On-Line Process Monitoring, Control and Supervision for an Industrial Polymerization Process

Copyright © IFAC On-line Fault Detection and Supervision in the Chemical Process Industries, Delaware, USA, 1992

ON-LINE PROCESS MONITORING, CONTROL AND SUPERVISION FOR AN INDUSTRIAL POLYMERIZATION PROCESS M.T. Vester Department of Process Technology, DSM Research, P.O. Box 18, 6160 MD Geleen, The Netherlands

Abstract. In industrial processes many key process variables (like conversions, selectivities, concentrations, viscosities etc.) can not be measured adequately (accurately and continuously). This paper presents the application of a Kalman filter to obtain continuous monitoring of such a key variable, which was otherwise not continuously measurable. A kinetic process model was developed and corrected with plant data information to estimate the conversion of the polymerization of butadiene into polybutadiene for a commercial-scale reactor. After tuning and testing for robustness a statistical evaluation of the estimator output has been performed, with a positive result. Given the estimator performance the possible applications range from monitoring and control to supervision of the process cooling margin. The applications ultimately chosen will depend on the estimator performance during a period of passive monitoring of both conversion and cooling margin. Keywords. monitoring; Kalman filters; plastics industry; industrial control; process supervision.

of derived information is for instance bound up with the circumstance that many key process variables (conversion, selectivity, concentration, viscosity, acid number etc.) can very often not be measured continuously, accurately or automatically. Deriving them from basic measurements (temperature, pressure, flow) provides process information otherwise unknown. This stresses the importance of techniques for the estimation of key process variables.

INTRODUCTION The increasing importance of human safety, the growing emphasis on environment protection, energy and materials conservation, along with the focus on higher quality and more specialized products, all lead to a higher technological complexity. Given this change in the context of the process industries, advanced control, on-line process diagnosis, supervision and optimization will play an important role. The application of these techniques requires adequate process information to build upon. An important step in the direction of higher level or more advanced control of processes therefore lies in providing more, more accurate and more reliable information.

This paper focusses on the application and evaluation of an estimation technique in the context of an industrial batch polymerization process: on-line monitoring making use of a Kalman filter (Kalman, 1960). Where inferential control pretends to derive unmeasured control variables from basic measurements on the basis of deterministic process knowledge, application of the Kalman filter technique makes use of a confrontation of process knowledge and process data. The safe and efficient control of an industrial batch polymerization reactor is a difficult task as the measurement of key variables as conversion and viscosity is often impossible, expensive or time-consuming (Eli~abe, 1988; MacGregor, 1984). Because of the poor information available, a safe margin is

At first glance, two kinds of process information can be distinguished: off-line information (design specifications, mechanistic or experimental models, experience etc.) and on-line information (measured data). Techniques for on-line estimation of unmeasurable process variables, on-line parameter estimation, observation of mass and energy balances, reconciliation of plant data etc. are all aimed at combining the two kinds of available information into a third kind, which we shall name 'derived information'. The importance

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required for exothermic reactions between the polymerization heat and the cooling capacity. The margin can be reduced by on-line monitoring of key variables, increasing productivity and maintaining safety (Peme, 1990).

most relevant overall reaction kinetics. For this purpose the model was divided into two phases: a first phase with monomer dispersed in water and absorbed into latex particles, and a second phase, where all the monomer has been absorbed into the latex particles. Some pilot plant data for isothermal batches and several normal production scale batches were used for model tuning. Given the reaction temperature, the most important variables predicted by the model are the conversion, the conversion rate, which is proportional to the heat of polymerization, and the pressure profile. The profiles predicted by the model are very realistic. However, as different batches can display different behaviour, e.g. due to fouling, inhibition, less active initiator, insufficient cooling capacity etc., the model can diverge from actual batch results.

In the application considered, a kinetic model of the process is developed to estimate the reaction conversion. The predicted conversion rate is corrected by means of a feedback mechanism using plant measurements. The feedback correction principle used is the Kalman filter.

THE PROCESS The following work is devoted to the exothermic batch polymerization reaction of butadiene (BTD) to polybutadiene (PB). This emulsion polymerization (Weerts, 1990) is one step in the preparation of ABS plastic. Because of their importance, the reaction kinetics have been studied extensively. However, the theory is still developing.

To be able to monitor actual conversions in the plant accurately, the model has to be corrected for unmodelled dynamics, e.g. due to simplifications, disturbances etc. In this application the pressure has been used as a correction variable. In the model the pressure is calculated from a Flory-Huggins relation and depends on reactor composition and temperature. The reactor composition is completely determined by the conversion. Therefore, accepting the temperature measurement as a model input, the difference between the predicted and the measured pressure is a measure ' of the correction that has to be applied to the predicted conversion. Using the Kalman filter technique, the conversion is corrected proportionally to the difference in measured and predicted pressure. Figure 1 shows the scheme of the conversion estimator.

The polymerization of BTD in PB is accomplished by mixing the mono mer BTD with a soap solution in a closed reactor (Vester, 1990). After initial heating, the exothermic reaction starts and the reactor has to be cooled. The reactor temperature is kept on a desired trajectory by means of adjusting the coolant water flow. After a desired degree of conversion of BTD into PB has been reached, the reaction is stopped with the addition of a radical scavenger. During the process several process variables are measured continuously: temperatures of the coolant flow into and out of the reactor, reactor temperatures, reactor pressure and coolant water flow. A crucial variable, however, is the conversion of BTD into PB. The actual conversion determines the state of the batch process. The conversion is measured by means of off-line gravimetric determination of the solids content of a sample, which is a time-consuming and operator unfriendly analysis. The samples are taken four to five times per batch with large time intervals. This implies that the operator has no continuous information on the actual state of the process. Active conversion monitoring would therefore provide means to enhance batch efficiency and improve safety.

pressure measurement

pressure estimation

coaection

conversion e8timation +

coaected ' - - - - - - -----i conversion estimation

In order to predict the reaction conversion, the reaction kinetics were modelled. As indicated above, the emulsion polymerization of BTD into PB is complex, making the development of a complete mechanistic model impractible. Therefore the choice was made to model only the

Fig. 1. Scheme of estimator. In this application the initial estimation of the conversion at the start of the batch is simple: zero. Because of the high accuracy of the initial estimate, the process state covariance matrix P

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in the Kalman filter can be relatively small. P is the estimation of the state prediction error and indicates the need for model correction by means of measurements. As the dynamic behaviour of a batch process is continuously changing, the P matrix should never become too small: divergence could occur. Periodic resetting is undesired, because of possible strong fluctuations in the conversion estimation due to the filter. For this purpose the P matrix was chosen constant over the whole batch.

The heat of polymerization is also estimated. All three figures are obtained with the same parameter set. The visual agreement between the estimated and the measured conversion is good and does not call for expansion of the kinetic model or fme-tuning. The predicted profiles for the heat of polymerization correspond with the staff expectations based on experience with the coolant energy consumptions during batches.

STATISTICAL EVALUATION After the development of the Kalman filter and some robustness tests for measurement biasses and computer breakdown, it was handed over to the plant staff. There the estimator was tested for its accuracy and reliability by comparing the conversion estimations for 47 batches with the corresponding manual analyses. This resulted in a statistical evaluation of the estimator performance. The results are given in table 1.

- -- - ... - - -- ,. " ,- ,', · _ - --

- - - - --.1

TABLE 1 Results of Statistical Evaluation of Estimator Performance

absolute error (%)

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.- -, .., ...

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-

interval number

mean

standard deviation

interval 1

no measurements

--'1

Fig. 2. Off-line model validation 1

interval 2

2.9

4.0

interval 3

-0.7

3.4

interval 4

1.6

5.5

interval 5

-0. 1

3.6

interval 6

-2.5

5.3

interval 7

-0.8

5.7

interval 8

1.6

4.1

interval 9

-0.4

1.5

-0.1

I 1.6

over all intervals

11

I

TUNING RESULTS In Fig. 2 the results of the off-line tuning for three typical batches are shown. Horizontally, the batch time is presented. On the vertical scale the profiles for three variables are plotted (after proper scaling). The dots are the measured conversion values, the line through them is the estimated conversion. The pressure profile is estimated and nearly equals the measured one.

The absolute error is the difference between the estimated conversion and the measured conversion (both in %) at a certain time during the batch. In table 1 all the measurements made during the 47 batches are classified into equidistant intervals of the measured conversion with a total span of appr. 100 %. During the

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the reaction heat does not exceed the cooling capacity. Therefore a fast indication of such a situation is desirable for it would increase process safety. As the first manual sample is taken only in conversion interval 2, the estimator is able to provide an earlier indication on such faster process take-off. The second abnormal process condition could be a situation where a batch which does not start up well, e.g. due to insufficient initiator dosing. For both situations, however, at the start of the batch the pressure, which is used by the estimator as a correcting variable on the model predictions, decreases very slowly. This implies that little information is contained in the pressure profile. Therefore extreme care has to be taken that the estimator does not present a normal conversion profile during abnormal process conditions. Possible solutions to this problem are to extend the estimator with more correcting process measurements: see also the above remark on reducing the standard deviation of the estimation error.

first conversion interval the solids content of the batch is so small that no reliable sampling can be perfonned. Therefore the only infonnation on the accuracy of the estimation in this interval can be obtained from extrapolation of the other intervals. The accuracy of the gravimetric determination is relatively good. The sampling procedure, however, can introduce significant errors as well (especially at the start of the batch) and will be further investigated. From table 1 it is concluded that the mean of the absolute error is small, indicating good overall perfonnance. The standard deviation shows that the 95 % confidence range (2<1) of the estimation runs from -7.5 % to 7.5 %. For each application of the estimator this confidence range has to be judged for its acceptability. It is possible to reduce the confidence range ( = reduce the standard deviation) by using more than only one feedback variable (here: pressure). Examples would be the reactor energy balance or agitator motor power consumption. The end value of the conversion (interval 9) is estimated more accurately than in the other intervals, which can be explained by the fast pressure decrease in this last interval. A large pressure decrease provides much information on the conversion.

Conversion Control If the estimator has proved to provide reliable conversion values, a control scheme can be designed which makes use of the estimator. This means that the current reproducing of a temperature profile can be replaced by a conversion profile: it will no longer be necessary to make the batches recipe-driven, but they can be actively controlled and corrected during the batch. Conversion can be controlled by means of the reaction temperature, resulting in an imprOVed product reproducibility if manual samples are used in a corrective feedback loop. The effect of direct conversion control on product quality, however, is uncertain. True quality control is only possible by means of a model extension with molecular weight distribution (MWD) and with the relation between MWD and physical product properties.

POSSIBLE APPLICATIONS Conversion Monitoring The original purpose of the estimator was to monitor the monomer conversion. By implementing the estimator technique on-line, the operator has continuous information on the actual conversion value. As the end conversion is relevant to the product quality, the last manual sample during the batch is used in the operation procedure to determine the time at which the reaction has to be stopped. Afterwards an extra manual sample is analyzed to determine the actually realized end conversion. With the continuous estimation of the conversion being available, the end conversion can be realized more accurately.

Cooling Margin Monitoring As the actual process is monitored by the estimator, the number of manual samples can be reduced, possibly to one sample at the start of the batch and one for the realized end conversion.

As a spin-off of the conversion estimator the conversion rate is estimated. The conversion rate is proportional to the reaction rate and therefore a direct measure of the reaction heat produced. The available cooling capacity can also be estimated directly from the available process measurements. The agreement between the estimated heat production profile (see fig. 2) and the cooling capacity is surprisingly good. The difference between them at a certain time

The estimator is capable of monitoring the conversion rate as well. At the start of the batch the estimated conversion rate can indicate two kinds of abnormal process conditions. The first one is if for some reason the batch starts faster than normal. In such cases care has to be taken that

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must retain a safe margin. Continuous monitoring of this margin can prevent thermal run-away of the reactor, e.g. due to an increase in coolant water inlet temperature on hot summer days.

be determined as accurately with an energy balance. Using the estimated polymerization heat together with the available cooling capacity, the safety margin can be monitored and supervised. Additionally, knowing the propfile of the polymerization heat for given recipes and the variations in the prop file over different batches, the estimated polymerization profhile can be compared to the normal situation and can be used as an indication of fault conditions.

Knowledge of the heat production profile during various batches can also provide information on 'normal' batch behaviour. By monitoring the heat production profiles estimated over a large amount of regular batches will indicate a pass band for this heat production profile. Deviation from this pass band is an indication of abnormal behaviour. Here too, care has to be taken that the estimator does not mislead the operator by giving no warnings during abnormal process behaviour.

The general conclusion drawn is that the combination of off-line process knowledge and measurement data by means of estimation techniques like Kalman filtering can constitute a sound basis for process monitoring, control and supervision. The introduction into a production environment can best be done in two steps: first as a monitor, giving the possibility to correct, extend, redesign or tune the estimator and to build a level of confidence. The second step is then to fully utilize all the potential possibilities by implementation in the process control system.

IMPLEMENTATION PROCEDURE The estimator algorithm was developed within AGNES, DSM's propietary dynamic simulation software environment. After tuning and testing for robustness, it was converted into a menudriven and user-friendly PC version. The estimator algorithm was handed over from the research department to the production plant staff. Before starting on-line implementation, the offline evaluation was performed in order to build confidence in the estimator output. Active use of the estimator information will only be made after a period of passive monitoring, focussing on the estimator behaviour under abnormal process conditions. This phase of passive use will also result in a better definition of the ultimate scope of the on-line application: monitoring, control and/or process supervision, possibly with an extended version of the estimator.

REFERENCES Elic;abe, G.E., and G.R. Meira (1988). Estimation and Control in Polymerization Reactors. - A Review. Polymer Engineering and Science, Vo!. 28, No. 3, pp. 121-135. Kalman, R.E. (1960). A new Approach to linear Filtering and Prediction Theory. Trans ASME, J. Bas. Engg., 82, pp. 35-45. MacGregor, J.F., Penlidis, A., and A.E. Hamielec (1984). Control of Polymerization Reactors: a Review. Polymer Process Engineering. Vo!. 2, pp. 179206. Peme, R. (1990). Model-based Control System for Exothermic Chemical Reactions. Preprints of 11th IFAC World Congress, Tallinn, Estonia, pp. 205207. Vester, M.T. (1990). On-line Conversion Estimation as an Example of Model Supported Process Control. In H. Th. Bussemaker and P.D. Iedema (ed.) Computer Application in Chemical Engineering, Process Techn. Proceedings, 9, Elsevier Science Publishers, Amsterdam, pp. 183-187. Weerts, P.A. (1990). Emulsion Polymerization of Butadiene: a Kinetic Study. Ph.D. Thesis, Eindhoven University of Technology, The Netherlands.

CONCLUSIONS The results show that the estimator can be used for monitoring purposes, which is in fact a passive use of the extra process information. Active use of the information will only be made when the confidence in the provided information is high enough. The conversion information will then provide possibilities for conversion control and batch supervision, in addition to the monitoring capabilities. It will no longer be necessary to make batches recipe-driven (prescribing temperature profiles etc.): they can be actively controlled and corrected during the batch. Together with conversion, the conversion rate can be estimated. The conversion rate is proportional to the polymerization heat, which could not

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