A Self-adaptive Optimization Strategy of Wastewater Treatment System Based on Artificial Immune Algorithm

In order to limit the effluent quality and optimize energy,a self-adaptive optimization scheduling strategy that is based on ordered sample clustering method is proposed for wastewater treatment plants. By partitioning the control periods adaptively in accordance with the influent condition,the optimal setting values of the control variables are calculated using the artificial immune algorithm with its global searching ability,thus achieving a dynamic optimal control of the wastewater system. Simulation experiments are implemented on the activated sludge biological treatment benchmark model No.1 (BSM1),and the results show the effectiveness of the presented self-adaptive optimization strategy in energy-saving field of the wastewater treatment process.