Prediction of amines capacity for carbon dioxide absorption in gas sweetening processes

Almost all gas reservoirs around the world produce sour gas that contains considerable amounts of acid gases including carbon dioxide and hydrogen sulfide. Because carbon dioxide in water tends to cause corrosion and the presence of CO2 in natural gas reduces its heating value, it must be removed prior to preparation of natural gas for marketing. Many technologies have offered various solutions to remove carbon dioxide from natural gas based on regenerable amine-based solvents. In order to make these technologies more efficient and economical, further research is required in terms of experiment and modeling to identify the main parameters which influence the capacity of amines for CO2 absorption. Numerous studies of amines have shown evidence that some relationships exist between the structure of amine and its capacity for carbon dioxide absorption. Quantitative Structure Property/Activity Relationship (QSPR/QSAR) provides an effective method for predicting amines capacity for CO2 absorption. In this paper, first, Density functional theory (DFT) method level of B3LYP and 6-311 + g (d,p) basis set was employed to complete molecular geometrical optimization. Then, the Quantitative relationship between the absorption capacities data and calculated descriptors was achieved by the multiple linear regression (MLR) and model variables were selected by genetic algorithms (GA). The accuracy of the model was verified by different statistical methods and the result proved high statistical qualities of the model. Unlike other QSPR researches, the reported equation in this paper consists of simple and easy-calculated descriptors which form a robust model for predicting amines capacity of carbon dioxide absorption.