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Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization

Author

Listed:
  • Ali Alahmer

    (Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA
    Department of Mechanical Engineering, Faculty of Engineering, Tafila Technical University, Tafila 66110, Jordan)

  • Rania M. Ghoniem

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

Abstract

This study aims to enhance the effectiveness of automobile air conditioning (AAC) systems through the use of composite nano-lubricants and fuzzy modeling optimization techniques. Composite nano-lubricants, which consist of varied metal oxide ingredients and content ratios, are projected to surpass single-component nano-lubricants in terms of improving the performance of AAC systems. Fuzzy modeling is used to simulate the AAC system based on experimental data using three input parameters: volume concentration of nano-lubricants (%), the refrigerant charge (g), and compressor speed (rpm). The output performance of the AAC system is measured using four parameters: cooling capacity (CC) in kW, compressor work (CW) in kJ/kg, coefficient of performance (COP), and power consumption (PC) in kW. Optimization is performed using the marine predators algorithm (MPA) to identify the best values for the input control parameters. The objective function is to minimize CW, COP, and PC while simultaneously maximizing CC and COP. Results showed that the performance of the AAC system improved from 85% to 88% compared to the experimental dataset, highlighting the potential benefits of using composite nano-lubricants and fuzzy modeling optimization for improving the energy efficiency of AAC systems. Furthermore, a comprehensive comparison with ANOVA was performed to demonstrate the superiority of the fuzzy modeling approach. The results indicate that the fuzzy model outperforms ANOVA, as evidenced by a reduced root mean square error (RMSE) for all data, from 0.412 using ANOVA to 0.0572 using fuzzy. Additionally, the coefficient of determination for training increased from 0.9207 with ANOVA to 1.0 with fuzzy, further substantiating the success of the fuzzy modeling phase.

Suggested Citation

  • Ali Alahmer & Rania M. Ghoniem, 2023. "Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9481-:d:1169903
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    References listed on IDEAS

    as
    1. Dina Diga & Irina Severin & Nicoleta Daniela Ignat, 2021. "Quality Study on Vehicle Heat Ventilation and Air Conditioning Failure," Sustainability, MDPI, vol. 13(23), pages 1-13, December.
    2. Huang, Xianghui & Li, Kuining & Xie, Yi & Liu, Bin & Liu, Jiangyan & Liu, Zhaoming & Mou, Lunjie, 2022. "A novel multistage constant compressor speed control strategy of electric vehicle air conditioning system based on genetic algorithm," Energy, Elsevier, vol. 241(C).
    3. Alaa Attar & Mohamed Rady & Abdullah Abuhabaya & Faisal Albatati & Abdelkarim Hegab & Eydhah Almatrafi, 2021. "Performance Assessment of Using Thermoelectric Generators for Waste Heat Recovery from Vapor Compression Refrigeration Systems," Energies, MDPI, vol. 14(23), pages 1-17, December.
    4. Huang, Yanjun & Khajepour, Amir & Ding, Haitao & Bagheri, Farshid & Bahrami, Majid, 2017. "An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems," Applied Energy, Elsevier, vol. 188(C), pages 576-585.
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