Fuzzy Neural Network Control Of DO In Wastewater Treatment Process

The activated sludge process has characteristics of multi-variable, strong coupling, high non-linearity, big-lag and indetermination, which results in laggard modeling and control of wastewater treatment process. The water quality changes violently in the sewage environment, and dissolved oxygen (DO) required is varied. To solve these problems, the paper proposes an adaptive fuzzy neural network controller to realize the control of DO in activated sludge model. For the sake of enhancing the quality of output water, decreasing the costs of wastewater treatment and ensuring the highly efficient operation of wastewater treatment system, the research of intelligent modeling and control methods of waste water treatment process can not only be valuable in theory and reality, but also provides useful reference for the modeling and control of other complex systems with nonlinear and big-lag features.Firstly introduces domestic and international wastewater treatment process control strategy, control and test methods, Analyzes the inherent mechanism of activated sludge, establishes mathematics model, which reflects the relations among dissolved oxygen, microorganism and substrate. Based on the stability and controlability analysis of uncertain parameters model, a method of condition feedback calm to system is given.And then puts forward the control scheme, and introduces the design method of fuzzy control and three typical fuzzy neural network, according to the four parts of the fuzzy control, design a four layers of fuzzy neural network, based on the standard neural network model,and the use of a variable add momentum BP algorithm as the fuzzy neural network controller learning algorithm.Finally, the results of simulation show that the above controller can adjust subjection function on-line, optimize control rules, come to expectation with high veracity quickly and have great robustness when applying to activated sludge system.