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<p>Energy efficiency and environmental sustainability are key drivers in the advancement of vapor compression refrigeration (VCR) systems. The impact of 1.6&#xa0;mass% SiO<sub>2</sub> + 0.2&#xa0;mass% Al<sub>2</sub>O<sub>3</sub> blended nanolubricants on the operation of a home refrigeration system based on the R600a is experimentally studied in this work. R600a&#xa0;charge quantities ranging from 30 to 70&#xa0;g were tested and performance metrics such as compressor work input, refrigeration effect (RE) and coefficient of performance (COP) were examined. The findings demonstrate that the addition of hybrid nanolubricants consistently improves performance under all charge conditions. At a charge of 50&#xa0;g, the refrigeration effect reached a maximum of 240&#xa0;W, representing an increase of 8–13&#xa0;W equivalent to a relative gain of 3–6%. The compressor power decreased by 11–39&#xa0;W equivalent to about 9&#xa0;29%, with a minimum of 84&#xa0;W at a charge of 50&#xa0;g; at the same time the COP enhanced by 0.3&#xa0;0.8 approximately 17–47%, reaching a maximum of 2.85. 50&#xa0;g of R600a was found to be the ideal charge level providing the highest refrigeration effect (RE), maximum COP and lowest consumption of energy. A two-layer feedforward artificial neural network (ANN) was used to verify and predict system behavior, and it matched the experiment results closely with a mean squared error (MSE) between 10<sup>−6</sup> and 10<sup>−4</sup> and a correlation coefficient (<i>R</i>) approximately equal to one. Results show that SiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> hybrid nanolubricants greatly improve thermodynamic performance and lower compressor energy consumption, and the ANN framework offers a reliable predictive tool for performance enhancement. The ANN model proved to be accurate enough in predicting the results obtained from the experimental data set used in this study. This combined strategy demonstrates how intelligent modeling and hybrid nanolubricants can advance next-generation, environmentally friendly and energy-efficient refrigeration technologies.</p>

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Experimental and ANN-based performance analysis of an R600a vapor compression system using SiO2–Al2O3 hybrid nanolubricants

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • C. F. Theresa Cenate,
  • S. Purushothaman,
  • P. Anitha,
  • J. Joshua Bapu,
  • Erdem Cuce

摘要

Energy efficiency and environmental sustainability are key drivers in the advancement of vapor compression refrigeration (VCR) systems. The impact of 1.6 mass% SiO2 + 0.2 mass% Al2O3 blended nanolubricants on the operation of a home refrigeration system based on the R600a is experimentally studied in this work. R600a charge quantities ranging from 30 to 70 g were tested and performance metrics such as compressor work input, refrigeration effect (RE) and coefficient of performance (COP) were examined. The findings demonstrate that the addition of hybrid nanolubricants consistently improves performance under all charge conditions. At a charge of 50 g, the refrigeration effect reached a maximum of 240 W, representing an increase of 8–13 W equivalent to a relative gain of 3–6%. The compressor power decreased by 11–39 W equivalent to about 9 29%, with a minimum of 84 W at a charge of 50 g; at the same time the COP enhanced by 0.3 0.8 approximately 17–47%, reaching a maximum of 2.85. 50 g of R600a was found to be the ideal charge level providing the highest refrigeration effect (RE), maximum COP and lowest consumption of energy. A two-layer feedforward artificial neural network (ANN) was used to verify and predict system behavior, and it matched the experiment results closely with a mean squared error (MSE) between 10−6 and 10−4 and a correlation coefficient (R) approximately equal to one. Results show that SiO2/Al2O3 hybrid nanolubricants greatly improve thermodynamic performance and lower compressor energy consumption, and the ANN framework offers a reliable predictive tool for performance enhancement. The ANN model proved to be accurate enough in predicting the results obtained from the experimental data set used in this study. This combined strategy demonstrates how intelligent modeling and hybrid nanolubricants can advance next-generation, environmentally friendly and energy-efficient refrigeration technologies.