Direct Adaptive Predictive Control For Wastewater Treatment Plant

Abstract

The purpose of this paper was to design a much simpler control method for a wastewater treatment plant. The work proposes a direct adaptive predictive control (DAMPC) also known as subspace predictive control (SPC) as a solution to the conventional one. The adaptive control structure is based on the linear model of the process and combined with numerical algorithm for subspace state space system identification (N4SID). This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. The performances of both estimation and control algorithms are illustrated by simulation results. Stability analysis is done to investigate the response of the system-proposed when parameter changes exist. This project proves that subspace-adaptive method has a large number of important and useful advantages, primarily the application ability to Multi Input Multi Output (MMO) systems, and the low requirements on prior system information. Given the advantages observed, the most likely areas of application for the proposed algorithm are multivariable processes, about which little information is known such as this wastewater treatment plant. Hence, direct adaptive predictive control (DAMPC) can provide simplicity and good performance in of an activated sludge process