MPC of Sewage Treatment Process

Abstract

Sewage treatment is one of the main methods to promote the recycling of water resources. The control goal of sewage treatment process is to reduce energy consumption under the premise that the effluent quality reaches the standard. In recent years, model predictive control (MPC) has attracted some attention in sewage treatment. Neural network is widely used in control field because of its strong online learning ability. BP neural network is selected as the prediction layer and control layer of MPC and applied to sewage treatment plant to realize on-line control of dissolved oxygen and nitrate. The training index of traditional neural network usually only selects the error between measured value and set value as the variable, and now the change of control quantity is also taken as the training index variable of control layer to adjust the weight relation between them to get the best control effect. Considering that different weather conditions will have a greater impact on the water inflow, different coefficients of the two can be selected to achieve better results in different weather.