Simulation and Control of an Aromatic Distillation Column

Document Type : Research Article


Department of Chemical and Petroleum Engineering, Sharif University of Technology, P.O. Box 11365-9465 Tehran, I.R. IRAN


In general, the objective of distillation control is to maintain the desired products quality. In this paper, the performances of different one point control strategies for an aromatic distillation column have been compared through dynamic simulation. These methods are: a) Composition control using measured composition directly. This method sufferes from large sampling delay of measuring devices. b) Composition control by controlling the temperature of a specific tray. In this strategy, the composition-temperature relationship is used to find the temperature setpoint corresponding to the desired composition. Since composition-temperature relation depends on feed condition, an artificial neural network has been proposed which receives the feed specifications and provides the setpoint of the temperature control loop. c) Using temperature measurements for predicting the composition and controlling the composition based on predicted values of composition (inferential contol). Simulation results indicate that controlling the 8th tray temperature and using an artificial neural network for calculating corresponding tray temperature setpoint, has the best performance. Due to negligible pressure drop along the column, controlling the tray temperature difference does not improve the control loop performance.  


Main Subjects

[1] Yu, C. C., Luyben, W. L., Use of  Multiple Temperatures for the Control of Multicomponent Distillation Columns, Ind. Eng. Chem. Res., 23, 590 (1984).
[2] Yu, C.C., Luyben, W.L., Control of Multicomponent Distillation Columns Using Rigorous Composition Estimators, in Distillation and Adsorption, IChemE Symposium Series, Brighton, UK (1987).
[3] Whithead, D.B., Parnis, M., Computer Control Improves Ethylene Plant Operation, Hydrocarbon Processing, Nov., 105 (1987).
[4] Brosilow, C., Joseph, B., Inferential Control of Process, Part I, Steady State Analysis and Design, AIChE J., 24, 485 (1978).
[5] Shen, G.C., Lee, W.K., Adaptive Inferential Control for Chemical Processes with Intermittent Measure-ments, I & EC Research, 28, 557 (1989).
[6] Guilandoust, M.T., Morris,  A.J., Tham,  M.T., An Adaptive Estimation  Algorithm for Inferential Control, I & EC Research, 27, 1658 (1988).
[7] Morari, M., Stephanopoulos, G., Optimal Selection of Secondary Measurements with the Framework of State Estimation in the Presence of Persistent Unknown Disturbances, AIChE J., 16, 247 (1980).
[8] Mejdell, T., Skogestad, S., Estimation of Distillation Composition from Multiple Temperature Measure-ments Using Partial-Least Squares Regression, Ind. Eng. Chem. Res., 30, 2543 (1991).
[9] Manabu, K., Koichi, M., Shinji, H., Iori, H., Inferential Control System of Distillation Compositions Using Dynamic Partial Least Squares Regression, Journal of Process Control, 10, 157 (2000).
[10] Morris,  A. J., Tham,  M. T., Montague,  G. A., Proceedings of a Workshop Kanaskis, Canada (1988).
[11] Willis, M. J., Di Massimo, C.,  Montague, G. A., Tham, M.T., Morris, A.J., Industrial Application of a New Adaptive Estimator for Inferential Control, IFAC Symposium on Intelligent Tuning and Adaptive Control, Singapore (1991).
[12] Moonyong, L., Sunwon, P., Process Control Using a Neural Network Combined with the Conventional PID Controllers, ICASE: The Iinstitute of Control, Automation and Systems Engineers, KOREA, June, 2, 136 (2000).
[13] Zamprogna, E., Barolo, M., Seborg, D.E., Neural Network Approach to Composition Estimation in a Middle-Vessel Batch Distillation Column, Proc. DINIP. Workshop on Nonlinear Dynamics and Control in Process Engineering, Italy (2000).
[14] Bansal, V., D. Perkins, J., Pistikopoulos, E.N., A Case Study in Simultaneous Design and Control Using Rigorous, Mixed-Integer Dynamic Opti-mization Models, Ind. Eng. Chem. Res., 41, 760 (2002).