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<p>An experimental evaluation of the thermal, tribological, rheological, and eco-friendly properties as well as the stability of polyolester (POE) oil combined with Al<sub>2</sub>O<sub>3</sub>–CuO hybrid nanoparticles has been carried out. The potential of hybrid nanolubricants has been investigated using refrigerant mixtures of R290/R600a (80/20 mass fraction) in a vapour compression refrigeration system. A stable suspension of Al<sub>2</sub>O<sub>3</sub>–CuO hybrid nanolubricant was prepared using four nanoparticle mixture ratios of 80:20, 60:40, 40:60, and 50:50 at a volume concentration of 0.15%. The viscosity, thermal conductivity, and friction coefficient exhibited improvements with varying hybrid nanolubricant proportions. The highest thermal conductivity increased by 28.2%, and the most significant reduction in the coefficient of friction (CoF) and the highest increase in the coefficient of performance was achieved with the 40:60 mixture ratio. The energy-efficiency analysis of the refrigeration system has been carried out using a machine learning approach, specifically utilizing the linear regression model to make predictions for a new dataset with an accuracy of 90%.</p> Graphical Abstract <p></p>

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Performance assessment of POE/Al2O3–CuO hybrid nanolubricants in refrigeration system with predictive machine learning

  • 期刊论文

  • 发表日期：

    2025年3月28日

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  • Mohammed Dilawar,
  • Adnan Qayoum,
  • Mukhtar Ahmad,
  • Gowhar Shafi Bhat,
  • Turali Narayana

摘要

An experimental evaluation of the thermal, tribological, rheological, and eco-friendly properties as well as the stability of polyolester (POE) oil combined with Al2O3–CuO hybrid nanoparticles has been carried out. The potential of hybrid nanolubricants has been investigated using refrigerant mixtures of R290/R600a (80/20 mass fraction) in a vapour compression refrigeration system. A stable suspension of Al2O3–CuO hybrid nanolubricant was prepared using four nanoparticle mixture ratios of 80:20, 60:40, 40:60, and 50:50 at a volume concentration of 0.15%. The viscosity, thermal conductivity, and friction coefficient exhibited improvements with varying hybrid nanolubricant proportions. The highest thermal conductivity increased by 28.2%, and the most significant reduction in the coefficient of friction (CoF) and the highest increase in the coefficient of performance was achieved with the 40:60 mixture ratio. The energy-efficiency analysis of the refrigeration system has been carried out using a machine learning approach, specifically utilizing the linear regression model to make predictions for a new dataset with an accuracy of 90%.

Graphical Abstract