article · Water and Environment Journal
Desalination methods, notably membrane-based approaches, offer vital solutions to water scarcity. Integrating advanced digital technologies, including machine learning, the Internet of Things, big data, and artificial neural networks, enhances these systems significantly. Data-driven methods deliver precise estimations of critical operating variables, such as membrane fouling, permeate flux, and overall energy consumption. They also enable real-time operational monitoring and early fault diagnosis. Reported predictive models, such as artificial neural networks and support vector machines, demonstrate high predictive accuracy with coefficients of determination exceeding 0.9. Deploying these smart systems increases process efficiency, improves final water quality, lowers operational expenditures, extends facility lifespan, and reduces environmental impact. However, widespread industrial adoption remains constrained by difficulties in obtaining operational data and challenges in interpreting complex model outputs, which must be resolved to modernise traditional water treatment infrastructure.
Water scarcity demands more efficient and cost-effective purification infrastructure. Applying artificial intelligence and smart monitoring to desalination plants improves water quality, cuts energy usage, and prevents equipment breakdown. Addressing current hurdles in data access and model transparency could accelerate the transition toward automated, environmentally sound water production facilities worldwide.
The applications target desalination plant operators, water utilities, and engineering firms seeking to cut operational costs and monitor equipment wear. The underlying tools, such as predictive models for fouling and energy use, show strong computational performance, but commercial adoption remains at an intermediate stage because barriers regarding data access and model interpretability still impede large-scale deployment across operational facilities.
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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.
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DOI: 10.1111/wej.70092
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