Solution of deterministic optimization tasks for biological processes in bioreactor: Single and multi-objective approach

Abstract

This paper addresses the optimisation of control strategies in a sequencing batch reactor (SBR) wastewater treatment plant (WWTP). A dynamic process model was developed using the activated sludge model no. 2d (ASM2d) and a data-based aeration model, calibrated with measurements from the Swarzewo WWTP. The main objective was to optimise key operational parameters: aerobic and anaerobic phase durations, the number of aeration phases in a cycle, and the reference dissolved oxygen concentration (DO_ref), to improve nutrient removal efficiency and reduce aeration energy consumption. Deterministic optimisation algorithms such as sequential quadratic programming (SQP), branch and bound (B&B), and direct multisearch (DMS) were employed, while grey relational analysis (GRA) was used as a decision-making tool to evaluate trade-offs in the multi-objective setting. A backstepping controller was designed to track the optimised DO_ref trajectory generated by a heuristic supervisory controller. Simulation results confirm that the proposed approach enhances treatment performance while reducing the specific aeration energy consumption by up to 45%, depending on the influent loading scenario. Under high-load conditions, the approach further improves total nitrogen and phosphorus removal efficiencies by 6.8 and 2.2 percentage points, respectively.