Control of activated sludge treatment process using pre-compensated multi-variable quantitative feedback theory-based controller

Abstract

This paper deals with the design of a pre-compensated multi-variable quantitative feedback theory (QFT)-based fully populated matrix controller for an activated sludge treatment process (ASTP) of a waste water treatment plant (WWTP). The regulation of the concentration of biochemical oxygen demand ( $\mathrm{BO}{D}_5$ ) and ammonium-ion ( ${\mathrm{NH}}_4^{+}$ ) is the control objective. The plant dynamics are obtained using physical laws available in the literature. The parametric uncertainty is quantified from the measurement data obtained from a real ASTP of an oil refinery. The model is duly cross-validated. A novel technique is proposed to design a pre-compensator that will enhance the diagonal dominance of the plant transfer function matrix. A diagonal controller and a pre-filter, are then designed using a sequential multi-input multi-output (MIMO) QFT-based methodology to meet a set of performance specifications such as relative stability, disturbance rejection, robust tracking and so forth. The simulation results validate the effectiveness of the proposed control scheme. A comparative analysis with reported works shows that the proposed control scheme outperforms some of the reported control strategies.