ARTIFICIAL INTELLIGENCEBASED SMART WASTEWATER TREATMENT SYSTEM FOR INDUSTRIES

  • Y. Divya, B. Achiammal, Santosh Kumar Sahoo
Keywords: Wastewater treatment plant, Effluent treatment plant, performance, artificial neural network, optimization algorithms (L–M, BR, and SCG)

Abstract

In this paper, ANN approaches are relevant to the prediction of input and effluent chemical oxygen demand (COD) for effluent treatment procedures. Artificial neural networks (ANNs) offer accurate technique modeling for complex systems using an artificial intelligence technique. Three distinct types of back-propagation ANN were devised to avoid the concentration of wastewater treatment facilities in the concentration of COD, suspended particles, and mixed liquid solids in an epidermal water treatment tank (MLSS). To anticipate COD levels in influential and effluent areas, two ANN-based techniques have been presented. The proper structure for the neural network models was identified via a variety of training and model testing methods. An efficient and robust forecasting tool has been created for the ANN model.

Published
2021-07-16
How to Cite
Santosh Kumar Sahoo, Y. D. B. A. (2021). ARTIFICIAL INTELLIGENCEBASED SMART WASTEWATER TREATMENT SYSTEM FOR INDUSTRIES . Design Engineering, 3604-3621. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/2773
Section
Articles