globalchange  > 气候减缓与适应
DOI: 10.1016/j.jcou.2018.04.025
Scopus记录号: 2-s2.0-85046857528
论文题名:
Machine learning predictive framework for CO2 thermodynamic properties in solution
作者: Zhang Z.; Li H.; Chang H.; Pan Z.; Luo X.
刊名: Journal of CO2 Utilization
ISSN: 22129820
出版年: 2018
卷: 26
起始页码: 152
结束页码: 159
语种: 英语
英文关键词: Amino acid salt ; Artificial neural network ; CO2 absorption ; Machine learning ; Solubility
Scopus关键词: Backpropagation ; Carbon dioxide ; Data mining ; Ethanolamines ; Forecasting ; Greenhouse gases ; Intelligent systems ; Learning systems ; Neural networks ; Solubility ; Solutions ; Thermodynamic properties ; Viscosity ; Amino acid salt ; Back-propagation neural networks ; Chemical absorption ; CO2 absorption ; General regression neural network ; Number of hidden neurons ; Predictive performance ; Solution viscosity ; Solution mining
英文摘要: CO2 is the major greenhouse gas (GHG) emission throughout the world. For scientific and industrial purposes, chemical absorption is regarded as an efficient method to capture CO2. However, the observation of thermodynamic properties of CO2 in solution environment requires too much time and resources. To address this issue and provide an ultra-fast solution, here, we use machine learning as a powerful data-mining strategy to predict the CO2 solubility, density and viscosity of potassium lysinate (PL) and its blended solutions with monoethanolamine (MEA), with totally 433 data groups extracted from previous experimental literatures. Specifically, we compared the predictive performances of back-propagation neural network (BPNN) and general regression neural network (GRNN). Results show that for BPNN with only one hidden layer and a small number of hidden neurons could provide good predictive performance for CO2 solubility and aqueous solution viscosity, while a GRNN could perform better for the prediction of aqueous solution density. Finally, it is concluded that such a machine learning based predictive framework could help to provide an ultra-fast prediction for CO2-related thermodynamic properties in solution environment. © 2018 Elsevier Ltd. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111843
Appears in Collections:气候减缓与适应

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作者单位: Key Laboratory of Jiangxi Province for Persistant Pollutants Control and Resources Recycle, Nanchang Hangkong University, Nanchang, 330063, China; School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing, 400054, China; Department of Chemistry, Institute for Computational and Engineering Sciences, University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, United States; College of Petroleum Engineering, Liaoning Shihua University, Fushun, 113001, China; Fujian Provincial Key Laboratory of Featured Materials in Biochemical Industry, Ningde Normal University, Ningde, 352100, China

Recommended Citation:
Zhang Z.,Li H.,Chang H.,et al. Machine learning predictive framework for CO2 thermodynamic properties in solution[J]. Journal of CO2 Utilization,2018-01-01,26
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