Detail publikace

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.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

1. 1. 2023

Nakladatel

Elsevier Ltd

ISSN

0360-5442

Periodikum

Energy

Číslo

262

Stát

Spojené království Velké Británie a Severního Irska

Strany od

125099

Strany do

125099

Strany počet

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"
}