Recycle Interaction Effects on the Control of Impurities in a Complex

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Recycle Interaction Effects on the Control of Impurities in a Complex Plant Alexandre C. Dimian,* Alexander J. Groenendijk, and Piet D. Iedema Universiteit van Amsterdam, Instituut voor Technische Scheikunde, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands

Proper inventory of impurities in a large plant is a combined flowsheet/equipment design and plantwide control problem. This problem can be solved efficiently by managing positive and negative feedback effects and by taking profit from interactions through recycles. Chemical conversion is an effective way to counteract the positive feedback through recycles. Changing the connectivity of units can modify the effect of interactions. This article proposes a methodology for evaluating the dynamic inventory of impurities that consists of a combination of steadystate and dynamic flowsheeting with controllability analysis. This approach is used to assess the best design alternative and/or to propose subsequent modifications. A VCM process with four recycle alternatives illustrates the approach. Taking advantage of the effect of interactions enables three impurities to be kept under control with only two controllers. The approach is generic and can be used to improve the environmental performances of chemical processes. Introduction The management of impurities is currently an important issue in both process design and operation. A key point is the interaction between these two aspects. The minimization of waste, aiming for zero effluents, increases the number of recycles, making plant control and operation much more difficult. Ideally, the inventory of each component should be traced from its source to its final destination. In the case of large-scale commodity plants, the treatment of byproducts and impurities is the major reason for differences in technologies, rather than the separation of main products, for which the flowsheet alternatives are rather limited. In some processes with complex reaction networks, as illustrated in this article, the purity control of some intermediate reactants can be as important as that of the final product(s). Moreover, the overall process economics, in terms of investment and operation costs, are greatly affected by the tolerable levels of impurities in these intermediates. The inventory of impurities is a plantwide control problem, because it involves both the reaction and the separation subsystems through recycles. This important issue has been acknowledged in industry, by Downs,1 among others, but it has largely been ignored by academic researchers. A quantitative approach has been reported only recently by Dimian et al.2 in some preliminary publications. An understanding of the role of recycles in complex plants is crucial for solving the above problem. Denn and Lavie3 demonstrated long ago that a recycle profoundly affects the dynamics of a process, but this observation has had little impact on plantwide control philosophy because of its formal nature. Only recently have Luyben and co-workers started systematic studies on the dynamics and control of recycle systems by introducing elements regarding chemistry, thermodynamics, and unit design. Their work has been collected * Author to whom correspondence should be addressed. E-mail: [email protected].

in a recent book of Luyben and Tyreus.4 They proposed a 10-step plantwide control design procedure. It is relevant that step 7 consists of “checking component balances: identify[ing] how chemical components enter, leave, and are generated or consumed in the process. At this stage it is necessary to find the specific mechanism or control loop to guarantee that there will be no uncontrollable build-up of any chemical component within the process.” We should mention that the “synthesis problem” is not solved by the proposed procedure. The above-mentioned control design methodology has the merit of pinpointing the major role of material balance in plantwide control, not only for the main components (production rate and product quality), but also for other components, such as impurities. However, it does not indicate how to solve this problem systematically and how to include design issues in analysis. As demonstrated in this paper, in the case of large complex plants, the inventories of the main components and of impurities cannot be managed separately, because they are coupled through recycles. The interactions can hinder or help the solution of the problem, depending on the competition between positive and negative feedback effects. The implementation of a control structure based on the point of view of stand-alone units can lead to conflicts. Hence, a systemic approach based on a quantitative evaluation of the recycle effects is needed. In a previous paper, Groenendijk et al.5 demonstrated how a systems approach could be used to analyze the dynamics and plantwide control of a complex plant by using rigorous simulation and controllability tools. This approach enables one to distinguish between undesirable and favorable interactions, as well as to suggest major process modifications, such as the introduction of a reactor for the chemical conversion of impurities. In this article, we proceed with an examination of the alternative flowsheets produced from this analysis. This paper begins by presenting an outline of the methodology, followed by a description of a case study with alternative flowsheets and of the plantwide control problem. Possible control structures, as well as the effects of recycles, are evaluated by a linear MIMO

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controllability analysis, both at steady state and in the frequency domain. A comparison of alternatives is performed by closed-loop simulation. This procedure enables to choose the final flowsheet and the appropriate solution for the control of impurities. Methodology The systems approach for evaluating the dynamics and plantwide control of complex plants presented by Groenendijk et al.5 is also suitable for controlling the inventory of impurities. Moreover, the application of the procedure enables design alternatives to be developed that are not visible at the process synthesis stage. In this respect, the most used process synthesis method, known as the hierarchical approach of Douglas,6 is based on a sequential development of the flowsheet structure and largely ignores the interactions between the reaction and separation sections. This limitation can be corrected by the proposed methodology. The procedure can be summarized in the following steps: 1. Problem Definition. First, the key components are identified. These are products, subproducts, and intermediates, as well as impurities with significant effects on product quality and plant operation. The amounts of impurities must be traced by means of tables containing sources, sinks, exit streams, transit units, and process streams. Their formation and depletion must be supported by a consistent stoichiometry. In this way, the material balance will also close the atomic balance. Here, the operating window must be defined in terms of production rate, operating parameters, and technological constraints. In principle, this step can identify a number of flowsheet alternatives, but the systematic synthesis of such alternatives is beyond the scope of this work. Supplementary alternatives might arise during the application of the procedure. 2. Calibration of a Steady-State Plant Simulation Model. This activity can be the most timeconsuming. For an existing plant, it can be combined with data reconciliation, although such as approach can be applied with reasonable accuracy only for the main components. Dimian7 showed that the tuning of the stoichiometry, as well as the identification of parameters in the thermodynamic models, can be done in a systematic manner. 3. Plantwide Control Problem. The inventory of key impurities is the plantwide control problem. The following elements have to be defined: Control Objectives. The strategic plantwide control objective is minimization of the total process waste. Potential toxic material must be converted into benign material that does not increase environmental problems or that can be stored and sent to posttreatment. In addition, the plantwide control objective might consider the control of key impurities in internal process streams, particularly in intermediate reactants, as well as in the end products. In this respect, key impurities are considered those that are the objects of analytical control procedures. Process Constraints. These are maximum tolerable amounts or/and concentrations of impurities in products and process effluent streams (purges, vents, and bleeds), as well as in internal streams or in the inventory of selected units. In addition, the maximum tolerable losses of reactants and products in effluent streams must be considered. These constraints are defined by

environmental regulations and product quality and equipment protection demands. Controlled Variables (Outputs). The types and locations of measurements are based on process engineering judgment. These are typically quality requirements of intermediate reactants and concentrations of key impurities in selected internal process streams. Manipulated Variables (Inputs). These are available degrees of freedom left after inventory control of the main components has been considered. They might include manipulated variables left for quality control of selected separation units, as well as streams belonging to the input/output structure of the process, such as makeup, purge, or bleed streams. Disturbances. These might be flow rates and concentrations of key impurities produced by the process or introduced with the inflow of reactants. Disturbances must be defined in terms of amplitude and frequency range. Set point changes, due to optimization or design modifications such as the rerouting of some streams, also have to be considered. Scaling of Variables and Disturbances. Proper scaling is necessary for a meaningful computation of controllability indices. 4. Steady-State Controllability Analysis. Following Luyben and Tyreus,4 we adopt the position that a simple and efficient plantwide control structure can be built with multiple SISO PID controllers. This step enables us to evaluate the control structures of decentralized (integral) feedback control. Chapter 10 in Skogestad and Postlethwaite8 describes this concept. The main actions include the determination of steadystate gains for plant and disturbances, the evaluation of the feasibility of input/output combinations by a single value decomposition (SVD) analysis, and the estimation of feasible pairing by a relative gain array (RGA) and Niederlinski index. 5. Dynamic Flowsheeting. The resolution of modeling depends on the dynamics of the units involved in the plantwide control problem. More detailed models are necessary for the key units, where impurities are generated and eliminated, such as kinetic models for reactors and dynamic models for some distillation columns. For other units, steady-state models might be sufficient. At an intermediate modeling degree, Weitz and Lewin9 proposed the use of simple transfer functions with parameters identified from steady-state simulation and sizing. However, the availability of suitable dynamic models for the wide variety of unit operations involved in practice is questionable. The simplification of a steady-state plant simulation model to a tractable dynamic model that is still able to represent the relevant dynamics of the actual problem is a practical alternative. 6. Dynamic Controllability Analysis. Based on the nonlinear plant model, a linear dynamic model is derived, either as a set of transfer functions (identification method) or as a state-space description. The last alternative is offered in some advanced packages, such as SpeedUp (now Aspen Dynamics), but its applicability to very large problems is questionable. Then, a standard frequency analysis can be performed. The main steps include the following: (a) Compute the RGA and RGA number. The latter is defined as |RGA - I|sum. Check the pairing suggested

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by the steady-state analysis over the frequency range where control is needed. Evaluate the effect of interactions. (b) Check the input constraints by means of the closed-loop disturbance gain (CLDG). Modify the design if necessary. (c) Estimate the controllability performances of the selected structures using the performance relative gain array (PRGA) and relative disturbance gain (RDG). (d) Repeat the procedure for each alternative decentralized control structure. For details about different controllability measures, see the book of Skogestad and Postlethwaite.8 7. Closed-Loop Simulation. Here, the first task consists of implementing and tuning the controllers. The use of prescribed local control structures, or the setting of perfect control for fast loops, simplifies this task and preserves the plantwide character of the analysis. Here, the plantwide control structures identified by the controllability analysis are tested by full nonlinear simulation. Evidently, this step is time-consuming and should be applied only to the most promising design alternatives. 8. Design Alternatives. For each flowsheet alternative identified at step 1, the procedure should be repeated and the alternatives ranked. Supplementary alternatives might arise during the analysis by design evolution. The generation of a base case, against which the other alternatives can be evaluated, is recommended. Design modifications might concern the (re)sizing of unit operations and/or alternative flowsheet (recycle) structures. Some alternatives can be rejected during steps 4-6 when the controllability analysis indicates clearly inferior dynamical behavior. However, some design improvements might be suggested by the controllability measures, as described at steps 4 and 6. Essentially, they should ensure that the effects of interactions do not prevent the implementation of a decentralized control system (RGA and RGA number) and that the magnitudes of the inputs are effective in controlling the outputs at steady state (SVD and CLDG analysis). 9. Conclusions. Such a study results in identification of the best flowsheet alternative and suggestions for plantwide control strategy, as well as design and sizing modifications of the units. In summary, in the above procedure, the inventory of impurities is characterized by allowable variations, as well as by set points in some particular cases. Decentralized control tools can be used to investigate its dynamic behavior. This is a new approach that solves the fundamental problem of the material balance of components quantitatively. The above methodology was applied in an evaluation of the dynamics and plantwide control of a VCM process by Groenendijk et al.5 The analysis of an initial design identified that the conversion of impurities reduced the positive feedback and improved the overall plant dynamics. As a consequence, this major modification generated four new alternatives, a base case plus three new recycle alternatives. These will be analyzed in the next sections. Process Description A balanced VCM process produces only vinyl chloride by combining three reaction steps: direct ethylene chlorination to 1,2-dichloroethane (DCE), cracking of

Figure 1. Base-case flowsheet of the balanced VCM process.

DCE to VCM, and recovery of HCl by oxychlorination with ethylene. The global stoichiometry can be described by the following simplified scheme

chlorination C2H4 + Cl2 f 1,2-C2H4Cl2 (DCE) + impurities (1) cracking DCE f C2H3Cl (VCM) + HCl + impurities (2) oxychlorination C2H4 + 2HCl + 1/2O2 f DCE + H2O + impurities (3) The above steps are conducted in three different reactors. Differences among individual VCM licensers are mainly due to the reaction systems used. These determine the amount of impurities and, consequently, the purification equipment and energy requirements. Details can be found in Ullmann.10 Proper handling of impurities is a crucial for the sustainability of such process. This problem is a complex combination of chemistry, thermodynamics, design, and control, whose solution has generic value for large-scale complex processes. Base-Case Flowsheet. Figure 1 presents a simplified flowsheet, which concentrates the essential features of an industrial process but does not reproduce a particular technology. The chlorination of ethylene to DCE takes place in a gas-liquid-type reactor (R1), working at near-atmospheric pressure and 353 K and using FeCl3 as the catalyst. After purification, the thermal cracking of DCE takes place in a tubular reactor (R2) at high temperature (500-600 K) and pressure (0.5-1.5 MPa). The byproduct HCl is recovered as DCE by oxychlorination of ethylene in a fluid-bed reactor (R3). Note that the flowsheet can consider the processing of an external DCE stream. Thus, the amount of ethylene and chlorine is “balanced” to consume all of the HCl produced in the cracking section. Despite the good selectivities of the reactors, the amount of impurities in a VCM process is considerable because of the large process scale. As an order of magnitude estimate, the waste production is 25 kg/ tonne of VCM lights and heavies, plus vents and aqueous streams. The removal of impurities in a VCM process is known to be difficult. These species can accumulate in the recycle loops and cause unstable operation. Among the large number of impurities, three are of particular importance: (I1) chloroprene (nbp 332.5 K), (I2) trichloroethylene (nbp 359.9 K), and (I3) tetrachloromethane

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(nbp 349.8). Both I1 and I2 are “bad” for operation, being nonsaturated components. The first can easy polymerize above 8% and plug the equipment. The second favors coke formation in the cracking reactor. In contrast, I3 is a “good” impurity, as it has catalytic action in the cracking reactor by reducing the temperature and improving the selectivity. In some patented processes, it is introduced deliberately. The source of chloroprene (I1) is the cracking section. Trichloroethylene (I2) appears mainly in the oxychlorination reaction. Tetrachloromethane (I3) is produced mainly in chlorination and oxychlorination but might also be present as an impurity in the initial feed. More details about the stoichiometry of these reactions can be found in the Supporting Information. These impurities must be removed selectively from the DCE (nbp 356.8 K) sent to cracking. The contents of both I1 and I2 must be kept low, between acceptable limits, whereas that of I3 must be kept close to an optimal value. This is the incentive of the plantwide control problem. Crude DCE produced in reactors R1 and R3 are sent to S0, a black-box unit that simulates the washing/ drying operation. An initial amount of dissolved gases and very light impurities is removed in unit S1 and sent to distillation column S4, which is the exit of light impurities. After pretreatment, the crude DCE is sent to purification in distillation column S2, the key unit of the separation system. This column receives DCE from three reactor systems and is the place where three large recycle loops cross. The top distillate of S2 should remove the light impurities mentioned above. The purification from the heavies is continued in distillation columns S3 and S5. However, the separation of impurities in S2 is affected by volatility constraints. At the top temperature of 350 K, the volatilities of I1, I2, and I3 relative to that of DCE are about 1.9, 0.94, and 1.6, respectively. Therefore, the top distillate of S2 can easily remove I1 and I3 but not I2. Note also that the top distillate of S2 cannot contain more than 8% I1. To prevent the accumulation of I2, a side stream drawn from S2 is sent to reactor R1, where chlorination to heavies takes place. Because of the constraint on I1, the top distillate of S2 carries with it a significant amount of DCE, which has to be recovered and recycled by column S4. By recycling the bottom of S4 to reactor R1, some amounts of impurities I1 and I2 are converted into heavies. This operation helps to reduce the accumulation of undesired impurities, particularly I2, but affects the operation of reactor R1. Therefore, it is rational to introduce a specialized reactor for the conversion of nonsaturated impurities into heavies by liquid-phase chlorination. This new reactor, designated R4 and placed between S2 and S4, provides an opportunity for flowsheet alternatives, as described in the next section. Note that all of the heavy impurities, produced by the process or by the conversion of lights into heavies, are removed by distillation columns S3 and S5. These two columns work in tandem to limit the losses of DCE. Unlike the distillation of light impurities, there is no heavy impurity that constrains the purification of DCE. Note that S2 is a large distillation column, with about 50 theoretical stages, operating at high reflux. The separation in S3 is easy and requires only a few stages,

Figure 2. Flowsheet alternatives.

Note that S4 and S5 are small units, but they of particular importance because of their function: S4 is the only exit of light impurities (lights), and S5 is the only exit of the heavy impurities (heavies). After thermal cracking, the reaction mixture is quenched and cooled (not presented). The recovery of HCl and the separation of VCM from unreacted DCE takes place in units S6 and S7, respectively. Alternative Flowsheets. The chemical conversion of impurities in R4 makes possible alternative recycle structures obtained by keeping the same equipment but changing the stream connections. The recycle of impurities to R1 can cancel. Figure 2 shows three supplementary alternatives to the base case. In alternative A, the bottom of S4 is sent directly to column S5. The removal loop of lights, S2-R4-S4-S5S2, crosses the removal loop of heavies, S2-S3-S5S2, in column S5. This modification diminished the heavies in both R1 and S2. Because of higher throughput, the design of S5 requires revision. In alternative B, the bottom of S4 returns directly to S2. The loop for the removal of lights is shorter, but a larger amount of heavies increases the danger of fouling incolumn S2. In alternative C, the bottom of S4 goes directly to the finishing column S3, which is now subject to more fouling. Moreover, a fault in S4 will immediately affect the cracking section. Of the three alternatives, A is technologically the safest, but it implies the revamping of S5. Alternative B is simpler but necessitates a review of the internals of S2, which is an expensive column. Alternative C is the simplest but less safe in operation. It can be expected that each alternative will have distinct controllability properties. The final selection must take into account this important feature, as well as the costs of modifications. In summary, the tracing mechanism of key impurities is as follows: I1 is generated in R2, converted to heavies in R1 and R4, and concentrated in S2 to leave by S4 in lights; I2 is generated in R3, converted to heavies in R1 and R4, concentrated in S2, and leaves by the path S2S3-S5 in heavies; I3 appears in R1 and R3 and leaves the process by the path S2-S4 in lights. Plant Simulation Model Steady-state simulation models have been built for the base case and alternatives. A steady-state plant simulation model of an existing VCM plant helped to calibrate the base-case model on a representative operating point. Some details of an industrial process were skipped, but the omission of these details influences neither the plantwide material balance nor the process

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dynamics. Units S0, S6, and S7 can be considered black boxes. In contrast, units S1-S5 are rigorous distillation columns, modeled as sieve trays. In the steady-state approach, all of the reactors are described using a stoichiometric approach. Kinetic modeling is used for R1 and R4 in the dynamic simulation. Note that the reaction network has been formulated to use a minimum of representative components but to respect the atomic balance. This approach is necessary because yield reactors can misrepresent the process. Details enabling a full simulation are given in the Supporting Information. The computations were performed with Aspen Plus, version 9.3, and SpeedUp, version 5.5, both from Aspen Technology.11 Plantwide Control Problem. In the VCM process, the quality of the intermediate reactant DCE sent to the production of VCM must fulfill strict purity specifications. Low impurity levels imply high energy consumption, but higher impurity amounts are not desired for operation. In this case, the intermediate DCE is conditioned mainly in distillation column S2. In the bottom product, the concentration of the two bad impurities I1 and I2 must not exceed upper limits of 100 and 600 ppm, respectively, whereas the concentration of the good impurity I3 must be kept around the optimal value of 2000 ppm. Because these impurities are implied in all three reaction systems through recycles that cross in a central separation system, their inventory is a plantwide control problem. Note also that the problem is constrained by technological and environmental considerations, as mentioned before. The advanced removal of I1 and I2 must find a compromise with the optimal concentration of I3 in the bottom product of column S2. These contradictory requirements cannot be fulfilled by any stand-alone design of S2, as demonstrated by Groenendijk et al.5 The control of impurities becomes possible only by exploiting the positive feedback effects of the recycle loops balanced by the negative feedback effects of chemical conversion and the exit streams. Hence, the plantwide control objective is the quality of DCE sent to the cracking section, for which three specifications are required: spec1, the maximum concentration of I1; spec2, the maximum concentration of I2; and spec3 the optimum concentration of I3. These are the outputs of the plantwide control problem. We assume that they are available by direct concentration measurements, such as IR spectroscopy or on-line chromatography. Note that both I1 and I2 contain double-bonded groups that are easily identified and that the -CCl3 in I3 is a group that can also be detected conveniently. Gas chromatography is a possibility for detection. In a first approximation, we suppose that composition analyzers are not constrained by the speed of response, which can be of some minutes, slow enough compared with the plant time constant, which is on the order of hours. An analysis of the degrees of freedom indicates manipulated variables belonging to column S2 as a first choice to be used for quality control: D2, the distillate flowrate; SS2, the side stream flow rate; and Q2, the reboiler duty. We can also consider manipulated variables belonging to column S4, which is adjacent to and connected with S2 by a recycle but dynamically much faster. Thus, supplementary outputs are D4, the distillate flow rate, and Q4, the reboiler duty. Hence, the inputs are the variables D2, SS2, Q2, D4, and Q4.

Table 1. Nominal Operating Points spec I1 I2 I3

wt ppm wt ppm wt ppm

D2 SS2 Q2 D4 Q4

kmol/h kmol/h GJ/h kmol/h GJ/h