INTELLIGENT OPTIMIZATION OF SOLAR-FOSSIL HYBRID REFRIGERATION SYSTEMS USING ANN SURROGATE MODELS AND METAHEURISTIC ALGORITHMS (OPTIMIZACIÓN INTELIGENTE DE SISTEMAS DE REFRIGERACIÓN HÍBRIDOS SOLAR-FÓSIL MEDIANTE MODELOS SUSTITUTOS CON REDES NEURONALES ARTIFICIALES Y ALGORITMOS METAHEURÍSTICOS)
Resumen
This study presents an optimized hybrid refrigeration system powered by solar energy and natural gas, aimed at improving energy efficiency, economic performance, and environmental sustainability. The methodology combines thermoenergetic analysis, climatic adaptation across various Mexican regions, and advanced tools such as artificial neural networks and metaheuristic algorithms (Particle Swarm Optimization and TOPSIS). Through climate-informed optimization, the system is tailored to specific environmental conditions, enhancing performance. Results show significant improvements in Net Present Value and carbon emissions reduction, especially in semi-arid climates. The integration of intelligent algorithms enables the design of context-sensitive refrigeration solutions, demonstrating the potential of hybrid systems to meet cooling demands while minimizing environmental impacts. This work highlights the importance of localized adaptations and strategic optimization for advancing renewable energy integration in thermal systems, offering a replicable model for sustainable cooling applications in diverse regions.
Keywords:Computational intelligence, hybrid refrigeration systems, metaheuristic optimization, solar-powered cooling, thermoenergetic analysis.
Resumen
Este estudio presenta un sistema de refrigeración híbrido optimizado, alimentado por energía solar y gas natural, orientado a mejorar la eficiencia energética, el desempeño económico y la sostenibilidad ambiental. La metodología combina análisis termoenergético, adaptación climática en diversas regiones de México y herramientas avanzadas como redes neuronales artificiales y algoritmos metaheurísticos (Optimización por Enjambre de Partículas y TOPSIS). A través de una optimización basada en condiciones climáticas, el sistema se adapta a entornos específicos, mejorando su rendimiento. Los resultados muestran mejoras significativas en el Valor Presente Neto y la reducción de emisiones de carbono, especialmente en climas semiáridos. La integración de algoritmos inteligentes permite diseñar soluciones de refrigeración sensibles al contexto, demostrando el potencial de los sistemas híbridos para satisfacer la demanda de enfriamiento minimizando los impactos ambientales. Este trabajo resalta la importancia de las adaptaciones localizadas y la optimización estratégica para impulsar la integración de energías renovables en sistemas térmicos, ofreciendo un modelo replicable para aplicaciones sustentables de refrigeración.
Palabras Clave: Análisis termoenergético, inteligencia computacional, optimización metaheurística, refrigeración solar, sistemas de refrigeración híbridos.
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Al-Yasiri Q., Szabó M., & Arıcı M., A Review on Solar-Powered Cooling and Air-Conditioning Systems for Building Applications, Energy Reports, vol. 8, pp. 2888–2907, 2022.
Alnowibet K. A., Mahdi S., El-Alem M., Abdelawwad M., & Mohamed A. W., Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems, Mathematics, vol. 10, no. 8, 2022.
Cetina-Quiñones A. J., Sánchez-Domínguez I., Casillas-Reyes A., & Bassam A., 9E Analysis of a Flat Plate Solar Collector System Implementation: A New Approach Based on Digital Twin Model Coupled with Global Sensitivity Analysis and Multi-Objective Optimization, J. Renew. Sustain. Energy, vol. 15, no. 3, 2023.
Cetina-Quiñones A. J., Xamán J., Bassam A., Escalante Soberanis M. A., & Perez-Quintana I., Thermo-Economic Analysis of a Flat Solar Collector with a Phase Changing Material Under Tropical Climate Conditions: Residential and Industrial Case, Appl. Therm. Eng., vol. 182, 2021.
Duda R., & Cyklis P., Auto-optimization Control of Hybrid Ecological Refrigeration System, IOP Conf. Ser. Earth Environ. Sci., vol. 214, 2019.
Gad A. G., Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review, Arch. Comput. Methods Eng., vol. 29, no. 5, pp. 2531–2561, 2022.
Herrando M., Pantaleo A. M., Wang K., & Markides C. N., Solar Combined Cooling, Heating and Power Systems Based on Hybrid PVT, PV or Solar-Thermal Collectors for Building Applications, Renew. Energy, vol. 143, pp. 637–647, 2019.
Jain V., Sharma N., Sachdeva G., & S. Kachhwaha S., Performance Analysis and Multi-Objective Optimization of Cooling Tower Assisted Vapor Compression-Absorption Cascaded and Hybrid Refrigeration Systems, Int. J. Green Energy, vol. 16, no. 13, pp. 1024–1045, 2019.
Jenkins P., Elmnifi M., Younis A., Emhamed A., & Alshilmany M., Design of a Solar Absorption Cooling System: Case Study, J. Power Energy Eng., vol. 08, no. 01, pp. 1–15, 2020.
Jiao C., & Li Z., An Updated Review of Solar Cooling Systems Driven by Photovoltaic–Thermal Collectors, Energies, vol. 16, no. 14, 2023.
Koholé Y. W., Fohagui F. C. V., & Tchuen G., Flat-Plate Solar Collector Thermal Performance Assessment Via Energy, Exergy and Irreversibility Analysis, Energy Convers. Manag. X, vol. 15, 2022.
Lei L., Liu N., & Zhou J., Nonlinear Function Optimization Based on Adaptive Genetic Algorithm, in Proceedings of the 4th International Conference on Computer Science and Application Engineering, 2020.
May Tzuc O., Bassam A., Ricalde L. J., Jaramillo O. A., Flota-Bañuelos M., & Escalante Soberanis M. A., Environmental-Economic Optimization for Implementation of Parabolic Collectors in the Industrial Process Heat Generation: Case Study of Mexico, J. Clean. Prod., vol. 242, 2020.
Rashid F. L., Eleiwi M. A., Mohammed H. I., Ameen A., & Ahmad, S., A Review of Using Solar Energy for Cooling Systems: Applications, Challenges, and Effects, Energies, vol. 16, no. 24, 2023.
Rosales-Pérez J. F., Villarruel-Jaramillo A., Romero-Ramos J. A., Pérez-García M., Cardemil J. M., & Escobar R., Hybrid System of Photovoltaic and Solar Thermal Technologies for Industrial Process Heat, Energies, vol. 16, no. 5, 2023.
Sengupta S., Basak S., & Peters R., Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives, Mach. Learn. Knowl. Extr., vol. 1, no. 1, pp. 157–191, 2018.
Settino J., Ferraro V., & Morrone P., Energy Analysis of Novel Hybrid Solar and Natural Gas Combined Cycle Plants, Appl. Therm. Eng., vol. 230, 2023.
Singh K. K., Kumar R., & Gupta A., Multi-Objective Optimization of Thermodynamic and Economic Performances of Natural Refrigerants for Cascade Refrigeration, Arab. J. Sci. Eng., vol. 46, no. 12, pp. 12235–12252, 2021.
Tseng H. Y., Chu P. H., Lu H. C., & Tsai M. J., Easy Particle Swarm Optimization for Nonlinear Constrained Optimization Problems, IEEE Access, vol. 9, pp. 124757–124767, 2021.
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