Intelligent Control of Flocculation Process Based on Radial Basis Probabilistic Neural Network for Sewage Treatment

Abstract

The flocculating process of sewage treatment is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The radial basis probabilistic neural network (RBPNN) has the ability of strong function approach and fast convergence. In this paper, an intelligent optimized control system based on radial basis probabilistic neural network is presented. We constructed the structure of radial basis probabilistic neural network that used for controlling the flocculation process, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We given the architecture of control system and analyzed the working process of system. In this system, the parameters of flocculation process were measured using sensors, and then the control system can control the flocculation process real-time. The system was used in the sewage treatment plant. The experimental results prove that this system is feasible.