The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. Forgeneral nonlinear processes, it is defficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous appoaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach suffers from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Progamming(NDP) approach was proposed by Bertsekas and Tsitsiklis[1996]. The NDP approach is to utilize all the data collected to generate an appoximation of optimal cost-to-go functionwhich was used to find the optimal input movement in real time control. The appoximation could be any type of function such as polynomials, neural networks, etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the approximator, the neural network which requires training and the k-nearest neighbor method which requires querying instead of training are investigated. The approximator has to use data from the optimal control strategy. If the optimal control strategy is not readily available, a suboptimal control strategy can be used instead. However, the laborious Bellman iterations are necessary inthis case. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Alse, the effects of comstraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

pH Neutralization Process The NDP(Neuro-Dynamic Programming) Constraint on Input Movement k-Nearest Neighbor Method Neural Network

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