Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method

Xia, Luyue; Wang, Jiachen; Liu, Shanshan; Li, Zhuo; Pan, Haitian

doi:10.3390/pr7050258

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…The results showed that COSMO-RS can be a useful and practical predictive tool. The employment of intelligent methods to predict the solubility of various gases such as N 2 O 26 , SO 2 27 , 28 , H 2 S 24 , 29 , and CO 2 30 in ILs has received much attention in the last decade, and the results confirm the reliability of these methods. Using the thermodynamic properties of ILs and 728 experimental data, Baghban et al 31 developed a multi-layer perceptron (MLP) model and an adaptive neuro-fuzzy interference system (ANFIS) to predict the solubility of CO 2 in ILs and then compared the results with the equations of state.…”

Section: Introductionmentioning

confidence: 89%

Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches

Nakhaei-Kohani

Atashrouz

Hadavimoghaddam

et al. 2022

Sci Rep

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Ionic liquids (ILs) have emerged as suitable options for gas storage applications over the past decade. Consequently, accurate prediction of gas solubility in ILs is crucial for their application in the industry. In this study, four intelligent techniques including Extreme Learning Machine (ELM), Deep Belief Network (DBN), Multivariate Adaptive Regression Splines (MARS), and Boosting-Support Vector Regression (Boost-SVR) have been proposed to estimate the solubility of some gaseous hydrocarbons in ILs based on two distinct methods. In the first method, the thermodynamic properties of hydrocarbons and ILs were used as input parameters, while in the second method, the chemical structure of ILs and hydrocarbons along with temperature and pressure were used. The results show that in the first method, the DBN model with root mean square error (RMSE) and coefficient of determination (R2) values of 0.0054 and 0.9961, respectively, and in the second method, the DBN model with RMSE and R2 values of 0.0065 and 0.9943, respectively, have the most accurate predictions. To evaluate the performance of intelligent models, the obtained results were compared with previous studies and equations of the state including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Redlich–Kwong (RK), and Zudkevitch–Joffe (ZJ). Findings show that intelligent models have high accuracy compared to equations of state. Finally, the investigation of the effect of different factors such as alkyl chain length, type of anion and cation, pressure, temperature, and type of hydrocarbon on the solubility of gaseous hydrocarbons in ILs shows that pressure and temperature have a direct and inverse effect on increasing the solubility of gaseous hydrocarbons in ILs, respectively. Also, the evaluation of the effect of hydrocarbon type shows that increasing the molecular weight of hydrocarbons increases the solubility of gaseous hydrocarbons in ILs.

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Section: Introductionmentioning

confidence: 89%

Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches

Nakhaei-Kohani

Atashrouz

Hadavimoghaddam

et al. 2022

Sci Rep

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“…An example of the application of machine learning for studying gas hydrates can be found in the work done by Xia et al [216]. They incorporated a fusion modeling method that could be used to predict CO 2 solubility in hydrates as related to nine ionic liquids.…”

Section: Machine Learningmentioning

confidence: 99%

Surfactants in Upstream E&P

2021

Petroleum Engineering

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“…Using the information entropy method to determine the weight coefficient of each sub-model can effectively reduce the impact of the weak sub-model on the model performance [29]. In this paper, the information entropy method is used to obtain the weight coefficient of the optimal sub-model [11].…”

Section: Sub-model Ensemblementioning

confidence: 99%

“…Although the mechanism modeling method has the advantage of strong model interpretability, thermodynamic model is relatively complex and requires complicated mathematical operations [10]. Considering the complex parameters of mechanism model, a multi-model fusion method was proposed to predict the solubility of CO 2 in ionic liquids [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

2020

Self Cite

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Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

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Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method

Cited by 16 publications

References 38 publications

Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches

Solubility of gaseous hydrocarbons in ionic liquids using equations of state and machine learning approaches

Surfactants in Upstream E&P

Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

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