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Gharagheizi F.,Saman Energy Giti Co. | Mirkhani S.A.,Sharif University of Technology | Tofangchi Mahyari A.-R.,Sharif University of Technology
Energy and Fuels | Year: 2011

The artificial neural network-group contribution (ANN-GC) method is applied to estimate the standard enthalpy of combustion of pure chemical compounds. A total of 4590 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 99, root mean square error of 12.57 kJ/mol, and average absolute deviation lower than 0.16% for the estimated properties from existing experimental values. © 2011 American Chemical Society. Source


Gharagheizi F.,Saman Energy Giti Co. | Salehi G.R.,Islamic Azad University
Thermochimica Acta | Year: 2011

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 (R2) 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. © 2011 Elsevier B.V. All Reserved rights. Source


Gharagheizi F.,Saman Energy Giti Co.
Journal of Hazardous Materials | Year: 2011

Accurate prediction of pure compounds autoignition temperature (AIT) is of great importance. In this study, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to evaluate the AIT of pure compounds. 1025 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient of 0.984, root mean square error of 15.44. K, and average percent error of 1.6% for the experimental values. © 2011 Elsevier B.V. Source


Gharagheizi F.,Saman Energy Giti Co. | Keshavarz M.H.,Malek-Ashtar University of Technology | Sattari M.,MAPNA Generator Engineering and Manufacturing Co. PARS
Journal of Thermal Analysis and Calorimetry | Year: 2012

In this study, a simple three-parameter linear model is presented for estimation of flash point (FP) of pure compounds. The parameters of the model contain experimental normal boiling point of the compound and two chemical structure-based parameters. A comprehensive database of FPs containing 1472 pure compounds of various chemical structures was used to develop the model. The squared correlation coefficient and average absolute error of the model calculation results for all of the compounds presented in the database are evaluated to be 0.982 and 7.2 K, respectively. © Akadémiai Kiadó, Budapest, Hungary 2011. Source


Gharagheizi F.,Saman Energy Giti Co. | Gohar M.R.S.,Sharif University of Technology | Vayeghan M.G.,Islamic Azad University at North Tehran
Journal of Thermal Analysis and Calorimetry | Year: 2012

In this study, the quantitative structure-property relationship method is applied to predict the enthalpy of fusion of pure chemical compounds at their normal melting point. A genetic algorithm-based multivariate linear regression is used to select the most statistically effective molecular descriptors for evaluating this property. To propose a comprehensive and predictive model, 3,846 pure chemical compounds are investigated. The root mean square of error and the average absolute deviation of the model are equal to 2.57 kJ/mol and 9.7%. © 2011 Akadémiai Kiadó, Budapest, Hungary. Source

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