Articles producció científica> Enginyeria Química

Searching for Sustainable Refrigerants by Bridging MolecularModeling with Machine Learning

  • Datos identificativos

    Identificador: imarina:9266829
    Handle: http://hdl.handle.net/20.500.11797/imarina9266829
  • Autores:

    Alkhatib, Ismail I. I.
    Alba, Carlos G.
    Darwish, Ahmad S.
    Llovell, Felix
    Vega, Lourdes F.
  • Otros:

    Autor según el artículo: Alkhatib, Ismail I. I.; Alba, Carlos G.; Darwish, Ahmad S.; Llovell, Felix; Vega, Lourdes F.;
    Departamento: Enginyeria Química
    Autor/es de la URV: Llovell Ferret, Fèlix Lluís
    Palabras clave: Vapor-liquid-equilibria Thermodynamic properties Soft-saft equation Saturated pressure measurements Pvt measurements Phase-equilibria Of-state Eutectic solvents Cosmo-rs Artificial-intelligence
    Resumen: 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
    Áreas temáticas: 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
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: felix.llovell@urv.cat
    Identificador del autor: 0000-0001-7109-6810
    Fecha de alta del registro: 2023-02-19
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://pubs.acs.org/doi/10.1021/acs.iecr.2c00719
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Industrial & Engineering Chemistry Research. 61 (21): 7414-7429
    Referencia de l'ítem segons les normes 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
    DOI del artículo: 10.1021/acs.iecr.2c00719
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    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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