Desalination Powered by Renewables: A Challenge and an AI Opportunity

Tawfiq Chekifi (), Amine Benmoussa and Moustafa Boukraa
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Tawfiq Chekifi: Unité de Recherche Appliqué en Energies Renouvelables, URAER, CDER
Amine Benmoussa: C-MAST, Universidade da Beira Interior
Moustafa Boukraa: Research Center in Industrial Technologies CRTI

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 14, No 2, 5419-5461

Abstract: Abstract Renewable energy sources such as solar, wind, and geothermal hold significant promise for desalination, particularly in remote regions where access to conventional power sources may be limited. These renewable sources are often integrated with desalination methods like reverse osmosis or multi-stage flash to harness their energy potential. While certain combinations demonstrate reliability and cost-effectiveness, the intermittent nature of renewable energy poses challenges in system design, requiring innovative strategies such as combining solar and wind with battery storage or fuel cells. However, determining the optimal configuration for such integrated systems remains challenging using traditional methods due to the complex and dynamic nature of renewable energy resources. In this study, we focus on the application of Artificial Intelligence (AI) in improving the effectiveness and dependability of renewable-powered desalination systems. Specifically, we explore how AI, through techniques like forecasting models, optimization algorithms, and advanced control systems, can enhance the efficiency and sustainability of these systems. Our research delves into how AI-driven solutions can revolutionize the design, operation, and management of renewable-powered desalination plants. By integrating AI in forecasting, optimization, and control techniques, we aim to address challenges associated with renewable energy integration, ultimately paving the way for more efficient and sustainable water desalination processes.

Keywords: Artificial intelligence; Efficiency; Neural network modelling; Optimization; Process control Sustainable energy and Water desalination (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03935-4

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