Dynamic Simulation and Optimization for the Start ... - ACS Publications

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Ind. Eng. Chem. Res. 2010, 49, 4360–4371

Dynamic Simulation and Optimization for the Start-up Operation of An Ethylene Oxide Plant Xiongtao Yang,† Qiang Xu,*,† Kuyen Li,†,‡ and Chirag D. Sagar† Dan F. Smith Department of Chemical Engineering, Lamar UniVersity, Beaumont, Texas 77710 and Department of Chemical Engineering, National Chang Kung UniVersity, Tainan 70101, Republic of China

Ethylene oxide (EO) is an important chemical intermediate for the production of various chemical products. The manufacturing of EO involves critical exothermic reactions at high temperature and high pressure, whose failure may cause catastrophic personal injury, severe air pollution, and tremendous economic loss. Thus, an EO plant must be well controlled under various situations, especially during its start-up operations. In this paper, a general methodology for improving chemical plant start-up operations through plant-wide dynamic simulation has been developed. It undergoes modeling and validations for steady-state, dynamic, and historian start-up operations. On the basis of the validated dynamic simulation model, the original plant start-up strategy is further examined and optimized to speed-up the plant start-up operation with enhanced safety considerations. A case study on an EO plant start-up has demonstrated the significant operational and economic benefits of the proposed methodology. 1. Introduction Ethylene oxide (EO) is a flammable and colorless gas at temperatures above 51.3 °F, which smells like ether at toxic levels. It is an important commodity chemical for the production of solvents, antifreeze, textiles, detergents, adhesives, polyurethane foam, and pharmaceuticals. Small amounts of EO are used in manufacturing fumigants and sterilants for spices and cosmetics, as well as hospital sterilization for surgical equipments. Figure 1 shows the EO based product family,1 which highlights the importance of EO production in daily life. Modern EO productions employ either air or oxygen (O2) to oxidize ethylene (C2H4) with Ag/Al2O3 catalyst packed in a fixed-bed reactor (plug-flow reactor). The oxygen-based reaction process is more desirable because of four major benefits: (i) higher productivity and selectivity; (ii) lower initial capital costs; (iii) less expensive catalyst required;2 and (iv) less air pollutants resulting from the purge gas. An EO manufacturing plant generally includes a reaction section and a recovery section. As the reaction section is much more important and complex than the recovery section, and most of the accidents occur in this section, this study will focus on the EO reaction section. A typical process diagram for an oxygen-based EO plant is shown in Figure 2. The C2H4 feed (>99.97% purity) merged with the scrubbed recycled gas in Mixer 1 is mixed with the O2 feed and the ballast gas of nitrogen in Mixer 2. The outflow of Mixer 2 is preheated by a feed/ effluent heat exchanger before being fed into the oxidation reactor. In turn, the reactor effluent stream gets cooled. In the reactor, the preheated feed flows through the tube side, which has been packed randomly with silver catalysts supported by alumina. The oxidation reactions take place on the catalyst surface to produce EO and other byproducts such as carbon dioxide (CO2) and water. Since the oxidation reactions are highly exothermic, a cooling water stream is used to recover the heat in the shell side of the reactor. The cooled reactor effluent is directed to an EO scrubber, where the EO-rich stream, also called the rich cycle water * To whom correspondence should be addressed. Tel.: 409-880-7818. Fax: 409-880-2197. E-mail: [email protected]. † Lamar University. ‡ National Chang Kung University.

(RCW), is collected from the bottom and sent to the downstream EO recovery section. The vapor stream from the scrubber top containing C2H4, O2, CO2, small amounts of EO, and argon is mainly recycled back to a compressor to increase the pressure to 330 psi. The compressed gas is split into two streams. One (S19) is recycled and mixed with the fresh C2H4 feed. Part of the S19 actually passes a CO2 scrubber to remove the contained CO2. The other (S18) is mixed with the cooled reactor effluent in Mixer 3. Then, the effluent (S10) flows to the EO scrubber. Under normal operations, S18 has zero flow rate, which means all the compressed gas is circulated back as the reaction feed with partial CO2 removal. During an EO plant start-up, however, the flow rate of S18 will not be zero and actually will function as an important manipulated variable. A chemical plant start-up operation is a highly complex operation that usually involves discontinuous and/or parallel operating procedures, as well as a wide change of many controllers’ set points.3 It is one of the most critical time periods with high risks of uncertainty occurrence.4 A large number of accidents have been reported during petrochemical plant startups.5 Thus, the start-up operational safety draws the most concerns in chemical plants. For an EO plant start-up, the safety requirements are extremely serious because it involves critical exothermic reactions at high temperature and high pressure, whose failure may cause catastrophic personal injury, severe air pollution, and tremendous economic loss. Especially during the initial start-up phase, the catalyst experiences a so-called “break-through phase”, during which the O2 conversion rate is very high, while the selectivity is low and the oxidation process is very difficult to control. Meanwhile, the reactant concentrations of C2H4 and O2 should be well controlled below the safety limits of 25% and 8%, respectively; otherwise, the reactants will explode directly. Note that the main product, EO, is a flammable and colorless toxic gas. Thus, an explosion not only causes the personnel injury/death and equipment damage, but also emits large amounts of toxic EO and highly reactive VOCs (HRVOC, defined in Texas air quality regulation as ethylene, propylene, isomers of butene and 1,3-butadiene), which results in severe environmental pollutions and raw material losses. Therefore, the safe start-up of an EO plant has significant economic and environmental potentials.

10.1021/ie9019038  2010 American Chemical Society Published on Web 04/08/2010

Ind. Eng. Chem. Res., Vol. 49, No. 9, 2010

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Figure 1. Ethylene oxide based product family (EPA, 1986).

Figure 2. Flowsheet for an EO plant reaction section.

Extensive studies for improving EO production and safety operation have been reported. Chen studied the impacts of operating variables on the EO reaction process.6 Gudekar carried out an optimization study to determine the optimal shell side inlet temperature profile over the run length of the operation by maximizing the net profit of the EO process.7 Thermal runaway scenarios for EO reactions and various prevention techniques were studied.8-10 Zhou and Yuan presented the steady-state and dynamic optimization approaches to determine optimal feed variables and operating

conditions to improve the reactor performance at normal operation conditions.11 Lou et al. gave a critical review of design and operation policy to secure operation by simulating and evaluating process behaviors under various disturbances.12 On the basis of the literature survey, however, it can be seen that quantitative studies on EO plant start-up operation and optimization are still lacking. In this paper, a general methodology for improving the startup operation strategy of a chemical plant has been developed. It employs plant-wide dynamic simulations to virtually test the

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Figure 3. General methodology framework.

start-up safety by systematically examining plant dynamic behaviors and transient responses of critical parameters. On the basis of the validated dynamic simulation model, the previous plant start-up strategy has been further examined and improved to speed-up the plant start-up with enhanced safety consideration. A case study on an EO plant start-up has demonstrated the efficacy of the proposed methodology. 2. General Methodology Framework A methodology framework on dynamic simulation and optimization for plant start-up operations is shown in Figure 3. It includes four major stages of work. To assist the plant-wide start-up modeling, simulation, and optimization, various supporting information is needed, which includes, but is not limit to, plant design data, process flow diagram (PFD), piping and instrument diagram (P&ID), distributed control system (DCS) historian, equipment sizing data, control parameters, and industrial expertise. Each stage of work presents unique features and forms a solid foundation for the next stage of work.

2.1. Steady-State (SS) Modeling and Validation. At the first stage, the model development starts from identifying the scope of modeling and optimization of the studied system. Then, a SS model is developed. Since a plant-wide simulation usually involves many unit models, an effective way is to divide the whole system into several small subsystems first, then develop and validate the SS model of each subsystem and hook-up all the submodels into a complete system model, and then further validate the entire SS model. Typical modeling support information includes updated plant PFD, thermodynamic methods and data, equipment dimension data, system input information, etc. The developed SS model is usually validated against plant design data first, which is collected from design documents. Then the model will be further validated by normal steady-state operating scenarios, which come from the plant DCS historian. Sometimes, the real plant data are not in mass or energy balance due to sensor noise, bias, malfunction, or unpredictable data loss. Under such conditions, data verification, data reconciliation, and the support of industrial expertise are needed.


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Table 1. Reaction Kinetic Data reaction 1

reaction 2 -0.5

9.547 × 10 [mol/L] /sec 4.289 × 109 [mol/L]-0.5/sec 28 1 0.5 9

ki, 0 (without ethyle chloride) ki, 0 (with ethyle chloride) ∆Ei kcal/mol mi ni

1.593 × 1011 [mol/L]-1/sec 7.081 × 1010 [mol/L]-1/sec 31 1 1

Table 2. SS Model Validation for the EO Plant

a

stream no.

description

unit

plant average

simulation result

S1 S2 S4 S4 S5 S5 S7 S7 S12 S13 S13 S14 S14 S17 S27 S28 S28

C2H4 feed flow rate O2 feed flow rate recycled gas flow rate pressure of the recycled gas C2H4 concentration at the reactor inlet O2 concentration at the reactor inlet reactor outlet pressure O2 concentration at the reactor outlet flow rate of LCW EO concentration at EO Scrubber top pressure at EO Scrubber top vent flow rate argon concentration of the vent stream compressor discharge pressure cooling water flow rate steam out flow rate steam out pressure

Klb/h Klb/h MMSCFH Psig mole % mole % Psig mole % GPM PPM Psig Lb/h mole % Psig KGPM Klb/h Psig

25.90 29.60 14.20 310.00 25.00 8.00 266.57 5.60 2163.25 269.83 256.29 493.80 6.87 316.00 7.73 93.00 634.93

25.90a 29.60a 14.46 310.00 25.10 8.00 266.30 5.70 2163.25a 269.00 255.30 521.32 10.80 316.09 7.73a 90.00 635.29

The values represent the SS model inputs.

Figure 4. EO process diagram with the control strategy.

2.2. Dynamic Simulation (DS) Modeling and Validation. Once the SS validation is satisfied, the system model should be transferred to the DS mode. Two major types of data should be prepared to support the model transition and validation. One is process control information including control strategies and parameters from updated plant P&ID. The other is dynamic scenarios from the plant DCS historian, which are used to examine if the system dynamic responses match the reality under given disturbances. DS model validation is a nontrivial task, because lots of data preprocessing, postprocessing, and modeling troubleshooting activities need to be conducted. Sometimes, quality dynamic data and scenarios are hard to obtain. Under this situation, the industrial expertise will play an important role for the DS model validation. The success of the DS validation depends on whether the timing and amplitude

of the dynamic responses of each subsystem match the real DCS historian.3 After the validation, the DS model can be transformed to the third modeling stage. 2.3. Historian Start-up Modeling and Validation. The start-up modeling and simulation are quite different from normal DS simulations. The major differences lie in the following two aspects: (1) The initial state of a plant start-up usually involves low-load running equipments, zero inflow and outflow rates, and full reflux of distillation columns as well as temporary multiple recycles and auxiliary streams. (2) The start-up simulation involves transiting the DS model from the initial state of start-up to the normal operation condition. During the transition, not only do process stream and unit operating statuses change (e.g., flow rate, temperature, pressure, concentration, and controller set point) but also process flowsheet topology changes

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Table 3. Control Strategy for the EO Production System

a

tag

process variable

manipulated variable

description

AIC-1 AIC-2a AIC-3 FIC-1 FIC-2 FIC-3a LIC-1 PIC-1a PIC-2 PIC-3 RC-1a SC-1a SpC-1a TIC-1a

C2H4 concentration of S5 N2 concentration of S5 CO2 concentration of S5 C2H4 feed flow rate O2 feed flow rate flow rate of S4 steam drum liquid level reactor outlet pressure steam drum pressure compressor discharge pressure O2/C2H4 molar ratio difference of O2 flow rate between S5 and S4 split ratio of Splitter 2 reactor inlet temperature

FIC-1 set point N2 feed flow rate split ratio of Splitter 3 C2H4 feed flow rate O2 feed flow rate SpC-1 set point cooling water flow rate reactor outlet flow rate steam outlet flow rate CH4 feed flow rate SC-1 Input 1 FIC-2 set point split ratio of Splitter 2 cooling duty of Cooler 1

concentration controller concentration controller concentration controller flow rate controller flow rate controller flow rate controller liquid level controller pressure controller pressure controller pressure controller ratio controller subtract controller splitter ratio controller temperature controller

Represent hypothetical controllers to mimic operators’ manual control behaviors.

Table 4. Initial Start-up and Normal Operation Conditions stream

description

unit

initial start-up condition

normal working condition

S1 S2 S4 S5 S5 S5 S5 S6 S17 S24 S28

C2H4 feed flow rate O2 feed flow rate recycled gas flow rate C2H4 concentration nitrogen concentration CO2 concentration O2/C2H4 ratio reactor inlet temperature system pressure flow rate of steam to the steam drum steam drum pressure

Klb/h Klb/h Klb/h mole % mole % mole % mole % °F Psi Klb/h Psi

0.00 0.00 275.50 17.00 17.30 0.02 0.04 340.00 250.00 22.00 365.00

25.51 28.78 902.00 25.00 0.01 6.00 0.31 375.00 330.00 0.00 650.00

(e.g., some recycles and auxiliary streams will be phased out at the end of start-up). Obviously, start-up modeling and validation present tremendous challenges, which need sufficient care. The first step at this stage is to adjust the entire system model to run at the initial state of start-up, which needs the initial feed data, initial unit operating status, and initial control parameters. One systematic way to accomplish this transition task has been reported.3 Once the initial start-up state is obtained, the DS model is ready to run the historian start-up procedures provided by the plant. The simulation results will be compared with historian start-up data from DCS. Such validation and possible troubleshooting are the last chances to systematically examine the quality of the developed start-up DS model. The success of this task must be guaranteed, because it provides the solid foundation for optimizing the start-up operating strategy on the fourth stage. 2.4. Start-up Strategy Optimization. When the historian start-up based model validation is successfully accomplished in the simulation environment, all the system dynamic responses are disclosed. On the basis of some economic objective and plant safety and environmental concerns, the optimal system dynamic responses need to be identified for improving the historian start-up strategy. This can be accomplished with the help of industrial expertise or some quantitative evaluation indices. Then the new start-up strategy will be virtually examined by the start-up DS model to check its feasibility, safety, and environmental performance. Further modifications of the start-up operating strategy may be involved, which needs iterative DS tests until the final satisfaction. Finally, the finetuned start-up strategy can be used for plant start-up decision supports. Note that since a chemical plant start-up operation is a transient process, its system parameters undergo a series of continuous changes. Thus, controller set points and even control strategies in the DS model should be adjusted accordingly in order to mimic plant operators’ controlling behaviors. These

adjustments usually involve system input changes and unit operating changes. To simulate the dynamic start-up procedure and the related system input and unit operating changes, a set of program scripts (so-called start-up automation “tasks”) must be developed and embedded into the start-up DS model13 such that the start-up dynamic simulation will be totally automatic, and the generated simulation results will be fully repeatable and retraceable. 3. EO Plant Start-up Simulation and Optimization On the basis of the developed methodology framework, a case study on dynamic simulation and optimization for an EO plant start-up has been conducted. The following four sections systematically present the results of the plant start-up simulation and optimization. 3.1. EO Reaction Modeling. EO reaction modeling is the most critical part for the EO plant simulation. The reaction mechanisms suggested by many literatures include two major reactions.14-19 The first reaction is the epoxidation of C2H4 to EO, which is the desired reaction. The second reaction is the total combustion of C2H4, which is obviously the undesired side reaction. 1 C2H4 + O2 f C2H4O 2

(1)

C2H4 + 3O2 f 2CO2 + 2H2O

(2)

In this study, the power law kinetics is used to calculate the reaction rate, which is ri ) ki × [C2H4]mi × [O2]ni,

i ) 1, 2

(3)

ki ) ki0 × exp(-∆Ei /(RT)),

i ) 1, 2

(4)

where mi and ni are the reaction orders of C2H4 and O2, respectively. Note that at 212 °F, both reaction rates are very


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Figure 5. Modeling C2H4 feed flow rate for the historian start-up. Figure 7. Modeling CO2 concentration at the reactor inlet for the historian start-up.

Figure 6. Modeling O2/C2H4 molar ratio at the reactor inlet for the historian start-up.

low, and the reaction product is almost EO. At 572 °F, both reaction rates are high, but the EO product selectivity decreases and reaction products have the majority of CO2 and H2O. Thus, there exists an optimal reaction temperature providing best economic trade-off between reaction rate and selectivity. For the oxygen-based reaction, the temperature range is from 428 to 500 °F. In this paper, the reaction temperature is required to be controlled around 496 °F at normal productions. The reactor consists of 7191 parallel tubes with 1.75 in. diameter and 33 feet of the tube length. For simplification, the radial temperature distribution is assumed homogeneous. Meanwhile, dangerous runaway situations will occur if the reaction temperature is above 520 °F. Also note that for both economic and operational considerations, there are escalating interests in increasing the reaction selectivity of EO, such that C2H4 consumption and heat generation by the side reaction can be reduced.20,21 As reported, by adding the EO reaction stimulator such as ethyle chloride, the EO selectivity will be increased.20,22,23 Meanwhile, the O2 conversion rate will be reduced so as to avoid hot spots inside the reactor. This is because chlorine inhibits the production rates of both EO and CO2. However, in the particular region of concentrations the production of CO2 is more inhibited, which causes the EO selectivity increment. Since the addition of small amounts of ethyle chloride results in the rapid change of production concentrations, the reaction rates need to be adjusted whenever the ethyle chloride has been injected into the reactor inlet. The formulas used to estimate the reaction rates have been studied and presented in several publications.17,24 However, the exact C2H4 oxidation mechanism and the precise kinetic parameters (pre-exponential factors,

Figure 8. Modeling the recycled gas flow rate for the historian start-up.

Figure 9. Comparisons of O2 feed flow rate for the historian start-up.

activation energies, and reaction orders) are still not fully available. In previous work, they were estimated from laboratory, pilot plant, or plant experimental data by using a variety of mathematical methods.25 In this study, the activation energy values for the above two reactions are provided by catalyst vendor. The values of k0 are estimated from the plant data of selectivity and conversion through the reactor. The complete set of reaction kinetic data used in this paper, which matches the plant conversion and selectivity, is shown in Table 1. It shows that the ratio of k10/k20 slightly increases when ethyl chloride is added. Thus, the EO selectivity will have a small increase. Meanwhile, because both k10 and k20 are significantly reduced after adding ethyl chloride, it suggests the production rates of both reactions are suppressed.

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Figure 10. Comparison of C2H4 concentration at the reactor inlet for the historian start-up.

Figure 13. Comparison of CO2 concentration at the reactor outlet for the historian start-up.

Figure 11. Comparison of O2 concentration at the reactor inlet for the historian start-up.

Figure 14. Comparison of the steam drum pressure for the historian startup.

Figure 12. Comparison of the EO concentration at the reactor outlet for the historian start-up.

Figure 15. Comparison of EO concentration at the EO scrubber top for the historian start-up.

3.2. Plant SS Modeling and Validation Results. The SS model of an EO plant with the normal productivity of 1.5 MMlb EO/day has been developed (see Figure 2). Because the process system contains polar and nonelectrolyte specie, and the components do not form liquid-liquid phases in all units, the thermodynamic package of NRTL (nonrandom two-liquid) is recommended for the simulation.26 To validate the developed SS model, the simulation results are compared with the plant data obtained from the DCS historian during a stable production period of 15 days. The average process data and the SS results are shown in Table 2. It can be seen that the simulation output values are generally in good agreement with the plant data. Thus,

the SS model is reliable and can be transformed to the dynamic modeling environment. 3.3. Plant DS Modeling and Validation Results. In the dynamic modeling environment, equipment sizing data, control strategies, and control parameters are added to the model, which constitute the DS diagram as shown in Figure 4. In this diagram, AIC-1 and FIC-1 are cascaded to control the C2H4 concentration in stream S5 by manipulating the fresh C2H4 feed flow rate. AIC-2 is used to control the nitrogen concentration of S5 by manipulating the fresh nitrogen feed flow rate. RC-1, SC-1, and FIC-2 are ratio control combined with a cascade control for regulating the O2 concentration of S5 by manipulating the fresh O2 feed flow rate. AIC-3 controls the CO2 concentration of S5


Figure 16. Comparison of start-up profiles for C2H4 feed flow rate.

Figure 17. Comparison of start-up profiles for the O2/C2H4 molar ratio at the reactor inlet.

by manipulating the split ratio of Splitter 3 to change the recycled flow rate of S21 to CO2 scrubber. FIC-3 and SpC-1 are cascade control for regulating the total recycled gas flow rate of S4 by manipulating the split ratio of Splitter 2. PIC-3 controls the discharge pressure by manipulating fresh methane feed flow rate. In the reaction system, methane is an inert component (because ethylene is much more reactive than methane, the selectivity of methane and ethylene combustion leans heavily toward ethylene) and is used to control the heat removal and increase the explosive concentration limits of C2H4 and O2. For the reactor control, TIC-1 is used to control the reactor inlet temperature by manipulating the cooling duty of Cooler 1. PIC-1 is employed to control the reactor output pressure by adjusting the reactor outlet flow rate. For the reaction temperature control, because the reaction heat is utilized for the steam generation, PIC-2 is used to control the recovered steam flow rate to maintain the shell-side pressure; while LIC-1 controls the cooling water flow rate to maintain the liquid level in the shell side. The complete control strategies have been summarized in Table 3. Note that during a plant start-up, operators sometimes will control some process variables by manually regulating the related control loops according to their on-site observations and judgments. Thus, a complete start-up simulation should simulate not only a predefined start-up procedure, but also operators’ manual control behaviors during the start-up. To accomplish this task, some hypothetical controllers are created. As shown

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in Table 3, the marked controllers are actually virtual controllers used to mimic operators’ manual control behaviors. For instance, the plant requires recycling the unreacted C2H4 for reuse from the top of EO scrubber. As the required amount of recycled gas is less than that of S17 during the start-up, the rest must be handled properly. In practice, the plant operators need to carefully and manually circulate the rest cycled gas (S18) to EO scrubber bypassing the reactor. This can be attained by introducing hypothetical cascade controllers FIC-3 and SpC-1. Another example of the hypothetical controller is TIC-1, which is added to mimic operators’ manual control on the reactor inlet temperature by manipulating the cooling duty. Also note that when a fresh catalyst is loaded into the reactor, it is typically subjected to a high temperature treatment with nitrogen gas passing over the catalyst for cleanup and demoisture prior to carrying out the start-up.27 Thus, the nitrogen flow is maintained at a high value in the initial start-up state, and its concentration during the start-up should be controlled. This is accomplished by the hypothetical controller of AIC-2. After the setup of the control strategy, the DS model should be validated and fine-tuned. In this paper, since a complete historian start-up data is available, the DS model is validated and fine-tuned directly against the historian start-up data. Note that this is the last chance to validate the plant-wide DS model. The first step at this stage is to transform the DS model from normal operation state to the initial state of start-up provided by the plant. Table 4 has shown the attained initial state of the start-up. As aforementioned, the reaction stimulator of ethylene chloride is added to suppress the reaction rates during the startup. This will be accounted by changing reaction parameters as shown in Table 1, when the stimulator is added at 4.6 h. Then, the historian start-up operation has been modeled and simulated, which includes the following 10 steps: Step 1: Set the C2H4 concentration controller (AIC-1) to manual mode. The controller’s output, which is the set point of the C2H4 feed flow rate controller (FIC-1), is designated as the plant measurement profile shown in Figure 5. For the convenience of simulation, the measurement profile is closely approximated by its simulation input profile, which consists of various step and ramp changes. Step 2: The set point of the O2/C2H4 ratio controller (RC-1) is designated as the plant measurement profile shown in Figure 6. The measurement profile has been approximated by various steps and ramps as the simulation input. Step 3: Designate the set point of the inlet CO2 concentration controller (AIC-3) as the plant measurement profile shown in Figure 7. The measurement profile has been approximated by various steps and ramps as the simulation input. Step 4: Designate the set point of the recycled gas flow rate controller (FIC-3) as the plant measurement profile shown in Figure 8. The measurement profile has been approximated by various steps and ramps as the simulation input. Step 5: Disable the steam drum pressure controller (PIC-2) during the initial stage of start-up. Then, enable the controller and work at the auto mode once the steam drum pressure reaches 650 psi. Step 6: Ramp the set point of the inlet nitrogen concentration controller (AIC-2) from 17.3 mol % down to 2.9 mol % during the first hour of start-up. Step 7: Ramp the steam flow rate of S24 from 22000 lb/h down to 0 during the first hour of start-up. Step 8: The stimulator (ethyle chloride) is added to the EO reactor at 4.6 h. It is estimated to stabilize the reaction selectivity around 3 h.

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Figure 18. Comparison of start-up profiles for the CO2 concentration at the reactor inlet.

Figure 19. Comparison of start-up profiles for the recycled gas flow rate.

Figure 20. Comparison of start-up profiles for the O2 feed flow rate.

Step 9: Ramp the set point of the pressure controller (PIC-3) from 250 psi at 0.52 h up to 330 psi within 1.8 h; Step 10: Ramp the set point of reactor inlet temperature controller (TIC-1) from 340 °F up to 375 °F during the first 2 h. On the basis of the historian start-up model, the simulated historian start-up performances are obtained and compared with real plant measurements as shown in Figures 9- 15. Figure 9 shows the comparison of O2 feed flow rate, which is determined by the C2H4 feed flow rate, O2/C2H4 molar ratio, and the recycled oxygen flow rate in S4; Figure 10 shows the C2H4 concentration profiles at the reactor inlet, which should be controlled below 25%; Figure 11 gives O2 concentration profiles at the reactor inlet, which should be controlled below 8%; Figure 12 shows EO concentration profiles at the reactor outlet; Figure 13 shows CO2 concentration profiles at the reactor outlet; Figure 14 shows the steam drum pressure profiles; and Figure 15 shows

EO concentration profiles at the EO scrubber top, which should be controlled below 600 ppm. It can be seen that the simulated results are generally in good agreement with the plant measurements. The largest error comes from the EO concentration profiles at the EO scrubber top during the second and the fourth hours (see, Figure 15). During this time period, the measurement seems unreasonably high. The possible reason might be that the flow rate of lean water was too small at that time, which caused the inefficiency of the EO absorption. Anyway, it does not influence the overall start-up operation and the start-up DS model validation. As aforementioned, the validation work should be conducted with the help of plant expertise, especially when some of the start-up data are lost or are very difficult or even impossible to obtain. In this case study the plant did not have the historian measurements of the reactor temperature changes during the start-up, thus, the reactor temperature validation is not available. However, from all the other dynamic validations such as the


component concentrations at the reactor inlet and outlet, the model validation has been accepted by plant engineers. Overall, the developed DS model is proved to be capable of simulating the historian EO start-up operation. Therefore, it can be used for further study to improve the plant start-up operation strategy. 3.4. Operating Strategy Optimization for EO Plant Start-up. Safety is the major concern for a chemical plant startup. From the historian start-up records in Figures 5-15, the previous plant start-up experienced unsmooth transient operations as shown by those big fluctuations in the profiles of temperature, pressure, and concentrations. These fluctuations may cause unsafe problems that may create hazardous situations or even severe accidents to the plant. Thus, a smooth start-up is foremost for the EO plant. On the other hand, the start-up is preferred to be completed as early as possible if plant safety is ensured, because that means a significant decrease of start-up costs and production down time, as well as the foreseeable reduction of waste discharges and environmental compliance costs. To optimize the EO plant start-up strategy in terms of both safety and start-up speed improvements, key variables such as the concentrations of O2 and C2H4 at the reactor inlet and EO product concentration at the EO scrubber top should be well controlled. These variables are affected by C2H4 feed flow rate, O2/C2H4 ratio, CO2 concentration at reactor inlet, and the recycled gas flow rate. Thus, the profiles of these four variables are selected for optimal manipulation. As shown in Figures 16-19, the dynamic trajectories of these four variables are designated based on the industrial experience. Generally, these variables increase gradually and smoothly from their initial startup status to their normal operating status. Note that the implementation of these four optimized profiles can be justified because they can be controlled as the designation, which are also equivalent as changing Steps 1 through 4 in the historian start-up. Because the new start-up strategy features quicker and smoother transitions of those key variables, it supposedly has better performance than that of the historian start-up. When the new start-up strategy is identified, its dynamic performance is virtually examined again through the dynamic simulation. Figures 20-28 show the simulated results of the optimized start-up strategy. The overall start-up performance is improved as expected, which is summarized as the following: (1) The O2 feed flow rate change is obviously smoother than that of the historian start-up (see Figure 20). It reaches the normal operation at around 25 h. (2) The C2H4 and O2 concentrations at the reactor inlet change smoother than those of the historian start-up, especially during the first 8 h (see Figures 21 and 22). The C2H4 and O2 concentrations at the reactor inlet settle down at 25 h. (3) The EO and CO2 concentration profiles at the reactor outlet are significantly improved compared with the historian start-up records (see Figures 23 and 24). No big fluctuations in their profiles. They finally settle down at about 25 h without violating safety limits and product quality. (4) During the start-up, the reactor temperature is always below the safety limit of 520 °F. As shown in Figure 25, the DS start-up model provides dynamic temperature profiles with respect to both time and the reactor length. (5) During the start-up, the steam drum pressure is always below the safety limit of 650 psi (see Figure 26). (6) The EO concentration at EO scrubber top changes very smoothly and reaches steady state at 25 h (see Figure 27). There are no big fluctuations compared with the historian start-up

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Figure 21. Comparison of start-up profiles for the C2H4 concentration at the reactor inlet.

Figure 22. Comparison of start-up profiles for the O2 concentration at the reactor inlet.

Figure 23. Comparison of start-up profiles for the EO concentration at the reactor outlet.

record, and the EO concentration at EO scrubber top is under specification (

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