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Abstract:The standard enthalpy of formation of 1115 compounds from all chemical groups, were predicted using genetic algorithm-based multivariate linear regression (GA-MLR). The obtained multivariate linear five descriptors model by GA-MLR has correlation coefficient ( 9830 . 0 2 = R ). All molecular descriptors which have entered in this model are calculated from chemical structure of any molecule. As a result, application of this model for any compound is easy and accurate.

The solubility parameters of 1228 solvents, from all the chemical groups, were predicted using Genetic Algorithm-Based Multivariate Linear Regression (GA-MLR) and Generalized Function Approximation Neural Network (GRNN). GA-MLR was used to select the molecular descriptors, as inputs for GRNN. The obtained multivariate linear seven descriptors model by GA-MLR had a correlation coefficient of R 2 ¼ 0:821. The generated GRNN in this work has a correlation coefficient of R 2 ¼ 0:98.

In the present work, a quantitative structure−property relationship (QSPR) study is performed to predict the aniline point temperature of pure hydrocarbon components. As a powerful tool, genetic algorithm-based multivariate linear regression (GA-MLR) is applied to select most statistically effective molecular descriptors on the aniline point temperature of pure hydrocarbon components. Also, a three-layer feed forward neural network (FFNN) is constructed to consider the nonlinear behavior of appearing molecular descriptors in GA-MLR result. The obtained results show that the constructed FFNN can accurately predict the aniline point temperature of pure hydrocarbon components.

In this work, physical properties of sulfur compounds (critical temperature (Tc), critical pressure (Pc), and Pitzer's acentric factor (omega)) 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.

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The simplest experimental method to determine the Hansen solubility parameters (HSPs) for a polymer is to evaluate whether or not it dissolves in selected solvents. Those solvents dissolving the polymer will have HSPs closer to those of the polymer than those that do not. A computer program or graphical method can then be used to find the HSP for the polymer. In this work, an improved method for calculating the HSP of polymers, based on the Nelder-Mead optimization algorithm, is presented. The results of this program fit the data very well.