Mehdi Sattari

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.

Modeling of the Properties of Gasoline and Petroleum Fractions Using a Robust Scheme

Gasoline is one of the most recognized products of the petroleum industry due to its use as a liquid fuel worldwide. As a result, it is of great importance to accurately determine the properties of gasoline, so as to evaluate its quality. In this article, an effective mathematical and predictive strategy, namely least squares support vector machines (LSSVM) is applied to predict some gasoline properties, viz. specific gravity (SG), motor octane number (MON), research octane number (RON), and Reid vapor pressure (RVP). A comprehensive error analysis is also undertaken to compare the values predicted from the model with actual data which enables one to evaluate the performance of the model developed in this study. The results indicate that the model developed has reasonable accuracy and prediction capability. The correlation indices, R2, are 0.990, 0.933, 0.955, and 0.920 for SG, MON, RON, and RVP, respectively.