Tehrān, Iran
Tehrān, Iran

Time filter

Source Type

Gharagheizi F.,University of Tehran | Gharagheizi F.,Saman Energy Giti Co. | Abbasi R.,Saman Energy Giti Co. | Tirandazi B.,Iran University of Science and Technology
Industrial and Engineering Chemistry Research | Year: 2010

In this work, a new model is presented for estimation of Henry's law constant of pure compounds in water at 25 °C (H). This model is based on a combination between a group contribution method and neural networks. The needed parameters of the model are the occurrences of a new collection of 107 functional groups. On the basis of these 107 functional groups, a feed forward neural network is presented to estimate the H of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation error, and root-mean-square error of the model over a diverse set of 1940 pure compounds used are, respectively, 0.9981, 2.84%, 2.4, and 0.1 (all the values obtained using logH based data). Therefore, the model is a comprehensive and an accurate model and can be used to predict the H of a wide range of chemical families of pure compounds in water better than previously presented models. © 2010 American Chemical Society.


Gharagheizi F.,Saman Energy Giti Co. | Babaie O.,Saman Energy Giti Co. | Mazdeyasna S.,Iran University of Science and Technology
Industrial and Engineering Chemistry Research | Year: 2011

In this work, the artificial neural network-group contribution (ANN-GC) method is applied to estimate the vaporization enthalpy of pure chemical compounds at their normal boiling point. A group of 4907 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 0.993, root mean square error of 1.1 kJ/mol, and average absolute deviation lower than 1.5% for the estimated properties from existing experimental values. © 2011 American Chemical Society.


Gharagheizi F.,Saman Energy Giti Co. | Sattari M.,Saman Energy Giti Co. | Tirandazi B.,Iran University of Science and Technology
Industrial and Engineering Chemistry Research | Year: 2011

In this study, a new group contribution-based model is presented to predict the enthalpy of sublimation of pure compounds. This model can also be used to predict the lattice crystal energy of such compounds. The model is a neural network using the number of occurrences of 172 chemical groups on the chemical structures of pure compounds to predict the enthalpy of sublimation. This comprehensive model is generated using a large data set of pure compounds (1384 pure compounds). The squared correlation coefficient, average percent error, and root-mean-square error of the model over all investigated compounds are 0.9854, 3.54%, and 4.21, respectively. © 2011 American Chemical Society.


Gharagheizi F.,Saman Energy Giti Company | 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.


Mirkhani S.A.,Sharif University of Technology | Gharagheizi F.,Saman Energy Giti Co. | Sattari M.,Saman Energy Giti Co.
Chemosphere | Year: 2012

Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15. K and atmospheric pressure. Four thousand five hundred and seventy nine organic compounds from broad spectrum of chemical families have been investigated to propose a comprehensive and predictive model. The final model is derived by Genetic Function Approximation (GFA) and contains five descriptors. Using this dedicated model, we obtain satisfactory results quantified by the following statistical results: Squared Correlation Coefficient = 0.9723, Standard Deviation Error = 0.003 and Average Absolute Relative Deviation = 0.3% for the predicted properties from existing experimental values. © 2011 Elsevier Ltd.


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.


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.


Gharagheizi F.,Saman Energy Giti Co. | Abbasi R.,Saman Energy Giti Co.
Industrial and Engineering Chemistry Research | Year: 2010

In this study, a new group contribution-based model is presented for the prediction of the upper flash point temperature of pure compounds based on a large data set containing 1294 pure compounds. The model is a neural network using a number of occurrences of 122 chemical groups in a pure compound to predict its related UFLT (Upper Flash Point Limit). The squared correlation coefficient, average percent error, mean average error, and root-mean-square error of the model over the main data set containing 1294 pure compounds are 0.99, 1.7%, 6, and 8.5, respectively. © 2010 American Chemical Society.


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.


Keshavarz M.H.,Malek-Ashtar University of Technology | Gharagheizi F.,Saman Energy Giti Co. | Pouretedal H.R.,Malek-Ashtar University of Technology
Fluid Phase Equilibria | Year: 2011

A new general method has been introduced for prediction of melting points of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. It extends earlier works, which were used for certain classes of energetic compounds, to estimate melting points of any compound containing at least one of the groups Ar-NO2, C-NO2, C-ONO2 or N-NO2 through additive and non-additive functions. Elemental composition of an energetic compound of composition CxHyNvOw was used as additive function. The methodology assumes that non-additive function of desired compound can be approximated as the difference between the positive and negative contributions of specific structural moieties. The new model is applied for 149 different energetic compounds including complex molecular structures. For those energetic compounds where one of well-developed group additivity methods can be applied, it is shown that average deviation of the new method is much lower. The new method also gives better predictions as compared to another new and suitable model, which is based on division of the calculated enthalpy of melting to entropy of melting. © 2011 Elsevier B.V.

Loading Saman Energy Giti Co. collaborators
Loading Saman Energy Giti Co. collaborators