Experimental simultaneous state and parameter identification of a pH neutralization process based on an extended Kalman Filter

The pH neutralization process is a representative nonlinear process. If a change in feed or buffer streams is introduced, the characteristics of the titration curve are altered and the way of change in titration curve is highly nonlinear. Moreover, if the changes are introduced in the middle of operation, then the nature of the process becomes nonlinear and time-varying. This is the one of the reason why conventional PID controller may fail. Even though the use of buffer solution may alleviate the nonlinearity, the improvement may be limited. A better way to tackle this type of process is to use nonlinear model-based control techniques with online parameter estimation. However, in most cases, the measurements of the process are not adequate enough so that the full state feedback control techniques can be utilized. If the states and crucial parameters are estimated online simultaneously, the effectiveness of the nonlinear state feedback control can be greatly enhanced. Thus, in this study, the capability of simultaneous estimation of states and parameters using Extended Kalman Filter (EKF) are experimentally investigated for a pH neutralization process. The process is modelled using reaction invariants and the concentrations of reaction invariants of the effluent stream (states) and the feed concentrations (parameters) are estimated online. From the comparison of experiments and simulations, it is found that the states and parameters can efficiently be identified simultaneously with EKF so that the estimated information can be exploited by state-feedback control techniques

[1]  M. A. Henson,et al.  ADAPTIVE INPUT–OUTPUT LINEARIZATION OF A pH NEUTRALIZATION PROCESS , 1997 .

[2]  P. Jutila,et al.  Control and estimation algorithms for physico-chemical models of pH-processes in stirred tank reactors , 1981 .

[3]  P. Jutila.  An application of adaptive pH-control algorithms based on physico-chemical modelling in a chemical waste-water treatment plant , 1983 .

[4]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[5]  Ai Poh Loh,et al.  Modeling pH neutralization processes using fuzzy-neural approaches , 1996, Fuzzy Sets Syst..

[6]  Tae Woong Yoon,et al.  Indirect Adaptive Nonlinear Control of a PH Process , 1999 .

[7]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[8]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice , 1993 .

[9]  Tore K. Gustafsson.  An experimental study of a class of algorithms for adaptive pH control , 1985 .

[10]  T. Gustafsson,et al.  Nonlinear and adaptive control of pH , 1992 .

[11]  T. Gustafsson,et al.  Dynamic modeling and reaction invariant control of pH , 1983 .

[12]  Costas Kravaris,et al.  On-line identification and nonlinear control of pH processes , 1998 .

[13]  Ai Poh Loh,et al.  Neural network modelling and control strategies for a pH process , 1995 .

[14]  JayHyung Lee,et al.  Nonlinear inferential control of pulp digesters , 1994 .

[15]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[16]  P. Mäkilä,et al.  Chemical reaction invariants and variants and their use in reactor modeling, simulation, and control , 1981 .

[17]  T. Gustafsson,et al.  Modeling of pH for control , 1995 .

[18]  Jay H. Lee,et al.  Extended Kalman Filter Based Nonlinear Model Predictive Control , 1993, 1993 American Control Conference.

[19]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[20]  Jin-Sung Kim,et al.  Fuzzy model predictive control of nonlinear pH process , 1999 .

[21]  Dale E. Seborg,et al.  Adaptive nonlinear control of a pH neutralization process , 1994, IEEE Trans. Control. Syst. Technol..

[22]  Costas Kravaris,et al.  On-line identification and nonlinear control of an industrial pH process , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[23]  Tae Woong Yoon,et al.  INDIRECT ADAPTIVE BACKSTEPPING CONTROL OF A PH NEUTRALIZATION PROCESS BASED ON RECURSIVE PREDICTION ERROR METHOD FOR COMBINED STATE AND PARAMETER ESTIMATION , 2001 .

[24]  R. A. Wright,et al.  Strong Acid Equivalent Control of pH Processes: An Experimental Study , 1991, 1991 American Control Conference.

[25]  T. J. McAvoy,et al.  Dynamics of pH in Controlled Stirred Tank Reactor , 1972 .

[26]  Lennart Ljung,et al.  The Extended Kalman Filter as a Parameter Estimator for Linear Systems , 1979 .

[27]  P. Jutila,et al.  A physico-chemical model and simulation of pH-process in continuous stirred tank reactors , 1981 .