Optimal Control for Purified Water System Using Adaptive Dynamic Programming

Purified water is an important part of the pharmaceutical production process, and the size of its purified water quality index directly affects pharmaceutical production, so the stable operation and control of the purified water system to produce a high standard of purified water quality is extremely important. The purified water production process is a system with multiple inputs, nonlinearities and high time lags, which makes it difficult to establish the corresponding accurate mathematical model and the stable control of the system. On this basis, this paper proposes a research on the control method of purified water system based on adaptive dynamic programming. Firstly, the Elman neural network model of the purified water system is established using a large amount of off-line experimental data. Secondly, the Adaptive Dynamic Programming (ADP) algorithm is used to achieve the optimal control of the purified water system. Finally, the effectiveness of the proposed algorithm is demonstrated by the simulation experiments of the purified water system.