Prediction of CO 2 loading of amines in carbon capture process using membrane contactors: A molecular modeling

Rezaei, Bijan; Riahi, Siavash

doi:10.1016/j.jngse.2016.05.003

Cited by 18 publications

(5 citation statements)

References 43 publications

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“…To evaluate the sensitivity and chance correlation of two bettest QSAR models (Model 7 and Model 33), Y-scrambling test was carried out for both models. 18 The dependent variable (CO 2 capacity form) is randomly applied to different amines, and new QSAR models are built according to the previous independent variables matrix. The newly built QSAR models should have low R 2 .…”

Section: Resultsmentioning

confidence: 99%

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Quantitative Relationship between CO₂ Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

Cheng

Zhu

et al. 2019

Ind. Eng. Chem. Res.

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CO 2 emissions reduction has become a hot issue to relieve global warming in recent years. CO 2 absorption with amines aqueous solutions is a promising method for its capture, and increasing CO 2 capacity and reducing energy consumption are essential for its application. In this work, a data set of 29 different amines was regressed with genetic function approximation (GFA) and artificial neural network (ANN) algorithm to find a predictive quantitative relationship between amine structure and CO 2 absorption capacity. Density functional theory (DFT) methods at levels B3LYP and 6-311+g (d,p) were used to optimize the structures. Both reactants and products were introduced in regression, and an optimal model with best relevance and predictability for CO 2 absorption at 313 K was obtained and experimentally confirmed. Descriptors analysis showed that reducing the number of hydroxyl groups, increasing molecular mass, and changing steric effect of amine structure would increase its CO 2 absorption capacity. Two new amine structures predicted with high absorption capacity were proposed.

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Section: Resultsmentioning

confidence: 99%

“…Singh et al found the CO 2 absorption capacity increased with the number of N groups in the amine molecule up to 3.03 mol CO 2 /mol amine (teraethylenepentamine). Quantitative research conducted by Rezaei et al also showed that the number of the hydroxyl groups and N atoms significantly impacted the CO 2 absorption capacity.…”

Section: Introductionmentioning

confidence: 96%

Quantitative Relationship between CO₂ Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

Cheng

Zhu

et al. 2019

Ind. Eng. Chem. Res.

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“…Using the QSPR method, Momeni obtained a simple and robust model to predict the amine capacity for carbon dioxide absorption. Their results showed that by increasing the number of primary and secondary amine functional groups (NH bonds) in the molecular structure of amine, the capacity of amine for carbon dioxide absorption (or rich loading), increases significantly. , Additionally, Rezaei has used the QSPR method to predict the solubility of carbon dioxide in amine solutions in the absorption and desorption process using membrane contactors and concluded that the steric hindrance effect on the desorption model has a more significant influence than the absorption model . In previous papers, nonlinear modeling was not investigated.…”

Section: Introductionmentioning

confidence: 99%

“…8,16 Additionally, Rezaei has used the QSPR method to predict the solubility of carbon dioxide in amine solutions in the absorption and desorption process using membrane contactors and concluded that the steric hindrance effect on the desorption model has a more significant influence than the absorption model. 17 In previous papers, nonlinear modeling was not investigated. Since the relationship between the structure of the compounds and their properties could be linear and nonlinear, in this work, to the best our knowledge, nonlinearity was studied for the first time.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Amines Capacity for Carbon Dioxide Absorption Based on Structural Characteristics

Khaheshi

Riahi

Mohammadi‐Khanaposhtani

et al. 2019

Ind. Eng. Chem. Res.

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Global warming is a major concern in environmental management. Among the greenhouse gases, carbon dioxide plays an important role in global warming. Many novel technologies offer different solutions to reduce carbon dioxide emissions to the environment. One of the most commonly used technologies to absorb carbon dioxide is the use of solvents, especially amine solutions. The chemical structure of amines has a powerful effect on carbon dioxide absorption. Due to the fact that laboratory studies on a large number of amines are costly and time-consuming, the modeling of the effect of different structural parameters on the absorption capacity of amine solutions plays an important role. Quantitative structure property relationship (QSPR) provides an effective method for predicting amines capacity for carbon dioxide absorption. In this work, the density functional theory (DFT-B3LYP) method was employed for optimization of the molecular geometries. GA-MLR (genetic algorithm-multilinear regression) and LS-SVM (least-squares support vector machines) were utilized to build linear and nonlinear QSPR models, respectively. The value of the square of correlation coefficient (R 2 ) for the MLR and SVM models are 0.962 and 0.992, respectively. The suggested models were evaluated by a variety of statistical tests. The results showed that using a nonlinear model is better than a linear model for predicting the amine capacity to absorb carbon dioxide. Based on the structural information that were appeared in the model,we concluded that the volume of the amine molecules and the length of the carbon chain in the structure of amines have significant effects on amine capacity. Comparing this work with others showed that this model is very accurate and versatile to the prediction of carbon dioxide absorption.

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“…Several CO 2 capture techniques, such as chemical and physical solvent absorption, membrane purification, cryogenic distillation, catalytic conversion, and adsorption processes, have been proposed for the separation and regeneration of CO 2 emitted in the atmosphere [7][8][9] . The most widely applied technique for CO 2 removal is the absorption of various solvent types 10,11 . Choosing an economically solvent system includes two essential criteria: high CO 2 selectivity over other gases such as CH 4 and N 2 , and high CO 2 solubility.…”

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confidence: 99%

Modeling and optimization of CO2 mass transfer flux into Pz-KOH-CO2 system using RSM and ANN

Pashaei

Mashhadimoslem

Ghaemi

2023

Sci Rep

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In this research, artificial neural networks (ANN) and response surface methodology (RSM) were applied for modeling and optimization of carbon dioxide (CO2) absorption using KOH-Pz-CO2 system. In the RSM approach, the central composite design (CCD) describes the performance condition in accordance with the model using the least-squares technique. The experimental data was placed in second-order equations applying multivariate regressions and appraised applying analysis of variance (ANOVA). The p-value for all dependent variables was obtained to be less than 0.0001, indicating that all models were significant. Furthermore, the experimental values obtained for the mass transfer flux satisfactorily matched the model values. The R2 and Adj-R2 models are 0.9822 and 0.9795, respectively, which, it means that 98.22% of the variations for the NCO2 is explained by the independent variables. Since the RSM does not create any details about the quality of the solution acquired, the ANN method was applied as the global substitute model in optimization problems. The ANNs are versatile utensils that can be utilized to model and anticipate different non-linear and involved processes. This article addresses the validation and improvement of an ANN model and describes the most frequently applied experimental plans, about their restrictions and generic usages. Under different process conditions, the developed ANN weight matrix could successfully forecast the behavior of the CO2 absorption process. In addition, this study provides methods to specify the accuracy and importance of model fitting for both methodologies explained herein. The MSE values for the best integrated MLP and RBF models for the mass transfer flux were 0.00019 and 0.00048 in 100 epochs, respectively.

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Prediction of CO 2 loading of amines in carbon capture process using membrane contactors: A molecular modeling

Cited by 18 publications

References 43 publications

Quantitative Relationship between CO₂ Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

Quantitative Relationship between CO₂ Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

Prediction of Amines Capacity for Carbon Dioxide Absorption Based on Structural Characteristics

Modeling and optimization of CO2 mass transfer flux into Pz-KOH-CO2 system using RSM and ANN

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Prediction of CO 2 loading of amines in carbon capture process using membrane contactors: A molecular modeling

Cited by 18 publications

References 43 publications

Quantitative Relationship between CO2 Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

Quantitative Relationship between CO2 Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

Prediction of Amines Capacity for Carbon Dioxide Absorption Based on Structural Characteristics

Modeling and optimization of CO2 mass transfer flux into Pz-KOH-CO2 system using RSM and ANN

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Product

Resources

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Quantitative Relationship between CO₂ Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

Quantitative Relationship between CO₂ Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study