Research And Application On Optimized RBF Neural Network Based On GA For Sewage Treatment

The aeration stage in the activated sludge sewage treatment process is most energy consuming. Hence, research to reduce energy consumption in aeration process is the first choice of sewage treatment plants. Activated sludge sewage treatment method takes advantage of microbial metabolism to break down organic pollutants in the water into inorganic. And aeration is to blow oxygen into biological pool to meet the metabolism needs of microorganisms.In the sewage treatment process, the aeration is controlled by the automatic control system. The control of dissolved oxygen concentration is a complex nonlinear process, for which is difficult to establish mathematical model. General control strategy is to maintain the dissolved oxygen concentration at2mg/L or so which is an experience value obtained upon years of practice. The concentration of dissolved oxygen in the water is determined by the amount of blast, which determines energy consumption directly. Energy consumption is bound to rise when aeration excesses. Therefore, building a dynamic control system that changes when water quality changes is the key to energy conservation. Conventional PID control or fuzzy control are largely dependent on a large number of water quality parameters, which are measured by instruments. Thus, control is limited by the accuracy and timeliness of the instrument. However, the actual working environment is complex in wastewater treatment plant. It often occurs that instruments damage results in inaccurate measurement.This paper is about to build a RBF neural network model to forecast dissolved oxygen concentration with the incoming water quality parameters as the model input. Furthermore, This model is optimized by genetic algorithms. With simulation analysis, the optimized RBF neural network model based on GA has a better effect than the traditional RBF neural network model. This strategy can control dissolved oxygen concentration stably, improve processing efficiency, save energy under the premise of the drainage water quality up to standard.