DOI: 10.1016/j.jcou.2018.03.008
Scopus记录号: 2-s2.0-85044279323
论文题名: Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN
作者: Abdi-Khanghah M. ; Bemani A. ; Naserzadeh Z. ; Zhang Z.
刊名: Journal of CO2 Utilization
ISSN: 22129820
出版年: 2018
卷: 25 起始页码: 108
结束页码: 119
语种: 英语
英文关键词: Artificial neural network modeling
; Multi-layer perceptron
; Radial basis function
; Solubility of n-alkane
; Supercritical CO2
Scopus关键词: Carbon dioxide
; Computer system recovery
; Crude oil
; Functions
; Network layers
; Neural networks
; Oil well flooding
; Paraffins
; Petroleum industry
; Petroleum reservoirs
; Solubility
; Supercritical fluid extraction
; Artificial neural network modeling
; Multi layer perceptron
; n-Alkanes
; Radial basis functions
; Supercritical CO2
; Radial basis function networks
英文摘要: Recently, due to declination of oil production the importance of enhancement of oil recovery becomes highlighted. CO2 injection as one of popular approaches because of economically and environmental friendly has wide applications in enhancement of oil recovery. Supercritical carbon dioxide is defined as CO2 which is placed at the pressure and temperature above the critical pressure and temperature which is used widely in petroleum industry. After CO2 injection to the reservoir, the light hydrocarbons of crude oil can be extracted by liquid CO2 and these processes are affected by different parameters such as solubility, so this study was performed to investigate solubility of alkanes in supercritical CO2. Two types of artificial neural networks, i.e., Radial Basis Function (RBF) and Networks Multi-layer Perceptron (MLP) were applied for this investigation. Results show that the MLP-ANN (artificial neural network) has better performance than RBF-ANN to predict solubility of n-alkane in supercritical carbon dioxide. © 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/111916
Appears in Collections: 气候减缓与适应
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作者单位: Chemical Engineering Department, Petroleum University of Technology, Ahwaz, Iran; Petroleum Engineering Department, Petroleum University of Technology, Ahwaz, Iran; College of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing, 400054, China; Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of China, Chongqing University, Chongqing, 400044, China
Recommended Citation:
Abdi-Khanghah M.,Bemani A.,Naserzadeh Z.,et al. Prediction of solubility of N-alkanes in supercritical CO2 using RBF-ANN and MLP-ANN[J]. Journal of CO2 Utilization,2018-01-01,25