Solar-aided cogeneration power and absorption cooling cycle optimized using priority machine learning approach
摘要
Rising international power requirements has enabled a sudden need of renewable energy-waste heat recovery solutions demand efficient thermodynamic models competent of binding solar and thermal energy successfully. This study proposes a novel integrated heliostat-based solar thermal power generation system coupled with an absorption refrigeration cycle, employing high initial heat source temperature to enhance overall performance. A comprehensive thermodynamic assessment is implemented to assess system behavior under varying irradiation levels, evaporator temperatures, pump work, and absorption characteristics. A priority-weighted machine learning method is employed to identify the dominant contributors to system losses and to highlight critical operating parameters. Results show that the central receiver and heliostat account for 31.37 and 27.40% of total irreversibility’s, respectively, while energy and exergy efficiencies hold the highest decision weightage (30% each). The optimal condition (dataset 5), defined by DNI of 800 W m–2, absorber efficiency of 0.91, evaporator temperature of 9 °C, and pump work of 52.01 kW, yields 24.18% energy efficiency and 33.11% exergy efficiency with stable molten salt (11.74 kg s–1) and steam (1.80 kg s–1) flow rates. The results confirm that the proposed solar–thermal–cooling configuration offers enhanced thermodynamic performance and improved sustainability compared to conventional systems, making it a promising candidate for future renewable energy applications. The findings validate the hybrid model, proving to have better thermodynamic performance and superior sustainability than standard systems, enabling it to be a viable candidate in future applications of renewable energy.