Documents disponibles écrits par cet auteur

Boiling points of ternary azeotropic mixtures modeled with the use of the universal solvation equation and neural networks / Alexander A. Oliferenko in Industrial & engineering chemistry research, Vol. 51 N° 26 (Juillet 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 26 (Juillet 2012) . - pp. 9123-9128 Titre : Boiling points of ternary azeotropic mixtures modeled with the use of the universal solvation equation and neural networks Type de document : texte imprimé Auteurs : Alexander A. Oliferenko, Auteur ; Polina V. Oliferenko, Auteur ; José S. Torrecilla, Auteur Année de publication : 2012 Article en page(s) : pp. 9123-9128 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Neural  network  Modeling  Azeotropic  mixture  Boiling  point Résumé : Azeotropic mixtures, an important class of technological fluids, constitute a challenge to theoretical modeling of their properties. The number of possible intermolecular interactions in multicomponent systems grows combinatorially as the number of components increases. Ab initio methods are barely applicable, because rather large clusters would need to be calculated, which is prohibitively time-consuming. The quantitative structure-property relationships (QSPR) method, which is efficient and extremely fast, could be a viable alternative approach, but the QSPR methodology requires adequate modification to provide a consistent treatment of multicomponent mixtures. We now report QSPR models for the prediction of normal boiling points of ternary azeotropic mixtures based on a training set of 78 published data points. A limited set of meticulously designed descriptors, together comprising the Universal Solvation Equation (J. Chem. Inf. Model. 2009, 49, 634), was used to provide input parameters for multiple regression and neural network models. The multiple regression model thus obtained is good for explanatory purposes, while the neural network model provides a better quality of fit, which is as high as 0.995 in terms of squared correlation coefficient. This model was also properly validated and analyzed in terms of parameter contributions and their nonlinearity characteristics. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26107469 [article] Boiling points of ternary azeotropic mixtures modeled with the use of the universal solvation equation and neural networks [texte imprimé] / Alexander A. Oliferenko, Auteur ; Polina V. Oliferenko, Auteur ; José S. Torrecilla, Auteur . - 2012 . - pp. 9123-9128. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 26 (Juillet 2012) . - pp. 9123-9128 Mots-clés : Neural  network  Modeling  Azeotropic  mixture  Boiling  point Résumé : Azeotropic mixtures, an important class of technological fluids, constitute a challenge to theoretical modeling of their properties. The number of possible intermolecular interactions in multicomponent systems grows combinatorially as the number of components increases. Ab initio methods are barely applicable, because rather large clusters would need to be calculated, which is prohibitively time-consuming. The quantitative structure-property relationships (QSPR) method, which is efficient and extremely fast, could be a viable alternative approach, but the QSPR methodology requires adequate modification to provide a consistent treatment of multicomponent mixtures. We now report QSPR models for the prediction of normal boiling points of ternary azeotropic mixtures based on a training set of 78 published data points. A limited set of meticulously designed descriptors, together comprising the Universal Solvation Equation (J. Chem. Inf. Model. 2009, 49, 634), was used to provide input parameters for multiple regression and neural network models. The multiple regression model thus obtained is good for explanatory purposes, while the neural network model provides a better quality of fit, which is as high as 0.995 in terms of squared correlation coefficient. This model was also properly validated and analyzed in terms of parameter contributions and their nonlinearity characteristics. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26107469

Determination of toluene, n-heptane, [emim][EtSO4], and [bmim][MeSO4] ionic liquids concentrations in quaternary mixtures by UV-vis spectroscopy / José S. Torrecilla in Industrial & engineering chemistry research, Vol. 48 N° 10 (Mai 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4998–5003 Titre : Determination of toluene, n-heptane, [emim][EtSO4], and [bmim][MeSO4] ionic liquids concentrations in quaternary mixtures by UV-vis spectroscopy Type de document : texte imprimé Auteurs : José S. Torrecilla, Auteur ; Ester Rojo, Auteur ; Julián García, Auteur Année de publication : 2009 Article en page(s) : pp. 4998–5003 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Toluene  Heptane  1-ethyl-3-methylimidazolium  ethylsulfate  1-butyl-3-methylimidazolium  methylsulfate  Ionic  liquids  Correlation  UV−vis  absorbance Résumé : This article reports a new computerized approach to the simultaneous determination of low concentrations of toluene, heptane and 1-ethyl-3-methylimidazolium ethylsulfate ([emim][EtSO4]) and 1-butyl-3-methylimidazolium methylsulfate ([bmim][MeSO4]) ionic liquids (ILs) in acetone using a correlation between their concentrations and UV−vis absorbance values. The essential information (absorbance data) from UV−vis spectrometer of quaternary mixtures in acetone was selected by a principal component analysis (PCA) and transferred into linear regressions (LRs) or neural network (NN) trained computer to estimate the concentrations. Such an integrated PCA/NN/UV−vis spectroscopy system is capable of estimating the concentrations of chemicals in acetone, based on the created models and patterns, without any previous phenomenological knowledge. The mean difference between the estimated concentrations using the PCA/NN/UV−vis and the real concentrations of toluene, heptane, [emim][EtSO4], and [bmim][MeSO4] is less than 2.5%. The PCA/NN/UV−vis is one of the first reliable approaches that can be used in the ILs mixtures field to determine the concentration of two ILs and hydrocarbons in quaternary mixtures while at the same time having simple applications. The short time required by PCA/NN/UV−vis to measure all four chemicals makes it especially useful in processes where long sample preparation times are required. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8014044 [article] Determination of toluene, n-heptane, [emim][EtSO4], and [bmim][MeSO4] ionic liquids concentrations in quaternary mixtures by UV-vis spectroscopy [texte imprimé] / José S. Torrecilla, Auteur ; Ester Rojo, Auteur ; Julián García, Auteur . - 2009 . - pp. 4998–5003. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4998–5003 Mots-clés : Toluene  Heptane  1-ethyl-3-methylimidazolium  ethylsulfate  1-butyl-3-methylimidazolium  methylsulfate  Ionic  liquids  Correlation  UV−vis  absorbance Résumé : This article reports a new computerized approach to the simultaneous determination of low concentrations of toluene, heptane and 1-ethyl-3-methylimidazolium ethylsulfate ([emim][EtSO4]) and 1-butyl-3-methylimidazolium methylsulfate ([bmim][MeSO4]) ionic liquids (ILs) in acetone using a correlation between their concentrations and UV−vis absorbance values. The essential information (absorbance data) from UV−vis spectrometer of quaternary mixtures in acetone was selected by a principal component analysis (PCA) and transferred into linear regressions (LRs) or neural network (NN) trained computer to estimate the concentrations. Such an integrated PCA/NN/UV−vis spectroscopy system is capable of estimating the concentrations of chemicals in acetone, based on the created models and patterns, without any previous phenomenological knowledge. The mean difference between the estimated concentrations using the PCA/NN/UV−vis and the real concentrations of toluene, heptane, [emim][EtSO4], and [bmim][MeSO4] is less than 2.5%. The PCA/NN/UV−vis is one of the first reliable approaches that can be used in the ILs mixtures field to determine the concentration of two ILs and hydrocarbons in quaternary mixtures while at the same time having simple applications. The short time required by PCA/NN/UV−vis to measure all four chemicals makes it especially useful in processes where long sample preparation times are required. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8014044

Development of an a priori ionic liquid design tool. 1. integration of a Novel COSMO-RS molecular descriptor on neural networks / José Palomar in Industrial & engineering chemistry research, Vol. 47 N° 13 (Juillet 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N° 13 (Juillet 2008) . - p. 4523–4532 Titre : Development of an a priori ionic liquid design tool. 1. integration of a Novel COSMO-RS molecular descriptor on neural networks Type de document : texte imprimé Auteurs : José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur ; Francisco Rodríguez, Auteur Année de publication : 2008 Article en page(s) : p. 4523–4532 Note générale : Bibliogr. p. 4530-4532 Langues : Anglais (eng) Mots-clés : Ionic  liquids;  Charge  distribution;  COSMO-RS  methodology Résumé : An innovative computational approach is proposed to design ionic liquids (ILs) based on a new a priori molecular descriptor of ILs, derived from quantum-chemical COSMO-RS methodology. In this work, the charge distribution on the polarity scale given by COSMO-RS is used to characterize the chemical nature of both the cations and anions of the IL structures, using simple molecular models in the calculations. As a result, a novel a priori quantum-chemical parameter, Sσ-profile, is defined for 45 imidazolium-based ILs, as a quantitative numerical indicator of their electronic structures and molecular sizes. Subsequently, neural networks (NNs) are successfully applied to establish a relationship between the electronic information given by the Sσ-profile molecular descriptor and the density properties of IL solvents. As a consequence, we develop here an a priori computational tool for screening ILs with required properties, using COSMO-RS predictions to NN design and optimization. Current methodology is validated following a classical quantitative structure−property relationship scheme, which is the main aim of this work. However, a second part of the current investigation will be devoted to a more useful design strategy, which introduces the desired IL properties as input into inverse NN, resulting in selections of counterions as output, i.e., directly designing ILs on the computer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800056q [article] Development of an a priori ionic liquid design tool. 1. integration of a Novel COSMO-RS molecular descriptor on neural networks [texte imprimé] / José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur ; Francisco Rodríguez, Auteur . - 2008 . - p. 4523–4532. Bibliogr. p. 4530-4532 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N° 13 (Juillet 2008) . - p. 4523–4532 Mots-clés : Ionic  liquids;  Charge  distribution;  COSMO-RS  methodology Résumé : An innovative computational approach is proposed to design ionic liquids (ILs) based on a new a priori molecular descriptor of ILs, derived from quantum-chemical COSMO-RS methodology. In this work, the charge distribution on the polarity scale given by COSMO-RS is used to characterize the chemical nature of both the cations and anions of the IL structures, using simple molecular models in the calculations. As a result, a novel a priori quantum-chemical parameter, Sσ-profile, is defined for 45 imidazolium-based ILs, as a quantitative numerical indicator of their electronic structures and molecular sizes. Subsequently, neural networks (NNs) are successfully applied to establish a relationship between the electronic information given by the Sσ-profile molecular descriptor and the density properties of IL solvents. As a consequence, we develop here an a priori computational tool for screening ILs with required properties, using COSMO-RS predictions to NN design and optimization. Current methodology is validated following a classical quantitative structure−property relationship scheme, which is the main aim of this work. However, a second part of the current investigation will be devoted to a more useful design strategy, which introduces the desired IL properties as input into inverse NN, resulting in selections of counterions as output, i.e., directly designing ILs on the computer. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800056q

Development of an a priori ionic liquid design tool. 2. Ionic liquid selection through the prediction of COSMO-RS molecular descriptor by inverse neural network / José Palomar in Industrial & engineering chemistry research, Vol. 48 N°4 (Février 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°4 (Février 2009) . - p. 2257–2265 Titre : Development of an a priori ionic liquid design tool. 2. Ionic liquid selection through the prediction of COSMO-RS molecular descriptor by inverse neural network Type de document : texte imprimé Auteurs : José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur Année de publication : 2009 Article en page(s) : p. 2257–2265 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Ionic  liquids  Inverse  neural  networks  Molecular  descriptor Résumé : In this work, the a priori computational tool for screening ILs, developed in previous part 1, is extended to the simultaneous prediction of a set of IL properties for 45 imidazolium-based ILs. In addition, current part 2 reports the development of a more useful design strategy, which introduces the target IL properties as input, resulting in the selections of counterions as output, that is directly designing ILs on the computer. For this purpose, inverse neural networks are used to estimate the Sσ-profile molecular descriptor of a potential IL solvent by the specification of its required properties, following a reverse quantitative structure−property relationship scheme. Subsequently, a statistical tool based on Euclidean distances is developed to select an adequate set of anion+cation combinations that fulfill the estimated Sσ-profile values, to obtain, in this case, the tailor-made ILs. Finally, the proposed computational tool for designing ILs is applied in liquid−liquid extraction of a system model (toluene/n-heptane). In view of the inherent modular nature of ILs, the proposed methodology is here used in the formulation of IL mixtures to enhance the performance of extractive solvents in the aromatic/aliphatic separation. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009507 [article] Development of an a priori ionic liquid design tool. 2. Ionic liquid selection through the prediction of COSMO-RS molecular descriptor by inverse neural network [texte imprimé] / José Palomar, Auteur ; José S. Torrecilla, Auteur ; Víctor R. Ferro, Auteur . - 2009 . - p. 2257–2265. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°4 (Février 2009) . - p. 2257–2265 Mots-clés : Ionic  liquids  Inverse  neural  networks  Molecular  descriptor Résumé : In this work, the a priori computational tool for screening ILs, developed in previous part 1, is extended to the simultaneous prediction of a set of IL properties for 45 imidazolium-based ILs. In addition, current part 2 reports the development of a more useful design strategy, which introduces the target IL properties as input, resulting in the selections of counterions as output, that is directly designing ILs on the computer. For this purpose, inverse neural networks are used to estimate the Sσ-profile molecular descriptor of a potential IL solvent by the specification of its required properties, following a reverse quantitative structure−property relationship scheme. Subsequently, a statistical tool based on Euclidean distances is developed to select an adequate set of anion+cation combinations that fulfill the estimated Sσ-profile values, to obtain, in this case, the tailor-made ILs. Finally, the proposed computational tool for designing ILs is applied in liquid−liquid extraction of a system model (toluene/n-heptane). In view of the inherent modular nature of ILs, the proposed methodology is here used in the formulation of IL mixtures to enhance the performance of extractive solvents in the aromatic/aliphatic separation. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009507

Optimization of an artificial neural network by selecting the training function / José S. Torrecilla in Industrial & engineering chemistry research, Vol. 47 N°18 (Septembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N°18 (Septembre 2008) . - p. 7072–7080 Titre : Optimization of an artificial neural network by selecting the training function : application to olive oil mills waste Type de document : texte imprimé Auteurs : José S. Torrecilla, Auteur ; José M. Aragón, Auteur ; María C. Palancar, Auteur Année de publication : 2008 Article en page(s) : p. 7072–7080 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Artificial  neural  network  Olive  oil  mill  waste  Fluidized-bed  dryer Résumé : This article describes the selection of the training algorithm of an artificial neural network (ANN) used to model the drying of olive oil mill waste in a fluidized-bed dryer. The ANN used was a three-layer perceptron that predicts the moisture value at time t + T from experimental data (solid moisture, input air, and fluidized-bed temperature) at t time; T is the sampling time. In this study, 14 training algorithms were tested. This selection was carried out by applying several statistical tests to the real and predicted moisture values. Afterward, an experimental design was carried out to analyze the influence of the training function parameters on the ANN performance. Finally, Polak−Ribiere conjugate gradient backpropagation was selected as the best training algorithm. The ANN trained with the selected algorithm predicted the moisture with a mean prediction error of 1.6% and a correlation coefficient of 0.998. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8001205 [article] Optimization of an artificial neural network by selecting the training function : application to olive oil mills waste [texte imprimé] / José S. Torrecilla, Auteur ; José M. Aragón, Auteur ; María C. Palancar, Auteur . - 2008 . - p. 7072–7080. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N°18 (Septembre 2008) . - p. 7072–7080 Mots-clés : Artificial  neural  network  Olive  oil  mill  waste  Fluidized-bed  dryer Résumé : This article describes the selection of the training algorithm of an artificial neural network (ANN) used to model the drying of olive oil mill waste in a fluidized-bed dryer. The ANN used was a three-layer perceptron that predicts the moisture value at time t + T from experimental data (solid moisture, input air, and fluidized-bed temperature) at t time; T is the sampling time. In this study, 14 training algorithms were tested. This selection was carried out by applying several statistical tests to the real and predicted moisture values. Afterward, an experimental design was carried out to analyze the influence of the training function parameters on the ANN performance. Finally, Polak−Ribiere conjugate gradient backpropagation was selected as the best training algorithm. The ANN trained with the selected algorithm predicted the moisture with a mean prediction error of 1.6% and a correlation coefficient of 0.998. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8001205

Principal component analysis/UV Spectroscopy for the determination of 1-ethyl-3-methylimidazolium ethylsulfate ionic liquid and toluene concentrations in aqueous solutions / José S. Torrecilla in Industrial & engineering chemistry research, Vol. 47 n°11 (Juin 2008) 

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