Enhancing efficiency in an innovative geothermal poly-generation system for electricity, cooling, and freshwater production through integrated multi-objective optimization: A holistic approach to energy, exergy, and enviroeconomic effects

This study offers a comprehensive exploration of a poly-generation system that integrates geothermal energy, comprising a geothermal primary loop incorporating double self-superheating, Organic Rankine Cycle (ORC), and Ejector Refrigeration Cycle (ERC) employing zeotropic mixtures, along with a Humidification Dehumidification (HDH) unit. The research aims to simultaneously enhance both total energy efficiency and total exergy efficiency through Multi-Objective Particle Swarm Optimization (MOPSO), utilizing Pareto front analysis to effectively balance conflicting objectives. Under specific conditions, energy efficiency and exergy efficiency peak at 35.11 % and 58.23 %, respectively, marking a 40.48 % and 2.32 % improvement over the baseline. The optimized scenarios consistently outperform the baseline in various performance metrics, achieving improvements. Within the Pareto front domain, the highest-performing metrics include 5.5414 [MW] power generation, 59.2069 [kg. s(-1)] cooling production, 0.9144 [MW] cooling load generation, Coefficient of Performance (COP) of 0.3248, 2.9132 [kg. s(-1)] freshwater production, and 3.4906 [MW] Exergy destruction. Additionally, the system's reliance on geothermal energy eliminates CO(2 )emissions at 5.6749 [ton.h(-1)] and reduces the Environmental Penalty Cost Rate (EPCR) to 0.9534 [MUS$.year(-1)]. This study highlights the effectiveness of a multi-objective approach in systematically designing high-efficiency, sustainable poly-generation systems. By carefully manipulating decision variables, this research offers valuable insights into achieving superior system performance and resource optimization.