Multi-objective controller for enhancing nutrient removal and biogas production in wastewater treatment plants

• A multi-objective controller (MOC) was developed for wastewater treatment plants.
• An MOGA was used to solve conflicting control objectives in WWTPs.
• The MOC with optimal set-points had a better control performance than the reference controller.
• Environmental loads and operational costs of WWTPs were reduced by using an optimal MOC.

A multi-objective genetic algorithm (MOGA) aimed at solving a contradictory problem that occurs while combining several single-loop controllers into a multi-loop multi-objective controller (ML-MOC) was proposed to enhance the nutrient removal and biogas production in wastewater treatment plants at a low operational cost. First, an ML-MOC that consists of three proportional–integral (PI) controllers for improving nutrient removal and biogas production was developed. Then, a multi-objective optimization (MOO) was performed using the MOGA in order to determine the optimal set-points of the ML-MOC. The conflicting objective functions are (1) to minimize the effluent loads, (2) to minimize the operational cost and (3) to maximize the biogas production. The proposed ML-MOC was applied to benchmark simulation model no. 2 (BSM2) which is a benchmark system for wastewater treatment plants for evaluating the performances of the control strategies. The results of this study demonstrates that the ML-MOC showed a better nutrient removal performance than the reference controller in BSM2 maintaining economic operational costs, where both nutrient treatment removal rate and biogas production were increased by 3.9% and 3.6%, respectively.