THE HYBRID ESTIMATION ALGORITHM FOR WASTEWATER TREATMENT PLANT ROBUST MODEL PREDICTIVE CONTROL PURPOSES AT MEDIUM TIME SCALE

Abstract

The paper proposes an approach to designing the hybrid estimation algorithm/module (HEA) with moving measurements window for Wastewater Treatment Plant (WWTP) Robust Model Predictive Control (RMPC) purposes at medium time scale. The RMPC uses a dedicated grey-box model of biological reactor for the system outputs prediction purposes. The grey-box model parameters are dependant on the plant operating point. Hence, these parameters and grey-box model state should be estimated according to the current operating plant condition. The needed parameters and state estimates at medium time scale are provided by the HEA algorithm. The HEA consist of the set-bounded parameter and state estimation algorithm cooperated with the Extended Kalman Filter (EKF) algorithm. The HEA algorithm is used to ensure robustness of grey-box model parameter and state estimates in situation of limited amount of hard measurements on the WWTP. The state estimates provided by the EKF are used as pseudo measurements of the states and the set-bounded estimation algorithm produce sets bounding the grey-box model parameters and states.There are two hybrid estimation algorithms presented and described in the paper: algorithm of explicit method and algorithm of implicit method. Both hybrid estimation algorithms are validated by simulation base on real data records and calibrated model of Kartuzy WWTP in northern Poland.