investigation on desalination system through machine learning and internet of thing approach

Abstract

Desalination, which is the process of removing salt and other pollutants from seawater or brackish water in order to produce freshwater, is an essential component in the fight against water scarcity issues that are prevalent all over the world. In recent years, the incorporation of Machine Learning (ML) and the Internet of Things (IoT) technologies has revolutionized the desalination sector by increasing the efficacy and sustainability of desalination systems. This has led to a significant increase in the demand for desalinated water. This inquiry focuses on the application of machine learning (ML) and internet of things (IoT) in desalination operations, providing an in-depth analysis of their potential benefits and problems. newlineThesis starts off by reviewing the fundamental concepts of several desalination techniques, such as multi-effect distillation and reverse osmosis, and highlighting the essential aspects that affect the performance of these techniques, such as energy consumption, membrane fouling, and system maintenance. It highlights the growing necessity for enhanced solutions for monitoring, control, and optimization to overcome these issues. newlineFollowing that, the inquiry looks into the incorporation of machine learning and the internet of things in desalination systems, with a focus on the implementation of data analytics platforms, sensors, and actuators. Real-time data gathering is accomplished by the utilization of Internet of Things (IoT)-enabled sensors, which enables the continuous monitoring of crucial factors such as water quality, pressure, and temperature. After that, machine learning algorithms are used to process this data in order to minimize energy consumption, increase system reliability, and forecast and avoid any problems. newlineIn addition, this paper examines a number of machine learning strategies, like as regression, clustering, and deep learning, as well as their applications in desalination systems. It addresses the benefits of predictive maintenance, early defect detection, and adaptive c