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: 2024-07-20
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
Repositori URV