Enhancement of energy and cost efficiency in wastewater treatment plants using hybrid bio-inspired machine learning control techniques

Abstract

Global population growth drives demand for clean water, energy, and wastewater production in urban areas. Most existing wastewater treatment plants (WWTPs) are energy- and cost-intensive. To achieve the UN Sustainable Development Goals (SDGs) by 2030, many regions have implemented strict policies for energy consumption and contaminated water discharge, promoting environmental sustainability. This paper attempts to enhance the achievement of SDGs in the wastewater treatment industry by proposing a novel method for hierarchical control of wastewater treatment plants to ensure energy and cost efficiency with less knowledge of the process model, fewer measurements, and fewer control loops. This controller is the combination of a machine learning control technique with a bio-inspired computational algorithm. To evaluate and validate the performance of the proposed controller on the WWTP, the benchmark simulation model no. 1 (BSM1) has been used. The most significant contributions of this paper show that 488.96 KWh/d, 3620.08 Kg/d, and 3454.55 KWh/d of aeration energy cost index, sludge production cost index, and operational cost index are respectively saved when compared to the default control strategy (DCS). Also, 893.69 Kg pollution units per day of effluent fine-related costs have been successfully recovered when compared to the DCS. The experimental results illustrate that the proposed control yields significant amelioration of the energy and cost profile of the WWTP with no effluent violation in dry, rain, and storm weather conditions.