Artificial intelligence-driven multi-facet study/optimization of a methane fueled low-carbon CCP-desalination system using oxyfuel combustion and Goswami cycle

While conventional energy processes frequently suffer from low efficiency, high emissions, and limited resource integration, the increasing global demand for electricity, cooling, and freshwater calls for efficient and sustainable multigeneration systems. Reducing environmental effects and guaranteeing energy security depend on addressing these issues. Using cascade heat recovery to optimize energy use, this study suggests a novel methane-fueled combined cooling and power (CCP)–desalination system that combines an oxygen generation system (OGS), Goswami cycle, oxy-combustion power unit (OPU), and desalination subsystems. In order to maximize thermodynamic, economic, and environmental performance, the methodology combines sophisticated simulation with machine learning predictions using a Random Forest Regressor (RFR) and multi-objective Particle Swarm Optimization (PSO). Model reliability is confirmed by the high accuracy of critical subsystem validation, with errors under 3 %. Key findings include a minimum carbon footprint of 0.245 kg/kWh, a TUCP of 16.05 $/GJ, an LCOE of 0.03 $/kWh, an NPV of 17.51 $M$, a freshwater production of 2.11 kg/s, and peak thermal and exergy efficiencies of 78.60 % and 48.00 %, respectively. The most important operating parameters, according to sensitivity analysis, are T11 and T16. The system offers a well-rounded improvement in terms of economic, environmental, and energy aspects. With useful applications in industrial, municipal, and remote areas, this integrated CCP design provides a feasible route for sustainable multigeneration energy production. It minimizes environmental impacts and supports low-carbon energy transitions while enabling simultaneous electricity, cooling, and freshwater generation.