Dynamic Setpoint Optimization Using Metaheuristic Algorithms for Wastewater Treatment Plants

Abstract

Wastewater Treatment Plants (WWTPs) are complex structures, with nonlinear characteristics, that have strict quality criteria, and carry out the treatment using various resources, with special emphasis on electricity. The optimization of WWTPs is a necessity to achieve sustainability. This work proposes the use of metaheuristic algorithms to optimize the functioning of a WWTP. Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing are used to minimize the aeration energy consumption prediction by an artificial neural network. The optimization framework aims dynamically adjusts the reference value for the proportional-integral (PI) controller. The proposed approach was able to dynamically adjust the oxygen pumping requirement and thus reduce the electricity consumption compared to the default PI controller adopted by the Benchmark Simulation Model no. 2 (BSM2).