An integrated renewable energy and machine learning framework for techno economic analysis of water and energy nexus management in arid climates

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 kgCO2 emissions. Smart grid systems enhanced operational efficiency, reducing energy wastage by 15% and improving water distribution by 25%. Machine learning models, including the M5 model tree and recurrent neural networks (RNN), were applied to predict and optimize system performance, highlighting their ability to handle complex, non-linear relationships between energy and water variables. The results proposed a scalable framework for integrating machine learning-driven renewable solutions into water-energy systems in water-stressed regions, addressing global challenges in water management, supporting climate adaptation strategies, and contributing to the United Nations Sustainable Development Goals (SDGs).

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