COSMO-RS prediction and experimental verification of 1,5-pentanediamine extraction from aqueous solution by ionic liquids

Jiang, Chenhao; Cheng, Hongye; Qin, Zexian; Wang, Ruizhuan; Chen, Lifang; Yang, Chen; Qi, Zhiwen; Liu, Xiucai

doi:10.1016/j.gee.2020.12.011

Cited by 56 publications

(28 citation statements)

References 48 publications

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“…This will extend the IL screening and application windows in extractive separation fields. Jiang et al 122 employed the COSMO‐RS model to screen the IL candidates for 1,5‐pentanediamine extraction from aqueous solution, and the prediction capacity of the model was verified by the LLE experiment. Therefore, these CAILD methods based on predictive molecular thermodynamic models can greatly promote the development of novel desulfurization technologies by screening desulfurizers while avoiding time‐consuming experimental investigations.…”

Section: Applications In Phase Equilibria and Separation Processesmentioning

confidence: 99%

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Predictive molecular thermodynamic models for ionic liquids

Wei

Chen

et al. 2022

AIChE Journal

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Predictive molecular thermodynamic models can bridge the gap between the microscopic molecular level and macroscopic system scale. Over the past few decades, ionic liquids (ILs), as a class of green solvents and functional materials, have received widespread research interest in the field of chemical processing owing to their unique physicochemical merits. This review aims to provide an easy‐to‐read and exhaustive reference regarding state‐of‐the‐art predictive thermodynamic models for ILs, with more focuses on UNIFAC‐ and COSMO‐based models and various applications involving phase equilibrium prediction, guidance for IL screening and design, and building equilibrium stage models for separation processes. This review provides a theoretical perspective on the structure–property relationships between molecular structures and phase behaviors for the systems and the constituted components covering a multi‐scale viewpoint from molecular level to industrial scale. Moreover, the predictive capacities of different thermodynamic models are comprehensively compared.

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Section: Applications In Phase Equilibria and Separation Processesmentioning

confidence: 99%

“…This will extend the IL screening and application windows in extractive separation fields. Jiang et al 122 the review article entitled "Extractive Distillation: A Review" published in 2003. 123 Since then, there has been increasing research on extractive distillation technologies with ILs.…”

Section: Liquid-liquid Extractionmentioning

confidence: 99%

Predictive molecular thermodynamic models for ionic liquids

Wei

Chen

et al. 2022

AIChE Journal

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“…Their predictive ability depends on the sensible group split and the availability of group interaction parameters which have been regressed for several decades 9–11 . Moreover, COSMO‐RS (COnductor‐like Screening MOdel for Real Solvents) and its variant COSMO‐SAC (COSMO segment activity coefficient) became alternative prediction methods, 12–14 because they are more flexible and only use individual component parameters as model inputs 15 . Other methods of predicting γ ∞ include modified separation of cohesive energy density (MOSCED) 16 and Hildebrand 17 .…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11] Moreover, COSMO-RS (COnductor-like Screening MOdel for Real Solvents) and its variant COSMO-SAC (COSMO segment activity coefficient) became alternative prediction methods, [12][13][14] because they are more flexible and only use individual component parameters as model inputs. 15 Other methods of predicting γ ∞ include modified separation of cohesive energy density (MOSCED) 16 and Hildebrand. 17 The models mentioned above have been constantly validated, yet large deviations still occur.…”

Section: Introductionmentioning

confidence: 99%

Prediction of infinite‐dilution activity coefficients with neural collaborative filtering

et al. 2022

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Accurate prediction of infinite dilution activity coefficient (γ ∞ ) for phase equilibria and process design is crucial. In this work, an experimental γ ∞ dataset containing 295 solutes and 407 solvents (21,048 points) is obtained through data integrating, cleaning, and filtering. The dataset is arranged as a sparse matrix with solutes and solvents as columns and rows, respectively. Neural collaborative filtering (NCF), a modern matrix completion technique based on deep learning, is proposed to fully fill in the γ ∞ matrix. Ten-fold cross-validation is performed on the collected dataset to test the effectiveness of the proposed NCF, proving that NCF outperforms the state-ofthe-art physical model and previous machine learning model. The completed γ ∞ matrix makes solvent screening and extension of UNIFAC parameters possible. Taking two typical hard-to-separate systems (benzene/cyclohexane and methyl cyclopentane/n-hexane mixtures) as examples, the NCF-developed database provides high-throughput screening for separation systems in terms of solvent selectivity and capacity.

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“…The study of solvent extraction mechanism is of great significance for solvent screening and molecular design. At present, there are many methods for solvent screening, such as empirical method, group contribution method, , and computer design method, , but they cannot explain the extraction mechanism of solvents from a micro perspective view. Therefore, it is of great significance to explore the micro mechanism of solvents in extractive distillation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Eutectic Solvents and Organic Solvent Effects on the Separation of Ternary Azeotropes by the Experimental Study and Molecular Simulation

Qiu

Zhou

et al. 2021

ACS Sustainable Chem. Eng.

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Extractive distillation is often used to separate difficult separation systems so as to realize the recovery and utilization of resources in industry. Based on the separation of ethylene glycol (EG), isoamyl alcohol, and water, the separation of ternary azeotropic mixtures by extractive distillation was studied. The study of extraction mechanism is very important for solvent screening and molecular design. To compare the selectivity of solvents, the interaction energies between solvent–water, solvent–EG, and solvent–isoamyl alcohol were calculated by the quantum chemical method. Atoms in molecules, reduced density gradient, and electrostatic potential were used to study the microextraction mechanism of solvents in the water–EG–isoamyl alcohol system. In extractive distillation, vapor–liquid equilibrium (VLE) data is used as the basic data for designing and optimizing the process. In order to solve the problem of lack of VLE data between N,N-dimethylformamide, dimethyl sulfoxide, choline-based deep eutectic solvents, and azeotropes, the VLE experiment of the solvent and azeotrope system was carried out. In this work, the selectivity of different solvents was compared by the quantum chemical method, the microextraction mechanism of solvents was explored, and the lack of VLE experimental data was supplemented, which provided theoretical guidance for the selection and design of solvent molecules with excellent performance.

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COSMO-RS prediction and experimental verification of 1,5-pentanediamine extraction from aqueous solution by ionic liquids

Cited by 56 publications

References 48 publications

Predictive molecular thermodynamic models for ionic liquids

Predictive molecular thermodynamic models for ionic liquids

Prediction of infinite‐dilution activity coefficients with neural collaborative filtering

Comparison of Deep Eutectic Solvents and Organic Solvent Effects on the Separation of Ternary Azeotropes by the Experimental Study and Molecular Simulation

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