CAS OpenIR  > 研究所(批量导入)
Density Prediction of Mixtures of Ionic Liquids and Molecular Solvents Using Two New Generalized Models
Alternative TitleInd. Eng. Chem. Res.
Huang, Ying1,2; Zhao, Yongsheng1; Zeng, Shaojuan1,2; Zhang, Xiangping1; Zhang, Suojiang1
2014-10-01
Source PublicationINDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
ISSN0888-5885
Volume53Issue:39Pages:15270-15277
AbstractEngineers often demand generalized models without sophisticated and long-time computations. To date, such models are still lacking for the density prediction of ionic liquid (IL) mixtures. In this paper, corresponding states principle combining with new mixing rules is employed to develop two new generalized models for density prediction of IL mixtures, including an extended Riedel (ER) model and an artificial neural network (ANN) model. A total of 1985 data points of binary and ternary mixtures of IL with molecular solvents, such as water, alcohols, ketones, ethers, hydrocarbons, esters, and acetonitrile, are used to verify the models. Average absolute relative deviations of the ER model and the ANN model are 0.92% and 0.37%, respectively, which indicates both the developed models can achieve a universal and accurate density prediction of IL mixtures. Moreover, the ER model does not contain any fitted parameters and thus provides a real predictive method.; Engineers often demand generalized models without sophisticated and long-time computations. To date, such models are still lacking for the density prediction of ionic liquid (IL) mixtures. In this paper, corresponding states principle combining with new mixing rules is employed to develop two new generalized models for density prediction of IL mixtures, including an extended Riedel (ER) model and an artificial neural network (ANN) model. A total of 1985 data points of binary and ternary mixtures of IL with molecular solvents, such as water, alcohols, ketones, ethers, hydrocarbons, esters, and acetonitrile, are used to verify the models. Average absolute relative deviations of the ER model and the ANN model are 0.92% and 0.37%, respectively, which indicates both the developed models can achieve a universal and accurate density prediction of IL mixtures. Moreover, the ER model does not contain any fitted parameters and thus provides a real predictive method.
KeywordArtificial Neural-network Binary-mixtures Thermodynamic Properties Acentric Factors Cosmo-rs Systems Viscosity Conductivity Temperature Equilibria
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
DOI10.1021/ie502571b
URL查看原文
Indexed BySCI
Language英语
WOS KeywordARTIFICIAL NEURAL-NETWORK ; BINARY-MIXTURES ; THERMODYNAMIC PROPERTIES ; ACENTRIC FACTORS ; COSMO-RS ; SYSTEMS ; VISCOSITY ; CONDUCTIVITY ; TEMPERATURE ; EQUILIBRIA
WOS Research AreaEngineering
WOS SubjectEngineering, Chemical
WOS IDWOS:000342609000038
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/11674
Collection研究所(批量导入)
Affiliation1.Chinese Acad Sci, Inst Proc Engn, Key Lab Green Proc & Engn, Beijing Key Lab Ion Liquids Clean Proc,State Key, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Chem & Chem Engn, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Huang, Ying,Zhao, Yongsheng,Zeng, Shaojuan,et al. Density Prediction of Mixtures of Ionic Liquids and Molecular Solvents Using Two New Generalized Models[J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,2014,53(39):15270-15277.
APA Huang, Ying,Zhao, Yongsheng,Zeng, Shaojuan,Zhang, Xiangping,&Zhang, Suojiang.(2014).Density Prediction of Mixtures of Ionic Liquids and Molecular Solvents Using Two New Generalized Models.INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,53(39),15270-15277.
MLA Huang, Ying,et al."Density Prediction of Mixtures of Ionic Liquids and Molecular Solvents Using Two New Generalized Models".INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 53.39(2014):15270-15277.
Files in This Item:
File Name/Size DocType Version Access License
Density Prediction o(892KB)期刊论文出版稿限制开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Huang, Ying]'s Articles
[Zhao, Yongsheng]'s Articles
[Zeng, Shaojuan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Huang, Ying]'s Articles
[Zhao, Yongsheng]'s Articles
[Zeng, Shaojuan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Huang, Ying]'s Articles
[Zhao, Yongsheng]'s Articles
[Zeng, Shaojuan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.