Artificial Neural Networks Application for Modelling of Wastewater Treatment Plant Performance

Biological oxygen demand (BOD) has been shown to be an important variable in water quality management and planning. However, BOD is difficult to measure and needs longer time periods (5 day) to get results. Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. The objective of this research was to develop artificial neural networks (ANNs) model to estimate daily biological oxygen demand (BOD) at the influent of wastewater biological treatment plant. The plantscale data set (364 daily records of the year 2005) were obtained from a local wastewater treatment plant. Various combinations of daily water quality data, namely chemical oxygen demand (COD), water discharge (Qw), suspended solid (SS), total nitrogen (N) and total phosphorus (P) are used as inputs into the ANN so as to evaluate the degree of effect of each of these variables on daily influent BOD. The results of the ANN model is compared with multiple linear regression model (MLR). Mean square error, average absolute relative error and coefficient of determination statistics are used as comparison criteria for the evaluation of the model performances. Based on the comparisons, it was found that the ANN model could be employed successfully in estimating the daily influent BOD of wastewater biological treatment plant and also ANN model is superior to MLR technique.