Adaptive Input-Output Linearization Control of pH Processes

Document Type: Research Article

Authors

1 Department of Chemical Engineering, Isfahan University of Technology, Isfahan, I.R. IRAN

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

Abstract

pH control is a challenging problem due to its highly nonlinear nature. In this paper the performances of two different adaptive global linearizing controllers (GLC) are compared. Least squares technique has been used for identifying the titration curve. The first controller is a standard GLC based on material balances of each species. For implementation of this controller a nonlinear state estimator is used. Some modifications are proposed to avoid the singularity of the observer gain. The second controller is designed based on the reduced state equation. Through computer simulations, it has been shown that the performances of the second GLC is superior and it is more robust to process model mismatch. It should be also noted that the design of reduced state-based GLC is much easier and dose not need observer for implementation.
 

Keywords


[1] McAvoy, T.J., Hsu, E. and Lowenthal, S., Dynamic of pH in a Controlled Stirred Tank Reactor, Ind. Eng. Chem. Process Des. Dev., 11, 68 (1972).

[2] Gustafsson, T.K., Waller, K.V., Dynamic Modeling and Reaction Invariant Control of pH, Chem. Eng. Sci., 38, 389(1983).

[3] Mellichamp, D.A., Coughanowr, D.R. and Koppel, L.B., Characterization and Gain Identification of Time Varying Flow Processes, AIChE J., 12, 75 (1966).

[4] Gupta, S.R., Coughanowr, D.R., On-Line Gain Identification of Flow Processes with Application to Adaptive pH Control, AIChE J., 24, 654 (1978).

[5] Buchholt, F., Kummel, M., Self-tuning Control of a pH Neutralization Process, Automatica, 15,665 (1979).

[7] Zhou, W., Dong, S. and Lee, P.L., Exponential Tracking with Disturbance Attenuation (ETDA) by Output Feedback, Computer & Chemical Engineering, 26, 1231 (2002). 

[8] Yoon, S.S., Yoon, T.W., Yang, D.R. and Kang, T.S., Indirect Adaptive Nonlinear Control of a pH Process, Computer & Chemical Engineering, 26, 1223 (2002).

[10] Wright, R.A., Kravaris, C., Non-linear Control of pH Processes Using the Strong Acid Equivalent, Ind. Eng. Chem. Res., 30, 1561 (1991a).

[11] Wright, R.A., Soroush, M. and Kravaris, C., Strong Acid Equivalent Control of pH Processes: An Experimental Study, Ind. Eng. Chem. Res., 30, 2437 (1991b).

[13] Wright, R.A., Smith, B.E. and Kravaris, C., On-Line Identification and Nonlinear Control of pH Processes, Ind. Eng. Chem. Res., 37, 2446 (1998).

[14] Lee, J.,  Choi,  J. Y.,  In-Line Mixer for Feedforward Control and Adaptive Feedback Control of pH Processes, Chemical Engineering Science, 55, 1337 (2000).

[16] Sun, D., Hoo, K. A., Dynamic  Transition  Control Structure for a Class of SISO Nonlinear Systems, IEEE Transactions on Control Systems Technology, 7, 622 (1999).

[17] Behera, L., Anand, K.K., Guaranteed Tracking and Regulatory Performance of Nonlinear Dynamic Systems Using Fuzzy Neural Networks, IEE Proceedings-Control Theory and App., 146, 484 (1999).

[19] Wang, H., Oh, Y. and Yoon, E.S., Strategies for Modeling and Control of Nonlinear Chemical Processes Using Neural Networks, Computer & Chemical Engineering, 22, 823 (1998).

[20] Akesson, B.M., Toivonen, H.T., Waller, J.B. and Nystr¨om R.H., Neural Network Approximation of a Nonlinear Model Predictive Controller Applied to a pH Neutralization Process, Computer & Chemical Engineering, 29, 323(2005).

[21] Slotine, J.J. E., Li, W., Applied Nonlinear Control, PrenticeHall Inc., Upper Saddle River, NJ., (1991).

[22] Baumann, W.T., Rugh, W.J., Feedback Control of Nonlinear Systems by Extended Linearization, IEEE Trans. Autom. Control, AC-31,40 (1986).

[24] Ciccarella, G., Dalla Mora, M., and Germani, A., A Luenberger-Like Observer for Nonlinear Systems, Int. J. Control, 57, 537 (1993).

[25] Valluri, S., Soroush, M., Nonlinear State Estimation in the Present of Multiple Steady States, Ind. Eng. Chem. Res., 53, 2645 (1996).

[26] Kazantzis, N., Kravaris, C., Nonlinear Observers for Process Monitoring, Ind. Eng. Chem. Res., 39, 408 (2000).

[27] Lopez, R.A., Maya-Yescas, R., State Estimation for Nonlinear Systems Under Model Uncertainties: A Class of Sliding-Mode Observers, Journal of Process Control, 15, 363 (2005).