Prediction the Performance of Full Scale Wastewater Treatment Plant with A-B Process Using Artificial Neural Network and Genetic Algorithm

Abstract

Aim: A reliable model for wastewater treatment plants (WWTPs) is essential to provide a tool for predicting their performance and to form a basis for controlling the operation of the process also; this would minimize the operation costs. In recent years, computer-based methods have been applied to many areas of environmental issues. Artificial neural networks (ANNs) and genetic algorithm (GA) techniques could be applied for modeling WWTP processes, owing to their high accuracy, adequacy, and quite promising applications in engineering. Materials and Methods: This study applied multilayer feed forward back propagation neural network and GA to predict and optimize the performance of the second phase of the Isfahan North WWTP. Experimental results, which demonstrated the performance of the plant over 6 years were applied for modeling. Results: A three-layer neural network was developed as a predictive model and the network was trained with Levenberg–Marquardt algorithm. The chemical oxygen demand (COD), biochemical oxygen demand (BOD), total suspended solids (TSS), total kjeldahl nitrogen (TKN), and total phosphorus (TP) were introduced as the model input and output. Neural network performance was evaluated with correlation coefficient ® and least mean square error. Proposed model demonstrated the high consistency of the results of modeling and experiments. GA achieved the best value of input parameters as 324.36, 457.37, 359.11, 60.09, and 14.15 mg/l for BOD, COD, TSS, TKN, TP, respectively. Conclusion: ANN and GA combination provides powerful analysis tool for modeling and optimization of nonlinear relationships between the parameters in WWTPs and could be used for proper design and operation of the WWTPs.