Optimization of the Activated Sludge Process

Abstract

This paper presents a multiobjective model for optimization of the activated sludge process (ASP) in a wastewater-treatment plant (WWTP). To minimize the energy consumption of the activated sludge process and maximize the quality of the effluent, three different objective functions are modeled [i.e., the airflow rate, the carbonaceous biochemical oxygen demand (CBOD) of the effluent, and the total suspended solids (TSS) of the effluent]. These models are developed using a multilayer perceptron (MLP) neural network based on industrial data. Dissolved oxygen (DO) is the controlled variable in these objectives. A multiobjective model that included these objectives is solved with a multiobjective particle swarm optimization (MOPSO) algorithm. Computation results are reported for three trade-offs between energy savings and the quality of the effluent. A 15% reduction in airflow can be achieved by optimal settings of dissolved oxygen, provided that energy savings take precedence over the quality of the effluent.