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Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods

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  • Noushabadi, Abolfazl Sajadi
  • Lay, Ebrahim Nemati
  • Dashti, Amir
  • Mohammadi, Amir H.
  • Chofreh, Abdoulmohammad Gholamzadeh
  • Goni, Feybi Ariani
  • Klemeš, Jiří Jaromír

Abstract

The thermophysical properties of refrigerating systems should be accurately understood for designing low-temperature refrigeration cycles of economic acceptance. The present work has tried to simplify this complicated procedure by proposing reliable and new correlative methods for determining thermodynamic and transport properties of four refrigerating substance classes, namely halocarbon, inorganic, hydrocarbon, and cryogenic fluids. New machine learning methods e.g., particle swarm optimisation adaptive neuro-fuzzy inference system (PSO-ANFIS), genetic programming (GP), and hybrid adaptive neuro-fuzzy inference system (Hybrid ANFIS) algorithms were utilised. The development of a new, simple and comprehensive correlation was for the first time introduced to estimate saturated vapour enthalpy, entropy, velocity of sound, and viscosity of refrigerants without having in-depth knowledge of complicated parameters. The accuracy and validity of the proposed models were assessed using a variety of statistical and graphical demonstrations. The findings were compared, and it was found that Hybrid ANFIS models are more accurate because Absolute Average Relative Errors (%AARD) for enthalpy, entropy, the velocity of sound, and viscosity were estimated as 0.5558, 1.3105, 0.5215, and 1.5727 in respective order. In addition, the proposed models' results were compared to the results of recently previously published models, and it confirms the reliability of our results. The innovation of this research is the design of reliable correlative methods having elevated precisions for thermodynamic and transport specifications of refrigerating substances.

Suggested Citation

  • Noushabadi, Abolfazl Sajadi & Lay, Ebrahim Nemati & Dashti, Amir & Mohammadi, Amir H. & Chofreh, Abdoulmohammad Gholamzadeh & Goni, Feybi Ariani & Klemeš, Jiří Jaromír, 2023. "Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222019946
    DOI: 10.1016/j.energy.2022.125099
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    as
    1. Chen, Lei & Wang, Shanyou & Tao, Wenquan, 2019. "A study on thermodynamic and transport properties of carbon dioxide using molecular dynamics simulation," Energy, Elsevier, vol. 179(C), pages 1094-1102.
    2. Bai, Tao & Yan, Gang & Yu, Jianlin, 2019. "Thermodynamic assessment of a condenser outlet split ejector-based high temperature heat pump cycle using various low GWP refrigerants," Energy, Elsevier, vol. 179(C), pages 850-862.
    3. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
    4. Liu, Yuanbin & Hong, Weixiang & Cao, Bingyang, 2019. "Machine learning for predicting thermodynamic properties of pure fluids and their mixtures," Energy, Elsevier, vol. 188(C).
    5. Su, Wen & Zhao, Li & Deng, Shuai, 2017. "Group contribution methods in thermodynamic cycles: Physical properties estimation of pure working fluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 984-1001.
    6. Kasaeian, Alibakhsh & Hosseini, Seyed Mohsen & Sheikhpour, Mojgan & Mahian, Omid & Yan, Wei-Mon & Wongwises, Somchai, 2018. "Applications of eco-friendly refrigerants and nanorefrigerants: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 91-99.
    7. Eyerer, Sebastian & Eyerer, Peter & Eicheldinger, Markus & Tübke, Beatrice & Wieland, Christoph & Spliethoff, Hartmut, 2018. "Theoretical analysis and experimental investigation of material compatibility between refrigerants and polymers," Energy, Elsevier, vol. 163(C), pages 782-799.
    8. Kim, Gahyeong & Choi, Hyung Won & Lee, Gawon & Lee, Jang Seok & Kang, Yong Tae, 2020. "Experimental study on diffusion absorption refrigeration systems with low GWP refrigerants," Energy, Elsevier, vol. 201(C).
    9. Qyyum, Muhammad Abdul & Lee, Moonyong, 2018. "Hydrofluoroolefin-based novel mixed refrigerant for energy efficient and ecological LNG production," Energy, Elsevier, vol. 157(C), pages 483-492.
    10. Lin, Kui & Zhao, Ya-Pu, 2021. "Entropy and enthalpy changes during adsorption and displacement of shale gas," Energy, Elsevier, vol. 221(C).
    11. Qin, Yanbin & Li, Nanxi & Zhang, Hua & Liu, Baolin, 2021. "Energy and exergy analysis of a Linde-Hampson refrigeration system using R170, R41 and R1132a as low-GWP refrigerant blend components to replace R23," Energy, Elsevier, vol. 229(C).
    12. Valero, Alicia & Valero, Antonio & Vieillard, Philippe, 2012. "The thermodynamic properties of the upper continental crust: Exergy, Gibbs free energy and enthalpy," Energy, Elsevier, vol. 41(1), pages 121-127.
    13. Matsuura, Riku & Watanabe, Kosuke & Yamauchi, Yuji & Sato, Haruka & Chen, Li-Jen & Ohmura, Ryo, 2021. "Thermodynamic analysis of hydrate-based refrigeration cycle," Energy, Elsevier, vol. 220(C).
    Full references (including those not matched with items on IDEAS)

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