Forecasting Oxygen Demand in Treatment Plant Using Artificial Neural Networks

Modeling the wastewater treatment plant is difficult due to nonlinear properties of most of its different processes. Due to the increasing concerns over environmental effects of treatment plants considering the poor operation, fluctuations in process variables and problems of linear analyses, algorithms developed using artificial intelligence methods such as artificial neural networks have attracted a great deal of attention. In this research, first using regression analysis, the parameters of biological oxygen demand, chemical oxygen demand, and pH of the input wastewater were chosen as input parameter among other different parameters. Next, using error analysis, the best topology of neural networks was chosen for prediction. The results revealed that multilayer perception network with the sigmoid tangent training function, with one hidden layer in the input and output as well as 10 training nodes with regression coefficient of 0.92 is the best choice. The regression coefficients obtained from the predictions indicate that neural networked are well able to predict the performance of the wastewater treatment plant in Yazd