IOT Based Smart Wastewater Treatment Model for Industry 4.0 Using Artificial Intelligence

• Main Authors: Singh, D.N., Murugamani, C., Kshirsagar, P.R., Tirth, V., Islam, S., Qaiyum, S., Suneela, B., Al Duhayyim, M., Waji, Y.A.
• Format: Article
• Published: Hindawi Limited 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126371194&doi=10.1155%2f2022%2f5134013&partnerID=40&md5=ae24acea3a302bf1a196cb3626f3ad5c; http://eprints.utp.edu.my/29082/

Wastewater is created by pharma firms and has become a huge worry for the ecosystem. There is a significant amount of toxins that are being dropped continuously from numerous pharmaceutical companies that causes serious damages to the environment and public health because of its comprising high organics as well as inorganic loadings and thus requirements appropriate treatment before final disposal to the ecosystem. Goal of this approach is to treat the wastewater treatment model with industrial data. Algorithms of the artificial neural network (ANN) were employed progressively to predict parameters for wastewater plants. This provision assists users to take remedial measures and function the process by the standards. It is proven as beneficial technology because of its complicated mechanism, dynamic and inconsistent changes in aspects, to overcome some of the limitations of common mathematical models for the wastewater treatment plant. The target is to achieve better prediction accuracy in wastewater treatment model. 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. © 2022 Narendar Singh D et al.