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      저자 : Siavash Riahi (검색결과 2 건)
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      • KCI등재

        Molecular investigation of amine performance in the carbon capture process: Least squares support vector machine approach

        Bijan Rezaei,Siavash Riahi,Ali Ebrahimpoor Gorji 한국화학공학회 2020 Korean Journal of Chemical Engineering Vol.37 No.1

        The growing threat of global warming has raised more attention towards carbon capture. Current amine plants used for carbon removal suffer from great costs inflicted by high energy demand of the solvent regeneration step. Recently, looking for amines with proper performance in reduced temperatures has been the subject of many researches. Clearly, conducting these researches without any criterion and based only on trial and error wastes large amounts of money and time; thus, it is highly needed that the effect of different amine structural parameters be studied on the amine’s cyclic capacity. Quantitative structure property relationship (QSPR) provides an effective method for predicting amines capacity for CO2 absorption. In this work, density functional theory (DFT) was employed for optimization of the molecular geometries, and linear and nonlinear models based on parameters related to the molecular structure are presented. The value of the square of the correlation coefficient (R2) for the MLR and SVM models are 0.894 and 0.973, respectively. Developed models can be used as a criterion for amine selection. Reliability and high predictability of the models are confirmed based on statistical tests. Moreover, mechanistic interpretation of models for better understanding of the reaction mechanism of carbon capture was discussed.

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        Quantitative structure-property relationship (QSPR) for prediction of CO2 Henry’s law constant in some physical solvents with consideration of temperature effects

        Ali Ebrahimpoor Gorji,Zahra Eshaghi Gorji,Siavash Riahi 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.5

        Different types of physical solvents have been utilized for CO2 removal from natural gas in the sweetening process. In this work, quantitative structure-property relationship (QSPR) method is suggested to build powerful models to predict Henry’s law constant (HLC) for CO2 in physical solvents. Modeling the HLC for CO2 as a function of molecular descriptors was achieved by multiple linear regression and descriptor selection was by genetic algorithm. The main proposed model has two simple descriptors, including the number of hydroxyl groups and molecular weight of solvents at fixed temperature. Also, the effect of temperature was studied, and this operational variable was added to the mentioned simple descriptors. In this case, the data set is comprised of 77 HLC for CO2 in solvents and at different temperatures. Several internal and external validation methods demonstrated the excellent ability for prediction, and the average relative deviation of main model was 6.48.

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