Articles producció científica> Enginyeria Química

Searching for Sustainable Refrigerants by Bridging MolecularModeling with Machine Learning

  • Identification data

    Identifier: imarina:9266829
    Authors:
    Alkhatib, Ismail I. I.Alba, Carlos G.Darwish, Ahmad S.Llovell, FelixVega, Lourdes F.
    Abstract:
    We present here a novel integrated approachemploying machine learning algorithms for predicting thermo-physical properties offluids. The approach allows obtainingmolecular parameters to be used in the polar soft-statisticalassociatingfluid theory (SAFT) equation of state using moleculardescriptors obtained from the conductor-like screening model forreal solvents (COSMO-RS). The procedure is used for modeling18 refrigerants including hydrofluorocarbons, hydrofluoroolefins,and hydrochlorofluoroolefins. The training dataset included sixinputs obtained from COSMO-RS andfive outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured byits high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysicalproperties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictionsprovided a good level of accuracy (AADs = 1.3-10.5%) compared to experimental data, and within a similar level of accuracy usingparameters obtained from standardfitting procedures. Moreover, the predicted parameters provided a comparable level of predictiveaccuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposedapproach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, whichhinders their technical evaluation and hence theirfinal application
  • Others:

    Author, as appears in the article.: Alkhatib, Ismail I. I.; Alba, Carlos G.; Darwish, Ahmad S.; Llovell, Felix; Vega, Lourdes F.;
    Department: Enginyeria Química
    URV's Author/s: Llovell Ferret, Fèlix Lluís
    Keywords: Vapor-liquid-equilibria Thermodynamic properties Soft-saft equation Saturated pressure measurements Pvt measurements Phase-equilibria Of-state Eutectic solvents Cosmo-rs Artificial-intelligence
    Abstract: We present here a novel integrated approachemploying machine learning algorithms for predicting thermo-physical properties offluids. The approach allows obtainingmolecular parameters to be used in the polar soft-statisticalassociatingfluid theory (SAFT) equation of state using moleculardescriptors obtained from the conductor-like screening model forreal solvents (COSMO-RS). The procedure is used for modeling18 refrigerants including hydrofluorocarbons, hydrofluoroolefins,and hydrochlorofluoroolefins. The training dataset included sixinputs obtained from COSMO-RS andfive outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured byits high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysicalproperties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictionsprovided a good level of accuracy (AADs = 1.3-10.5%) compared to experimental data, and within a similar level of accuracy usingparameters obtained from standardfitting procedures. Moreover, the predicted parameters provided a comparable level of predictiveaccuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposedapproach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, whichhinders their technical evaluation and hence theirfinal application
    Thematic Areas: Química Nutrição Materiais Interdisciplinar Industrial and manufacturing engineering Geociências General chemistry General chemical engineering Farmacia Engineering, chemical Engenharias iv Engenharias iii Engenharias ii Engenharias i Educação física Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Chemistry (miscellaneous) Chemistry (all) Chemical engineering (miscellaneous) Chemical engineering (all) Biotecnología Biodiversidade Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: felix.llovell@urv.cat
    Author identifier: 0000-0001-7109-6810
    Record's date: 2024-09-07
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://pubs.acs.org/doi/10.1021/acs.iecr.2c00719
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Industrial & Engineering Chemistry Research. 61 (21): 7414-7429
    APA: Alkhatib, Ismail I. I.; Alba, Carlos G.; Darwish, Ahmad S.; Llovell, Felix; Vega, Lourdes F.; (2022). Searching for Sustainable Refrigerants by Bridging MolecularModeling with Machine Learning. Industrial & Engineering Chemistry Research, 61(21), 7414-7429. DOI: 10.1021/acs.iecr.2c00719
    Article's DOI: 10.1021/acs.iecr.2c00719
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Chemical Engineering (Miscellaneous),Chemistry (Miscellaneous),Engineering, Chemical,Industrial and Manufacturing Engineering
    Vapor-liquid-equilibria
    Thermodynamic properties
    Soft-saft equation
    Saturated pressure measurements
    Pvt measurements
    Phase-equilibria
    Of-state
    Eutectic solvents
    Cosmo-rs
    Artificial-intelligence
    Química
    Nutrição
    Materiais
    Interdisciplinar
    Industrial and manufacturing engineering
    Geociências
    General chemistry
    General chemical engineering
    Farmacia
    Engineering, chemical
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Educação física
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Chemistry (miscellaneous)
    Chemistry (all)
    Chemical engineering (miscellaneous)
    Chemical engineering (all)
    Biotecnología
    Biodiversidade
    Astronomia / física
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