Adaptive MPC for ozone dosing process of drinking water treatment based on RBF modeling

Abstract

The practical ozone dosing process of drinking water treatment is essentially a complicated nonlinear process with time delay. It is difficult to establish an exact mathematical model and implement a satisfying real-time control for the frequent changes of water quality, water flow rate and process operational conditions. In this paper, the control strategy of keeping a constant ozone exposure is attempted instead of conventional keeping a constant ozone dosage or dissolved ozone residual. To this end, an adaptive model predictive control (MPC) scheme based on the radial basis function (RBF) neural network model is proposed to maintain a constant ozone exposure by adjusting ozone dosage. With the proposed control scheme, a RBF neural network model is established as the prediction model of practical ozone dosing process. Then an adaptive model predictive controller is designed. Owing to the online updating of RBF neural-network weights, the proposed MPC scheme can cope with the frequent changes of water quality, water flow rate and process operational conditions. Both simulation and experimental results demonstrate the effectiveness and practicality of this real-time control method.