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<p>Water and energy shortages are intensifying globally due to rapid population growth, urbanization, and industrial expansion. While desalination offers a viable solution to water scarcity, it remains hindered by high energy consumption and environmental trade-offs. This study provides a critical and comparative review of widely used desalination technologies—including reverse osmosis (RO), multiple-effect distillation (MED), multi-stage flash (MSF), adsorption desalination (AD), and electrodialysis (ED)-organized into thermal- and membrane-based categories. Emphasis is placed on hybrid desalination systems that integrate multiple methods to improve water recovery and energy efficiency. Benchmark performance metrics such as specific energy consumption (SEC), recovery ratios (RR), and product water costs are discussed to evaluate technological viability. A distinctive contribution of this review is the integration of recent advancements in artificial intelligence (AI), particularly neural networks, fuzzy logic, and genetic algorithms, in optimizing desalination processes. The study highlights how AI tools can reduce SEC by up to 20% and enhance predictive maintenance, offering real-time system control. Furthermore, it identifies critical gaps in current literature—such as the lack of field-scale validation, limited cost–benefit analysis of AI integration, and insufficient environmental impact assessments of hybrid systems. Finally, the paper proposes concrete multidisciplinary research directions, including AI-material science collaboration for next-generation membranes and policy frameworks supporting modular hybrid desalination units. This synthesis aims to guide future innovations toward scalable, cost-effective, and environmentally sustainable desalination technologies.</p>

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An Overview Study on Water Desalination, Hybrid RO Systems, and How to Utilize Artificial Intelligence with the System

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Salwa Abobeh,
  • Mohamed M. Abdelsalam,
  • Moustafa A. El Bouz

摘要

Water and energy shortages are intensifying globally due to rapid population growth, urbanization, and industrial expansion. While desalination offers a viable solution to water scarcity, it remains hindered by high energy consumption and environmental trade-offs. This study provides a critical and comparative review of widely used desalination technologies—including reverse osmosis (RO), multiple-effect distillation (MED), multi-stage flash (MSF), adsorption desalination (AD), and electrodialysis (ED)-organized into thermal- and membrane-based categories. Emphasis is placed on hybrid desalination systems that integrate multiple methods to improve water recovery and energy efficiency. Benchmark performance metrics such as specific energy consumption (SEC), recovery ratios (RR), and product water costs are discussed to evaluate technological viability. A distinctive contribution of this review is the integration of recent advancements in artificial intelligence (AI), particularly neural networks, fuzzy logic, and genetic algorithms, in optimizing desalination processes. The study highlights how AI tools can reduce SEC by up to 20% and enhance predictive maintenance, offering real-time system control. Furthermore, it identifies critical gaps in current literature—such as the lack of field-scale validation, limited cost–benefit analysis of AI integration, and insufficient environmental impact assessments of hybrid systems. Finally, the paper proposes concrete multidisciplinary research directions, including AI-material science collaboration for next-generation membranes and policy frameworks supporting modular hybrid desalination units. This synthesis aims to guide future innovations toward scalable, cost-effective, and environmentally sustainable desalination technologies.