Prediction of Effluent Treatment Plant Performance in a Diary Industry Using Artificial Neural Network Technique
Amrutha Vijayan* and Gayathri S Mohan
KMCT College of Engineering for Women, Kozhikode, India
- *Corresponding Author:
- Amrutha Vijayan
KMCT College of Engineering for Women
E-mail: [email protected]
Received Date: October 28, 2016; Accepted Date: November 07, 2016; Published Date: November 09, 2016
Citation: Vijayan A, Mohan GS (2016) Prediction of Effluent Treatment Plant Performance in a Diary Industry Using Artificial Neural Network Technique. J Civil Environ Eng 6:254. doi: 10.4172/2165-784X.1000254
Copyright: © 2016 Vijayan A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.