DOI: 10.1016/j.jcou.2018.03.004
Scopus记录号: 2-s2.0-85044294126
论文题名: Accurate prediction of miscibility of CO2 and supercritical CO2 in ionic liquids using machine learning
作者: Mesbah M. ; Shahsavari S. ; Soroush E. ; Rahaei N. ; Rezakazemi M.
刊名: Journal of CO2 Utilization
ISSN: 22129820
出版年: 2018
卷: 25 起始页码: 99
结束页码: 107
语种: 英语
英文关键词: CO2
; Ionic liquid
; Machine learning
; Miscibility
; Supercritical CO2
Scopus关键词: Artificial intelligence
; Carbon dioxide
; Forecasting
; Ionic liquids
; Liquids
; Network layers
; Solubility
; Accurate prediction
; Empirical correlations
; Experimental values
; Ionic liquid (ils)
; Machine learning methods
; Multi-layer perceptron neural networks
; Statistical assessment
; Supercritical CO2
; Learning systems
英文摘要: In this study, the solubility of CO2 and supercritical (SC) CO2 in 20 ionic liquids (ILs) of different chemical families over a wide range of pressure (0.25-100.12 MPa) and temperature (278.15-450.49 K) were predicted, using a robust machine learning method of multi-layer perceptron neural network (MLP-NN). The developed model with the R2 of 0.9987, MSE of 0.6293 and AARD% of 1.8416 showed a great accuracy in predicting experimental values. In another approach for predicting the CO2 solubility, an empirical correlation with several constants was developed. With the R2 of 0.9922, MSE of 3.7874 and AARD% of 3.5078 the empirical correlation showed acceptable results; nevertheless weak compared to the ANN. The significance of this correlation is that it needs no physical property of the ILs or their mixture, and for its estimation, even a simple calculator is sufficient. A comprehensive statistical assessment conducted to assure the robustness and generality of the model. In addition, the applicability of the model and quality of experimental data was fully investigated by Leverage approach. © 2018 Elsevier Ltd. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111924
Appears in Collections: 气候减缓与适应
There are no files associated with this item.
作者单位: Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran; Young Researchers and Elites Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran; Young Researchers and Elites Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran; Islamic Azad University, Science and Research Branch, Chem. Eng. Dept., Hesarak, Tehran, Iran; Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran
Recommended Citation:
Mesbah M.,Shahsavari S.,Soroush E.,et al. Accurate prediction of miscibility of CO2 and supercritical CO2 in ionic liquids using machine learning[J]. Journal of CO2 Utilization,2018-01-01,25