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<p>The escalating consumption of fossil fuels, coupled with environmental pollution and resource depletion, has intensified the need for clean energy alternatives, particularly in the industrial and mining sectors. This study addresses this challenge by proposing a novel geothermal-solar multi-generation system for the co-production of power, hydrogen, and freshwater. The system integrates single, double, and triple-flash geothermal cycles with a Kalina cycle, a photovoltaic-powered proton exchange membrane (PEM) electrolyzer, and a multi-effect distillation (MED) unit. Its primary novelty lies in the comparative energy, exergy, and exergoeconomic analysis of the three distinct geothermal configurations within this integrated framework, followed by an advanced multi-objective optimization methodology. This methodology employs a Grey Wolf Optimizer (GWO) algorithm, with input data generated through an integrated Artificial Neural Network (ANN) model, to identify optimal trade-offs between conflicting objectives. The results identify the triple-flash system (System C) as the superior configuration, achieving an exergy efficiency of 28.37% and a unit product cost of 75.99 $/GJ under design conditions. Multi-objective optimization further enhanced performance, with one optimal scenario for System C yielding an exergy efficiency of 30.17%, a net power output of 223.52 kW, and a reduced unit cost of 61.59 $/GJ. This work demonstrates that the synergistic integration of renewable sources with a systematic optimization framework is pivotal for developing high-performance, cost-effective multi-generation systems that simultaneously address energy and water sustainability.</p>

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Exergoeconomic analysis and machine learning-based optimization of a combined geothermal/solar-based power, freshwater and hydrogen cogeneration system

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • A. R. Hosseini Asl,
  • S. A. Mousavi Parsa,
  • Z. Namazian,
  • M. Faramarzi

摘要

The escalating consumption of fossil fuels, coupled with environmental pollution and resource depletion, has intensified the need for clean energy alternatives, particularly in the industrial and mining sectors. This study addresses this challenge by proposing a novel geothermal-solar multi-generation system for the co-production of power, hydrogen, and freshwater. The system integrates single, double, and triple-flash geothermal cycles with a Kalina cycle, a photovoltaic-powered proton exchange membrane (PEM) electrolyzer, and a multi-effect distillation (MED) unit. Its primary novelty lies in the comparative energy, exergy, and exergoeconomic analysis of the three distinct geothermal configurations within this integrated framework, followed by an advanced multi-objective optimization methodology. This methodology employs a Grey Wolf Optimizer (GWO) algorithm, with input data generated through an integrated Artificial Neural Network (ANN) model, to identify optimal trade-offs between conflicting objectives. The results identify the triple-flash system (System C) as the superior configuration, achieving an exergy efficiency of 28.37% and a unit product cost of 75.99 $/GJ under design conditions. Multi-objective optimization further enhanced performance, with one optimal scenario for System C yielding an exergy efficiency of 30.17%, a net power output of 223.52 kW, and a reduced unit cost of 61.59 $/GJ. This work demonstrates that the synergistic integration of renewable sources with a systematic optimization framework is pivotal for developing high-performance, cost-effective multi-generation systems that simultaneously address energy and water sustainability.