Artificial Neural Network Model to Predict Compositional Viscosity over a Broad Range of Temperatures

摘要：

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) Cn-mimNTf2 with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model.

作者: Yiqing Miao Quan Gan David Rooney

作者单位: School of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast BT9 5AG, UK

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 668-673

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）