Farhad Gharagheizi

A new group contribution-based model for estimation of lower flammability limit of pure compounds

In the present study, a new method is presented for estimation of lower flammability limit (LFL) of pure compounds. This method is based on a combination of a group contribution method and neural networks. The parameters of the model are the occurrences of a new collection of 105 functional groups. Basing on these 105 functional groups, a feed forward neural network is presented to estimate the LFL of pure compounds. The average absolute deviation error obtained over 1057 pure compounds is 4.62%. Therefore, the model is an accurate model and can be used to predict the LFL of a wide range of pure compounds.

Prediction of upper flammability limit percent of pure compounds from their molecular structures

In this study, a quantitative structure-property relationship (QSPR) is presented to predict the upper flammability limit percent (UFLP) of pure compounds. The obtained model is a five parameters multi-linear equation. The parameters of the model are calculated only from chemical structure. The average absolute error and squared correlation coefficient of the obtained model over all 865 pure compounds used to develop the model are 9.7%, and 0.92, respectively.

A Molecular‐Based Model for Prediction of Solubility of C60 Fullerene in Various Solvents

Abstract In this presented work, a quantitative structure‐property relationship study (QSPR) was done for prediction of solubility of C60 fullerene in various solvents. In this study, genetic algorithm‐based multivariate linear regression (GA‐MLR) was applied to obtain most statistically effective molecular descriptors on solubility of C60 in various solvents. All of these molecular descriptors are only calculated from the chemical structure of solvents. For considering nonlinear behavior of appearing molecular descriptors in GA‐MLR section, a feed forward neural network (FFNN) was constructed and optimized for prediction of solubility of C60 fullerene in solvents. Obtained models considerably showed better accuracy in comparison with the previous models.

Prediction of molecular diffusivity of pure components into air: a QSPR approach.

The molecular diffusivity of 378 pure components into air was predicted using genetic algorithm-based multivariate linear regression (GA-MLR) and feed forward neural networks (FFNN). GA-MLR was used to select the molecular descriptors, as inputs for FFNN. The correlation coefficient (R2) of obtained multivariate linear seven-descriptor model by GA-MLR is 0.9334 and the same value for generated FFNN is 0.9643. These models can be applied for prediction of molecular diffusivity of pollutants into air in case of air pollution studies.

Prediction of some important physical properties of sulfur compounds using quantitative structure–properties relationships

In this work, physical properties of sulfur compounds (critical temperature (Tc), critical pressure (Pc), and Pitzer’s acentric factor (ω)) are predicted using quantitative structure–property relationship technique. Sulfur compounds present in petroleum cuts are considered environmental hazards. Genetic algorithm based multivariate linear regression (GA-MLR) is used to select most statistically effective molecular descriptors on the properties. Using the selected molecular descriptors, feed forward neural networks (FFNNs) are applied to develop some molecular-based models to predict the properties. The presented models are quite accurate and can be used to predict the properties of sulfur compounds.

Prediction of the Standard Enthalpy of Formation of Pure Compounds Using Molecular Structure

A predictive approach has been presented to calculate the standard enthalpy of formation of pure compounds based on a quantitative structure–property relationship technique. A large number (1692) of pure compounds were used in this study. A genetic algorithm based on multivariate linear regression was used to subset variable selection. Using the selected molecular descriptors an optimized feed forward neural network was presented to predict the ΔHfo of pure compounds.

An accurate model for prediction of autoignition temperature of pure compounds

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 15.44K, and average percent error of 1.6% for the experimental values.

A QSPR model for estimation of lower flammability limit temperature of pure compounds based on molecular structure

In this study, a quantitative structure-property relationship was presented to estimate lower flammability limit temperature (LFLT) of pure compounds. This relationship is a multi-linear equation and has six parameters. These chemical structure-based parameters were selected from 1664 molecular-based parameters by genetic algorithm multivariate linear regression (GA-MLR). Since 1171 compounds were used to develop this equation, the model can be used to estimate the LFLT of a wide range of pure compounds.

A Molecular Approach for the Prediction of Sulfur Compound Solubility Parameters

A quantitative structure–property relationship (QSPR) study was performed to construct a multivariate linear model and a three-layer feed-forward neural network model. This model relates the solubility parameters of 82 sulfur compounds to their structures. Molecular descriptors, which are extracted from the molecular structure of compounds, have been used as model parameters. The multivariate linear model was gained by a genetic algorithm–based multivariate linear regression; the results showed that the squared correlation coefficient (R2) between predicted and experimental values was 0.964. Next, a three-layer feed-forward neural network model with optimized structure was employed; the results showed that the squared correlation coefficient (R2) is 0.9874, and with this model we can predict the solubility parameter more accurately than the linear model. Supplemental materials are available for this article. Go to the publishers online edition of Phosphorus, Sulfur, and Silicon and the Related Elements to view the free supplemental file.

A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition.

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 298.15K 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.