Research Article IOTBasedSmartWastewaterTreatmentModelforIndustry4.0 Using Artificial Intelligence NarendarSinghD, 1 MurugamaniC, 2 PravinR.Kshirsagar , 3 VineetTirth, 4 SaifulIslam, 5 Sana Qaiyum, 6 SuneelaB, 7 MesferAlDuhayyim, 8 andYosefAsratWaji 9 1 Department of Electronics and Communication Engineering, Anurag University, Hyderabad, India 2 HoD-IT, Bhoj Reddy Engineering College for Women, Hyderabad, India 3 Department of Artificial Intelligence, G. H. Raisoni College of Engineering, Nagpur 440016, India 4 Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61411, Asir, Saudi Arabia 5 Civil Engineering Department, College of Engineering, King Khalid University, Abha-61411, Asir, Saudi Arabia 6 Center for Research in Data Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia 7 Department of Electronics and Communication Engineering, Lords Institute of Engineering &Technology, Hyderabad, India 8 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia 9 Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia Correspondence should be addressed to Pravin R. Kshirsagar; pravinrkshirsagarphd@gmail.com and Yosef Asrat Waji; yosef.asrat@aastu.edu.et Received 21 December 2021; Revised 6 January 2022; Accepted 7 February 2022; Published 25 February 2022 Academic Editor: M Pallikonda Rajasekaran Copyright © 2022 Narendar Singh D et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Wastewater is created by pharma firms and has become a huge worry for the ecosystem. ere 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. is 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. e 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. ree 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. e 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. 1.Introduction Nowadays, intelligent models are advanced in wastewater process simulation such that they are extensively employed for modeling complicated processes. It is difficult to analyze and anticipate their performances exactly in complex in- teractions between the elements of ecological system ac- tivities [1]. Environmental impacts and their environmental engineers mainly have two main features: they depend on numerous factors and the complicated interactions between Hindawi Scientific Programming Volume 2022, Article ID 5134013, 11 pages https://doi.org/10.1155/2022/5134013