DOI: 10.1016/j.jcou.2017.03.011
Scopus记录号: 2-s2.0-85016434935
论文题名: Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l-phenylalanine
作者: Garg S. ; Shariff A.M. ; Shaikh M.S. ; Lal B. ; Suleman H. ; Faiqa N.
刊名: Journal of CO2 Utilization
ISSN: 22129820
出版年: 2017
卷: 19 起始页码: 146
结束页码: 156
语种: 英语
英文关键词: Artificial neural network
; Carbon dioxide
; Modified Kent-Eisenberg
; Sodium salt of l-phenylalanine
; Solubility
Scopus关键词: Amino acids
; Carbon
; Deep neural networks
; Equilibrium constants
; Neural networks
; Salts
; Solubility
; Temperature
; Effect of temperature
; Kent-Eisenberg
; l-Phenylalanine
; Modeling technique
; Neural network model
; Pressure ranges
; Solubility data
; Solvent concentration
; Carbon dioxide
英文摘要: In this study, experimental CO2 solubility in aqueous sodium salt of l-phenylalanine (Na-Phe) was investigated at concentrations (w = 0.10, 0.20, and 0.25) mass fractions. The solubility was measured in a high-pressure solubility cell at temperatures 303.15, 313.15 and 333.15 K, over a CO2 pressure range of (2-25) bar. The effect of temperature, equilibrium CO2 pressure and Na-Phe concentration on CO2 loading were examined. Two different models namely modified Kent-Eisenberg and artificial neural network (ANN) were used to correlate the CO2 solubility data. Carbamate hydrolysis and amine deprotonation equilibrium constants were estimated as a function of temperature, pressure and solvent concentration from modified Kent-Eisenberg model. Also, the comparison of prediction results obtained from both modeling techniques was carried out. It was found that ANN model performed better than modified Kent-Eisenberg model. © 2017 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/52748
Appears in Collections: 影响、适应和脆弱性
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Recommended Citation:
Garg S.,Shariff A.M.,Shaikh M.S.,et al. Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l-phenylalanine[J]. Journal of CO2 Utilization,2017-01-01,19