Publication detail

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

Noushabadi, A.S., Lay, E.N., Dashti, A., Mohammadi, A.H., Chofreh, A.G., Goni, F.A., Klemeš, J.J.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Refrigerants; Thermodynamic properties; Transport properties; Machine learning; Correlation

Authors

Noushabadi, A.S., Lay, E.N., Dashti, A., Mohammadi, A.H., Chofreh, A.G., Goni, F.A., Klemeš, J.J.

Released

1. 1. 2023

Publisher

Elsevier Ltd

ISBN

0360-5442

Periodical

Energy

Number

262

State

United Kingdom of Great Britain and Northern Ireland

Pages from

125099

Pages to

125099

Pages count

16

URL

https://www.sciencedirect.com/science/article/pii/S0360544222019946

BibTex

@article{BUT179228,
  author="Abdoulmohammad {Gholamzadeh Chofreh} and Feybi Ariani {Goni} and Jiří {Klemeš}",
  title="Insights into modelling and evaluation of thermodynamic and transport properties of refrigerants using machine-learning methods",
  journal="Energy",
  year="2023",
  number="262",
  pages="125099--125099",
  doi="10.1016/j.energy.2022.125099",
  issn="0360-5442",
  url="https://www.sciencedirect.com/science/article/pii/S0360544222019946"
}