Representation and Prediction of Molecular Diffusivity of Nonelectrolyte Organic Compounds in Water at Infinite Dilution Using the Artificial Neural Network-Group Contribution Method

Organic Compounds ◽

Infinite Dilution ◽

Group Contribution ◽

Molecular Diffusivity ◽

Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.7b02753 ◽

2017 ◽

Vol 56 (42) ◽

pp. 12236-12245 ◽

Cited By ~ 16

Author(s):

William L. Kubic ◽

Rhodri W. Jenkins ◽

Cameron M. Moore ◽

Troy A. Semelsberger ◽

Andrew D. Sutton

Keyword(s):

Neural Network ◽

Organic Compounds ◽

Group Contribution ◽

Determination of Parachor of Various Compounds Using an Artificial Neural Network−Group Contribution Method

Industrial & Engineering Chemistry Research ◽

10.1021/ie102464t ◽

2011 ◽

Vol 50 (9) ◽

pp. 5815-5823 ◽

Cited By ~ 31

Author(s):

Farhad Gharagheizi ◽

Ali Eslamimanesh ◽

Amir H. Mohammadi ◽

Dominique Richon

Keyword(s):

Neural Network ◽

Group Contribution ◽

Estimation of Pure Component Parameters of PC-SAFT EoS by an Artificial Neural Network Based on a Group Contribution Method

Fluid Phase Equilibria ◽

10.1016/j.fluid.2021.113179 ◽

2021 ◽

pp. 113179

Author(s):

Hiroaki Matsukawa ◽

Masayuki Kitahara ◽

Katsuto Otake

Keyword(s):

Neural Network ◽

Pure Component ◽

Group Contribution ◽

Representation/Prediction of Solubilities of Pure Compounds in Water Using Artificial Neural Network−Group Contribution Method

Journal of Chemical & Engineering Data ◽

10.1021/je101061t ◽

2011 ◽

Vol 56 (4) ◽

pp. 720-726 ◽

Cited By ~ 41

Author(s):

Farhad Gharagheizi ◽

Ali Eslamimanesh ◽

Amir H. Mohammadi ◽

Dominique Richon

Keyword(s):

Neural Network ◽

Group Contribution ◽

Pure Compounds ◽

Artificial Neural Network Investigation of the Structural Group Contribution Method for Predicting Pure Components Auto Ignition Temperature

Industrial & Engineering Chemistry Research ◽

10.1021/ie0300373 ◽

2003 ◽

Vol 42 (22) ◽

pp. 5708-5714 ◽

Cited By ~ 35

Author(s):

Tareq A. Albahri ◽

Reena S. George

Keyword(s):

Neural Network ◽

Ignition Temperature ◽

Group Contribution ◽

Structural Group ◽

Auto Ignition ◽

Liquid density prediction of five different classes of refrigerant systems (HCFCs, HFCs, HFEs, PFAs and PFAAs) using the artificial neural network-group contribution method

International Journal of Refrigeration ◽

10.1016/j.ijrefrig.2014.09.007 ◽

2014 ◽

Vol 48 ◽

pp. 188-200 ◽

Cited By ~ 9

Author(s):

Majid Moosavi ◽

Elaheh Sedghamiz ◽

Maryam Abareshi

Keyword(s):

Neural Network ◽

Group Contribution ◽

Liquid Density ◽

Density Prediction ◽

Prediction of the specific volume of polymeric systems using the artificial neural network-group contribution method

Fluid Phase Equilibria ◽

10.1016/j.fluid.2013.07.004 ◽

2013 ◽

Vol 356 ◽

pp. 176-184 ◽

Cited By ~ 11

Author(s):

Majid Moosavi ◽

Nima Soltani

Keyword(s):

Neural Network ◽

Specific Volume ◽

Group Contribution ◽

Polymeric Systems ◽

Prediction of hydrocarbon densities using an artificial neural network–group contribution method up to high temperatures and pressures

Thermochimica Acta ◽

10.1016/j.tca.2013.01.038 ◽

2013 ◽

Vol 556 ◽

pp. 89-96 ◽

Cited By ~ 20

Author(s):

Majid Moosavi ◽

Nima Soltani

Keyword(s):

Neural Network ◽

High Temperatures ◽

Group Contribution ◽

Artificial Neural Network-Group Contribution Method for Predicting Standard Enthalpy of Formation in the Solid State: C–H, C–H–O, C–H–N, and C–H–N–O Compounds

International Journal of Thermophysics ◽

10.1007/s10765-015-1928-x ◽

2015 ◽

Vol 36 (10-11) ◽

pp. 2820-2832 ◽

Cited By ~ 5

Author(s):

Soufiane Guella ◽

Kadda Argoub ◽

Ali Mustapha Benkouider ◽

Ahmed Yahiaoui ◽

Rachid Kessas ◽

...

Keyword(s):

Neural Network ◽

Solid State ◽

Enthalpy Of Formation ◽

Standard Enthalpy ◽

Group Contribution ◽

Standard Enthalpy Of Formation ◽

Use of Artificial Neural Network-Group Contribution Method to Determine Surface Tension of Pure Compounds

Journal of Chemical & Engineering Data ◽

10.1021/je2001045 ◽

2011 ◽

Vol 56 (5) ◽

pp. 2587-2601 ◽

Cited By ~ 70

Author(s):

Farhad Gharagheizi ◽

Ali Eslamimanesh ◽

Amir H. Mohammadi ◽

Dominique Richon

Keyword(s):

Neural Network ◽

Surface Tension ◽

Group Contribution ◽

Pure Compounds ◽