may also experience the so-called “stonewall” problem, which means the intake of CGC has exceeded its maximum capacity that it can push through. Stonewall can cause vibration and fatigue failure that can damage the entire compressor over time. Thus, a limit for the high flowrate operation has to be controlled. It should be noted that the molecular weight (MW) changes at the CGC inlet will also affect the compressor operating status, because MW changes under a given normal volume flowrate may cause the intake mass flowrate to change out of its tolerance. Under plant normal operations, the MW of the CGC inlet is almost a constant because its inlet composition is near stable. However, in the developed shutdown procedure, two recycle streams will mix with the crack gases in the early operating stage (see, Step 2) to go through the CGC, and later they will go through the CGC independently (see, Step 3). Meanwhile, during the shutdown operation, the compositions of these two recycled streams will keep changing, which means the overall MW at the CGC inlet will also dynamically change with time. Therefore, the operating status of CGC must be dynamically and continuously examined during the entire shutdown operation. Conceivably, compressor vendors will provide compressor performance curves to disclose the safe operating zone of a compressor. Figure 3 shows an illustrative example of a compressor.

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In this figure, the horizontal axis is volume flow and the vertical axis is pressure ratio (discharge pressure/inlet pressure). Generally, the safe operating zone has four operating boundaries: (1) the left-side surge control line to prevent compressor surge; (2) the right-side choke control line to avoid stonewall problems; (3) the top boundary line representing the maximum running speed that a compressor can operate; and (4) the bottom boundary line representing the minimum running speed line. The region within these four boundaries is the safe operating zone that a compressor could be operated. Note that the studied CGC has four stages in total, which are equivalent to four compressors. Each compressor will have its own designated safe operating zone as shown in the example. Therefore, the performance of four-stage CGC compressors during the shutdown operation will be examined through rigorous dynamic simulations in this paper. The dynamic simulation results have been utilized to help fine tune the final obtained new shutdown strategy to ensure the CGC always working under safe operating conditions.

Figure 3. Sketch of the safe operating zone of a compressor.

5. Simulation Results and Comparison between Different Plant Shutdown Strategies

To virtually examine the newly developed shutdown strategy and compare its performance with the normal shutdown strategy, a dynamic simulation model for the entire plant has been developed by Aspen Plus Dynamics (AspenTech, 2015). Based on the plant-wide steady-state model described by Figure 2, the dynamic simulation model needs two additional modeling inputs: equipment size and controller information. In this study, the equipment sizing data and normal control loop information, such as feed flowrate, level, and pressure controls of various drums,

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tanks, and columns, as well as temperature and reflux controls of different columns, are obtained from a real olefin plant.

Additionally, the two flowrate controllers associated with the new

designed vapor-recycle lines (from C2 splitter and DeC3 tops respectively to the CGC inlet) as shown in Figure 2 should also be modeled. Figure 4 shows the dynamic model with all modeled control loops by Aspen Plus Dynamics.

Figure 4. Dynamic model of the studied olefin plant.

5.1 Base Case: dynamic simulation based on the normal plant shutdown strategy As aforementioned, when the olefin plant undergoes the normal shutdown, it will firstly reduce its feed and cracking capacity to 60% of its original value by shutting down two furnaces. The two furnaces will be linearly ramped down within 0.5 hours; while the other three furnaces still work under normal production conditions, which is shown in Figure 5. Meanwhile, liquid holdups from all drums and columns will be decreased to their minimum values. This action is accomplished by ramping down the set point (SP) of their level controllers. In the developed model, the minimal value of the liquid level is set as 10%, which is enough to run all related facilities normally. Figure 6 shows examples of dynamic level profiles for important units during this deinventory process in Step 1. From the figure, the inflow rate of C2 splitter experiences minor upsets due to its upper-stream unit de-inventory impact. In general, the transition is still smooth. It takes a longer time for drums in CGC section because the discharge flowrate is smaller compared with other units. This entire de-inventory operation takes near 3.5 hours.

Figure 5. Furnace shutdown schedule for the Base Case.

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Figure 6. Examples of liquid-level changes during the de-inventory operation in the Base Case.

After the de-inventory operation, the entire plant is running at the ready-to-shutdown status. In Step 2, CGC will be shut down first and then all other facilities will be shut down thereafter. Note that the left three furnaces will take 0.5 hours to completely shut down. During the furnace shut down period, the cracked from these furnaces will be released from the top of the quench tower to the flare system. In Steps 3 and 4, all leftover hydrocarbons in both liquid and gas phases will be dumped to the flare system. In the Base Case, the flaring sources come from the cracked gas during the three-furnace shutdown and all leftover hydrocarbon inventories in plant facilities, which is shown in Table 1. The information can be obtained through dynamic simulation results. Note that the data shown in Table 1 is the flaring source amount, which means the quantities of hydrocarbons sent to the flare system before the flare combustion.

Table 1. Flaring Amount Statistics for the Base and Improved Cases

5.2 Improved Case: dynamic simulation based on the new plant shutdown strategy The Improved Case is based on the developed new plant shutdown strategy. The detailed operating procedure is shown in Figure 7. The first step is the same as the Base Case. Starting from the ready-to-shutdown status at the second step, the strategy will shut down all left furnaces and quench section first and keep the CGC running in both Steps 2 and 3. All furnace shutdown operations are summarized in Figure 8. Note that when the left three furnaces are shutting down, the cracked gas feed to CGC will be gradually decreasing. To maintain a stable flow for the CGC inlet (about 123,000 m3•h-1) and the 4th-stage CGC inlet (about 13,200 m3•h-1), the opening of the 15

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two designed vapor-recycle lines (from C2 splitter and DeC3 tops respectively to the CGC inlet) needs to be well controlled together with the CGC 3-to-1 and 4-to-4 anti-surge recycle lines. In the dynamic simulation, each anti-surge recycle line has a PID controller set in the auto mode. The process variable (PV) is the safe operating percentage above the surge flowrate, the controller output is the flowrate valve opening of the anti-surge line. This can help the CGC running away from potential surge problems. During the time period of Steps 2 and 3, the material flow in the system will keep going as the CGC is still providing the driving force. The material flow will be cycled back to CGC inlet through the two designed vapor-recycle lines and finally directed to the fuel gas pipeline. Temperature controllers of different columns/drums will be set as the manual mode, and their cooling duties will be gradually turned off according to the schedule shown in Figure 7 to vaporize their liquid holdups to support the recycled vapor flows and ensure the adequate vapor flowrate at the CGC inlet. The timing to turn off cooling duties is identified through try and error via dynamic simulations. As an example, C2 splitter has the most amount of hydrocarbon holdup to be recovered in the process. When its condenser is turned off, the continuous running reboiler will evaporate liquid hydrocarbons in the column, it takes about 3.5 hours (from 4.0 to 7.5 h as shown in Figure 7) to vaporize and recycle all its liquid holdups on each stage including the sump. The dynamic changes of these liquid holdups via the developed simulation model are shown in Figure 9. It can be seen that at the end of 7.5 h, all stage holdups become zero, which means all liquid inventory in this column has been recovered.

Figure 7. Detailed shutdown operating schedule for the Improved Case

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Figure 8. Furnace shutdown schedule for the Improved Case.

Figure 9. Dynamic liquid-holdup changes on each stage of C2 splitter during its shutdown operation for the Improved Case.

Once the majority of liquid inventory in the process has been recovered as fuel gas, the pressure relief for each facility will be conducted to further recover all possible vapor inventories recessively. Again, the recycled vapor inventories will be sent to CGC inlet and directed to the fuel gas line to be recycled as the fuel gas. When the last two facilities have released their pressures, most of liquid/vapor inventories have been recovered. Thus, the CGC will be shut down and decommission and nitrogen purge operations will be performed in Step 4. It should be noted that for Step 4, all the leftover hydrocarbons in the process system will be sent out to the flare system. Thus, the flaring amount just needs to count the leftover vapor and liquid inventories for each facility, which could be identified based on the operating status at the end of operating Step 3 from the dynamic simulation model. This also means dynamic simulations for Step 4 can be neglected, which does not affect our accurate estimation of the total flaring amount and flaring species for the Improved Case. Therefore, the operating schedule for Steps 1 through 3 in Figure 7 has rigorous timing information; however, for operations of each facility in Step 4 are illustrative. During the time period of hydrocarbon recovery on Steps 2 and 3, the dynamic profile of the total vapor flowrate at the fuel gas pipeline is shown in Figure 11. It shows that after all furnace shutdown, the total flow, which mainly comes from original process gas products (H2 and C1), will continuously decrease. After the hydrocarbon recovery is started, it will take some time delay to pick up its flowrate. It shows after 5.2 h, it will start to increase with the recycled vapor coming

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to the pipeline. The recovered liquid inventories of all facilities are mainly vaporized out from 5.2 to 8.5 h. After that, from 8.5 to 10.0 h, the recovery is mainly from hydrocarbons released by the pressure relief operation from each facility.

Figure 10. Dynamic flowrate profile of recovered hydrocarbons at the fuel-gas pipeline outlet.

Also note that the plant shutdown operations based on the new plant shutdown strategy have been programmed as many simulation tasks and embedded into the dynamic model, so that the entire plant shutdown simulation could be automatically run without user manual operations to intervene the simulation process, which can ensure the preciseness and repeatability of obtained simulation results. With the help of dynamic simulation, not only the scheduling time of each main operation can be confirmed, but also the detailed operating status and stream information for each unit can be recorded and analyzed.

5.3 Dynamic performance of CGC based on the new plant shutdown strategy During operations of Steps 2 and 3 of the new shutdown strategy, CGC will accept lots of recycled hydrocarbon vapors from its downstream. Its inlet flowrate and average MW will change with time due to the recycled gas mixtures from different units with different composition and flowrates.

Figure 11(a) and (b) provide the dynamic profiles of CGC inlet flowrate and

composition during the plant shutdown operations, respectively. Figure 11(a) shows that during furnace shutdown operations from 0 to 0.5 h and from 4.0 to 4.5 h, the inlet volume flowrate of CGC will reduce a little bit, but through pickups from anti-surge control lines (3-to-1 and 4-to-4 CGC recycle lines) and two new vapor-recycle lines (from C2 splitter and DeC3 tops respectively to the CGC inlet), the flowrates will be stabilized. Other operations will also upset the flowrate at 18

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the CGC inlet, but because of the well controls of the two vapor-recycle lines, such upsets are all very minor. Figure 11(b) shows before the start of Step 2 (from 0 to 4.0 h) of the Improved Case, the opening of anti-surge control lines of CGC almost does not change the composition at the CGC inlet, because it is actually CGC internal recycles. Starting from 4.0 h (see, Figure 7), because the recovered vapor from C2 splitter will be firstly recycled to CGC, the mass fraction of C2 (ethylene and ethane) start to increase. Certainly, ethylene takes the major increment. When the recovered vapor from DeC3 kicks in starting at 5.5 h (see, Figure 7), C3 components (mainly propylene) begin to pick up and last for about 1.5 h prior to its drop. After that, ethane component begins to pick up and dominate the recycled vapor mixture at the CGC inlet, because most of the ethylene has been recovered at that time, the remaining gas in C2 splitter are mainly ethane.

Figure 11. Dynamic profiles of CGC inlet (a) flowrate and (b) composition during the plant shutdown operation for the Improved Case.

Under such dynamic upsets at the CGC inlet, the shaft rotation speed of CGC has been proactively controlled to prevent the CGC from surge problems. Figure 12 shows the shaft speed changing profile. The shaft speed is decreasing through several stages from 6,150 RPM to 5,600 RPM in the first hour (4.0~5.0 h) of Steps 2 and 3. After that, the shaft speed will keep 5,600 RPM until the CGC is shutdown in Step 4. During this 4.0~5.0 h running time, the average MW at the CGC inlet is also changing. Figure 12 also shows feed MW at the 1st and 4th stages of CGC during this time period.

Both MWs are increasing because the cycled mixtures from the

downstream are slightly heavier than the cracked gas from the quench column.

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Figure 12. CGC rotation speed and MW changes during the 1st hour (4.0~5.0 h) of Steps 2 and 3 for the Improved Case.

As the CGC inlet flowrate, composition, MW, and rotation speed are all changes during the plant shutdown operation, our most concern is to know if the CGC operation is in the safe operating zone all the time. Thus, Figure 13 has been developed based on the dynamic simulation results and the given performance curves to check the dynamic performance of each stage of CGC during the shutdown operation. Compared with Figure 3, Figure 13 presents 3D figures as a new dimension of operating time has to be involved in the safety evaluation. gives the operation points and envelope profiles for each stage. Thus, in each subfigure, the safe operating zone represents an enclosed space surrounded by a 3D boundary. The compressor operating status with respect to time represents a line, which should not touch the 3D boundary at any operating time. As shown in Figure 13, the entire 6 hours of CGC running time period during the plant shutdown operation, the operating status of its four stages is always within their safety boundary, which means all stages of CGC work safely during the plant shutdown operation based on the developed shutdown strategy. Such a dynamic performance overview is very valuable to develop new operating strategies and examine the corresponding safety operations.

Figure 13. Dynamic CGC performance during the plant shutdown operation (Steps 2 and 3) for the Improved Case.

5.4 Flare emission comparison and analysis Based on dynamic simulations for both cases, Table 1 summarizes the total flaring amount 20

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for both Base Case and Improved Case. It can be seen that the main flaring source for both two cases comes from columns (DeC1, DeC2, DeC3, and C2 splitter). Among them, the biggest plant column, C2 splitter, contributes the most. In the Base Case, the furnace effluent that is flared during furnaces shutdown is the second largest flaring source. However, this flaring will be fully avoided in the Improved Case because these materials will be fully sent to downstream by CGC and recovered through the fuel gas line. It should be noted that in the Base Case, the process leftover inventories including liquid and vapor hydrocarbons after the CGC shutdown, including gases, especially the great amount of liquid leftover will be flared. In the Improved Case, however, with all process leftover liquids and a part of leftover vapor being recovered, only some vapor leftovers after the CGC shutdown will be flared. Thus, the significant flare savings is understandable. In details, the flaring of the Improved Case could reduce nearly 90.23% of flaring compared with the Base Case. This significant savings not only benefits the plant for its profitability, but also benefits the local environment and communities for their sustainability. Another salient advantage with the plant-wide dynamic simulation is that the flaring source speciation could be clearly identified. Table 2 provides such information for both Base and Improved Cases. It can be seen that ethylene is the most flared species in the Base Case. It is also the most recycled species in the Improved Case. This information provides an effort roadmap indicating the effectiveness of recovery on each species for the developed new plant shutdown strategy. Meanwhile, it could help evaluate economic performances. Table 3 provides the detailed economy analysis to evaluate both two cases. Note that some chemical species are neglected in the economic analysis because their flaring amounts are comparably too small. Table 3 indicates that the Improved Case can reduce 91.03% of resultant economic losses due to flaring. Additionally, the corresponding CO2 emissions can be estimated by 98% of destruction efficiency (TCEQ, 2013).

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Thus, the Base Case generates more than 606 ton of CO2; while the Improved Case only generated no more than 6 ton of CO2. Based on the criteria of social cost of carbon (U.S. EPA, 2016), which is a measure of the economic harm from CO2 impacts, the Improved Case could significantly decrease the social cost of carbon by 90.37% compared with the Base Case. Therefore, the Improved Case has salient economic and environmental benefits.

Table 1. Flaring Source Amount Statistics for the Base and Improved Cases

Table 2. Flaring Speciation Statistics for the Base and Improved Cases

Table 3. Economy Analysis and Comparison for the Base Case and Improved Case

6. Concluding Remarks and Future Works

Fare minimization benefits both economic viability and environmental sustainability. In this paper, a systematic flare minimization methodology for shutdown operation of an olefin plant has been developed. Plant-wide steady-state and dynamic simulations have been used to explore and evaluate flare minimization opportunities in the context of optimal design and operation. Based on the developed methodology, an improved shutdown strategy for a front-end de-ethanizer olefin plant has been identified and virtually examined. Particularly, the dynamic performance of the critical equipment of CGC has been thoroughly investigated via dynamic simulations to ensure the operating safety associated with the developed shutdown strategy. Major findings of this study are listed below:

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

There are significant economic and environmental potentials to improve conventional shutdown procedures for olefin plants. As demonstrated in this study, the newly developed one can significantly reduce flared raw materials and emissions by 90.23% compared with the conventional plant shutdown strategy, which result in estimated economic savings by 91.03% and the social cost of carbon saving by 90.37%.



Plant-wide dynamic simulations can not only help deeply understanding when, where, and how flaring sources are generated, but also provide important technical support for new flare minimization strategy development with adequate safety considerations.

The future work based on this study will explore new flare minimization strategies in both normal and abnormal operating conditions via the systematic integration of material and energy exchanges among multiple production lines/plants.

Acknowledgments

This work was supported impart by the Qatar National Research Fund (NPRP 5-351-2136), Texas Air Research Center and President Visionary Initiative Project from Lamar University in USA.

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Abbreviation

AGRU-SRU

Acid gas removal unit and sulfur recovery unit

CGC

Cracked gas compressor

C1

Methane

C2

Two-carbon hydrocarbon

C3

Three-carbon hydrocarbon

C4

Four-carbon hydrocarbon

C5

Five-carbon hydrocarbon

DCS

Distributed control system

DeC1

De-methanizer

DeC2

De-ethanizer

DeC3

De-propanizer

DEC1SEP2

Second stage drum of De-methanizer

HRVOCs

Highly reactive volatile organic compounds

MMT/yr

Million metric tons per year

MW

Molecular weight

NOx

Oxides of nitrogen

PV

Process variable

PFD

Process flow diagram

SS

Steady state

SP

Set Point

VOCs

Volatile organic compounds

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References AspenTech, 2015. Aspen Plus Dynamics, V8.8 ed, Bedford, Massachusetts 01730 USA. Bernard, A., 2007. Start-up planning and support following a plant revamp, AIChE Spring National Meeting, Houston, TX. Chellino, M., 2001. Recycle method to reduce ethylene plant startup flaring, AIChE Spring National Meeting, Houston, TX. Chenevert, D., Harry, C., Walker, J.H., Unterbrink, B., Cain, M., 2005. Flare minimization practices improve olefins plant start-ups, shutdowns. Oil gas journal 103, 54-60. Cullen, S., Saionz, J., 2006. Flare reduction strategy for Lyondell’s Clinton complex, AIChE Spring National Meeting, Orlando, FL. Dinh, H., Eljack, F., Wang, S., Xu, Q., 2016. Dynamic simulation and optimization targeting emission source reduction during an ethylene plant start-up operations. Journal of Cleaner Production 135, 771-783. Dinh, H., Zhang, S., Xu, Y., Xu, Q., Eljack, F., El-Halwagi, M., 2014. Generic approach of using dynamic simulation for industrial emission reduction under abnormal operations: scenario study of an ethylene plant start-up. Industrial Engineering Chemistry Research 53, 15089-15100. Eia.gov. (2019). United States Natural Gas Industrial Price (Dollars per Thousand Cubic Feet). [online] Available at: https://www.eia.gov/dnav/ng/hist/n3035us3m.htm [Accessed 17 Dec. 2019]. Eramo, M., 2017. Global ethylene market outlook: low cost feedstocks fuel the next wave of investments in North America and China. [WWW Document]. Inaugural ethylene forum. URL http://media.corporateir.net/media_files/IROL/11/110877/05_Global_Ethylene_Market_Outlook _Eramo.pdf (accessed 4.16.19). Gore, S., 2005. Plant Startup in CPI Via Dynamic Simulation-case Study. ProQuest. ICIS.

(2019).

CHEMICAL

PROFILE:

Butene-1.

[online]

Available

at:

https://www.icis.com/explore/resources/news/2007/02/12/4500803/chemical-profile-butene-1/ [Accessed 17 Dec. 2019]. Kagay, E., Taylor, M., Genty, N., 2005. Development of a parking mode at Shell Chemical’s Deer Park Plant Olefin Unit OP-III, AIChE Spring National Meeting, Atlanta, GA. Kamrava, S., Gabriel, K.J., El-Halwagi, M.M., Eljack, F.T.J.C.T., Policy, E., 2015.

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Managing abnormal operation through process integration and cogeneration systems. Clean Technologies Environmental Policy 17, 119-128. Kazi, M.-K., Eljack, F., Elsayed, N.A., El-Halwagi, M.M., 2016. Integration of energy and wastewater treatment alternatives with process facilities to manage industrial flares during normal and abnormal operations: Multiobjective extendible optimization framework. Industrial Engineering Chemistry Research 55, 2020-2034. Krientenstein, S., 2005. Flare minimization strategy during plant startup, AIChE Spring National Meeting, Atlanta, GA. Li, K., Xu, Q., Lou, H., Gossage, J., Singh, A., Vragolic, S., Kelly, T., 2007. Flare minimization during plant start-up via dynamic simulation. PSE ASIA, 15-18. Loring, M.P., Smith, S.K., 1995. Flare load reduction in ethylene plants can ensure smokeless start-ups, AIChE Spring National Meeting, Houston, TX. Nam, J., Kimura, Y., Vizuete, W., Murphy, C., Allen, D.T., 2006. Modeling the impacts of emission events on ozone formation in Houston, Texas. Atmospheric Environment 40, 53295341. Patel, V., Feng, J., Dasgupta, S., Ramdoss, P., Wu, J., 2007. Application Of Dynamic Simulation In The Design, Operation, And Troubleshooting Of Compressor Systems, Proceedings of the 36th Turbomachinery Symposium. Texas A&M University. Turbomachinery Laboratories. Rubber & Plastics News. (2019). Kumho expects butadiene, SR prices to continue rising. [online] Available at: https://www.rubbernews.com/article/20180808/NEWS/180809940/kumhoexpects-butadiene-sr-prices-to-continue-rising [Accessed 17 Dec. 2019]. Rutherford, P., Persad, W., Lauritsen, M., 2003. Consider dynamic simulation tools when planning new plant startup. Hydrocarbon processing. Shaikh, A., Lee, C., 1995. Minimize flaring during ethylene plant startup. Hydrocarbon Processing 74. Texas Commission on Environmental Quality (TCEQ). 2013 Emission Inventory Guidelines, Appendix A: Technical Supplements. https://www.tceq.texas.gov/assets/public/comm_exec/pubs/rg/rg360/rg36012/appendix_a.pdf. Accessed December 17, 2019. TechnipFMC., 2017. Ethylene in our daily life. [WWW Document]. TechnipFMC. https://www.technipfmc.com/-/media/Files/what-

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wedo/onshore/on_ethyleneproduction_lr.pdf?la=en&hash=801A1317CE46A3EC212640DA672 4C3A094852246 (accessed 4.16.19). Tovar-Facio, J., Eljack, F., Ponce-Ortega, J.M., El-Halwagi, M.M., 2016. Optimal design of multiplant cogeneration systems with uncertain flaring and venting. ACS Sustainable Chemistry & Engineering 5, 675-688. United States Environmental Protection Agency (EPA), Social Cost of Carbon, 2016; https://www.epa.gov/sites/production/files/201612/documents/social_cost_of_carbon_fact_sheet.pdf. [Accessed 17 December, 2019] Waldheim, J. (2019). US December propylene contracts settle down 8 cents/lb. [online] Icis.

Available

at:

https://www.icis.com/explore/resources/news/2018/12/19/10297466/us-

december-propylene-contracts-settle-down-8-centslb/ [Accessed 17 December, 2019]. Wang, L., Makita, H., Kano, M., Hasebe, S., 2007. Dynamic start-up model of heat integrated distillation column. PSE ASIA. Wang, Z., Wang, S., Xu, Q., Ho, T., 2016. Impacts of flare emissions from an ethylene plant shutdown to regional air quality. Atmospheric environment 138, 22-41. Wang, Z., Xu, Q., Ho, T.C., 2014. Emission source characterization during an ethylene plant shutdown. Chemical Engineering Technology 37, 1170-1180. Wei, T., Hou, X., Yu, J., Zhang, J., Wang, Z., Xu, Q., Zhao, J., Qiu, T., 2014. Shutdown strategy for flare minimization at an olefin plant. Chemical Engineering Technology 37, 605-610. Westlake.com. (2019). Industry Product Pricing | Westlake Chemical. [online] Available at: https://www.westlake.com/industry-product-pricing [Accessed 17 December, 2019]. Xu, Q., Li, K., 2008. Dynamic simulation for chemical plant turnaround operation, Integrated Environmental Management Consortium Meeting, Houston, TX. Xu, Q., Yang, X., Liu, C., Li, K., Lou, H.H., Gossage, J.L., 2009. Chemical plant flare minimization via plantwide dynamic simulation. Industrial Engineering Chemistry Research 48, 3505-3512. Yang, X., Xu, Q., Li, K., 2011. Safety‐Considered Proactive Flare Minimization Strategy under Ethylene Plant Upsets. Chemical Engineering Technology 34, 893-904. Yang, X., Xu, Q., Zhao, C., Li, K., Lou, H.H., 2009. Pressure-driven dynamic simulation for improving the performance of a multistage compression system during plant startup. Industrial Engineering Chemistry Research 48, 9195-9203.

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LIST OF TABLES

Table 1.

Flaring Source Amount Statistics for the Base and Improved Cases

Table 2.

Flaring Speciation Statistics for the Base and Improved Cases

Table 3

Economy Analysis and Comparison for the Base Case and Improved Case

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Table 1. Flaring Source Amount Statistics for the Base and Improved Cases Types Quench Section

CGC Section

Chilling Train Section

Flash Drums

Columns

Furnaces

Base Case

Improved Case

(kg)

(kg)

W-QUENCH

6,742.37

6,742.37

OW-SEP

72.56

72.56

CGC-F1

123.94

162.51

CGC-F2

246.94

289.79

CGC-F3

1,347.94

1,406.92

DEC2-SUC

417.79

322.25

CB-F1

245.91

100.4

CB-F2

43.86

13.99

CB-F3

42.6

14.7

PRIM-DRY

248.14

100.81

DC1FSEP1

955.58

687.06

DEC1SEP2

763.18

532.42

DeC2

10,456.74

1,822.44

DeC3

1,106.36

229.74

DeC1

12,330.32

1,626.99

C2split

143,231.58

5,335.79

Cracking Furnace

20,794.21

0

Total Flaring Amount

199,170.02

19,460.74

Flare Reductions from Base Case (%)

---

90.23

Process Units

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Table 2. Flaring Speciation Statistics for the Base and Improved Cases Base Case (kg)

Improved Case (kg)

Reduction from Base Case (%)

CH4

2,202.89

160.28

92.72

C2H2

240.87

18.65

92.26

C2H4

169,055.79

3,763.18

97.77

C2H6

17,103.72

9,295.33

45.65

C3H4

93.82

28.11

70.03

C3H6

2,747.51

746.81

72.82

C3H8

104

25.82

75.18

BUTADIEN

1,043.10

66.27

93.65

I-BUTENE

1,156.67

943.13

18.46

N-BUTANE

786.05

567.98

27.74

C5H6

3,817.38

3,794.85

0.59

CO

26.91

1.58

94.15

H2

791.31

48.75

93.84

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Table 3. Economy Analysis and Comparison for the Base Case and Improved Case Unit Price*

Flaring Loss of

Flaring Loss of

($/kg)

Base Case ($)

Improved Case ($)

CH4

0.18

388

28

C2H4

0.30

50,717

1,129

C2H6

0.16

2,685

1,459

C3H6

1.10

3,022

821

BUTADIEN

1.66

1,729

110

I-BUTENE

1.50

1,735

1,415

N-BUTANE

0.99

778

562

H2

2.30

1,820

112

Total Flaring Loss ($)

--

62,874

5,636

--

--

91.03%

Flare induced CO2 emission (kg)

--

606,466

58,432

Social Cost of Carbon ($)

$42/ton**

25,472

2,454

Flare Savings Compared with Base Case

Social Cost of Carbon Savings Compared with Base Case

--

90.37%

Note: *: Unit price data is collected in December 17, 2019 from websites of Independent Commodity Intelligence Services (ICIS), Energy Information Administration (EIA), Rubber & Plastics News, Westlake.com **: Social cost of carbon is obtained from U.S. EPA (2016).

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LIST OF FIGURES

Figure 1. General methodological framework. Figure 2. Process flow diagram of the studied front-end de-ethanizer olefin plant. Figure 3. Sketch of the safe operating zone of a compressor. Figure 4. Dynamic model of the studied olefin plant. Figure 5. Furnace shutdown schedule for the Base Case. Figure 6. Examples of liquid-level changes during the de-inventory operation in the Base Case. Figure 7. Detailed shutdown operating schedule for the Improved Case Figure 8. Furnace shutdown schedule for the Improved Case. Figure 9. Dynamic liquid-holdup changes on each stage of C2 splitter during its shutdown operation for the Improved Case. Figure 10. Dynamic flowrate profile of recovered hydrocarbons at the fuel-gas pipeline outlet. Figure 11. Dynamic profiles of CGC inlet (a) flowrate and (b) composition during the plant shutdown operation for the Improved Case. Figure 12. CGC rotation speed and MW changes during the 1st hour (4.0~5.0 h) of Steps 2 and 3 for the Improved Case Figure 13. Dynamic CGC performance during the plant shutdown operation (Steps 2 and 3) for the Improved Case.

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Start Build Steady-state Model

Simulation Satisfied?

Model Tuning

NO

NO

YES

Model Tuning

Build Dynamic Model

YES

Steady State Validation

Dynamic Model Problem?

NO

Simulation Satisfied?

Dynamic Validation

YES Adjust Dynamic Model to the Ready-to-shutdown Status

New Shutdown Operation simulation

Normal Shutdown Operation simulation

Shutdown Operations Tuning

CGC Safety Testing Flare Minimization Opportunity

Testing Satisfied?

NO

YES

Plant-wide Flare Account and Analysis

New Plant-wide Flare Account and Analysis Results Comparison YES

NO

Satisfied? Improved Shutdown Strategy Identified

Figure 1. General methodological framework.

33

Shutdown Process Simulation

Vapor Recycle 2 ANTI-SURG

C3RECYCLE

4-4RECY

Caustic Furnace

CGC-STG1

CGC-STG2

CGC-STG3

CGC-STG4 DEC2SUC

Furnace Sour Gas

Dryer DeC2

I-2

AGRU-SRU

DeC3

Furnace CGC-F1

Furnace

Furnace

CGC-F2

CGC-F3

Acetylene Reactor

Quench PR-IN

PR-OUT

Chilling Train Section

Fuel Gas To PSA

C2RECYCLE CB-F1

CB-F2

CB-F3

Vapor Recycle1 DEC1SEP2

Ethylene

DeC1 41-32B

DC1FSEP1

C2spliter

Figure 2. Process flow diagram of the studied front-end de-ethanizer olefin plant. 34

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nN

tro

lL ine

Maximum Speed

Pressure Ratio

Su

rge

Co n

...

n3 n2 n1

ke Cho

Minimum Speed

Li rol t n Co

Volume Figure 3. Sketch of the safe operating zone of a compressor.

35

ne

Figure 4. Dynamic model of the studied olefin plant.

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CGC shutdown

Shutdown operation starts

Feed Flowrate (ton•h-1)

30

20

10

0 -1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Time (h) Legend:

Furnaces 1 & 2

Furnaces 3, 4 & 5

Figure 5. Furnace shutdown schedule for the Base Case.

37

4.0

4.5

60

60

50

50

40

40

Liquid Level (%)

liquid Level (%)

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30 20 10 0 0

0.5

1

1.5 (h) Time (hr)

2

2.5

30 20 10 0

3

0

0.5

60

60

50

50

40

40

Liquid Level (%)

Liquid Level (%)

1.5 (h) Time (hr)

2

2.5

3

2.5

3

2.5

3

(b) DeC2 Sump Level

(a) DeC1 Sump Level

30 20 10

30 20 10 0

0 0

0.5

1

1.5 Time (h) (hr)

2

2.5

0

3

0.5

(c) DeC3 Condenser Drum Level

1

1.5 Time (h) (hr)

2

(d) DeC3 Sump Level

60

60

50

50

40

40

Liquid Level (%)

Liquid Level (%)

1

30 20 10 0

30 20 10 0

0

0.5

1

1.5 Time (h) (hr)

2

2.5

3

0

(e) C2splitter Condenser Drum Level

0.5

1

1.5 (h) Time (hr)

2

(f) C2splitter Sump Level

Figure 6. Examples of liquid-level changes during the de-inventory operation in the Base Case.

38

Step 1: De-inventory and feed reduction

Step 4 : CGC Shutdown, Decommission and Nitrogen Purge

Step 2 & 3: Vaporization and material recovery Keep CGC running

Activate 3-to-1 & 4-to-4 recycles

CGC CGC suction durm DEC2SUC DeC2 DC1FSEP1 DEC1SEP2 Chilling train drums DeC1 C2splitter DeC3 Quench Furnaces Shutdown 3 furnaces

Shutdown 2 furnaces

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10

10.5 11.0 11.5 12.0 12.5

Time (h) Legengd:

Furnace shutdown

Keeping normal operations

CGC running with anti-surge recycles

De-inventory

Ready-to-shutdown

Quench shutdown

Flowing through and recovery

Cooling duty off & liquid vaporization

Pressure relief

Decommission & nitrogen purge

Figure 7. Detailed shutdown operating schedule for the Improved Case

39

Shutdown operation starts

Step 2 starts

Feed Flowrate (ton•h-1)

30

20

10

0 -1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Time (h) Legend:

Furnaces 1 & 2

Furnaces 3, 4 & 5

Figure 8. Furnace shutdown schedule for the Improved Case. 40

4.0

4.5

40 35 30 25

Liquid

20 15 10 5 0 4.0

4.5 Legend:

5.0 Stage 2 Stage 40

5.5

6.0 Time (h) Stage 10 Stage 50

6.5 Stage 21 Stage 60

7.0

7.5

8.0

Stage 30 Stage 71

Figure 9. Dynamic liquid-holdup changes on each stage of C2 splitter during its shutdown operation for the Improved Case.

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80

Mass Flowrate (ton•hr-1)

70

DeC1 & DeC3 vaporization started

60 50

Furnace 3~5 shutdown

40

DeC2 vaporization started

C2 splitter pressure relief started

30 C2 splitter CGC pressure shutdown relief ended

Recovered vapor arrived

20 10

C2 splitter liquid recycle ended

0 4.0

5.0

6.0

7.0 Time (h)

8.0

9.0

10.0

Figure 10. Dynamic flowrate profile of recovered hydrocarbons at the fuel-gas pipeline outlet.

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Flowrate (103 m3•h-1)

125

124

123

122

12.0

120

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10

Time (h)

(a) 1.0 0.9

Mass Fraction

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

Time (h)

Legend： H2

CH4

H2O

C2H2

C2H4

C2H6

C3H4

C3H6

C3H8

Butadiene

Isobutane

Butane

C5H6

CO

(b) Figure 11. Dynamic profiles of CGC inlet (a) flowrate and (b) composition during the plant shutdown operation for the Improved Case. 43

29

6200

27

6100

25

6000

23

5900

21

5800

19

5700

17

5600

15

4.0

4.1

Legend:

4.2

4.3

CGC rotation speed

4.4

4.5 Time (h)

4.6

MW of inlet at CGC 1st stage

4.7

4.8

4.9

5.0

MW of inlet at CGC 4th stage

Figure 12. CGC rotation speed and MW changes during the 1st hour (4.0~5.0 h) of Steps 2 and 3 for the Improved Case

44

5500

CGC Rotation Speed (rpm)

Molecular Weight

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Pressure Ratio 3.0

Pressure Ratio 4.0

2.5 3.0 2.0

2.0 1.5

1.0 4.0

1.0 4.0 10

0.4

10 2.0

1.0 5

3

4.0

6.0

8.0

Volume (104 m3•h-1)

-1

Volume (10 m •h )

(b) 2nd Stage of Compressor

(a) 1st Stage of Compressor

Pressure Ratio

Pressure Ratio

4.0

4.0 3.5 3.5 3.0 3.0 2.5 2.5 2.0 2.0 1.5 1.5 1.0 4.0

1.0 1.5

2.0

2.5

3.0

3.5

4.0

4.0

9.0

9.0

Volume (104 m3•hr-1)

0.5

2.0

1.5

1.0 4

3

-1

Volume (10 m •hr )

(c) 3rd Stage of Compressor

(d) 4th Stage of Compressor

Figure 13. Dynamic CGC performance during the plant shutdown operation (Steps 2 and 3) for the Improved Case.

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Credit Author Statement

Yiling Xu: Conceptualization, Investigation, Methodology, Investigation, Writing - Original Draft Ha DinH: Formal Analysis, Investigation, Methodology Qiang Xu: Conceptualization, Investigation, Methodology, Writing - Review & Editing, Funding acquisition Fadwa T. Eljack: Investigation, Resources, Funding acquisition Mahmoud M. El-Halwagi: Investigation, Resources, Funding acquisition

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Highlights 

A systematic flare minimization approach for an olefin plant shutdown is presented



The shutdown of a front-end de-ethanizer ethylene plant has been improved



Shutdown strategy and unit operating safety are investigated by dynamic simulations



The flare minimization study benefits industrial and environmental sustainability