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Abstract
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The study investigates a highly efficient multi-generation system, an advanced version of a combined cooling, heating, and power (CCHP) plan for building application. This innovative system effectively utilizes energy from a solar field to produce cold, heat, power, hydrogen, and fresh water. The study’s primary objective is to minimize energy wastage and enhance the system’s overall efficiency. The integrated system includes a heliostat field connected to a solar tower, absorption refrigerator, Kalina cycle, thermoelectric generator (TEG), electrolyzer, and reverse osmosis water treatment (RO). The proposed system was subjected to rigorous thermodynamic assessment through advanced modeling in the Engineering Equation Solver (EES) environment, complemented by an extensive parametric sensitivity analysis. Baseline simulations revealed a net power output of 15.57 MW, with corresponding energy and exergy efficiencies of 47.71% and 66.34%, respectively. The system demonstrated substantial co-generation capabilities, achieving a hydrogen production rate of 2.016 kg s− 1 and a water treatment capacity of 0.4981 kg s− 1. To maximize operational performance, a genetic algorithm-driven artificial intelligence optimization was implemented, simultaneously targeting net power output, exergy efficiency, and economic cost rate. The multi-objective optimization process markedly improved the system’s performance, yielding a net power output of 16,876.93 kW, an exergy efficiency of 65.19%, and a reduced cost rate of 28.00 $ h−1. Comparative analysis confirms significant enhancements in power generation capacity, cost-effectiveness, and thermodynamic efficiency. These results decisively demonstrate the robustness and efficacy of the genetic algorithm-based optimization framework in refining key design parameters, thereby unlocking substantial performance gains and underscoring the potential of the proposed system for high-efficiency, multi-generation energy applications.
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