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Fabrice Mutelet - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Partition Coefficients of Organic Compounds in Ionic Liquids Using a Temperature-Dependent Linear Solvation Energy Relationship with Parameters Calculated through a Group Contribution Method
Journal of Chemical and Engineering Data, 2011Co-Authors: Fabrice Mutelet, Jeannoel Jaubert, Virginia Ortega-villa, Jean-charles Moise, William E.,jr. AcreeAbstract:A temperature-dependent linear solvation energy relationship (LSER) is proposed for estimating the gas-to-ionic liquid partition coefficients. The model calculates the LSER parameters using a Group Contribution Method. Large sets of partition coefficients were analyzed using the Abraham solvation parameter model to determine the Contributions of 21 Groups: 12 Groups characterizing the cations and 9 Groups for the anions. The derived equations correlate the experimental gas-to-ionic liquid coefficient data to within 0.13 log units. The 21 Group parameters are used to predict the partition coefficients of solutes in alkyl or functionalized ionic liquids with good accuracy.
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prediction of partition coefficients of organic compounds in ionic liquids use of a linear solvation energy relationship with parameters calculated through a Group Contribution Method
Industrial & Engineering Chemistry Research, 2010Co-Authors: Annelaure Revelli, Fabrice Mutelet, Jeannoel JaubertAbstract:A Group Contribution Method is proposed to determine linear solvation energy relationship parameters (GC-LSER) in view of estimating the gas-to-ionic liquid partition coefficients and water-to-ionic liquid partition coefficients. Large sets of partition coefficients were analyzed using the Abraham solvation model to determine the Contributions of 21 Groups: 12 Groups characterizing the cations and 9 Groups for the anions. The derived equations correlate the experimental gas-to-ionic liquid and water-to-ionic liquid partition coefficient data to within 0.15 and 0.17 log units, respectively. The 21 Group-parameters can be used to predict the partition coefficients of solutes in alkyl or functionalized ionic liquids with a good accuracy.
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addition of the hydrogen sulfide Group to the ppr78 model predictive 1978 peng robinson equation of state with temperature dependent kij calculated through a Group Contribution Method
Industrial & Engineering Chemistry Research, 2008Co-Authors: Romain Privat, Fabrice Mutelet, Jeannoel JaubertAbstract:In 2004, we started to develop the PPR78 model which is a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state. In our previous papers, 13 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused_aromatic_rings, CH2,cyclic, CHcyclic or Ccyclic, CO2, and N2. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, carbon dioxide, and nitrogen whatever the temperature. In this study, the PPR78 model is extended to systems containing hydrogen sulfide. To do so, the Group H2S was added. From a general overview on the results obtained from the whole constituted experimental data bank, one can see that the PPR78 model is able to quite accurately predict the behavior of the systems containing H2S.
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addition of the sulfhydryl Group sh to the ppr78 model predictive 1978 peng robinson eos with temperature dependent kij calculated through a Group Contribution Method
The Journal of Chemical Thermodynamics, 2008Co-Authors: Romain Privat, Jeannoel Jaubert, Fabrice MuteletAbstract:In 2004, we started to develop a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state (EOS). This model was called PPR78 (predictive 1978, Peng–Robinson EOS) because it relies on the Peng–Robinson EOS as published by Peng and Robinson in 1978 and because the addition of a Group Contribution Method to estimate the kij makes it predictive. In our previous papers, 14 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic, CHcyclicCcyclic, CO2, N2, and H2S. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, CO2, N2, and H2S whatever the temperature. In this study, the PPR78 model is extended to systems containing thiols (also called mercaptans). To do so, the sulfhydryl Group: –SH was added.
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predicting the phase equilibria of co2 hydrocarbon systems with the ppr78 model pr eos and kij calculated through a Group Contribution Method
Journal of Supercritical Fluids, 2008Co-Authors: Stephane Vitu, Romain Privat, Jeannoel Jaubert, Fabrice MuteletAbstract:Abstract In 2004, we started to develop a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state (EOS). Because our model relies on the Peng–Robinson EOS as published by Peng and Robinson in 1978 and because the addition of a Group Contribution Method to estimate the kij makes it predictive, this model was called PPR78 (predictive 1978, Peng Robinson EOS). In our previous papers eleven Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic and CHcyclic Ccyclic. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics and naphthenes at any temperature. In this study, the PPR78 model is extended to systems containing carbon dioxide. To do so, the Group CO2 was added. The results obtained in this study are in many cases accurate.
Jeannoel Jaubert - One of the best experts on this subject based on the ideXlab platform.
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phase equilibria in hydrogen containing binary systems modeled with the peng robinson equation of state and temperature dependent binary interaction parameters calculated through a Group Contribution Method
Journal of Supercritical Fluids, 2013Co-Authors: Junwei Qian, Jeannoel Jaubert, Romain PrivatAbstract:Abstract The study of phase equilibria in hydrogen-containing mixtures is essential for petroleum and chemical engineering, electricity production, transportation and for many other energy needs. Fluid-phase diagrams are however atypical because of the size-asymmetric nature of these mixtures and the quantum behavior of hydrogen. Therefore, the development of a thermodynamic model able to accurately predict the phase behavior of such systems over wide ranges of pressure and temperature is a difficult and challenging task. In this work, the H2 Group is added to the well-established PPR78 model in order to predict mutual solubility and critical loci of hydrogen-containing systems. Such a model combines the widely used Peng–Robinson equation of state (EoS) with a Group-Contribution Method aimed at estimating the temperature-dependent binary interaction parameters [kij(T)]. In our previous papers, 15 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic, CHcyclic ⇔ Ccyclic, CO2, N2, H2S, and SH. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, CO2, N2, H2S and mercaptans regardless of the temperature. In this study, the addition of the H2 Group makes it possible to extend the PPR78 model to hydrogen-containing systems.
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Prediction of Partition Coefficients of Organic Compounds in Ionic Liquids Using a Temperature-Dependent Linear Solvation Energy Relationship with Parameters Calculated through a Group Contribution Method
Journal of Chemical and Engineering Data, 2011Co-Authors: Fabrice Mutelet, Jeannoel Jaubert, Virginia Ortega-villa, Jean-charles Moise, William E.,jr. AcreeAbstract:A temperature-dependent linear solvation energy relationship (LSER) is proposed for estimating the gas-to-ionic liquid partition coefficients. The model calculates the LSER parameters using a Group Contribution Method. Large sets of partition coefficients were analyzed using the Abraham solvation parameter model to determine the Contributions of 21 Groups: 12 Groups characterizing the cations and 9 Groups for the anions. The derived equations correlate the experimental gas-to-ionic liquid coefficient data to within 0.13 log units. The 21 Group parameters are used to predict the partition coefficients of solutes in alkyl or functionalized ionic liquids with good accuracy.
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prediction of partition coefficients of organic compounds in ionic liquids use of a linear solvation energy relationship with parameters calculated through a Group Contribution Method
Industrial & Engineering Chemistry Research, 2010Co-Authors: Annelaure Revelli, Fabrice Mutelet, Jeannoel JaubertAbstract:A Group Contribution Method is proposed to determine linear solvation energy relationship parameters (GC-LSER) in view of estimating the gas-to-ionic liquid partition coefficients and water-to-ionic liquid partition coefficients. Large sets of partition coefficients were analyzed using the Abraham solvation model to determine the Contributions of 21 Groups: 12 Groups characterizing the cations and 9 Groups for the anions. The derived equations correlate the experimental gas-to-ionic liquid and water-to-ionic liquid partition coefficient data to within 0.15 and 0.17 log units, respectively. The 21 Group-parameters can be used to predict the partition coefficients of solutes in alkyl or functionalized ionic liquids with a good accuracy.
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addition of the hydrogen sulfide Group to the ppr78 model predictive 1978 peng robinson equation of state with temperature dependent kij calculated through a Group Contribution Method
Industrial & Engineering Chemistry Research, 2008Co-Authors: Romain Privat, Fabrice Mutelet, Jeannoel JaubertAbstract:In 2004, we started to develop the PPR78 model which is a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state. In our previous papers, 13 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused_aromatic_rings, CH2,cyclic, CHcyclic or Ccyclic, CO2, and N2. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, carbon dioxide, and nitrogen whatever the temperature. In this study, the PPR78 model is extended to systems containing hydrogen sulfide. To do so, the Group H2S was added. From a general overview on the results obtained from the whole constituted experimental data bank, one can see that the PPR78 model is able to quite accurately predict the behavior of the systems containing H2S.
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addition of the sulfhydryl Group sh to the ppr78 model predictive 1978 peng robinson eos with temperature dependent kij calculated through a Group Contribution Method
The Journal of Chemical Thermodynamics, 2008Co-Authors: Romain Privat, Jeannoel Jaubert, Fabrice MuteletAbstract:In 2004, we started to develop a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state (EOS). This model was called PPR78 (predictive 1978, Peng–Robinson EOS) because it relies on the Peng–Robinson EOS as published by Peng and Robinson in 1978 and because the addition of a Group Contribution Method to estimate the kij makes it predictive. In our previous papers, 14 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic, CHcyclicCcyclic, CO2, N2, and H2S. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, CO2, N2, and H2S whatever the temperature. In this study, the PPR78 model is extended to systems containing thiols (also called mercaptans). To do so, the sulfhydryl Group: –SH was added.
Romain Privat - One of the best experts on this subject based on the ideXlab platform.
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phase equilibria in hydrogen containing binary systems modeled with the peng robinson equation of state and temperature dependent binary interaction parameters calculated through a Group Contribution Method
Journal of Supercritical Fluids, 2013Co-Authors: Junwei Qian, Jeannoel Jaubert, Romain PrivatAbstract:Abstract The study of phase equilibria in hydrogen-containing mixtures is essential for petroleum and chemical engineering, electricity production, transportation and for many other energy needs. Fluid-phase diagrams are however atypical because of the size-asymmetric nature of these mixtures and the quantum behavior of hydrogen. Therefore, the development of a thermodynamic model able to accurately predict the phase behavior of such systems over wide ranges of pressure and temperature is a difficult and challenging task. In this work, the H2 Group is added to the well-established PPR78 model in order to predict mutual solubility and critical loci of hydrogen-containing systems. Such a model combines the widely used Peng–Robinson equation of state (EoS) with a Group-Contribution Method aimed at estimating the temperature-dependent binary interaction parameters [kij(T)]. In our previous papers, 15 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic, CHcyclic ⇔ Ccyclic, CO2, N2, H2S, and SH. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, CO2, N2, H2S and mercaptans regardless of the temperature. In this study, the addition of the H2 Group makes it possible to extend the PPR78 model to hydrogen-containing systems.
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addition of the hydrogen sulfide Group to the ppr78 model predictive 1978 peng robinson equation of state with temperature dependent kij calculated through a Group Contribution Method
Industrial & Engineering Chemistry Research, 2008Co-Authors: Romain Privat, Fabrice Mutelet, Jeannoel JaubertAbstract:In 2004, we started to develop the PPR78 model which is a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state. In our previous papers, 13 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused_aromatic_rings, CH2,cyclic, CHcyclic or Ccyclic, CO2, and N2. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, carbon dioxide, and nitrogen whatever the temperature. In this study, the PPR78 model is extended to systems containing hydrogen sulfide. To do so, the Group H2S was added. From a general overview on the results obtained from the whole constituted experimental data bank, one can see that the PPR78 model is able to quite accurately predict the behavior of the systems containing H2S.
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addition of the sulfhydryl Group sh to the ppr78 model predictive 1978 peng robinson eos with temperature dependent kij calculated through a Group Contribution Method
The Journal of Chemical Thermodynamics, 2008Co-Authors: Romain Privat, Jeannoel Jaubert, Fabrice MuteletAbstract:In 2004, we started to develop a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state (EOS). This model was called PPR78 (predictive 1978, Peng–Robinson EOS) because it relies on the Peng–Robinson EOS as published by Peng and Robinson in 1978 and because the addition of a Group Contribution Method to estimate the kij makes it predictive. In our previous papers, 14 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic, CHcyclicCcyclic, CO2, N2, and H2S. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics, naphthenes, CO2, N2, and H2S whatever the temperature. In this study, the PPR78 model is extended to systems containing thiols (also called mercaptans). To do so, the sulfhydryl Group: –SH was added.
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predicting the phase equilibria of co2 hydrocarbon systems with the ppr78 model pr eos and kij calculated through a Group Contribution Method
Journal of Supercritical Fluids, 2008Co-Authors: Stephane Vitu, Romain Privat, Jeannoel Jaubert, Fabrice MuteletAbstract:Abstract In 2004, we started to develop a Group Contribution Method aimed at estimating the temperature dependent binary interaction parameters (kij(T)) for the widely used Peng–Robinson equation of state (EOS). Because our model relies on the Peng–Robinson EOS as published by Peng and Robinson in 1978 and because the addition of a Group Contribution Method to estimate the kij makes it predictive, this model was called PPR78 (predictive 1978, Peng Robinson EOS). In our previous papers eleven Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic and CHcyclic Ccyclic. It was thus possible to estimate the kij for any mixture containing alkanes, aromatics and naphthenes at any temperature. In this study, the PPR78 model is extended to systems containing carbon dioxide. To do so, the Group CO2 was added. The results obtained in this study are in many cases accurate.
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addition of the nitrogen Group to the ppr78 model predictive 1978 peng robinson eos with temperature dependent kij calculated through a Group Contribution Method
Industrial & Engineering Chemistry Research, 2008Co-Authors: Romain Privat, Jeannoel Jaubert, Fabrice MuteletAbstract:In 2004, we started to develop a Group Contribution Method aimed at estimating the temperature-dependent binary interaction parameters (kij(T)) for the widely used Peng−Robinson equation of state (EOS). This model was called PPR78 (predictive 1978, Peng Robinson EOS), because it relies on the Peng−Robinson EOS as published by Peng and Robinson in 1978 and because the addition of a Group Contribution Method to estimate the kij value makes it predictive. In our previous papers, 12 Groups were defined: CH3, CH2, CH, C, CH4 (methane), C2H6 (ethane), CHaro, Caro, Cfused aromatic rings, CH2,cyclic, CHcyclic = Ccyclic, and CO2. Thus, it was possible to estimate the kij value for any mixture that contains alkanes, aromatics, naphthenes, and CO2, regardless of the temperature. In this study, the PPR78 model is extended to systems that contain nitrogen. To do so, the Group N2 was added.
Dominique Richon - One of the best experts on this subject based on the ideXlab platform.
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Development of a Group Contribution Method for determination of viscosity of ionic liquids at atmospheric pressure
Chemical Engineering Science, 2012Co-Authors: Farhad Gharagheizi, Amir H Mohammadi, Deresh Ramjugernath, Poorandokht Ilani-kashkouli, Dominique RichonAbstract:In this study, a wide literature survey has been carried out to collect an extensive set of liquid viscosity data for ionic liquids (ILs). A data set consisting of 1672 viscosity values and comprising 443 ILs was collated from 204 different literature sources. Using this data set, a reliable Group Contribution Method has been developed. The Method employs a total of 46 sub-structures in addition to the temperature to predict the viscosity of ILs. In order to differentiate the effects of the anion and cation on the viscosity of ILs, 24 sub-structures related to the chemical structure of anions, and 22 sub-structures related to the chemical structure of cations were implemented. The proposed model produces a low average relative deviation (AARD) of less than 6.4% taking into consideration all 1672 experimental data values.
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determination of parachor of various compounds using an artificial neural network Group Contribution Method
Industrial & Engineering Chemistry Research, 2011Co-Authors: Farhad Gharagheizi, Amir H Mohammadi, Ali Eslamimanesh, Dominique RichonAbstract:In this communication, an Artificial Neural Network−Group Contribution algorithm is applied to represent/predict the parachor of pure chemical compounds. To propose a reliable and predictive tool, 227 pure chemical compounds are investigated. Using the developed Method, we obtain satisfactory results that are quantified by the following statistical parameters: absolute average deviations of the represented/predicted parachor values from existing experimental ones, %AAD = 1.2%; and squared correlation coefficient, R2 = 0.997.
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Representation and Prediction of Molecular Diffusivity of Nonelectrolyte Organic Compounds in Water at Infinite Dilution Using the Artificial Neural Network-Group Contribution Method
Journal of Chemical and Engineering Data, 2011Co-Authors: Ali Eslamimanesh, Amir H Mohammadi, Dominique RichonAbstract:The determination of diffusion coefficients of pure compounds in water at infinite dilution is of utmost interest in chemical and environmental engineering, especially wastewater treatment processes. In this work, the artificial neural network-Group Contribution (ANN-GC) Method is applied to represent and predict the molecular diffusivity of nonelectrolyte organic compounds in water at infinite dilution and 298.15 K. A total of 4852 pure compounds from various chemical families has been investigated to propose a predictive model. The obtained results show the squared correlation coefficient of 0.996, root-mean-square error of about 0.02, and average absolute deviation lower than 1.5 % for the calculated or predicted property from existing experimental values.
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Use of Artificial Neural Network-Group Contribution Method to Determine Surface Tension of Pure Compounds
Journal of Chemical and Engineering Data, 2011Co-Authors: Farhad Gharagheizi, Amir H Mohammadi, Ali Eslamimanesh, Dominique RichonAbstract:This work aims at applying an artificial neural network-Group Contribution Method to represent/predict the surface tension of pure chemical compounds at different temperatures and atmospheric pressure. To propose a comprehensive, reliable, and predictive tool, about 4700 data belonging to experimental surface tension values of 752 chemical compounds at different temperatures and atmospheric pressure have been studied. The investigated compounds belong to 78 chemical families containing 151 functional Groups (Group Contributions), which include organic and inorganic liquids. Using this dedicated strategy, we obtain satisfactory results quantified by the following statistical parameters: absolute average deviations of the represented/predicted properties from existing experimental values, 1.7 %, and squared correlation coefficient, 0.997.
Farhad Gharagheizi - One of the best experts on this subject based on the ideXlab platform.
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a Group Contribution Method for determination of thermal conductivity of liquid chemicals at atmospheric pressure
Journal of Molecular Liquids, 2014Co-Authors: Farhad Gharagheizi, Poorandokht Ilanikashkouli, Mehdi Sattari, Amir H Mohammadi, Deresh RamjugernathAbstract:Abstract In this communication, a Group Contribution Method (GC) for the representation/prediction of liquid thermal conductivity of pure chemical compounds, most of which are organic in nature, is presented. Nearly 19,000 liquid thermal conductivity data at different temperatures compiled for 1635 chemical compounds were extracted from the DIPPR 801 database and used to develop the proposed model, as well as to validate and optimize its parameters and evaluate its predictive capability. The parameters of the model comprise the occurrences/existence of 49 chemical substructures plus temperature. Nearly 80% of the data set (15,450 data points) is used to develop the model parameters, 10% of the data set (1931 data points) was employed to validate and optimize the model parameters, and the remaining data (1931 data points) were implemented to assess its predictive capability. The average absolute relative deviation of the model results with respect to the DIPPR 801 data is less than 7.1%. In terms of its simplicity and wide range of applicability, the model shows reasonable accuracy.
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A Group Contribution Method for determination of the standard molar chemical exergy of organic compounds
Energy, 2014Co-Authors: Farhad Gharagheizi, Amir H Mohammadi, Poorandokht Ilani-kashkouli, Deresh RamjugernathAbstract:Exergy analysis can be used to achieve the optimal conditions at which a system or a process can precede; as close as possible to the environmental conditions (with minimum loss of energy). In order to do such an analysis, the chemical exergy of each compound should be available. Since there are a limited number of organic compounds for which the chemical exergy values have been reported in the literature, it would be of great interest to have a reliable Method for the estimation of this parameter. In this communication, a Group Contribution Method is proposed for the prediction of the chemical exergy of pure organic compounds at the standard condition of 1 atm and 298.15 K for pressure and temperature respectively. In order to develop and validate the model, and also to evaluate its predictive capability, a dataset of 133 pure organic compounds composed of carbon, hydrogen, nitrogen, oxygen, and sulfur was used. The model proposed has a low average absolute relative deviation of 1.6% from literature data and indicates the reliability of the Method. It can be used as a predictive tool for the estimation of the standard chemical exergy of pure organic compounds.
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Development of a Group Contribution Method for determination of viscosity of ionic liquids at atmospheric pressure
Chemical Engineering Science, 2012Co-Authors: Farhad Gharagheizi, Amir H Mohammadi, Deresh Ramjugernath, Poorandokht Ilani-kashkouli, Dominique RichonAbstract:In this study, a wide literature survey has been carried out to collect an extensive set of liquid viscosity data for ionic liquids (ILs). A data set consisting of 1672 viscosity values and comprising 443 ILs was collated from 204 different literature sources. Using this data set, a reliable Group Contribution Method has been developed. The Method employs a total of 46 sub-structures in addition to the temperature to predict the viscosity of ILs. In order to differentiate the effects of the anion and cation on the viscosity of ILs, 24 sub-structures related to the chemical structure of anions, and 22 sub-structures related to the chemical structure of cations were implemented. The proposed model produces a low average relative deviation (AARD) of less than 6.4% taking into consideration all 1672 experimental data values.
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prediction of enthalpy of fusion of pure compounds using an artificial neural network Group Contribution Method
Thermochimica Acta, 2011Co-Authors: Farhad Gharagheizi, Gholamreza SalehiAbstract:Abstract In this work, the Artificial Neural Network-Group Contribution (ANN-GC) Method is applied to estimate the enthalpy of fusion of pure chemical compounds at their normal melting point. 4157 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the Squared Correlation Coefficient ( R 2 ) of 0.999, Root Mean Square Error of 0.82 kJ/mol, and average absolute deviation lower than 2.65% for the estimated properties from existing experimental values.
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determination of parachor of various compounds using an artificial neural network Group Contribution Method
Industrial & Engineering Chemistry Research, 2011Co-Authors: Farhad Gharagheizi, Amir H Mohammadi, Ali Eslamimanesh, Dominique RichonAbstract:In this communication, an Artificial Neural Network−Group Contribution algorithm is applied to represent/predict the parachor of pure chemical compounds. To propose a reliable and predictive tool, 227 pure chemical compounds are investigated. Using the developed Method, we obtain satisfactory results that are quantified by the following statistical parameters: absolute average deviations of the represented/predicted parachor values from existing experimental ones, %AAD = 1.2%; and squared correlation coefficient, R2 = 0.997.
