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This paper examines the application of artificial intelligence (AI) in desalination. The study explored AI techniques, including machine learning, neural networks, and genetic algorithms, to enhance system efficiency and reduce energy costs. Case studies assessed the impact of AI on desalination systems, including those powered by renewable energy sources. Key findings revealed that AI-driven systems improved water quality, reduced energy consumption by up to 50%, and enabled predictive maintenance, minimizing downtime. Challenges in integrating AI with renewable energy-powered water treatment and desalination systems were addressed by analyzing hybrid setups combining solar, wind, and battery storage with reverse osmosis (RO) and multi-stage flash (MSF) technologies. These systems demonstrated critical improvements in energy efficiency, making desalination more viable for arid and remote areas. Fault detection algorithms and predictive maintenance emerged as pivotal AI applications, significantly reducing maintenance costs and enhancing reliability. This study was concluded by identifying challenges such as the intermittent nature of renewable energy and the complexities of designing scalable hybrid AI systems. Future research should further refine AI techniques, advancing sustainable and energy-efficient water treatment solutions.
Alenezi et al. (Mon,) studied this question.
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