Prediction of Effluent Treatment Plant Performance in a Diary Industry Using Artificial Neural Network Technique

Abstract

Use of Artificial Neural Network (ANN) models is progressively increasingly to predict waste water treatment plant variables. This forecasting helps the operators to take corrective action and manage the process accordingly as per the norms. It is a proved useful device to surmount a few of the limitations of usual mathematical models for wastewater treatment plants for the reason that of their complex mechanisms, changing aspects-dynamics and inconsistency. This analysis considers the relevance of ANN techniques to predict influent and effluent biochemical oxygen demand (BOD), Chemical Oxygen Demand (COD), Total suspended solids (TSS) for effluent treatment process. Here, a feed forward ANN, using a back propagation learning algorithm, has been applied for predicting effluent BOD, COD, TSS. After collecting historical plant data from effluent treatment plant at Diary industry. The suitable architecture of the neural network models was ascertained after several steps of training and testing of the models. Efficiencies of the plant for BOD, COD, TSS were 85%,78%,75% respectively. The ANN based models were established to offer an efficient and a robust tool in prediction and modelling.