DOI: 10.1016/j.jcou.2018.06.008
Scopus记录号: 2-s2.0-85048689904
论文题名: Mining the intrinsic trends of CO2 solubility in blended solutions
作者: Li H. ; Zhang Z.
刊名: Journal of CO2 Utilization
ISSN: 22129820
出版年: 2018
卷: 26 起始页码: 496
结束页码: 502
语种: 英语
英文关键词: Chemical absorption
; CO2 solubility
; Data-mining
; Machine learning
; Trisodium phosphate (TSP)
Scopus关键词: Carbon dioxide
; Data mining
; Intelligent systems
; Learning systems
; Mean square error
; Neural networks
; Solubility
; Chemical absorption
; CO2 absorption
; CO2 solubility
; Experimental conditions
; General regression neural network
; Representation method
; Root mean square errors
; Trisodium phosphate
; Solution mining
英文摘要: CO2 solubility in trisodium phosphate (TSP) and its mixed solutions is a crucial information for CO2 absorption and utilization. However, with limited experimental data and large variations of experimental conditions, intrinsic trends of CO2 solubility under a specific set of conditions are difficult to be determined without comprehensive experiments. To address this, here, a machine learning based data-mining is proven a powerful method to explore the intrinsic trends of CO2 solubility trained from 299 data groups extracted from previous experimental literatures. A generalized machine learning input representation method was applied, for the first time, by quantifying the types and concentrations of the blended solutions. With a general regression neural network (GRNN) as the algorithm, we found that the intrinsic trends of CO2 solubility could be well-fitted with a limited amount of experimental data, having the average root mean square error (RMSE) lower than 0.038 mol CO2/mol solution. More importantly, it is shown that with a generalized input representation, machine learning can mine the relationships between CO2 solubility and various experimental conditions, which could help to better understand the intrinsic trends of CO2 solubility in blended solutions. © 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/111841
Appears in Collections: 气候减缓与适应
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作者单位: Department of Chemistry, Institute for Computational and Engineering Sciences, University of Texas at Austin, 105 E. 24th Street, Austin, TX 78712, United States; School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing, 400054, China; Fujian Provincial Key Laboratory of Featured Materials in Biochemical Industry, Ningde Normal University, Ningde, 352100, China
Recommended Citation:
Li H.,Zhang Z.. Mining the intrinsic trends of CO2 solubility in blended solutions[J]. Journal of CO2 Utilization,2018-01-01,26