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I n d . Eng. Chem. Res. 1987, 26, 2442-2444
Inferential Composition Control of an Extractive Distillation Tower Kim P. Choo and Avinash C. Saxena* Polysar Limited, Sarnia, Ontario, Canada N7T 7M2
An overhead composition control scheme was developed for an extractive distillation tower to improve its product recovery and consistency. T o eliminate total dependence of the new control strategy on composition analyzers, an inferential composition control technique was developed. The tower overhead composition was estimated from measurement of multiple tray temperatures, tower pressure, and feed composition. A material balance control scheme was successfully implemented by using the calculated compositions. The existing distributed control system was utilized to implement the new control strategy. The new strategy has reduced variability of the overhead composition by a factor of 3 compared to a manual operation. It is possible to maintain good control of composition even under variable feed compositions as well as during some analyzer failures. 1,3-Butadiene is the major monomer in many types of synthetic rubber manufactured by Polysar Limited. At its Sarnia, Ontario, Canada, operations, 1,3-butadiene is recovered from a mixed C4-hydrocarbon feed by an extractive distillation process. To improve product consistency and l,&butadiene recovery, it was decided to implement a composition control strategy. It was felt that the new strategy could not be based on the process analyzers alone. To maintain automatic composition control even when the analyzer was unavailable, or gave erroneous results, a technique of inferring composition from the extractor tower variables was developed. The various steps in the development and implementation of the new control strategy are detailed, and the changes in tower performance and controllability are discussed. It is believed that our methodology will prove to be of value in many other distillation processes.
The Process and the Control Problem The extraction of 1,3-butadiene from the mixed C4feed is accomplished in three main towers: an extractor tower using a butadiene selective solvent, a solvent stripping tower to recover 1,Bbutadiene and recycle the solvent, and a finishing tower for the final purification of the butadiene product. In the extractor tower, 1,3-butadiene forms a complex with the solvent and leaves with the bottom stream. The overhead product consisting of other C4 hydrocarbons is sent to another unit for the recovery of isobutylene, another major monomer used by Polysar for the manufacture of butyl rubber. Any 1,3-butadiene that leaves with the overhead stream is essentially degraded. If the concentration of butadiene in this stream is higher than a small value, the performance of the isobutylene extraction process may be adversely affected due to polymer formation. Attempting to achieve an extremely low butadiene content in the overhead stream threatens to reduce purity of the butadiene product in the bottom stream and production rates. It is possible to control the purity of the overhead stream manually to make the specification product. The process consistency and economics, however, are highly variable under a manual operation. It was decided to implement an automatic overhead composition control strategy on the extractor tower. It was felt, however, that reliance entirely on the process analyzer was inadvisable due to a finite probability of analyzer breakdowns or unacceptable operation. An independent technique of estimating overhead composition was needed that could, through a mutual cross-check, permit unin0888-5885/87/2626-2442$01.50/0
terrupted composition control of the overhead stream.
Development of a Composition Estimator (a) Literature Survey. In a distillation tower, the desired composition profile generally corresponds to a definite temperature profile. Thus, tray temperatures appeared to be the most promising variables to provide an estimate of the overhead composition. In a binary system, the temperature at the top of the tower, at a given pressure, would be enough to exactly fix the composition. The composition of a multicomponent system is more difficult to estimate due to the additional degrees of freedom. In the literature, many successful approaches to estimate composition from the measurement of other process variables have been reported. Buckley et al. (1985) have provided a thorough treatment of many of these attempts. Some of these techniques, which have utilized tray temperatures as the primary measurement for composition estimation, will be discussed here. To reduce the effect of pressure on composition estimates, various workers have proposed the use of differential temperatures (Boyd, 1975; Luyben 1969; Webber, 1959; Yu and Luyben, 1984). Use of a pressure-corrected temperature has also been discussed (Buckley et al., 1985; Rademaker et al., 1975; Shinskey, 1984). Many authors have made use of multiple tray temperatures for composition estimation (Grote, 1955; Johnson, 1984; Luyben, 1972). Brosilow and co-workers (Brosilow and Tong, 1978; Joseph and Brosilow, 1978; Weber and Brosilow, 1972) have utilized multiple tray temperature as well as flow rate measurements to estimate composition. They have coined the term “Inferential Control” for this method of composition estimation and control in which conveniently available measurements are used Two types of estimators are used: the “projection estimator” is based on a linear input-output model of the process and the “regression estimator” utilizes curve-fitting techniques on process measurements. A nonlinear composition estimator for binary systems based on rigorous tray-by-tray calculations has been proposed by Shah (1978). Patke and Deshpande (1982) have successfully applied inferential control methodology to the control of a laboratory-scale binary distillation column. (b) Our Approach. Many considerations influenced our approach to the development of an extractor overhead composition estimator. Although a significant improvement over the existing manual control strategy was desired, 0 1987 American Chemical Society
Ind. Eng. Chem. Res., Vol. 26, No. 12, 1987 2443 Table I. Composition Estimators Nomenclature y = 1,3-butadiene in the overhead, wt % z = 1,3-butadiene in feed, wt % T = tray or stream temp, OC P = tower pressure, kPa a,b,c,t = subscripts, refer to the three trays selected for temp measurement and tower top conditions, respectively Correlation Equation (1) y 0.00032 - 0.002751; - 0.06547Ta + 0.25698Tb - 0.18266Tc + 0.00051P correlation coeff = 0.78034 statistical significance level (F-test) = 99.9% std. error of estimate in y = 0.074 Correlation Equation (2) y = -0.79271 - 0.017431; - 0.10247Ta + 0.17197Tb - 0.O6444Tc + 0.00069P + 0.009232 correlation coeff = 0.78819 statistical significance level (F-test) = 99.9% std error of estimate in y = 0.11
minimizing cost and development time and utilizing the existing distributed control system were necessary requirements. From the experiences reported in the literature, a process-model-based estimator was expected to be most suitable for our application. In our process, recycles of variable composition from the solvent recovery and finishing towers are major sources of disturbance. It was not possible to obtain reliable process data to develop a suitable model to take into account the effect of solvent quality and various trace impurities. I t was therefore decided to develop a regression-type estimator based on a historical data base. For this purpose an extensive data base of tower variables spanning a period of 3 months was developed. The data used to develop the correlations were selected to ensure that they corresponded to a steady-state operation of the extractor tower. A plant test was run to select those tray temperatures which were most sensitive to composition profile changes. The four temperatures considered most suitable for statistical correlation studies were temperature of overhead vapors and liquid temperatures on three appropriate trays in the rectifying section. For the data base, the composition was expressed in terms of a pseudobinary: 1,3-butadiene as one component and all other components lumped together as the second component. Reliable composition measurements for the data base were obtained by using a laboratory analyzer. The initial attempts at developing a suitable correlation for the extractor overhead composition consisted of using single tray temperatures, differential temperatures, double-differential temperatures, and weighted average temperatures. It was evident that temperatures alone would not be sufficient. Since the tower was on a floating pressure operation, it was decided to use pressure as one of the correlation variables. Used in this manner, pressure alone accounted for all changes in tower flows. The resulting correlation equation (1)is summarized in Table I. I t proved to be an acceptable estimator of overhead composition. It was found however that when the feed composition was 1% or more outside the range used in the data base, the estimates were poor. In other words, the temperature profile and the composition profile were quite sensitive to variations in feed composition. I t proved difficult to build in the effect of feed composition change in the estimator. A feed analyzer was to be installed to help with manual control, and an independent
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feed analysis was available from the supplier. Furthermore, variation in feed composition was quite gradual. As a result, it was felt that feed composition could be used as a variable in the estimator. This was equivalent to, but simpler than, the use of composition-dependent estimator constants. In a sense, use of feed composition in the estimator is self-defeating. It was felt that a good estimate of feed composition was readily available and could be entered manually in case of a feed analyzer breakdown. The overhead composition analysis due to the presence of solvent components was much more difficult and had a prohibitively large dead time for tower control purposes. Due to these reasons, it was expected that estimator-based overhead composition control would be superior to manual control even with feed composition as a variable. The correlation equation (2) of Table I appeared to be very promising for the proposed inferential control purposes. A typical comparison between calculated overhead composition and the measurements of the process analyzer is shown in Figure 1. This correlation equation provides a satisfactory estimate of composition over a much wider feed composition range compared to the correlation equation (1).
Controller Design and Implementation The correlation equation (2) of Table I was used to close a tower material balance control loop. The overhead draw-off rate was varied according to the estimated composition. The existing distributed control system was used to implement the new control scheme. As shown in Figure 2, the computer estimated the overhead composition by using the correlation equation and generated a set point for the overhead draw-off controller by using a PI algorithm. The flow control loop was also
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Ind. Eng. Chem. Res., Vol. 26,No. 12, 1987 Process
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Figure 3. Logic of composition control loop.
of the PI type, and both controllers were tuned in the field to give a satisfactory response. The sampling cycle for all process variables used in the correlation equation was 5 min, and the controller cycle had a frequency of 10 min. The scanning cycle for both the feed and the overhead analyzer was 12 min. The following check points were built into the control strategy. 1. If the absolute difference between the analyzer reading and the overhead composition estimate exceeded a specified limit, an alarm was printed. The operator then had to decide which value to trust. Generally, the estimated value was more reliable, and the control loop was left in automatic. 2. In the event of a feed analyzer breakdown, the operator had the option of entering the feed composition obtained through laboratory analysis. The loop could be put on automatic if estimated, and overhead analyzer readings were in reasonable agreement. No tray temperature measurement/transmission problems have been encountered to date, but a similar operator intervention strategy would be used during such an event. 3. The usable range of the correlation equation was limited by upper and lower limits on both temperature and composition measurements. If this range was exceeded, the supervisory computer was programmed to switch its control mode from automatic to manual and print a message to the operator. The incremental output of the supervisory controller was restricted to specified limits to prevent excessive rates of changes in draw off flows. Tower dynamics were not included in the design of the controller. It was felt that the feed composition and rate changes were so gradual that the additional complexity of lead/lag elements was unnecessary. Futhermore, the instantaneous control actions confer a built-in factor of safety through overcorrection to a process change. The main extractor control loops are shown in Figure 3.
Results of Implementation By making use of the existing distributed control system, the implementation of the new control strategy was accomplished at virtually no cost. Many significant gains have resulted from this change. The new control strategy has reduced variability of overhead composition about its set point by a factor of 3, from 0.3% to 0.1%. After a set point change or a feed composition change, the settling time has also been reduced from about 6 h to less than 3 h. The inclusion in the estimator of feed composition and tray temperatures, near the feed entry point, has provided continuous response to changes in the feed composition. The frequency of operator-initiated actions is almost zero; the only exceptions are in the event of analyzer failures. The mutual
cross-checking capability between the estimated and measured composition has enabled automatic control of composition even under conditions of severe analyzer failure as well as under variable feed compositions. The 1,3-butadiene loss via the overhead stream is controlled. This allows implementation of any desired tower management strategy. For example, 1,S-butadiene production is maximized by controlling its concentration in the overhead at the highest allowable level. Its recovery is maximized by reducing the concentration on 1,3-butadiene in the overhead to the lowest practical level. The operating costs are lowest at some intermediate level of 1,3-butadiene in the overhead stream. In conjunction with a tower energy input control strategy, a fully automated tower operation has been possible. The performance of the extractor is crucial to the entire unit, and the new control strategy has resulted in a very stable operation. Our experience shows that with an existing distributed control system, the implementation of an inferential control strategy is quite cost-effective. This option should be considered if any analyzer-related control problems are present. A new control system must be justified on the basis of its cost relative to the cost of poor control and reliability, with the strategy in use.
Acknowledgment We thank Polysar Limited for permission to publish this work. Registry No. H 2 C = C H C H = C H ,106-99-0.
Literature Cited Boyd, D. M. “Fractionation Column Control”, CEP 1975,71, 55-60. Brosilow, C. B.; Tong, M. “Inferential Control of Processes: Part 11, The Structure and Dynamics of Inferential Control Systems”, AZChE J. 1978,24,492-500. Buckley, P. S.; Luyben, W. L.; Shunta, J. P. “Design of Distillation Column Control Systems”, Report, 1985;Instrument Society of America, Research Triangle Park, NC. Grote, H. W. “Fractionation Control System”, US. Patent 2725351, Nov 29,1955. Johnson, T. S. “Computerized Control Scheme Developments of Distillation Columns Using Multiple Temperature Inputs”, ISA Trans. 1984,23,73-83. Joseph, B.; Brosilow, C. B. “Inferential Control of Processes: Part I, Steady-state Analysis and Design”, AIChE J. 1978,24,485-492. Luyben, W.L. “Feedback Control of Distillation Columns by Double Differential Temperature Control”, Ind. Eng. Chem. Fundam. 1969,8,739-744. Luyben, W. L. “Profile Position Control of Distillation Columns with Sharp Temperature Profiles”, AIChE J . 1972,24,238-240. Patke, N. G.; Deshpande, P. B. “Experimental Evaluation of the Inferential System for Distillation Control”, Chem. Eng. Commun. 1982,13, 343-359. Rademaker, 0.; Rijnsdorp, J. E.; Maarleveld, A. Dynamics and Control of Continuous Distillation Units;Elsevier: Amsterdam, Holland, 1975. Shah, M. K.“Control of a Binary Distillation Column Using Nonlinear Composition Estimators”, Ph.D Thesis, Lehigh University, Bethlehem, PA, 1978. Shinskey, F. G. DistiElation Control for Productivity and Energy Conseruation; McGraw-Hill: New York, 1984. Webber, W. 0. “Control by Temperature Difference”, Pet. Ref. 1959, 38,187-191. Weber, R.; Brosilow, C. B. “Use of Secondary Measurements to Improve Control”, AZChE J . 1972,18, 614-623. Yu, C.; Luyben, W. L. “Use of Multiple Temperatures for the Control of Multicomponent Distillation Columns”, Ind. Eng. Chem. Process Des. Dev. 1984,23,590-597.
Received for reuiew July 11, 1986 Revised manuscript received June 30, 1987 Accepted August 26, 1987
