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

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU145618" target="_blank" >RIV/00216305:26210/23:PU145618 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://www.sciencedirect.com/science/article/pii/S0360544222019946" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360544222019946</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.energy.2022.125099" target="_blank" >10.1016/j.energy.2022.125099</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

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

  • Popis výsledku v původním jazyce

    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.

  • Název v anglickém jazyce

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

  • Popis výsledku anglicky

    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.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    20704 - Energy and fuels

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Ostatní

  • Rok uplatnění

    2023

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Energy

  • ISSN

    0360-5442

  • e-ISSN

    1873-6785

  • Svazek periodika

    neuveden

  • Číslo periodika v rámci svazku

    262

  • Stát vydavatele periodika

    GB - Spojené království Velké Británie a Severního Irska

  • Počet stran výsledku

    16

  • Strana od-do

    125099-125099

  • Kód UT WoS článku

    000861153300004

  • EID výsledku v databázi Scopus

    2-s2.0-85138099992