Flare Minimization during Start-Ups of an Integrated Cryogenic

Article pubs.acs.org/IECR

Flare Minimization during Start-Ups of an Integrated Cryogenic Separation System via Dynamic Simulation Yongchen Zhao,† Jian Zhang,‡ Tong Qiu,*,† Jinsong Zhao,† and Qiang Xu*,‡ †

Department of Chemical Engineering, Tsinghua University, Beijing 100084, China Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, Texas 77710, United States

‡

ABSTRACT: The integrated cryogenic separation system (ICSS), which includes a chilling train and a demethanization section, is a crucial production system in an ethylene plant. It accounts for up to 50% of the total start-up time and flare emissions of the whole ethylene plant. Traditional start-up scenarios are developed on experience and improved by trial and error, which is inefficient and dangerous. This paper employs rigorous dynamic simulations to examine the potential infeasibilities and operational risks of different start-up scenarios. The best start-up scenario with minimal start-up time and flare emissions is determined. To obtain the initial start-up state with ambient nitrogen filling in the whole system, an initialization algorithm based on parameter modification for the dynamic ICSS model is presented. This novel initialization algorithm greatly saves time and effort of the initialization of the start-up model compared with previous studies. In addition, if time-related variables are important for scenario study or temperature of the dynamic model changes greatly, heat capacity must be added into dynamic start-up model, which was not mentioned in published literatures. They are critical to determine accurate start-up time, control the cooling rate, and verify the feasibility of scenarios during the dynamic start-up process under arbitrary temperature changes. The dynamic simulation provides insight into process dynamic behavior, which is crucial for the plant to evaluate and improve start-up scenarios. A real case study has demonstrated the efficacy of the dynamic simulation with heat capacity added.

1. INTRODUCTION Ethylene is one of the most important chemical products with the largest productivity in the petrochemical industry. Global ethylene production in 2012 reached about 145.6 million metric tons and is expected to increase by 4.4% per year from 2013 to 2017. Generally, an ethylene plant undergoes start-ups and shutdowns every 3−6 years. During this process large quantities of off-spec gas products are generated and flared for safety. It has been estimated that an ethylene plant with a capacity of 1.1 billion pounds of ethylene production per year will easily flare about 5.0 million pounds of ethylene during one single start-up and generate about 40.0 klb of CO, 7.5 klb of NOx, 15.1 klb of hydrocarbon, and 100.0 klb of highly reactive volatile organic compounds (HRVOCs).1 Integrated cryogenic separation system (ICSS) is one of the key factors to the whole plant start-up. It has been reported that ICSS accounted for about 46.8% and 55.6% of the total fare emissions and start-up time respectively.2 Generally, ICSS includes a chilling train section and a demethanization section. It is the coldest system in an ethylene plant. It plays a critical role in separating hydrogen and methane from ethylene and heavier components at about −165 °C with multilevels of refrigerants and cryogenic separation equipment.3,4 The traditional start-up procedures include ramping up furnace feeds, starting up the charge-gas compressor (CGC), starting up the ICSS, and starting up downstream units in sequence.5 After ICSS is fed with cracked gas, it will be chilled from ambient temperature down to very low temperature (lower than −160 °C). This procedure is time-consuming and emissions are large. Even more, material leakage is prone to occur due to the uneven stress of equipment during the cooling © 2014 American Chemical Society

procedure. Hence, ICSS becomes a bottleneck of the whole plant start-up. Several precooling techniques are proposed in recent years, which transfer the cooling process from feeding to precommission stage. The first technique is precooling with N2 before furnaces are fed. The CGC and refrigeration system need to start up first to provide the driving force and cooling duty for N2. It can chill the ICSS to about −140 °C, which greatly reduces the cooling time after feeding. The second is precooling with natural gas (NG), which can be easily obtained in most ethylene plants.5 The main component of NG is methane, one of components of cracked gas, so it can reduce the time and emissions of N2 replacement process by cracked gas in the first precooling technique. In addition, there are some other flare minimization strategies, such as recycling the offspec products to the CGC or furnaces6−8 starting CGC with artificial feeds with the similar composition as the cracked gas.9 They all have similar procedures for precooling the ICSS but need extra investments to add pipelines. All techniques mentioned above are developed based on theory and experiences, and there are a lot of unexpected factors which can result in unplanned shutdowns even accidents. For instance, during the shutdown of Lyondell Chemical Co. Channelview, TX in 2007, the molecular weight of the actual recycling materials was much heavier than during normal operations. This caused large quantities of the process gas to condense in the compressor train, which resulted in a Received: Revised: Accepted: Published: 1553

September 28, 2013 December 16, 2013 January 3, 2014 January 14, 2014 dx.doi.org/10.1021/ie403223y | Ind. Eng. Chem. Res. 2014, 53, 1553−1562

Industrial & Engineering Chemistry Research

Article

Figure 1. Typical flow sheet of ICSS.

the plant start-up with safety consideration. Xu et al.15 developed a plant-wide dynamic simulation and tested a startup procedure with total recycles. The flaring of the dynamic simulation assisted start-up was reduced by about 60% compared with the shortest start-up in the past. However, the plant-wide dynamic model was mainly composed of distillation columns, and ICSS was simplified to improve overall convergence. Practice shows that start-up of ICSS is completely different from that of the distillation column. Therefore, a dynamic ICSS model was developed by AspenPlus Dynamics V7.3 in this paper, in order to study the different start-up scenarios and minimize the flare emissions. Heat capacity data were added into Aspen dynamic start-up model, which had been not mentioned in the published literature. If time-related variables are important for a scenario study or the temperature of the dynamic model changes greatly, heat capacity data must be added into the dynamic model to determine accurate start-up time and flare emissions and control the cooling rate. An initialization algorithm base on parameter modification was proposed to obtain the initial state of the dynamic start-up ICSS model, which saved much time and effort compared with the algorithm developed by Xu et al.15 The paper is organized as follows. Section 2 introduces the ICSS process briefly. Section 3 is the general methodology of developing a dynamic start-up model. Two main differences from similar studies are the addition of heat capacity and initialization algorithm based on parameter modification. Section 4 describes the scenarios and assumptions, and section 5 analyzes the results.

premature shutdown because the system could not remove these condensed liquids from the compressor train.9 If rigorous dynamic simulation were established to evaluate the new shutdown technique, the unexpected event could be spotted in advance and avoided. Dynamic behavior of multistream plate-fin heat exchangers was first studied numerically by Pingaud et al.10 Roetzel et al.11proposed a general model for dynamic responses of onedimensional flow multistream heat exchangers and their networks, which could calculate dynamic responses to arbitrary inlet temperature changes and mass flow disturbances by means of Laplace transform and the FFT (fast Fourier transform) inverse technique. Xin12 proposed a matrices model based on the simplified Pingaud model to describe the multistream exchanger and obtained the general analytical solution of dynamic response for multistream exchangers and their networks. However, these mathematical models are hard to apply to the ICSS start-up process. Because there are two additional distillation columns coupled with the multistream heat exchangers. The mathematical study of ICSS, including multistream heat exchangers, separation tanks, and distillation columns, is still lacking. Due to the advantages of commercial simulators, such as state-of-the-art physical property, easy combination of different models, and extensive use in plant, recently, dynamic simulations developed by commercial simulators have been used to virtually test and optimize the new start-up or shutdown operations and examine critically the potential process operational risks and infeasibilities. Singh13 developed a dynamic simulation of recovery area in an olefin plant for flare minimization, including deethanizer, depropanizer, acetylene converter, and the C2 splitter and examined a startup procedure with ethane. It helped the plant reduce flaring by 75%. Xiongtao14 developed a plant-wide dynamic simulation for improving start-up operations of an ethylene oxide plant. The original start-up strategy had been optimized to speed up

2. INTEGRATED CRYOGENIC SEPARATION SYSTEM DESCRIPTION A typical flow sheet of the ICSS is illustrated in Figure 1. The system inputs are vapor distillate (S1) and liquid distillate (S2) 1554

dx.doi.org/10.1021/ie403223y | Ind. Eng. Chem. Res. 2014, 53, 1553−1562


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Table 1. Integrated Cryogenic Separation System Steady-State Model Validationa T

P

T

EV-1 plant measurement simulation

−50 −50

plant measurement simulation

32 31

3485 3484

−70 −70

3352 3365

−42 −46

S28

a

P

T

EV-2

P

T

EV-3 3461 3472

−97 −99

3365 3375

−78 −76

S25

P

T

EV-4 3432 3443

−128 −126

3377 3385

−106 −110

S22

P EV-5

3418 3420

−162 −165

3388 3395

−133 −136

S19

3404 3415 S16 3397 3405

Units: P [kPa], T [°C].

Figure 2. Temperature profiles of ET-1 and ET-2 at normal steady-state.

ET-2. As aforementioned, ET-2 has four external feeds. The first three are from the bottoms of demethanizer feed separators, EV-2, EV-3, and EV-4, respectively, and the forth is from the top of ET-1. The ET-2 bottom stream (S43) is directed to downstream units, while the top stream (S42), mainly methane, is separated in flash drum (EV-6). The vapor of EV-6, S46, is fed into methane expander (EX-1), while the liquid of EV-6, S44, is mixed with the outlet of EX-1. The EV-6 is designed to protect the EX-1 impeller from the damage of liquid droplets. The mixture of S44 and S47 with the name of low pressure methane (LP CH4) is used as a refrigerant to cool the hot stream of the chilling train and warmed it to ambient temperature. The whole ICSS system is divided into two subsystems, chilling train section (including the cold boxes and flash drums) and demethanization section (including ET-1, ET-2, and EX-1) for convenience.

from the reflux drum of depropanizer (DeC3) column, respectively. S1 normally includes 21.6 mol % of H2, 40.5 mol % CH4, 31.5 mol % C2H4, 3.0 mol % C2H6, 3.2 mol % C3H6, and 0.2 mol % C3H8. S2, which is in equilibrium with S1, includes 1.2 mol % of H2, 14.6 mol % CH4, 49.5 mol % C2H4, 7.2 mol % C2H6, 26.5 mol % C3H6, and 1.0 mol % C3H8. The temperature and pressure for both streams are −34 °C and 3.5 MPa, respectively. The vapor distillate (S1) is refrigerated to −50 °C in EH-1 by propylene and separated into the vapor and liquid phase in gas−liquid separator (EV-1). The liquid stream of EV-1 is mixed with the liquid distillate (S2) and then directed to the top of demethanizer stripper (ET-1). The vapor stream (S4) of EV-1 is refrigerated to −70 °C by propylene in EH-2 and separated in the first demethanizer feed separator (EV-2). The liquid stream (S38) is sent to the demethanizer tower (ET-2). The vapor stream (S6) is refrigerated to −97 °C in No. 3 cold box (CB-3) and EH-3 by ethylene and separated in the second demethanizer feed separator (EV-3). Again, the liquid stream (S39) is directed to the ET-2, while the vapor stream (S9) is refrigerated to −128 °C in CB-4 and separated in the third demethanizer feed separator (EV-4). The liquid stream (S41) is utilized as a refrigerant of CB-4 to recover cooling duty and then directed to ET-2. The vapor stream (S11) is refrigerated to −165 °C and sent to the flash drum (EV-5) for separation of hydrogen and methane. The bottom products (S15) of EV-5 are mainly methane, which will flow through a Joule−Thomson expansion valve (V5) to provide the cooling duty to chill the hot stream in the chilling train, CB-5, CB-4, CB-3, CB-2, and CB-1 in sequence. A part of the top vapor, S14, is diverted to valve (V6) and mixed with the bottom stream in order to control the stream S12 at a specific temperature of −165 °C. The rest of EV-5 top, S13, which mainly contains hydrogen, is also directed to the chilling train for recover cooling duty. ET-1 has only one feed stream at the top. The bottom products (S36), which include nearly no CH4, are directed to downstream units, while the top products (S37) are directed to

3. GENERAL METHODOLOGY A systematic modeling methodology15,16 has been used in this paper. The rigorous dynamic simulation is performed based on the integration of process models, plant design data, PFD (Process Flow Diagram), P&ID (Piping & Instruments Diagram), DCS (Distributed Control System) historical data, industrial expertise, equipment dimension data, and heat capacity data. Note that heat capacity data are added into the dynamic model, which is not mentioned in the published literatures of dynamic start-up simulation.14−16 If time-related variables are important for a scenario study or if temperature change of the dynamic model is arbitrary, heat capacity is essential. Such as ICSS, if the cooling rate exceeds 60 °C/h, the material leakage is prone to occur due to the uneven stress of equipment during the cooling procedure. In addition, the time of precooling from 25 °C down to −165 °C has a great impact on flare emissions. 1555



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However, a dynamic model without heat capacity cannot give a reasonable response time to temperature changes. For example, a separating tank made of aluminum alloy will give quite a different response time for temperature change. The two experimental models including only a flash tank (EX1 without heat capacity data and EX2 with heat capacity data) have been developed. The response times of the step function of the feed temperature from 25 to 50 °C are 0 h (EX1) and 0.60 h (EX2) respectively, which has eliminated the residence time (tank volume/feed volume flow rate) 0.15 h. The results show that the dynamic model without heat capacity data is impractical and gives incorrect results. As the number of models in the simulation increases, the influence of time-related variables becomes more and more complicated. The Aspen Plus V7.3 is used to develop steady-state simulation (SS) and dynamic simulation (DS) models, and the sequential modular approach is used because the whole simulation task is more convenient to be decomposed into subsystems for troubleshooting. Because the components in ICSS are nonpolar or mildly polar hydrocarbons under high pressure, the thermodynamic package of PR-BM is used.17 3.1. Steady-State Modeling and Validation. The SS model is developed according to plant design data and PFD which are collected from plant design documents. Then it is validated by normal steady-state data obtained from a DCS historian. In this stage, data verification and data reconciliation will be extensively involved due to the uncertainty and complexity of plant data with the support of industrial expertise. Typical model tuning parameters include flow rate of heat transfer medium, column tray efficiency, etc. Table 1 and Figure 2 show the comparison between SS model and plant measurements for the chilling train and demethanization system. It is clear that the simulation results match very well with the plant measurements, which lays a solid foundation for further dynamic simulation. 3.2. Dynamic Modeling and Validation. When exported from the SS model to DS model, equipment dimension data (such as vessel type and geometry, tray geometry, and details), control strategy, and controller parameters, equipment heattransfer methods are needed to provide the process capacity, control, and thermodynamic information.15,18 When the DS model has been built, the control strategy will be modified according to the P&ID. The PID parameters including gain, integral time and derivative time should also be modified according to DCS instruments configuration. After that, the DS model is validated by the DCS historical data involving some process upset which are shown in Figure 3. Figures 4−6 show the time and amplitude of dynamic response, which are pretty well consistent with most plant measurements. The plant measurement of the ET-1 top pressure is greater than the simulation results. The reason for that is zero drift of the pressure gauge, which is confirmed by plant technical staff. The validation is a key process to ensure the effectiveness of the dynamic model. 3.3. Initialization of Start-Up. Conventionally, equipment is filled with nitrogen before starting up. However, the initial state of DS model is the results of SS model in normal operation conditions, which is not suitable for start-up study. In addition, the initialization of start-up usually involves zero inflow and outflow rate. Xu and Yang15 proposed a general algorithm to accomplish the status-adjusting task, as shown in Figure 7a, which required not only system model input changes but also process topology changes and operating status changes

Figure 3. Flow rate of input stream, FEEDV and FEEDL.

Figure 4. Dynamic response flow rate of products.

Figure 5. Dynamic response of ET-1.

(e.g., temperature, pressure, concentration, and control parameters changes). However, temperature, pressure, and composition of the nitrogen state in this paper are quite different from the initial state with hydrocarbon feed of the plant-wide model in Xu and Yang’s paper. Furthermore, there are five MHeatX models with quite poor convergence in dynamic model. The step, indicated as a red dotted arrow, in Figure 7a, used by Xu, cannot guarantee the convergence of multistream heat exchanger network during arbitrary inlet temperature changes and mass flow disturbances. Therefore, an initialization algorithm based on parameter modification is developed to accomplish the transition of model status, as shown in Figure 7b, which is applicable to dynamic 1556



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Step 2. Set the SS model developed in section 3.1 run in ambient nitrogen state. First, change the FEEDV and FEEDL from hydrocarbon to ambient nitrogen. It is better to add two nitrogen streams and splitters than to change stream composition. Second, set the whole system at ambient temperature by changing the set points of all heat exchangers’ outlet temperature. Mark this model as SS-N2. Step 3. Export SS-N2 to flow-driven dynamic model, DS2. As mentioned in section 3.2, modify the control strategy and controller PID parameters according to the plant data. Step 4. Update the key parameters of DS2 to get DS3, the final dynamic model ready for start-up test. The key parameters can be found by comparing the corresponding heat transfer related parameters in DS1 and DS2. This requires a lot of repetitive work to identify all the key parameters. Step 5. Test the DS3 model with a start-up scenario mentioned in section 4. If all the procedures in the start-up scenario can be executed, the initialization is successful. Otherwise, go back to step 5 to identify more heat transfer related parameters until the DS3 is ready for start-up test. Note that in step 4, key parameters are different for each model in Aspen Plus model library, such as heat transfer medium temperature and heat exchange area for exchanger, UAs for MHeatX, temperature of heat transfer medium for column, etc. The reason for this modification is that the parameters of the DS2 model, which are the results of the SSN2 model, are not suitable for the real plants while the parameters of DS1 are. Similarly, the state of DS1 is not the right state, while DS2 is. In other words, the parameters of DS3 are provided by DS1 and the initial state of DS3 is provided by DS2. The essence of the algorithm based on parameters

Figure 6. Dynamic response of ET-2.

start-up simulation including multistream exchanger networks combined with other unit operation models. The main difference between our algorithm and Xu’s is the parameter modification steps as shown in the red dotted box, Figure 7b. This parameter modification algorithm reduces not only the possibility of divergence of the dynamic transition but much time spent in the initialization of the start-up mentioned in Xu and Yang’s paper. As a comparison, dynamic models are developed by two algorithms respectively. The results show that they have the same dynamic response for the same process upset. However, modeling time can be reduced about 60% with the algorithm developed in this paper, which avoids the dynamic transition from normal operational conditions to ambient nitrogen state. Step 1. Make the DS model developed and validated in section 3.2 (named as DS1) run at the normal steady state.

Figure 7. Initialization algorithm of start-up model. (a) Initialization algorithm of Xu and (b) initialization algorithm based on parameter modification. 1557



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Table 2. Three Typical ICSS Start-Up Scenariosa scenario 1 initial state procedures final state a

ambient N2 (a) Precooling with CG (b) Displacement with CG (c) Feeding and cooling products on-spec

scenario 2

scenario 3

ambient N2 (a) Precooling with N2 (b) Displacement with CG (c) Feeding and adjustment products on-spec

ambient N2 (a) Precooling with CH4 (b) Total reflux of column (c) Feeding and adjustment products on-spec

CG: cracked gas. N2: nitrogen. CH4: methane.

Figure 8. Dynamic temperature variation (a) scenario 1, (b) scenario 2, and (c) scenario 3 (CG feeding times are 2.5, 9.5, and 7.5 h, respectively).

total reflux with mixture of methane and ethylene. The reason for choosing a mixture of methane and ethylene is that total reflux with pure component is effective for two-component distillation but ineffective for multicomponent distillation.5 The two kinds of total reflux streams, pure ethylene and mixture of methane and ethylene, are tested to identify the cause. The results show that the composition and temperature profiles of total reflux with ethylene is far from the normal profiles, which takes ET-2 nearly the same time as in scenario 1 to stabilize. Note that a plant start-up operation is a complex and planned transient process, involving all kinds of controller tuning according to operators’ observations and judgments. Thus, to simulate the predefined start-up procedure and operators’ control behaviors, the advanced function “task” in Aspen Dynamics is used and a set of program scripts is developed and embedded into the dynamic model. Then, the start-up DS model is ready to run, while the simulation results will be fully repeatable. In addition, the following assumptions are reasonably made for start-up simulation: (a) Off-spec products are flared directly. (b) The end of the N2 replacement is marked by nitrogen mole fraction of all streams less than 1%. (c) Emission leakage and other unexpected situations are negligible. (d) To reduce the computational load, the feed rate is simplified to increase from zero to its normal flow rate by a ramp with a 2 h duration, which does not affect the results of contrast.

modification is to avoid the dynamic transform from DS1 to DS3, in Figure 7a, which easily causes divergence of the dynamic model.

4. SCENARIOS AND ASSUMPTIONS Before new start-up scenarios are implemented in practice, detailed analysis and risk assessment were performed to evaluate the feasibility and safety. Even so, there are a lot of unexpected or unsafe operation conditions which can only be found in practice operation. This trial and error process is costly and hazardous. However, it is cost-effective to build a rigorous dynamic simulation model to mimic the plant start-up scenarios and check the operational feasibility, reliability, and safety issues. As mentioned in section 3.2, the dynamic ICSS model has been built and validated by plant DCS historical data. Thus, the dynamic ICSS model can be used to test different start-up scenarios. The start-up procedure in this paper means the operation steps to transition the plant from the initial state (namely, the end of N2 replacement) to its normal operation. Table 2 shows three typical ICSS start-up scenarios. Scenario 1 is the traditional start-up steps. Furnaces and CGC (charge-gas compressor) will be started before ICSS. After that, a small flow rate of the compressed cracked gas will be used to pressurize the ICSS and to replace N2. Once the replacement is done, the compressed cracked gas will be ramped to its normal flow rate in 2 h. Scenario 2 has been gradually adopted in recent years. Start the CGC with N2 before the start of the furnaces and precool the ICSS with compressed N2. Then naphtha begins to feed into furnaces and then the cracked gas is induced into CGC and ICSS in sequence. Facts have proved that scenario 2 greatly reduced start-up time and flare emissions. Even if leakage happened in the precooling process, it was safer than organic gas leakage because it would not fire. Scenario 3 is developed based on a large number of dynamic simulations. Precool the ICSS with a special methane stream (33 bar, −95 °C) and then preinventory and operate ET-2 on

5. RESULTS AND ANALYSIS Figure 8 gives dynamic temperature variation of the five flash separator drums. In scenario 1, 0−2.5 h is the precooling process with CG mentioned in Table 2. Because there are no refrigerants in EH-1, EH-2, and EH-3 in this stage, all drums are precooled to about −38 °C, the same temperature as the feedstock. Then refrigerants are induced into corresponding 1558



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Figure 9. Dynamic mole fraction variation of key components in important product streams (a) scenario 1, (b) scenario 2, and (c) scenario 3.

Figure 10. Dynamic temperature profile variation of ET-1.

Figure 11. Dynamic temperature profile variation of ET-2.

exchangers to refrigerate the chilling train section to their operational temperatures as shown in Figure 8a: 2.5−9.9 h. Similarly, in scenario 2, 0−9.5 h is the precooling process with N2, illustrated in Figure 8b. It is clear that a better temperature profile is established in the chilling train section with the support of refrigerants before CG feeding. This reduces the gas−liquid separation time of flash drums from 7.5 to 2 h after CG feeding. This separation time is important to stabilize ET-2. Because the liquids from flash drums are directed to ET-2, separation time is feed stabilization time of ET-2. Therefore, reduction of separation time of flash drums can speed up the stabilization of ET-2. In scenario 3, 0−7.5 h is the precooling process with CH4 without refrigerants, in Figure 8c. Even so, the coldest temperature can still reach about −90 °C, which also reduces gas−liquid separation time to 2 h. This could save a lot of refrigerant, but a cryogenic methane stream is required. Thus,

the application of scenario 3 depends on the availability of the special methane stream. According to the vendor cooling rate curve, adjust the flow rate of refrigerant (ethylene and propylene) and precooling stream to make sure the cooling rate never exceeds equipment constrains (60 °C/h), to avoid material leakage due to unequal stress. This is very important to actual operations in plants. Figure 9 shows the qualified time of products, which is used to determine flare time and emissions. The time of H2 in stream S28 reaching 95 mol % after feeding, as an example, is 7.7, 3.8, and 2.3 h in the three scenarios, respectively. This great reduction of time not only reduces the flare emissions but also speeds up the start-up of the hydrogenation reactor, which is a key device downstream. Because hydrogen is one of the feedstocks of the hydrogenation reactor in the back-end hydrogenation process, it is a bottleneck of the whole plant start-up. This is the reason why many manufactures try different 1559



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Figure 12. Temperature variation of chilling train precooled with flow rate of CH4: (a) 2000, (b) 1500, (c) 1000, (d) 500, (e) 200, and (f) 100 kg/h.

Figure 13. Temperature variation of chilling train under different solutions: (a) extending precooling time, (b) put refrigerant of EH-3 into service, and (c) open the flare emission valve of EV-5.

time of ET-2 chilling and establishment of temperature profile is only about 4 h, it takes another 5 h to stabilize, which is clearly shown by the mole fraction variation of CH4 in bottom in Figure 9a. The mole fraction of CH4 in the bottom is not allowed to exceed 10 ppm before S36 is sent to downstream units, because it is hard for ethylene products to be on-spec with excessive CH4. Flare is inevitable. Therefore, some measures are taken to speed up the stabilizing of ET-2. The first is precooling with N2 (0−9.5 h), as shown in Figure 11b. However, ET-2 still takes 9.1 h to stabilize. Detailed analysis of ET-2 during the dynamic start-up process reveals that 9.1 h contains 4 h stage level establishment and 5.1 h

kinds of precooling procedures to reduce the hydrogen qualification time. Figure 10 illustrates the variation of the temperature profile of ET-1 in different start-up processes. The liquid stream from the bottom of EV-1 is only 15% of the total feed rate, so it has little effect on stabilization of ET-1. Hence, Figure 10a−c has a similar variation of temperature profile. However, things are quite different for ET-2 as shown in Figure 11. In scenario 1, ET-2 takes 9.9 h to achieve stability, while the chilling train section takes 7.9 h and ET-1 takes 4.5 h. Therefore, ET-2 has become the bottleneck of the start-up of ICSS. From the comparison of Figures 11a and 9a, although 1560



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temperature profile establishment. Both times can be reduced and transferred from after feeding to preparation in scenario 3. The major improvements of scenario 3 are preinventory and operating ET-2 on total reflux with mixture of methane and ethylene. The temperature profile variation in this preparation is illustrated in Figure 11c, 0−7.5 h. The results show that the temperature profile of 7.5 h is closely similar to normal conditions. Therefore, ET-2 requires only 4.7 h to stabilize after feeding. Time of preparation of ET-2 in scenarios 2 and 3 before feeding can be merged into precommissioning of the plant, so it will not increase the time of start-up. The start-up time of scenario 3 is the shortest. However, before application, operational feasibilities and safety issues need to be investigated in detail. A key factor to precooling is flow rate of CH4 in scenario 3. As the flow rate reduces, the precooling process becomes more and more difficult, shown in Figure 12. When the flow rate of CH4 is lower than 500 kg/h, Figure 12d, the temperatures of EV-3, EV-4, and EV-5 remain constant. The reason is that S22, S23, and S24 have taken the most cooling duty of S6 to CB-2 and CB-1, so that the remaining part of cooling duty is not enough to chill the equipment in the square illustrated in Figure 1 (namely, heat transfer dead end). If this happens in actual operations, the planned precooling process cannot proceed. Therefore, if the flow rate of CH4 is less than 500 kg/h, three solutions are proposed as follows:

Figure 15. Emissions of different scenarios.

Scenario 3 is the best one for ICSS start-up, because the time of start-up is reduced by 53%, total emissions by 73% and total olefins by 76%. Second is scenario 2, which is widely adopted due to the easy availability of N2. Therefore, different plants can chose appropriate scenarios based on our calculations and their specificity.

6. CONCLUSION Dynamic simulation serves as a powerful tool in a feasibility study of start-up scenarios of a chemical plant. This paper performed the dynamic start-up simulation of ICSS based on plant design data, P&ID, DCS historical data, etc. Heat capacity data of the equipment are added into the Aspen Dynamic model, which is used to determine the proper flow rate of the precooling stream, control the cooling rate, and identify potential operational problems, such as heat transfer dead end. An initialization algorithm based on parameter modification is developed to obtain the proper start-up state. Dynamic simulation reasonably predicts the dynamic behavior of the start-up process. More importantly, it provides much more data on the start-up process, which cannot be obtained in practice. It also helps to identify the bottleneck of the start-up and determine the optimal start-up scenario with the shortest time and minimum flare emissions. The time and emissions of different scenarios will be validated with actual data by the next plant start-up.

(a) Extend the precooling time. (b) Put refrigerant of EH-3 (ethylene) into service. (c) If ethylene refrigerant is not available, open the valve in the flare emission line of EV-5 bottom to diminish the flow rate of S22, S23, and S24. The three solutions are tested with the flow rate of CH4 at 200 kg/h, see Figure 13. Compared with extending time solution (b) takes 30 h to complete the precooling, while (c) takes 40 h. Contrastive analysis of start-up time and emissions is easily accomplished in simulation, while it is impossible in an actual plant. The start-up time is counted based on feeding as a starting point, as shown in Figure 14. Individual and total emissions of all product streams during the start-up are shown in Figure 15. In addition, the two higher-value components in all products, ethylene and propylene, have been counted specially.

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AUTHOR INFORMATION

Corresponding Authors

*Tel: +86-10-62784513. E-mail: [email protected]. *Tel: 409-880-7818. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the Ministry of Science and Technology of PRC (National Basic Research Program of China, 973 Program) for its financial support (Grant No. 2012CB720500). The authors are also grateful for the financial support from the Ministry of Science and Technology of PRC (National Key Technology R&D Program; Grant No. 2012BAF05B00). This work was in part supported by Daqing Petrochemical Branch, China National Petroleum Corporation.

Figure 14. Start-up times of different scenarios. 1561



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Flare Minimization during Start-Ups of an Integrated Cryogenic

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