CO2 solubility and diffusivity in 1-ethyl-3-methylimidazolium cation-based ionic liquids; isochoric pressure drop approach

Sohaib, Qazi; Kazemi, Mohammad Amin; Charmette, Christophe; Cartier, Jim; Younas, Mohammad; Azarafza, Abouzar; Rezakazemi, Mashallah; Sánchez-Marcano, José

doi:10.1016/j.fluid.2022.113581

Cited by 5 publications

(2 citation statements)

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“…To perform external validation another database including 77 experimental data points was collected from the work of Han et al 54 and of Qazi Sohaib et al 55 The same selected number of features was used for model mining, that is, for the prediction of CO 2 solubility in ILs from an unused data set, called a validation set or external test.…”

Section: Resultsmentioning

confidence: 99%

Quantitative structure‐property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

Benmouloud,

Euldji,

Si‐Moussa

et al. 2024

Int J of Quantum Chemistry

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Ionic liquids (ILs) are considered unique and attractive types of solvents with great potential to capture carbon dioxide (CO2) and reduce its emissions into the atmosphere. On the other hand, carrying out experimental measurements of CO2 solubility for each new IL is time‐consuming and expensive. Whereas, the possible combinations of cations and anions are numerous. Therefore, the preparation and design of such processes requires simple and accurate models to predict the solubility of CO2 as a greenhouse gas. In the present study, two different models, namely: artificial neural network (ANN) and support vector machine optimized with dragonfly algorithm (DA‐SVM) were used in order to establish a quantitative structure–property relationship (QSPR) between the molecular structures of cations and anions and the CO2 solubility. More than 10 116 CO2 solubility data measured in various ionic liquids (ILs) at different temperatures and pressures were collected. 13 significant PaDEL descriptors (E2M, MATS8S, TDB6I, TDB1S, ATSC4V, MATS8M, ATSC7V, Gats2S, Gats5S, Gats5C, ATSC6V, DE, and Lobmax), temperature and pressure were considered as the model input data. For the test set data (2023 data point), the estimated mean absolute error (MAE) and R2 for the ANN model are of 0.0195 and 0.9828 and 0.0219 and 0.9745 for the DA‐SVM model. The results obtained showed that both models can reliably predict the solubility of CO2 in ILs with a slight superiority of the ANN model. Examination of sensitivity and outlier diagnosis examinations confirmed that the QSPR model optimized using the ANN algorithm is better suited to correlate and predict this property.

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Section: Resultsmentioning

confidence: 99%

Quantitative structure‐property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

Benmouloud,

Euldji,

Si‐Moussa

et al. 2024

Int J of Quantum Chemistry

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show abstract

“…Among the mentioned methods, the solvent based-absorption method and cryogenic process-based CO 2 separation are not economically favourable due to some problems including solvent loses, corrosion problems, high pressure and temperature, and high energy demand for refrigeration purposes. [22,23] The adsorption technique is a promising method due to its high selectivity, high adsorption capacity, and low regeneration energy demand. [24,25] Usually, solid sorbents such as activated carbon (AC), [26,27] zeolites, [28] metal organic frameworks (MOFs), [29] and porous organic polymers (POPs) [30,31] are used in adsorption processes.…”

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confidence: 99%

CO₂ capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

2023

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In this research, porous benzene‐based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi‐empirical quadratic model for adsorption capacity was presented based on RSM‐central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO2 uptake capacity was obtained around 167 (mg/g).

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Electrochemical Approaches Toward CO2 Capture and Concentration

Muhammad,

Mulk,

Zeeshan

et al. 2023

Handbook of Energy Materials

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CO2 solubility and diffusivity in 1-ethyl-3-methylimidazolium cation-based ionic liquids; isochoric pressure drop approach

Cited by 5 publications

References 68 publications

Quantitative structure‐property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

Quantitative structure‐property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

CO₂ capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

Electrochemical Approaches Toward CO2 Capture and Concentration

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CO2 solubility and diffusivity in 1-ethyl-3-methylimidazolium cation-based ionic liquids; isochoric pressure drop approach

Cited by 5 publications

References 68 publications

Quantitative structure‐property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

Quantitative structure‐property relationship techniques for predicting carbon dioxide solubility in ionic liquids using machine learning methods

CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

Electrochemical Approaches Toward CO2 Capture and Concentration

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CO₂ capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology