Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method
Abstract
:1. Introduction
2. Methods
2.1. Single Modeling Method
2.1.1. Back Propagation Neural Networks
2.1.2. Support Vector Machine
2.1.3. Extreme Learning Machine
2.2. Linear Fusion Method
2.2.1. Minimum Squared Error
2.2.2. Information Entropy
2.3. Implementation Steps
3. Results and Discussion
3.1. Data Collecting and Grouping
3.2. Fusion Model Development
3.2.1. Sub-Models Development
3.2.2. Sub-Models Evaluation
3.2.3. Sub-Models Fusion
3.3. Fusion Model Testing
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No. | Ionic Liquids | MW (g/mol) | Tc (k) | Pc (MPa) | Acentric Factor (w) |
---|---|---|---|---|---|
1 | [BMIM][BF4] | 226.03 | 623.3 | 2.04 | 0.8489 |
2 | [EMIM][TF2N] | 391.30 | 788.05 | 3.31 | 1.225 |
3 | [EMIM][ETSO4] | 236.29 | 1061.1 | 4.04 | 0.3368 |
4 | [HMIM][TF2N] | 447.92 | 1292.78 | 2.3888 | 0.3893 |
5 | [HMIM][TFO] | 316.34 | 1055.6 | 2.4954 | 0.489 |
6 | [HMIM][BF4] | 254.08 | 716.61 | 1.7941 | 0.6589 |
7 | [HMIM][MESO4] | 278.37 | 1110.84 | 2.9611 | 0.4899 |
8 | [BMMIM][TF2N] | 433.4 | 1255.8 | 2.031 | 0.3193 |
9 | [HMIM][PF6] | 312.24 | 759.16 | 1.5499 | 0.9385 |
No. | Ionic Liquids | Temperature Range (K) | Pressure Range (MPa) | CO2 Solubility Range (Mole Fraction) | No. of Samples | Refs. |
---|---|---|---|---|---|---|
1 | [BMIM][BF4] | 278.47–368.22 | 0.587–67.620 | 0.102–0.602 | 104 | [35,36] |
2 | [EMIM][TF2N] | 312.10–410.90 | 0.626–14.329 | 0.123–0.593 | 77 | [35,37] |
3 | [EMIM][ETSO4] | 303.15–353.15 | 0.122–1.546 | 0.008–0.132 | 39 | [35,38] |
4 | [HMIM][TF2N] | 303.15–373.15 | 0.420–45.280 | 0.165–0.824 | 64 | [20,39] |
5 | [HMIM][TFO] | 303.15–373.15 | 1.420–100.120 | 0.267–0.816 | 64 | [20,39] |
6 | [HMIM][BF4] | 303.15–373.15 | 1.200–41.690 | 0.212–0.622 | 48 | [20,39] |
7 | [HMIM][MESO4] | 303.15–373.15 | 0.870–50.140 | 0.158–0.602 | 48 | [20,39] |
8 | [BMMIM][TF2N] | 298.15–343.15 | 0.010–1.900 | 0.002–0.211 | 36 | [20,40] |
9 | [HMIM][PF6] | 243.15–373.15 | 0.220–55.630 | 0.216–0.691 | 64 | [20,39] |
No. of Hidden Layer Neurons | MAE | RMSE | R2 | STD |
---|---|---|---|---|
3 | 0.0081 | 0.0114 | 0.9973 | 0.0659 |
4 | 0.0062 | 0.0085 | 0.9985 | 0.0499 |
5 | 0.0076 | 0.0099 | 0.9979 | 0.0616 |
6 | 0.0064 | 0.0088 | 0.9983 | 0.0521 |
7 | 0.0063 | 0.0090 | 0.9983 | 0.0512 |
8 | 0.0082 | 0.0108 | 0.9975 | 0.0661 |
9 | 0.0064 | 0.0092 | 0.9982 | 0.0521 |
10 | 0.0070 | 0.0096 | 0.9981 | 0.0567 |
Type of Kernel Function | MAE | RMSE | R2 | STD |
---|---|---|---|---|
Polynomial kernel function | 0.0135 | 0.0196 | 0.9922 | 0.1091 |
Radial basis kernel function | 0.0122 | 0.0180 | 0.9928 | 0.0992 |
Sigmoid kernel function | 0.0269 | 0.0363 | 0.9727 | 0.2180 |
No. of Neurons | Type of Activation Function | MAE | RMSE | R2 | STD |
---|---|---|---|---|---|
148 | sigmoid | 0.0124 | 0.0176 | 0.9970 | 0.1007 |
149 | sigmoid | 0.0106 | 0.0149 | 0.9938 | 0.0856 |
150 | sigmoid | 0.0113 | 0.0158 | 0.9959 | 0.0912 |
151 | sigmoid | 0.0112 | 0.0157 | 0.9940 | 0.0911 |
152 | sigmoid | 0.0120 | 0.0176 | 0.9928 | 0.0969 |
151 | sine | 0.0122 | 0.0182 | 0.9953 | 0.0989 |
152 | Sine | 0.0115 | 0.0166 | 0.9945 | 0.0928 |
153 | sine | 0.0113 | 0.0172 | 0.9933 | 0.0911 |
Model | MAE | RMSE | R2 | STD |
---|---|---|---|---|
BP | 0.0068 | 0.0090 | 0.9982 | 0.0538 |
SVM | 0.0105 | 0.0174 | 0.9933 | 0.0854 |
ELM | 0.0093 | 0.0136 | 0.9961 | 0.0752 |
Linear fusion model I | 0.0062 | 0.0090 | 0.9983 | 0.0533 |
Linear fusion model II | 0.0060 | 0.0084 | 0.9985 | 0.0506 |
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Xia, L.; Wang, J.; Liu, S.; Li, Z.; Pan, H. Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method. Processes 2019, 7, 258. https://doi.org/10.3390/pr7050258
Xia L, Wang J, Liu S, Li Z, Pan H. Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method. Processes. 2019; 7(5):258. https://doi.org/10.3390/pr7050258
Chicago/Turabian StyleXia, Luyue, Jiachen Wang, Shanshan Liu, Zhuo Li, and Haitian Pan. 2019. "Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method" Processes 7, no. 5: 258. https://doi.org/10.3390/pr7050258
APA StyleXia, L., Wang, J., Liu, S., Li, Z., & Pan, H. (2019). Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method. Processes, 7(5), 258. https://doi.org/10.3390/pr7050258