Modelling and optimisation of activated sludge process using artificial neural networks and genetic algorithms

Mathematical modelling of activated sludge process (ASP) is done using multi-layer perceptron neural networks (MLP-ANN) to predict effluent water quality parameters and multi objective genetic algorithm (MOGA) is employed to optimise influent water quality parameters so that the concentration of contaminants in the effluent stream is minimized. The study area selected was in a central district of southern state of India. The effluent parameters to be investigated are pH, suspended solids (SS) and biochemical oxygen demand (BOD) and the influent parameters to be optimised are pH, suspended solids (SS), biochemical oxygen demand (BOD) and oil and grease (O&G). The model is evaluated based on statistical parameters of correlation coefficient R and mean square error (MSE). MATLAB R2019a are used for modelling and optimisation study. It has been found that effluent pH, SS and BOD were predicted with an overall R of 0.9207 and MSE of 0.0091. During optimisation of influent parameters, it was found that optimum values of the decision variables {pHinf} lies between 6-8, optimum values of {SSinf} lies between 68-380, optimum values of {BODinf} lies between 155-692 and optimum values of {O&Ginf} lies between 8-45 when the objective functions were minimised simultaneously.