| Neutralization process is a typical nonlinear process with pure time delay. This process can't be controlled effectively with conventional linear control method. Feed back control base on Nonlinear PID with self-adjust parameter and feed forward control base on experience model are employed in the practical application. The realization of this control system and its result are introduced in this paper. Because the process of the reaction is complicated, it's difficult to model for it. The Nonlinear PID can't fit the change of the reaction. The feed forward control base on model also can't fit the change of pH value and flux of the original water. So the control method can't get a good result.Fuzzy control is a control method base on the experience of the operator. Modeling doesn't need for the fuzzy control. It has excellent robustness. Fuzzy control fit the control of nonlinear, time lag system. It especially fits the control of Neutralization Process. So fuzzy control is employed in the control system as a feed forward control.Neural networks can be viewed as a universal approximator for nonlinear functions, but the multi-layer feed-forward neural network which be used usually is a static state network in nature, it is disagree with the real-time identification for dynamic system. Moreover, recurrent neural networks can simulate the state memory mechanism of dynamic system, so it can be utilized as the model of dynamic time delay system. In this paper, one step ahead predictive control strategy based on neural network (NMPC) is presented. Model training algorithm using modified Elman nets is described in details, also does NMPC algorithm using network's gradient information. In addition, the realization of whole neural network predictive control system and the control results are given. Real time control test show that this control system has good dynamic performance and excellent robustness.At last, a summary of the paper is given and further research interests are also proposed.
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