ArticleScientific reports2025
An integrated renewable energy and machine learning framework for techno economic analysis of water and energy nexus management in arid climates.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
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Abstract
Water management in arid regions, such as Basra, Iraq, faces escalating challenges due to water scarcity and increasing energy demand. This study investigates the integration of machine learning with renewable energy technologies to optimize water and energy efficiency in such environments. A multi-scenario approach was employed, combining advanced water treatment technologies, energy recovery systems, and smart grid integration to assess their impact on sustainability. This study evaluated a comprehensive techno-economic analysis of the integration of machine learning models and renewable energy technologies, marking a significant step toward more sustainable and efficient water-energy nexus management in arid climates. The solar-powered UV disinfection system reduced energy consumption by 30%, while membrane filtration techniques minimized water loss by 20%. The adoption of pressure recovery turbines improved energy efficiency by 25%, resulting in significant energy savings of 800 kWh annually and a reduction of 400 kgCO
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