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

Abstract

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.

Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

“…A proper role and responsibility of each person is prepared to take the necessary follow-up of every step to sustain the improvement. To control the action plan, there are needed to make proper outline of initiatives considered to measure ecological and functional efficiencies (Singh et al. , 2022aSingh et al.…”

“…Therefore, it is more essential to make changes within established production lines without affecting its impact on customers (Trappey et al, 2017) (Kannan and Garad, 2021). Some of advance technologies such as blockchain (Joshi et al, 2022), artificial intelligence (AI) (Munim et al, 2020) (Goyat et al, 2019b), machine learning (ML), smart city and Internet of things (IoTs) (Singh et al, 2022a) have been playing crucial and trajectories role over last decade with imperceptible overlap in their own. In the form of data and their processing, there is a more evident association among all technologies.…”

“…, 2020) (Goyat et al. , 2019b), machine learning (ML), smart city and Internet of things (IoTs) (Singh et al. , 2022a) have been playing crucial and trajectories role over last decade with imperceptible overlap in their own.…”

Blockchain-Orchestrated Intelligent Water Treatment Plant Profiling Framework to Enhance Human Life Expectancy

“…Ref. [28] presented the use of an artificial neural network (ANN) algorithm to enhance the performance of wastewater treatment plants. Their proposed system predicted the influential and effluent chemical oxygen demand for effluent treatment procedures with an accuracy of 92.67% with one model and 89.23% with another model.,…”

“…The authors [21], [23], [25], [28] incorporated ML into their solution. In some of their research work, the authors have not focused on the water potability prediction and their effects on human life; rather, they have discussed how different components of water affect the quality of water, such as authors of [21] predicted the effluent concentration of total nitrogen a few hours ahead, authors of [28] predicted the input and effluent chemical oxygen demand, authors of [25] predicted sludge volume index and authors of [23] predicted water salinity. However, some of the researchers have not considered the effects of poor-quality water on human life expectancy and security aspect.…”