Density Prediction of Mixtures of Ionic Liquids and Molecular Solvents Using Two New Generalized Models
文献类型：期刊论文
| 作者 | Huang, Ying1,2; Zhao, Yongsheng1; Zeng, Shaojuan1,2; Zhang, Xiangping1; Zhang, Suojiang1 |
| 刊名 | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
 |
| 出版日期 | 2014-10-01 |
| 卷号 | 53期号:39页码:15270-15277 |
| 关键词 | ARTIFICIAL NEURAL-NETWORK BINARY-MIXTURES THERMODYNAMIC PROPERTIES ACENTRIC FACTORS COSMO-RS SYSTEMS VISCOSITY CONDUCTIVITY TEMPERATURE EQUILIBRIA |
| ISSN号 | 0888-5885 |
| 其他题名 | Ind. Eng. Chem. Res. |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Engineering, Chemical |
| 研究领域[WOS] | Engineering |
| 关键词[WOS] | ARTIFICIAL NEURAL-NETWORK ; BINARY-MIXTURES ; THERMODYNAMIC PROPERTIES ; ACENTRIC FACTORS ; COSMO-RS ; SYSTEMS ; VISCOSITY ; CONDUCTIVITY ; TEMPERATURE ; EQUILIBRIA |
| 收录类别 | SCI |
| 原文出处 | |
| 语种 | 英语 |
| WOS记录号 | WOS:000342609000038 |
| 公开日期 | 2014-12-01 |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/11674]  |
| 专题 | 过程工程研究所_研究所（批量导入） |
| 作者单位 | 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：过程工程研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。