A Critical Review of Artificial Intelligence, Machine Learning and Data‐Driven Technologies in Water Desalination Plants
TL;DR
This research provides a thorough overview of the incorporation of technological advancements like Machine Learning, Internet of Things, Big Data, and Artificial Neural Networks into desalination systems that allow accurate estimation of important parameters like membrane fouling, permeate flux and energy consumption.
Abstract
Desalination techniques, especially membrane‐based methods, provide effective solution to water scarcity. This research provides a thorough overview of the incorporation of technological advancements like Machine Learning, Internet of Things, Big Data, and Artificial Neural Networks into desalination systems. These methods allow accurate estimation of important parameters like membrane fouling, permeate flux and energy consumption in addition to facilitating real‐time monitoring as well as the early detection of faults. The results reported show an excellent predictive capability from AI models, including ANN, SVM, and SVM, with their coefficients of derivation (R2) that exceed 0.9. Additionally, smart systems help improve water quality and efficiency of processes as well as reduce operating costs and prolong the life of plants and have minimal environmental impact. But issues related to access to data and the ability to interpret models are major obstacles to massive installation. The use of modern technologies will transform traditional desalination processes into more efficient, robust, and smart systems; the existing operational and technical limitations are remediated.