Adaptive dissolved oxygen control based on dynamic structure neural network

Activated sludge wastewater treatment processes (WWTPs) are difficult to control because of their complex nonlinear behavior. In this paper, an adaptive controller based on a dynamic structure neural network (ACDSNN) is proposed to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). The proposed ACDSNN incorporates a structure variable feedforward neural network (FNN), where the FNN can determine its structure on-line automatically. The structure of the FNN is adapted to cope with changes in the operating characteristics, while the weight parameters are updated to improve the accuracy of the controller. A particularly strong feature of this method is that the control accuracy can be maintained during adaptation, and therefore the control performance will not be degraded when the character of the model changes. The performance of the proposed ACDSNN is illustrated with numerical simulations and is compared with the fixed structure fuzzy and FNN approaches; it provides an effective solution to the control of the DO concentration in a WWTP.