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| Density Prediction of Mixtures of Ionic Liquids and Molecular Solvents Using Two New Generalized Models | |
| Alternative Title | Ind. Eng. Chem. Res. |
| Huang, Ying1,2; Zhao, Yongsheng1; Zeng, Shaojuan1,2; Zhang, Xiangping1; Zhang, Suojiang1 | |
| 2014-10-01 | |
| Source Publication | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
 |
| ISSN | 0888-5885 |
| Volume | 53Issue:39Pages:15270-15277 |
| Abstract | 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.; 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. |
| Keyword | Artificial Neural-network Binary-mixtures Thermodynamic Properties Acentric Factors Cosmo-rs Systems Viscosity Conductivity Temperature Equilibria |
| Subtype | Article |
| WOS Headings | Science & Technology ; Technology |
| DOI | 10.1021/ie502571b |
| URL | 查看原文 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | ARTIFICIAL NEURAL-NETWORK ; BINARY-MIXTURES ; THERMODYNAMIC PROPERTIES ; ACENTRIC FACTORS ; COSMO-RS ; SYSTEMS ; VISCOSITY ; CONDUCTIVITY ; TEMPERATURE ; EQUILIBRIA |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Chemical |
| WOS ID | WOS:000342609000038 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ipe.ac.cn/handle/122111/11674 |
| Collection | 研究所（批量导入） |
| Affiliation | 1.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-SA | Application Full Text | |
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