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Design and control of batch reactors
Computers them. Engng Vol. 19, Suppl., pp. S357-5368, 1995 Copyright 0 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 009%1354(95)00042-9 009%1354/95 $9.50 + 0.00
Pergamon
DESIGN AND CONTROL OF BATCH REACTORS -ANINDusTRIALvIEWPOINTM. Friedrich and R. Perne Bayer AG, Zentmle Forschung, 5 1368 Leverkusen, Germany
ABSTRACT Design and control of batch reactors provide challenging problems with respect to basic functionalitv and safetv of a process as well as product quality and yield related issues. In this paper examples for ihe use of modelling, dynamic simulation and advanced control techniques for industrial problems are given. They are evaluated with respect to economic benefit as well as effort for development and implementation. KExwoRDs Batch reactors, process design, advanced control, operator training, dynamic simulation, model based control, fuzzy control INTRODUCTION A large number of processes in the production of polymers, specialty and fine chemicals, as well as pharmaceuticals are operated in batch. In many cases such batch plants are used for manufacturing a variety of products that exhibit significantly different characteristics as far as time for conversion, enthalpy of reaction, or viscosity are concerned, to name just a few. Furthermore, different objectives may be critical for different products to obtain safe and economic plant operation. In one case, because of safety reasons, the removal of heat of reaction may be the critical issue, whereas in other cases tight control along a temperature or concentration trajectory may be most important to satisfy quality specifications. Different product characteristics as well as different objectives require high versatility of process design and control. Like for continuous processes, by process design not only the design of equipment is meant, but also the design of process operation. For batch processes this includes the sequence of actions taken to obtain the desired product, e. g. initial loading and feeding of reactants as well as heating and cooling. In other words, design of operation leads to desired trajectories for temperature and concentrations. Whereas in most cases vessels and piping can be designed relatively easily to meet the needs also of multiproduct plants, the design of operation and control for batch plants poses challenging problems. As far as the control problem is concerned, one must also observe the economic restriction that it is often not possible to develop a dedicated control strategy for each product or to implement any control technique, no matter how complex it may be, on a distributed control system (DCS). Instead, control methods have to be used that are robust against product change, easy to implement and, even more important, easy to maintain. 5357
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With respect to these aspects the purpose of this paper is l
to share experiences with techniques for the design of operation and control of batch processes under industrial constraints,
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to give estimates of the economic benefit of using such techniques,
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to evaluate the effort for development and implementation,
. and to outline deficits as well as needs for further research and development. Such an evaluation must necessarily rely on personal experiences that are, of course, limited to a certain number of techniques. So it can obviously not be of general validity. We do hope, however, to contribute to an open discussion about what is needed for the efficient design and control of batch reactors. Scope All our discussion is limited to the operation of a single batch reactor. It is not directed towards the problem of scheduling batches for multiple products in a sequence of different processing units (reactors, separation, and conditioning). Q&ctives and worl&
areas
The economic performance of batch processes is determined by a number of factors. We discern three different types of objectives to obtain an economically beneficial batch reactor. These are to ensure basic functionality, l
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process safety,
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and optimum yield at the desired product quality.
Ensuring basic functionality means that vessel, piping, and all other equipment, like sensors and actuators, are designed in such a way that the process can function properly. This includes appropriate choice of vessel size and material, supply of raw materials at the necessary rates, as well as addition or removal of heat. These aspects may be intimately linked, like the choice of vessel material and design of heating/cooling system since heat exchange with a steel reactor is much easier than with a glass-lined reactor, especially since the lining may be prohibitive to the construction of internal heat exchangers or pump-arounds for external heat exchangers. The treatment of process sajkty in this paper is restricted to avoiding high temperatures and, thereby, high pressures that may be caused by a number of effects, like pump or valve failure, highly exothermic side reactions, or operational errors. Product quality is determined by very different properties for different types of products.
Whereas the quality of a mixture of fluids is characterized by its composition, the quality of a polymer may also be affected by chain length distribution or particle size in the case of emulsion or suspension polymerization. Similarly, other quality related variables are important for dispersions of crystals, dyes or fibres. Generally, product quality depends on the thermodynamic state of the system under consideration and may be expressed as a function of it. Several authors have made use of this fact for quality oriented process design and control (e. g. Kozub and MacGregor, 1992; Soroush and Kravaris, 1993a), and a more general treatment has been given by Friedrich and Gilles (1991). Using such a definition, yield can be expressed as the amount of product satisfying given quality specifications that is obtained from a given amount of reactants. To achieve these goals three different working areas may be discerned during planning, start up and operation of the process. Besides process design and control, as mentioned earlier, education of the operating personnel is an important aspect despite the fact that many processes are automated to a large extent. During the design phase laboratory recipes for the desired products have to be adapted and optimized so that under economic restrictions concerning vessel type, size, and material as well as heat exchange system size feasible operating conditions can be found.
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By developing an appropriate connof system one has to ensure that the desired operating conditions can be maintained as closely as possible during process operation. Operator training helps making plant personnel familiar with basic physical and chemical relationships, plant equipment, and automation system as well as safety and quality related events that require proper manual action.
In the following sections we will give examples for the way the objectives mentioned, basic functionality, process safety, and product quality can be achieved by the steps of action taken during process design, control system design, as well as operator instruction. The experience gained during the work on these examples is evaluated afterwards, and conclusions drawn from this experience are given at the end. TECHNIQUES AND TOOLS FOR DESIGN, CONTROL, AND EDUCATION cess Design The design problem for a chemical reactor can be viewed as the problem of translating a number of specifications concerning the goals that are considered here into a set of specifications for the reactor as well as material end energy flows into the reactor. Besides, it is necessary to observe constraints, that are given by plant operability, availability of utilities, safety considerations, capital cost and others. For continuous plants a number of these issues have been addressed in industrial applications for more than twenty years by steady state flowsheet calculations, often in connection with optimization. For those cases, where it is not possible to formalize the design problem completely so that it can be solved as a whole using optimization techniques there exist well established procedures to combine empirical with mathematically formal design steps (e. g. Douglas, 1988). An analogous procedure for batch processes must lead to the use of dynamic simulation and optimization because of the inherently dynamic nature of a batch process. A formal procedure for integrated design and control of batch processes has only recently been published by Soroush and Kravaris (1993a,b). They recognized that it is helpful to decompose the complete problem into subproblems before starting the optimization. The decomposition leads to an optimization problem in which optimal trajectories for those variables are calculated that have the strongest impact on product quality. If a formal quality function as described in the previous section is used, an optimal trajectory for this function can be calculated. The optimizing variables are in any case reactor temperature and/on: oncentrations of raw materials. Once the trajectories for the optimizing variables are calculated one has to provide, in a second step, means that those trajectories can be realized by supplying appropriate piping, heating/cooling system as well as an adequate control system. In industrial practice one usually has to resort to empirical methods for a number of reasons that include lack of knowledge about reaction mechanism and kinetics, availability of efficient software tools (especially for dynamic optimization) and restrictions that can not easily be formalized. However, the use of dynamic simulations for process design is becoming increasingly important. Design examule: To illustrate the value of dynamic simulation for the design and control of a batch reactor and the analysis of the interdependence of both consider the following example of the production of a specialty chemical. It involves the exothermic reaction of species A and B to product P in aqueous phase where a proton H+ and a base Y’ arc released: A+B
-_) P+H+
+Y-.
It is known from laboratory experiments that it is crucial for product quality to keep pH and temperature of the mixture within a certain range and to keep the concentration of B at a very low level so that byproduct formation is inhibited. These restrictions together with the need for the shortest batch time possible form the objectives for design calculations. The objectives can be achieved in a batch reactor by loading the vessel initially with reactant A in water and feeding reactant B as well as a strong base 2 as the neutralizing agent. The performance of the process is determined by the amount of byproducts as well as batch time,
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whereas the performance determining variables are concentration cB of B, pH, and temperature. Although the relation between these variables and the amount of byproducts is not known explicitly, one can design the process so that cB, pH, and temperature are within the tolerable ranges. Clearly, the feed rates of B and the base 2 as well as feed rate of cooling water are the manipulated variables. Whereas there must be a strong relationship between the feed rates of B and Z, the problem of keeping the desired temperature can be treated independently as long as the feed rates are such that the capacity of the cooling system is not exceeded. The most difficult problem during process operation is to determine concentration cB since it can not be measured directly. Here, two ways of running the process without knowing the exact value of cB are compared. Both make use of the fact that in the accepted temperature range the formation of byproducts is very low as long as cB is small. Therefore the feed rates of B and Z must be close to equimolar. With strutegy I a mixture of B and Z is fed which has a small excess of Z in the order of a few mole percent to compensate for pH change caused by side reactions. The total feed rate is pHcontrolled. As long as the main reaction runs there is demand for base to keep the PH. This again supplies fresh B. As the concentration of A decreases the reaction slows down and the feed rate is lowered until it eventually approaches zero. Strategy
II leaves the feed rates of B and Z largely independent in the first place. B is supp lied at a certain rate, which may be constant in the simplest case, until a certain value of conversion (e. g. 80%) of A has been reached. Base Z is fed at a rate that is a certain fraction of B (e. g. 90%) and the resulting change in pH is compensated by a second, but small, stream of Z. After the specified value of conversion has been reached the flow rate of B is lowered and, accordingly, the flow rate of Z. The reaction will proceed further until conversion of A is complete. From that point on pH will rise since acid is no longer formed but base Z is still fed. The end point of the reaction is detected when pH reaches a given bound.
Using a reactor model that includes material balances for those species whose reaction kine tics can be described with sufficient accuracy as well as an energy balance, both strategies can be tested in a dynamic simulation. In Fig. 1 the feed rates of reactant B and base Z are plotted together with the concentration cB in the mixture (strategy I: left, strategy II: right). In both cases cB can be kept at about the same level so that both strategies should work equally well. Therefore, the decision which strategy should be used is mainly determined by economic factors like costs for investment, maintenance or operating personnel. With respect to those aspects both strategies have there distinct advantages and drawbacks. Using strategy I the process is simple to control and the point of complete conversion is easily detected. It is necessary, however, that the concentrations of B and Z in the feed stream are tightly kept because otherwise a substantial buildup of B in the mixture can occur or, if there is too much Z in the feed stream, the reaction will slow down or even stop prematurely. Strategy II allows
Fig. 1: Comparison of two different strategies of process operation (see text for explanation).
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more flexible operation because the feed rates of B and Z ate not diily coupled. Beyond, it is not necessary to provide exact composition control of the feed stream. On the other hand, development and implementation of this strategy is more elaborate. Partwise constant feed rate of B as shown in Fig. 1 is definitely not optimal. The benefit of modelling and simulation for this problem is, besides a more profound understanding of the system, a reduction in the number of laboratory experiments since many of those operating conditions that lead to useless results can be identified in the simulation so that they are avoided during the experiments. Furthermore, the developed model is used in the design of the reactor vessel and heat exchange system. Contrary to the situation in process design a large variety of methods to control batch reactors have been published. These include algorithmic approaches, like advanced PID, adaptive, predictive (Eaton and Rawlings, 1990) or geometric nonlinear control (Isidori, 1989; Soroush and Kravaris, 1992; Wang et al., 1994) as well as approaches based on fuzzy logic (Peme, 1992) or neural nets (Willis et al., 1992). In many cases state estimation is a crucial part of the control system (e. g. Gilles, 1986; Kozub and MacGregor, 1992). Despite the large number of papers from academia few industrial applications of advanced control methods to batch reactors have been reported, and those are restricted to a small number of approaches. Considering three process examples we will evaluate the use of different approaches of advanced control. 01 examnle l_:The objective is to obtain tight temperature control for a jacketed stirred tank reactor that is operated in semibatch mode. The control problem is complicated by a number of facts. The frost is the increase of reaction mass during the feed phase that changes the heat capacity, and thereby the dominant time constant, by a factor of eight. Secondly, reaction rate and conversion related heat production are strongly temperature dependent and follow an Arrhenius function so that system dynamics is nonlinear. Another problem is the increase of solids during the reaction from about 5% by weight to 55% which leads to an increase in viscosity and a related decrease of the overall heat transfer coefficient at the reactor wall. Finally, heat removal from the reaction mixture is affected by changing heat transfer area as well as fouling of reactor walls. With respect to heat transfer area two effects may partly compensate each other. On one hand the area of liquid/wall contact is increased during the batch, so that heat removal is enhanced, but on the other hand the complementary gas/wall area decreases, so that less vapor can condense and therefore cooling by evaporation is inhibited. Reactor fouling has an adverse effect on heat removal and becomes worse from one batch to the next by formation of a solids layer on the wall. All of these effects cause a time variant behavior also of heat transfer coefficient and area. Conventional PID control with fixed parameters can not lead to satisfactory results for such system dynamics if good control performance is required. Therefore, a model based control concept has been used, a simpler form of which was suggested and implemented by a number of authors (Gilles, 1986; Juba and Hamer, 1986). In this control structure, the setpoint of the jacket temperature controller is driven by a model based controller which contains two observers for heat of reaction and heat transfer to the jacket, respectively. The observer for heat of reaction is based on a dynamic energy balance of the reactor vessel, while the heat transfer estimator is based on an equivalent model for the jacket. Figure 2 shows that the performance of the model based controller is significantly better than that of the PID-cascade, even more so since the latter had to be taken on manual occasionally, to prevent reactor temperature from drifting away even further. The performance of the model based controller was an essential prerequisite, though not the only one, for a substantial increase in productivity of the plant. Within the course of a year batch time was reduced by about 50% of the original value. An alternative way to solve this control problem is to use input/output-linearization (Isidori, 1989). With this approach a nonlinear state feedback law is constructed which leads to linear I/O-behavior of the process. For the problem of temperature control this approach is very simple to apply if the process is minimum phase and the unknown rate of heat production can be estimated. The resulting structure is similar to the PI-controller structure with feedforward described above and it is not surprising that both approaches lead to comparable results in a
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Fig. 2: Comparison of control performance of conventional PID and model based control. PI-CONTROLWITH FEEDFORWARDAND STATE ESTIMATION
IX>UNEARKING CONTFIOLWITH STATE ESTIMATION
Fig. 3: Comparison of PI-controller with fecdforward term (left) and I/O-linearizing control (right), both using an estimate of heat production. simulation with a reactor model that describes the real process very well. In Fig. 3 the results using both approaches are compared. The error in the estimation of the heat release has only a moderate effect on the nonlinear controller since an additional PI-controller has been used that accounts for plant-model mismatch. Operating experience revealed that the precision of the observers is sufficient to calculate conversion and solids content from the estimated heat of reaction on line, although they had not been designed explicitly for that purpose. In Fig. 4 the calculated solids content is compared to measured values during one batch. The quality of the estimation enables now an online trend display of these variables as opposed to the previous situation in which it was only measured once for each batch. Control examnle 2: In this example supervision and control of an exothermic process with a potentially hazardous autocatalytic decomposition is presented. Here it is essential to detect the onset of an autocatalytic decomposition at a very early stage, in time to take counter action. Multi-Kalman-Filter techniques as proposed by Gilles and coworkers (Gilles and Schuler, 1981; King and Gilles, 1986) are applied and suitably modified allowing a detection of undesired process states hours before runaway of the reaction. Furthermore, with the information of the filters, controller parameters are optimally adapted to the process dynamics resulting in a considerable reduction of batch time. This concept has resulted in time-optimal and safe operation of the chemical reactions under all process conditions (Peme, 1992).
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Fig. 4: Comparison of estimated solids content (solid line) with measurements (circles). The process considered is an exothermic chemical reaction operated in batch whose stoichiometry is described as follows: A+B
+
C
(-+)
Besides the desired reaction, product C may decompose autocatalytically C
-> D+E
(-&)
start reaction
C+D
-> 2D+E
(-AhA)
exothermic decomposition.
The start of the autocatalytic decomposition is favored by high temperatures. To avoid exothermic decomposition of product C, an upper limit is set for the reaction temperature T and care has to be taken to ensure efficient removal of heat so that the critical temperature is never reached. In a conventional control concept temperature used to be controlled by a PI0 controller with futed parameters acting on the jacket cooling system. Even slight changes in process parameters, however, have significant effect on the overall behavior of the plant, due to the highly nonlinear dependence of the reaction on process state, so that the conventional controller could not work satisfactorily at all process states. Because of this difficulty the slope of the temperature set point ramp was taken to be very small in order to avoid rapid changes in exothermic heat release. This measure lead to long batch times and a subsequent loss of productivity although the occurrence of dangerous peaks in reaction rate followed by temporal shut-down of the reaction could still not be completely avoided. The potential occurrence of local temperature peaks in the reactor that could start the decomposition made it necessary to develop a system for the early detection of such an event. The detection principle applied involves the construction of extended Kalman Filters for normal and each type of abnormal reaction. A decision stage based on a Bayes-Markov process discriminates between the different process states: normal and different types of disturbance. Thereby, probabilities derived from the Kalman filters are used to update a Markov process which models the probability of the process state to belong to one of the different process states and gives a priori estimates for this probability. This model contains rates for the transition between the different process states - an example is the transition rate from normal reaction to autocatalytic decomposition which is a function of temperature and concentrations. Different from the approach of King and Gilles (1986), only two models are used: one for the normal reaction and one for the disturbed processes. The second model contains parameters which are estimated along with the process states and can describe different types of disturbances at the same time. Much of the effort saved by reducing the number of filters has to be reinvested in constructing and calculating a more complicated filter. However, this approach
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will automatically deal with commonly observed model deviations. The ability of the Bayes-Markov formalism to detect undesired process states depends crucially on the choice of the covariance matrix of process noise Q. Whereas the covariance matrix of measurement noise R is chosen such that it corresponds to the actual measurement noise, Q and the initial values of the covariance matrix of the states PO are considered as design variables for the Kalman filter. PO is chosen such that the desired speed and necessary degree of model adaptation is achieved at the start of each batch. Q for the normal reaction is a compromise between speed of process tracking and accuracy of estimation. Care has to be taken choosing this variable for the adaptive filter which includes the decomposition and parameters to be estimated along with process states. For the normal reaction both models should be equivalent and give similar results. It has been observed, however, that the higher dimension of the second model leads to a much broader estimated probability density distribution p for the measured variables effecting the ability of the subsequent decision logic to discriminate between the two cases. A steeper probability density distribution can be obtained by assuming smaller values for the “process noise”, which, however will reduce the speed of model adaptation. The optimum Q of the Kalman filter is a compromise between speed of detecting autocatalytic decomposition on one side and certainty of discrimination and accuracy of estimation on the other side. This detection mechanism has been implemented in a process with a reaction mechanism similar to the one shown above. Figure 5 shows results for different batch samples: In the frost case, the reaction proceeded normally, in the second case, deviation of the start concentrations required the addition of one component during the heating phase. The resulting heat of mixing is interpreted by the detection mechanism as an additional potentially dangerous heat source and a warning is given. At this point the temperature is still low and an autocatalytic decomposition not very probable. The performance of the supervision and control system for exothermic batch reaction is encouraging for further practical applications, It should be mentioned, however, that models accurate enough to discriminate between different process states are not readily available and often difficult to establish, limiting a straightforward extension of this approach to other reactions. Control example 3: In the next example a process is considered in which crystal growth is unnonnal
prooess
mod-
0bww.r Symtam
o /._...........;_-
Fig. 5:Different situations in the detection of hazardous reaction states: normal operation (left), abnormal operation causing a warning by the detection system.
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controlled by Fise feeding of different reactants. The process is monitored by measuring the concentratron of certain tonic species in the dispersion via the voltage between two electrodes, similar to a pH-measurement. The properties of the crystals are strongly related to the amount of the different species used, but also by the order in which they are built into the solid. One way to control crystal structure is to move the concentration of a key component in the solution along a predefmed trajectory during the phase of crystal growth. Several conventional control concepts that had been used in the past failed to work properly. By an appropriate adjustment of parameters it had been possible to obtain satisfactory perforting point or along a mance at a single operation point, moving the process to another 0 trajectory lead to deterioration of control quality and thereby an ofF”” spec product. A detailed process analysis revealed that it is not possible to obtain the desired performance with conventional concepts. Advanced control concepts appeared to be promising yet it seemed not to be possible to find a solution that could be developed in the available time and that was simple to implement. Instead the use of fuzxy logic was favored, especially since expert lcnowledge was available. The control structure developed is similar to a PI-controller with fcedforward term and gain scheduling. Considering the fact that the plant model was not known exactly fuzzy methods seemed to be appropriate to design the parameter adaptation part of the controller, They permit fuzzy inversion of the product specific nonlinearity with minimum a priori knowledge: only three points of the nonlinearity have to be known. The calculation of the integral term is also done by fuzzy logic. The inputs for this part are the control error as well as its appropriately filtered time derivative. The feedforward term of the controller is designed in the conventional way. It has to compensate for fluctuations in the main feed streams. Satisfactory results were achieved immediately after implementation of this control concept on the process control computer for the reactor. After a short period of tuning the superiority of the new approach over the previously used algorithmic controller was demonstrated. Fig. 6 shows results for the old and new concept for a laboratory process. Currently all production reactors are being equipped with the new controller.
Time
Time
Fig. 6: Comparison between performance of conventional (left) and advanced PID-control using parameter adaptation by Fuzzy-logic (right). tor m
. .
Advanced control systems help to run batch processes automatically in most situations. It is often difficult, however, for the plant operators to understand how the process control system is working. Therefore they do not know what to do if an exceptional event occurs, like the failure of a pump or a valve. One possibility is to give the operator as much information as possible about the control system on the screen of the DCS. For control example 1 it has been
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helpful to supply not only information about the control action of the temperature controller, but also to display the estimated heat of reaction as well as the degree of conversion. Beyond this, a process simulator can help to understand basic physical and chemical relationships, the way the control system works, or specific phenomena that can only be observed during unusual situations. Thereby, the information given by the DCS can better be appreciated by the operator. A profound overview about possible applications of process simulators for operator training, the requirements for such a snnulator, as well as different possibilities for implementation has been given by Ho11and Schuler (1992). We will just show one solution that has been developed for the process described in control example 1, where a realistic reactor model could be used that also served to test different control schemes. The system was configured in a way that it appeared to be as similar to the real plant as possible. Therefore, the computer on which the simulation runs was linked to a DCS of the same type that was used at the plant site, and the software of that DCS was taken to be the interface to the simulation. Thereby, the user has the same opportunities to interact with the process as in the real plant. Beyond, different scenarios can be examined and it is simple to study the behavior of the control system. The result of one of those scenarios is plotted in Fig. 7, where a situation was simulated in which a short term failure of the reactant feed system is assumed that leads to a significant temperature drop. After restart of feeding the reaction is very slow due to the lower temperature so that a large amount of reactant builds up in the vessel which poses a substantial safety risk. As the temperature rises the reaction rate is increased in an uncontrolled way because the accumulated reactant undergoes conversion in a very short time
Fig. 7: Temperature and heat release curves in a simulated scenario during operator training. as can be seen by the extreme rise of temperature and heat release. Although such a situation can not occur in the real plant since restart of feeding is made impossible by the automated safety system once reactor temperature has dropped below a certain value, such a scenario is still very instructive for an operator, e. g. to show how much time there remains for restart of feeding. DISCUSSION AND CONCLUSIONS In the previous sections it was shown how dynamic simulation and advanced control methods can be used to design and control batch reactors to improve safety and profitability of production processes. For many projects the use of these techniques is still restricted to problems concerning the basic functionality of the process. In many cases the work on product quality related issues is hampered by the lack of appropriate process knowledge. However, like shown in the design as well as the first control example, it is often possible to use comparatively simple models in appropriate connection with experiments or experience from the production plant to gain more information about the process. Beyond, the need for optimal productivity and consistent product quality leads to systematic exploration of basic physical and chemical relationships that can be used for process modeling.
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The benefits gained by using dynamic simulation and advanced control methods, the effort
that has to be undertaken as well as current deficits can be summarized for the different areas of application as follows: Process design: Dynamic simulation is an efftcient tool among others (like statistical me
thods) for planmng and analysis of experiments during process development. It is useful to evaluate different hypotheses about reaction mechanisms and to determine relevant kinetic parameters. A model derived by such a procedure can be used to design at least those elements of a process that ensure its basic functionality (vessel and piping size, heating/cooling system) and helps as such to reduce the number of experiments that have to be conducted in pilot plants and thereby the time for development of a process. Safety and quality relevant aspects, like an exact feeding strategy, require high fidelity models that am often not available. But even in those cases a simulation can help to sort out feeding strategies that lead to difficult operating conditions and should therefore be avoided so that they do not need to be tested experimentally. The effort for the development and application of models that can be used in the design for basic functionality of a process is very moderate, and efficient simulation tools are readily available. However, quality related issues are still difficult to model adequately since kinetic mechanisms and parameters are not lrnown in many cases. In those cases a close interaction between simulation and experiment is most valuable. Being able to efficiently obtain kinetic data for process models that are used for design purposes will be crucial for future applications. Here, the integration of systematic methods for planning of experiments with model based analysis is needed to be able to determine reaction mechanisms. To be able to make the best use possible of the resulting models requires the integration of dynamic simulation and optimization in one software tool. Process control: The highest benefit of using advanced control methods is to obtain consi-
stent product quality, better yield and, in many cases, increased reactor capacity. The graph in Fig. 8 shows estimates of return on investment obtained from the use of advanced control for about 25 applications, most of which are operated in batch. The list of applications has been collected in a survey by NAMUR from different chemical companies in Germany (Weymans, 1994). It is far from being complete since only those examples have been included whose benefit was stated explicitly by the plant manager. Using techniques like those described in the previous section the effort for development and implementation of a control concept is reasonably low. In most cases the main part in a project is that for process analysis, whereas the actual control design part takes usually less time. The techniques mentioned can be implemented easily on standard distributed control systems. Techniques that require the use of additional process computers (e.g based on optimization) are not favored since reliability and maintenance over several years are difficult issues.
ROI [a] Fig. 8: Time for return on investment for a number of control application of different chemical companies.
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Operator training: The benefits of simulation based operator training, better understanding of process aud control system as well as training for exceptional situations, have been discussed above. The effort that is needed is still quite high, especially in those situations where an interaction between simulation computer and DCS is required. Besides the need for coordinated exchange of data which can be handled quite easily on different DCSs the main problem is lacking flexibility of DCSs with respect to specific requirements for operator training, like changing time base for slow motion or accelerated simulations. REFERENCES Douglas, J. M. (1988). s
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McGraw-Hill, N. Y.
Eaton, J. W. and J. B. Rawlings (1990). Feedback control of chemical processes using on-line optimization techniques. a. Cu, & 469479. Friedrich, M. and E. D. Gilles (1991). On a process control framework for quality assurance. r. Adv. Contr. of Chem. Proc.. ADa 1991, Toulouse, France, 235240. Gilles, E.D. and H. Schuler (1981). Zur frlihzeitigen Erkemmng gefihrlicher Reaktionszustinde in chemischen Reaktoren. Chem.?. Tech, 53, 673-682. Gilles, E. D. (1986). Some new approaches for controllmg complex processes in chemical engineering. In: k (M. Momri and T. J. McAvoy, eds.), Elsevier, 689-747. Holl, P. and H. Schuler (1992). Simulatoren zur Unterstiitzung der Prozea- und Betriebsftihrung. Chem.-Ina.-Tech, 64, 679-692. Isidori, A. (1989. Nonlinear Control Systems. Springer, Berlin, 2. ed. Juba, M. R. and J. W. Hamer (1986). Progress and challenges in batch process control. In: Chemical Process Control-CPC III (M. Morari and T. J. McAvoy, eds.), Elsevier, 139183. King, R. and E. D. Gilles (1986). Early detection of hazardous states in chemical reactors. . In: Bpr. IFAC Work&o nFault Detection and Safetv in Chemical Pl ants”, 137-143. Kozub, D. J. and J. F. MacGregor (1992). Feedback control of polymer quality in semi-batch copolymerization reactors. Chem.EnP Sci, a, 929-942. Peme, R. (1992). Supervision and Control of an Exothermic Batch Process. In: Prepr. 1992 IFAC/IFIP/IMACS Int. Sva on Artificial Intelligence in Real-Time Control, 553-557. Peme, R. (1992). Fuzzy Control Anwendungen in der chemisch-verfahrenstechnischen dustrie. Chem. Ing. Tech, 64, 824-825.
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Peme, R. (1994). Erfahrungen mit einem regelungstechnisch orientierten Simulationswerkzeug. In: ProzeDsimulation. (H. Schuler, ed.), VCH Weinheim (1994). Soroush, M. and C. Kravaris (1992). Nonlinear control of a batch polymerization reactor: An experimental study. AIChE J, %,1429-1448. Soroush, M. and C. Kravaris (1993a). Optimal design and operation of batch reactors. 1. Theoretical framework. Ind. Ena. Chem. Res,, 2,866-881. Soroush, M. and C. Kravaris (1993b). Optimal design and operation of batch reactors. 2. A case study. Ind. Eng Chem. Res,, %,882-893. Wang, Z. L., J. P. Corriou and F. Pla (1994). Nonlinear adaptive control of batch polymerization. ComD. l& Suppl. S397S401. Weymans, G. (1994). NAMUR Work Group “Funktionen der ProzeSftihrung”; presented at NAMUR-Hauptsitzung. Lahnstein, Germany. Willis, M. J., G. A. Montague, C. di Massimo, M. T. Tham and A. J. Morris (1992). Artificial neural networks in process estimation and control. Automatica. 28,118 l- 1187.
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DESIGN AND CONTROL OF BATCH REACTORS -ANINDusTRIALvIEWPOINTM. Friedrich and R. Perne Bayer AG, Zentmle Forschung, 5 1368 Leverkusen, Germany
ABSTRACT Design and control of batch reactors provide challenging problems with respect to basic functionalitv and safetv of a process as well as product quality and yield related issues. In this paper examples for ihe use of modelling, dynamic simulation and advanced control techniques for industrial problems are given. They are evaluated with respect to economic benefit as well as effort for development and implementation. KExwoRDs Batch reactors, process design, advanced control, operator training, dynamic simulation, model based control, fuzzy control INTRODUCTION A large number of processes in the production of polymers, specialty and fine chemicals, as well as pharmaceuticals are operated in batch. In many cases such batch plants are used for manufacturing a variety of products that exhibit significantly different characteristics as far as time for conversion, enthalpy of reaction, or viscosity are concerned, to name just a few. Furthermore, different objectives may be critical for different products to obtain safe and economic plant operation. In one case, because of safety reasons, the removal of heat of reaction may be the critical issue, whereas in other cases tight control along a temperature or concentration trajectory may be most important to satisfy quality specifications. Different product characteristics as well as different objectives require high versatility of process design and control. Like for continuous processes, by process design not only the design of equipment is meant, but also the design of process operation. For batch processes this includes the sequence of actions taken to obtain the desired product, e. g. initial loading and feeding of reactants as well as heating and cooling. In other words, design of operation leads to desired trajectories for temperature and concentrations. Whereas in most cases vessels and piping can be designed relatively easily to meet the needs also of multiproduct plants, the design of operation and control for batch plants poses challenging problems. As far as the control problem is concerned, one must also observe the economic restriction that it is often not possible to develop a dedicated control strategy for each product or to implement any control technique, no matter how complex it may be, on a distributed control system (DCS). Instead, control methods have to be used that are robust against product change, easy to implement and, even more important, easy to maintain. 5357
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With respect to these aspects the purpose of this paper is l
to share experiences with techniques for the design of operation and control of batch processes under industrial constraints,
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to give estimates of the economic benefit of using such techniques,
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to evaluate the effort for development and implementation,
. and to outline deficits as well as needs for further research and development. Such an evaluation must necessarily rely on personal experiences that are, of course, limited to a certain number of techniques. So it can obviously not be of general validity. We do hope, however, to contribute to an open discussion about what is needed for the efficient design and control of batch reactors. Scope All our discussion is limited to the operation of a single batch reactor. It is not directed towards the problem of scheduling batches for multiple products in a sequence of different processing units (reactors, separation, and conditioning). Q&ctives and worl&
areas
The economic performance of batch processes is determined by a number of factors. We discern three different types of objectives to obtain an economically beneficial batch reactor. These are to ensure basic functionality, l
l
process safety,
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and optimum yield at the desired product quality.
Ensuring basic functionality means that vessel, piping, and all other equipment, like sensors and actuators, are designed in such a way that the process can function properly. This includes appropriate choice of vessel size and material, supply of raw materials at the necessary rates, as well as addition or removal of heat. These aspects may be intimately linked, like the choice of vessel material and design of heating/cooling system since heat exchange with a steel reactor is much easier than with a glass-lined reactor, especially since the lining may be prohibitive to the construction of internal heat exchangers or pump-arounds for external heat exchangers. The treatment of process sajkty in this paper is restricted to avoiding high temperatures and, thereby, high pressures that may be caused by a number of effects, like pump or valve failure, highly exothermic side reactions, or operational errors. Product quality is determined by very different properties for different types of products.
Whereas the quality of a mixture of fluids is characterized by its composition, the quality of a polymer may also be affected by chain length distribution or particle size in the case of emulsion or suspension polymerization. Similarly, other quality related variables are important for dispersions of crystals, dyes or fibres. Generally, product quality depends on the thermodynamic state of the system under consideration and may be expressed as a function of it. Several authors have made use of this fact for quality oriented process design and control (e. g. Kozub and MacGregor, 1992; Soroush and Kravaris, 1993a), and a more general treatment has been given by Friedrich and Gilles (1991). Using such a definition, yield can be expressed as the amount of product satisfying given quality specifications that is obtained from a given amount of reactants. To achieve these goals three different working areas may be discerned during planning, start up and operation of the process. Besides process design and control, as mentioned earlier, education of the operating personnel is an important aspect despite the fact that many processes are automated to a large extent. During the design phase laboratory recipes for the desired products have to be adapted and optimized so that under economic restrictions concerning vessel type, size, and material as well as heat exchange system size feasible operating conditions can be found.
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By developing an appropriate connof system one has to ensure that the desired operating conditions can be maintained as closely as possible during process operation. Operator training helps making plant personnel familiar with basic physical and chemical relationships, plant equipment, and automation system as well as safety and quality related events that require proper manual action.
In the following sections we will give examples for the way the objectives mentioned, basic functionality, process safety, and product quality can be achieved by the steps of action taken during process design, control system design, as well as operator instruction. The experience gained during the work on these examples is evaluated afterwards, and conclusions drawn from this experience are given at the end. TECHNIQUES AND TOOLS FOR DESIGN, CONTROL, AND EDUCATION cess Design The design problem for a chemical reactor can be viewed as the problem of translating a number of specifications concerning the goals that are considered here into a set of specifications for the reactor as well as material end energy flows into the reactor. Besides, it is necessary to observe constraints, that are given by plant operability, availability of utilities, safety considerations, capital cost and others. For continuous plants a number of these issues have been addressed in industrial applications for more than twenty years by steady state flowsheet calculations, often in connection with optimization. For those cases, where it is not possible to formalize the design problem completely so that it can be solved as a whole using optimization techniques there exist well established procedures to combine empirical with mathematically formal design steps (e. g. Douglas, 1988). An analogous procedure for batch processes must lead to the use of dynamic simulation and optimization because of the inherently dynamic nature of a batch process. A formal procedure for integrated design and control of batch processes has only recently been published by Soroush and Kravaris (1993a,b). They recognized that it is helpful to decompose the complete problem into subproblems before starting the optimization. The decomposition leads to an optimization problem in which optimal trajectories for those variables are calculated that have the strongest impact on product quality. If a formal quality function as described in the previous section is used, an optimal trajectory for this function can be calculated. The optimizing variables are in any case reactor temperature and/on: oncentrations of raw materials. Once the trajectories for the optimizing variables are calculated one has to provide, in a second step, means that those trajectories can be realized by supplying appropriate piping, heating/cooling system as well as an adequate control system. In industrial practice one usually has to resort to empirical methods for a number of reasons that include lack of knowledge about reaction mechanism and kinetics, availability of efficient software tools (especially for dynamic optimization) and restrictions that can not easily be formalized. However, the use of dynamic simulations for process design is becoming increasingly important. Design examule: To illustrate the value of dynamic simulation for the design and control of a batch reactor and the analysis of the interdependence of both consider the following example of the production of a specialty chemical. It involves the exothermic reaction of species A and B to product P in aqueous phase where a proton H+ and a base Y’ arc released: A+B
-_) P+H+
+Y-.
It is known from laboratory experiments that it is crucial for product quality to keep pH and temperature of the mixture within a certain range and to keep the concentration of B at a very low level so that byproduct formation is inhibited. These restrictions together with the need for the shortest batch time possible form the objectives for design calculations. The objectives can be achieved in a batch reactor by loading the vessel initially with reactant A in water and feeding reactant B as well as a strong base 2 as the neutralizing agent. The performance of the process is determined by the amount of byproducts as well as batch time,
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whereas the performance determining variables are concentration cB of B, pH, and temperature. Although the relation between these variables and the amount of byproducts is not known explicitly, one can design the process so that cB, pH, and temperature are within the tolerable ranges. Clearly, the feed rates of B and the base 2 as well as feed rate of cooling water are the manipulated variables. Whereas there must be a strong relationship between the feed rates of B and Z, the problem of keeping the desired temperature can be treated independently as long as the feed rates are such that the capacity of the cooling system is not exceeded. The most difficult problem during process operation is to determine concentration cB since it can not be measured directly. Here, two ways of running the process without knowing the exact value of cB are compared. Both make use of the fact that in the accepted temperature range the formation of byproducts is very low as long as cB is small. Therefore the feed rates of B and Z must be close to equimolar. With strutegy I a mixture of B and Z is fed which has a small excess of Z in the order of a few mole percent to compensate for pH change caused by side reactions. The total feed rate is pHcontrolled. As long as the main reaction runs there is demand for base to keep the PH. This again supplies fresh B. As the concentration of A decreases the reaction slows down and the feed rate is lowered until it eventually approaches zero. Strategy
II leaves the feed rates of B and Z largely independent in the first place. B is supp lied at a certain rate, which may be constant in the simplest case, until a certain value of conversion (e. g. 80%) of A has been reached. Base Z is fed at a rate that is a certain fraction of B (e. g. 90%) and the resulting change in pH is compensated by a second, but small, stream of Z. After the specified value of conversion has been reached the flow rate of B is lowered and, accordingly, the flow rate of Z. The reaction will proceed further until conversion of A is complete. From that point on pH will rise since acid is no longer formed but base Z is still fed. The end point of the reaction is detected when pH reaches a given bound.
Using a reactor model that includes material balances for those species whose reaction kine tics can be described with sufficient accuracy as well as an energy balance, both strategies can be tested in a dynamic simulation. In Fig. 1 the feed rates of reactant B and base Z are plotted together with the concentration cB in the mixture (strategy I: left, strategy II: right). In both cases cB can be kept at about the same level so that both strategies should work equally well. Therefore, the decision which strategy should be used is mainly determined by economic factors like costs for investment, maintenance or operating personnel. With respect to those aspects both strategies have there distinct advantages and drawbacks. Using strategy I the process is simple to control and the point of complete conversion is easily detected. It is necessary, however, that the concentrations of B and Z in the feed stream are tightly kept because otherwise a substantial buildup of B in the mixture can occur or, if there is too much Z in the feed stream, the reaction will slow down or even stop prematurely. Strategy II allows
Fig. 1: Comparison of two different strategies of process operation (see text for explanation).
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more flexible operation because the feed rates of B and Z ate not diily coupled. Beyond, it is not necessary to provide exact composition control of the feed stream. On the other hand, development and implementation of this strategy is more elaborate. Partwise constant feed rate of B as shown in Fig. 1 is definitely not optimal. The benefit of modelling and simulation for this problem is, besides a more profound understanding of the system, a reduction in the number of laboratory experiments since many of those operating conditions that lead to useless results can be identified in the simulation so that they are avoided during the experiments. Furthermore, the developed model is used in the design of the reactor vessel and heat exchange system. Contrary to the situation in process design a large variety of methods to control batch reactors have been published. These include algorithmic approaches, like advanced PID, adaptive, predictive (Eaton and Rawlings, 1990) or geometric nonlinear control (Isidori, 1989; Soroush and Kravaris, 1992; Wang et al., 1994) as well as approaches based on fuzzy logic (Peme, 1992) or neural nets (Willis et al., 1992). In many cases state estimation is a crucial part of the control system (e. g. Gilles, 1986; Kozub and MacGregor, 1992). Despite the large number of papers from academia few industrial applications of advanced control methods to batch reactors have been reported, and those are restricted to a small number of approaches. Considering three process examples we will evaluate the use of different approaches of advanced control. 01 examnle l_:The objective is to obtain tight temperature control for a jacketed stirred tank reactor that is operated in semibatch mode. The control problem is complicated by a number of facts. The frost is the increase of reaction mass during the feed phase that changes the heat capacity, and thereby the dominant time constant, by a factor of eight. Secondly, reaction rate and conversion related heat production are strongly temperature dependent and follow an Arrhenius function so that system dynamics is nonlinear. Another problem is the increase of solids during the reaction from about 5% by weight to 55% which leads to an increase in viscosity and a related decrease of the overall heat transfer coefficient at the reactor wall. Finally, heat removal from the reaction mixture is affected by changing heat transfer area as well as fouling of reactor walls. With respect to heat transfer area two effects may partly compensate each other. On one hand the area of liquid/wall contact is increased during the batch, so that heat removal is enhanced, but on the other hand the complementary gas/wall area decreases, so that less vapor can condense and therefore cooling by evaporation is inhibited. Reactor fouling has an adverse effect on heat removal and becomes worse from one batch to the next by formation of a solids layer on the wall. All of these effects cause a time variant behavior also of heat transfer coefficient and area. Conventional PID control with fixed parameters can not lead to satisfactory results for such system dynamics if good control performance is required. Therefore, a model based control concept has been used, a simpler form of which was suggested and implemented by a number of authors (Gilles, 1986; Juba and Hamer, 1986). In this control structure, the setpoint of the jacket temperature controller is driven by a model based controller which contains two observers for heat of reaction and heat transfer to the jacket, respectively. The observer for heat of reaction is based on a dynamic energy balance of the reactor vessel, while the heat transfer estimator is based on an equivalent model for the jacket. Figure 2 shows that the performance of the model based controller is significantly better than that of the PID-cascade, even more so since the latter had to be taken on manual occasionally, to prevent reactor temperature from drifting away even further. The performance of the model based controller was an essential prerequisite, though not the only one, for a substantial increase in productivity of the plant. Within the course of a year batch time was reduced by about 50% of the original value. An alternative way to solve this control problem is to use input/output-linearization (Isidori, 1989). With this approach a nonlinear state feedback law is constructed which leads to linear I/O-behavior of the process. For the problem of temperature control this approach is very simple to apply if the process is minimum phase and the unknown rate of heat production can be estimated. The resulting structure is similar to the PI-controller structure with feedforward described above and it is not surprising that both approaches lead to comparable results in a
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Fig. 3: Comparison of PI-controller with fecdforward term (left) and I/O-linearizing control (right), both using an estimate of heat production. simulation with a reactor model that describes the real process very well. In Fig. 3 the results using both approaches are compared. The error in the estimation of the heat release has only a moderate effect on the nonlinear controller since an additional PI-controller has been used that accounts for plant-model mismatch. Operating experience revealed that the precision of the observers is sufficient to calculate conversion and solids content from the estimated heat of reaction on line, although they had not been designed explicitly for that purpose. In Fig. 4 the calculated solids content is compared to measured values during one batch. The quality of the estimation enables now an online trend display of these variables as opposed to the previous situation in which it was only measured once for each batch. Control examnle 2: In this example supervision and control of an exothermic process with a potentially hazardous autocatalytic decomposition is presented. Here it is essential to detect the onset of an autocatalytic decomposition at a very early stage, in time to take counter action. Multi-Kalman-Filter techniques as proposed by Gilles and coworkers (Gilles and Schuler, 1981; King and Gilles, 1986) are applied and suitably modified allowing a detection of undesired process states hours before runaway of the reaction. Furthermore, with the information of the filters, controller parameters are optimally adapted to the process dynamics resulting in a considerable reduction of batch time. This concept has resulted in time-optimal and safe operation of the chemical reactions under all process conditions (Peme, 1992).
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Fig. 4: Comparison of estimated solids content (solid line) with measurements (circles). The process considered is an exothermic chemical reaction operated in batch whose stoichiometry is described as follows: A+B
+
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Besides the desired reaction, product C may decompose autocatalytically C
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The start of the autocatalytic decomposition is favored by high temperatures. To avoid exothermic decomposition of product C, an upper limit is set for the reaction temperature T and care has to be taken to ensure efficient removal of heat so that the critical temperature is never reached. In a conventional control concept temperature used to be controlled by a PI0 controller with futed parameters acting on the jacket cooling system. Even slight changes in process parameters, however, have significant effect on the overall behavior of the plant, due to the highly nonlinear dependence of the reaction on process state, so that the conventional controller could not work satisfactorily at all process states. Because of this difficulty the slope of the temperature set point ramp was taken to be very small in order to avoid rapid changes in exothermic heat release. This measure lead to long batch times and a subsequent loss of productivity although the occurrence of dangerous peaks in reaction rate followed by temporal shut-down of the reaction could still not be completely avoided. The potential occurrence of local temperature peaks in the reactor that could start the decomposition made it necessary to develop a system for the early detection of such an event. The detection principle applied involves the construction of extended Kalman Filters for normal and each type of abnormal reaction. A decision stage based on a Bayes-Markov process discriminates between the different process states: normal and different types of disturbance. Thereby, probabilities derived from the Kalman filters are used to update a Markov process which models the probability of the process state to belong to one of the different process states and gives a priori estimates for this probability. This model contains rates for the transition between the different process states - an example is the transition rate from normal reaction to autocatalytic decomposition which is a function of temperature and concentrations. Different from the approach of King and Gilles (1986), only two models are used: one for the normal reaction and one for the disturbed processes. The second model contains parameters which are estimated along with the process states and can describe different types of disturbances at the same time. Much of the effort saved by reducing the number of filters has to be reinvested in constructing and calculating a more complicated filter. However, this approach
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will automatically deal with commonly observed model deviations. The ability of the Bayes-Markov formalism to detect undesired process states depends crucially on the choice of the covariance matrix of process noise Q. Whereas the covariance matrix of measurement noise R is chosen such that it corresponds to the actual measurement noise, Q and the initial values of the covariance matrix of the states PO are considered as design variables for the Kalman filter. PO is chosen such that the desired speed and necessary degree of model adaptation is achieved at the start of each batch. Q for the normal reaction is a compromise between speed of process tracking and accuracy of estimation. Care has to be taken choosing this variable for the adaptive filter which includes the decomposition and parameters to be estimated along with process states. For the normal reaction both models should be equivalent and give similar results. It has been observed, however, that the higher dimension of the second model leads to a much broader estimated probability density distribution p for the measured variables effecting the ability of the subsequent decision logic to discriminate between the two cases. A steeper probability density distribution can be obtained by assuming smaller values for the “process noise”, which, however will reduce the speed of model adaptation. The optimum Q of the Kalman filter is a compromise between speed of detecting autocatalytic decomposition on one side and certainty of discrimination and accuracy of estimation on the other side. This detection mechanism has been implemented in a process with a reaction mechanism similar to the one shown above. Figure 5 shows results for different batch samples: In the frost case, the reaction proceeded normally, in the second case, deviation of the start concentrations required the addition of one component during the heating phase. The resulting heat of mixing is interpreted by the detection mechanism as an additional potentially dangerous heat source and a warning is given. At this point the temperature is still low and an autocatalytic decomposition not very probable. The performance of the supervision and control system for exothermic batch reaction is encouraging for further practical applications, It should be mentioned, however, that models accurate enough to discriminate between different process states are not readily available and often difficult to establish, limiting a straightforward extension of this approach to other reactions. Control example 3: In the next example a process is considered in which crystal growth is unnonnal
prooess
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0bww.r Symtam
o /._...........;_-
Fig. 5:Different situations in the detection of hazardous reaction states: normal operation (left), abnormal operation causing a warning by the detection system.
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controlled by Fise feeding of different reactants. The process is monitored by measuring the concentratron of certain tonic species in the dispersion via the voltage between two electrodes, similar to a pH-measurement. The properties of the crystals are strongly related to the amount of the different species used, but also by the order in which they are built into the solid. One way to control crystal structure is to move the concentration of a key component in the solution along a predefmed trajectory during the phase of crystal growth. Several conventional control concepts that had been used in the past failed to work properly. By an appropriate adjustment of parameters it had been possible to obtain satisfactory perforting point or along a mance at a single operation point, moving the process to another 0 trajectory lead to deterioration of control quality and thereby an ofF”” spec product. A detailed process analysis revealed that it is not possible to obtain the desired performance with conventional concepts. Advanced control concepts appeared to be promising yet it seemed not to be possible to find a solution that could be developed in the available time and that was simple to implement. Instead the use of fuzxy logic was favored, especially since expert lcnowledge was available. The control structure developed is similar to a PI-controller with fcedforward term and gain scheduling. Considering the fact that the plant model was not known exactly fuzzy methods seemed to be appropriate to design the parameter adaptation part of the controller, They permit fuzzy inversion of the product specific nonlinearity with minimum a priori knowledge: only three points of the nonlinearity have to be known. The calculation of the integral term is also done by fuzzy logic. The inputs for this part are the control error as well as its appropriately filtered time derivative. The feedforward term of the controller is designed in the conventional way. It has to compensate for fluctuations in the main feed streams. Satisfactory results were achieved immediately after implementation of this control concept on the process control computer for the reactor. After a short period of tuning the superiority of the new approach over the previously used algorithmic controller was demonstrated. Fig. 6 shows results for the old and new concept for a laboratory process. Currently all production reactors are being equipped with the new controller.
Time
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Fig. 6: Comparison between performance of conventional (left) and advanced PID-control using parameter adaptation by Fuzzy-logic (right). tor m
. .
Advanced control systems help to run batch processes automatically in most situations. It is often difficult, however, for the plant operators to understand how the process control system is working. Therefore they do not know what to do if an exceptional event occurs, like the failure of a pump or a valve. One possibility is to give the operator as much information as possible about the control system on the screen of the DCS. For control example 1 it has been
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helpful to supply not only information about the control action of the temperature controller, but also to display the estimated heat of reaction as well as the degree of conversion. Beyond this, a process simulator can help to understand basic physical and chemical relationships, the way the control system works, or specific phenomena that can only be observed during unusual situations. Thereby, the information given by the DCS can better be appreciated by the operator. A profound overview about possible applications of process simulators for operator training, the requirements for such a snnulator, as well as different possibilities for implementation has been given by Ho11and Schuler (1992). We will just show one solution that has been developed for the process described in control example 1, where a realistic reactor model could be used that also served to test different control schemes. The system was configured in a way that it appeared to be as similar to the real plant as possible. Therefore, the computer on which the simulation runs was linked to a DCS of the same type that was used at the plant site, and the software of that DCS was taken to be the interface to the simulation. Thereby, the user has the same opportunities to interact with the process as in the real plant. Beyond, different scenarios can be examined and it is simple to study the behavior of the control system. The result of one of those scenarios is plotted in Fig. 7, where a situation was simulated in which a short term failure of the reactant feed system is assumed that leads to a significant temperature drop. After restart of feeding the reaction is very slow due to the lower temperature so that a large amount of reactant builds up in the vessel which poses a substantial safety risk. As the temperature rises the reaction rate is increased in an uncontrolled way because the accumulated reactant undergoes conversion in a very short time
Fig. 7: Temperature and heat release curves in a simulated scenario during operator training. as can be seen by the extreme rise of temperature and heat release. Although such a situation can not occur in the real plant since restart of feeding is made impossible by the automated safety system once reactor temperature has dropped below a certain value, such a scenario is still very instructive for an operator, e. g. to show how much time there remains for restart of feeding. DISCUSSION AND CONCLUSIONS In the previous sections it was shown how dynamic simulation and advanced control methods can be used to design and control batch reactors to improve safety and profitability of production processes. For many projects the use of these techniques is still restricted to problems concerning the basic functionality of the process. In many cases the work on product quality related issues is hampered by the lack of appropriate process knowledge. However, like shown in the design as well as the first control example, it is often possible to use comparatively simple models in appropriate connection with experiments or experience from the production plant to gain more information about the process. Beyond, the need for optimal productivity and consistent product quality leads to systematic exploration of basic physical and chemical relationships that can be used for process modeling.
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The benefits gained by using dynamic simulation and advanced control methods, the effort
that has to be undertaken as well as current deficits can be summarized for the different areas of application as follows: Process design: Dynamic simulation is an efftcient tool among others (like statistical me
thods) for planmng and analysis of experiments during process development. It is useful to evaluate different hypotheses about reaction mechanisms and to determine relevant kinetic parameters. A model derived by such a procedure can be used to design at least those elements of a process that ensure its basic functionality (vessel and piping size, heating/cooling system) and helps as such to reduce the number of experiments that have to be conducted in pilot plants and thereby the time for development of a process. Safety and quality relevant aspects, like an exact feeding strategy, require high fidelity models that am often not available. But even in those cases a simulation can help to sort out feeding strategies that lead to difficult operating conditions and should therefore be avoided so that they do not need to be tested experimentally. The effort for the development and application of models that can be used in the design for basic functionality of a process is very moderate, and efficient simulation tools are readily available. However, quality related issues are still difficult to model adequately since kinetic mechanisms and parameters are not lrnown in many cases. In those cases a close interaction between simulation and experiment is most valuable. Being able to efficiently obtain kinetic data for process models that are used for design purposes will be crucial for future applications. Here, the integration of systematic methods for planning of experiments with model based analysis is needed to be able to determine reaction mechanisms. To be able to make the best use possible of the resulting models requires the integration of dynamic simulation and optimization in one software tool. Process control: The highest benefit of using advanced control methods is to obtain consi-
stent product quality, better yield and, in many cases, increased reactor capacity. The graph in Fig. 8 shows estimates of return on investment obtained from the use of advanced control for about 25 applications, most of which are operated in batch. The list of applications has been collected in a survey by NAMUR from different chemical companies in Germany (Weymans, 1994). It is far from being complete since only those examples have been included whose benefit was stated explicitly by the plant manager. Using techniques like those described in the previous section the effort for development and implementation of a control concept is reasonably low. In most cases the main part in a project is that for process analysis, whereas the actual control design part takes usually less time. The techniques mentioned can be implemented easily on standard distributed control systems. Techniques that require the use of additional process computers (e.g based on optimization) are not favored since reliability and maintenance over several years are difficult issues.
ROI [a] Fig. 8: Time for return on investment for a number of control application of different chemical companies.
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Operator training: The benefits of simulation based operator training, better understanding of process aud control system as well as training for exceptional situations, have been discussed above. The effort that is needed is still quite high, especially in those situations where an interaction between simulation computer and DCS is required. Besides the need for coordinated exchange of data which can be handled quite easily on different DCSs the main problem is lacking flexibility of DCSs with respect to specific requirements for operator training, like changing time base for slow motion or accelerated simulations. REFERENCES Douglas, J. M. (1988). s
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Peme, R. (1994). Erfahrungen mit einem regelungstechnisch orientierten Simulationswerkzeug. In: ProzeDsimulation. (H. Schuler, ed.), VCH Weinheim (1994). Soroush, M. and C. Kravaris (1992). Nonlinear control of a batch polymerization reactor: An experimental study. AIChE J, %,1429-1448. Soroush, M. and C. Kravaris (1993a). Optimal design and operation of batch reactors. 1. Theoretical framework. Ind. Ena. Chem. Res,, 2,866-881. Soroush, M. and C. Kravaris (1993b). Optimal design and operation of batch reactors. 2. A case study. Ind. Eng Chem. Res,, %,882-893. Wang, Z. L., J. P. Corriou and F. Pla (1994). Nonlinear adaptive control of batch polymerization. ComD. l& Suppl. S397S401. Weymans, G. (1994). NAMUR Work Group “Funktionen der ProzeSftihrung”; presented at NAMUR-Hauptsitzung. Lahnstein, Germany. Willis, M. J., G. A. Montague, C. di Massimo, M. T. Tham and A. J. Morris (1992). Artificial neural networks in process estimation and control. Automatica. 28,118 l- 1187.