An Optimized Dissolved Oxygen Concentration Control in SBR with the Use of Adaptive and Predictive Control Schemes

This paper addresses the problem of optimizing control of the aeration process in a water resource recovery facility (WRRF) using sequencing batch reactor (SBR), one that affects the efficiency of wastewater treatment by stimulating metabolic reactions of microorganisms through dissolved oxygen (DO) level control, and accounts for the predominant part of operating costs. Two independent approaches to DO control algorithm design based on nonlinear model-based predictive control (NMPC) with constraints and direct model reference adaptive control (DMRAC) are proposed and compared. Both algorithms were developed on the basis of utility models obtained by cognitive model simplification, however, both algorithms are characterized by a distinct mechanism to achieve control optimality and incorporate uncertainty. The NMPC-based algorithm solves an online optimization task by reducing the impact of uncertainty through feedback and estimating its influence by evaluating the differences between the internal model and measurements on a sliding prediction window. In contrast, DMRAC reduces the impact of uncertainty through the adaptation of control law parameters. Meanwhile, optimality is encoded in the reference model parameters reflecting the operation of the closed-loop system and in the independent parameters of the adaptation mechanism. Illustrations of the algorithms' operation were provided by simulation experiments using a three-layer SBR model of the Swarzewo wastewater treatment plant with ASM3e-based reactions.