Ionic liquid screening for CO2 capture and H2S removal from gases: The syngas purification case

Taheri, Mohsen; Zhu, Ruisong; Yu, Gangqiang; Lei, Zhigang

doi:10.1016/j.ces.2020.116199

Cited by 77 publications

(26 citation statements)

References 87 publications

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“…All the operating temperature and pressure were converted into K and kPa, respectively, while the concentration of the absorbent was considered in weight fraction. As a result, H 2 S solubility of 46 absorbents at various operating temperatures and pressures with 2526 data points were collected from 37 literature studies as presented in Table S1 in the Supporting Information. ,,,,,,− ,− The collected absorbents can be classified as amines, ionic liquids, physical organic absorbents, and hybrid absorbents (amine + physical absorbent and amine + ionic liquid).…”

Section: Data Collection and Processingmentioning

confidence: 99%

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

et al. 2021

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Solubility of hydrogen sulfide (H2S) in 46 single and blended physical absorbents, amines, ionic liquids, and hybrid absorbents of amines + ionic liquids and amines + physical absorbents was successfully predicted based on artificial neural networks (ANNs). Three neural network algorithms of Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) were applied for architecting the ANN models. The results showed that both the number of hidden neurons and the prediction algorithm affected the prediction of H2S solubility. Based on the mean square error (MSE) and determination coefficient (R 2), the most attractive model was the LM-ANN model with 17 hidden neurons. As a result, very satisfactory prediction performance (for the testing data set) with an MSE of 0.0014 and an R 2 of 0.9817 was obtained from the developed LM-ANN model. Additionally, a parity chart confirmed that the predicted solubility of H2S well aligned with the experimental data. To effectively absorb H2S and maintain high solubility of H2S, the absorbent should be well complied with the operating pressure. For a low-pressure range of less than 100 kPa, amines are very attractive. As the pressure elevated to 100–1000 kPa, amines and hybrid amine + physical absorbents are suggested. Lastly, at a high pressure over 1000 kPa, physical absorbents and ionic liquids are recommended.

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Section: Data Collection and Processingmentioning

confidence: 99%

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

et al. 2021

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“…ML has been used to perform solvent screening for different applications 80,81 . Some studies related to solvent screening for CO 2 absorption have been done using the COSMO-RS thermodynamic model 82,83 . However, it should be noted that the number of studies related to the application of ML in the solvent selection and design for CO 2 absorption is considerably less than the application of ML in other types of studies related to CO 2 absorption, which are reviewed in previous sections.…”

Section: Review Of the Ml-based Models Used To Solvent Selection And Designmentioning

confidence: 99%

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

Yan

Borhani

Subraveti

et al. 2021

Energy Environ. Sci.

124

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“…Another chemical which has gained traction in the field of CO 2 separation is IL, a type of salt that exists as liquid at temperature below 100 • C [236]. The interest on IL is largely driven by its operational stability and large CO 2 uptake [237]. IL, especially those consisting of imidazolium-based cations and fluorinated anions showed excellent CO 2 -affinity and can be processed into supported IL membranes [38,238] and poly(ionic liquid) membranes [239,240] which exhibit good separation performance [37,97,241,242].…”

Section: Enhancing Gas Separation Performancementioning

confidence: 99%

“…These studies depicted the potential of IL-based CO 2 capture technologies which are still being vigorously developed. In fact, the variety of cations and anions currently available (and still expanding) is so vast that advanced computer simulation and machine learning could play significant roles to screen, identify and optimize IL structures [237,[270][271][272]. Analysis of the permeability in Robeson's plot provides information about the intrinsic separation characteristic of the materials but does not represent the entirety of the membrane.…”

Section: Future Prospectsmentioning

confidence: 99%

The State-of-the-Art Functionalized Nanomaterials for Carbon Dioxide Separation Membrane

et al. 2022

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Nanocomposite membrane (NCM) is deemed as a practical and green separation solution which has found application in various fields, due to its potential to delivery excellent separation performance economically. NCM is enabled by nanofiller, which comes in a wide range of geometries and chemical features. Despite numerous advantages offered by nanofiller incorporation, fabrication of NCM often met processing issues arising from incompatibility between inorganic nanofiller and polymeric membrane. Contemporary, functionalization of nanofiller which modify the surface properties of inorganic material using chemical agents is a viable approach and vigorously pursued to refine NCM processing and improve the odds of obtaining a defect-free high-performance membrane. This review highlights the recent progress on nanofiller functionalization employed in the fabrication of gas-separative NCMs. Apart from the different approaches used to obtain functionalized nanofiller (FN) with good dispersion in solvent and polymer matrix, this review discusses the implication of functionalization in altering the structure and chemical properties of nanofiller which favor interaction with specific gas species. These changes eventually led to the enhancement in the gas separation efficiency of NCMs. The most frequently used chemical agents are identified for each type of gas. Finally, the future perspective of gas-separative NCMs are highlighted.

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Ionic liquid screening for CO2 capture and H2S removal from gases: The syngas purification case

Cited by 77 publications

References 87 publications

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

The State-of-the-Art Functionalized Nanomaterials for Carbon Dioxide Separation Membrane

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