A molecular-based model for prediction of liquid viscosity of pure organic compounds: A quantitative structure property relationship (QSPR) approach

作者：Gharagheizi Farhad^*; Mirkhani Seyyed Alireza; Keshavarz Mohammad Hossein; Farahani Nasrin; Tumba Kaniki

来源：Journal of the Taiwan Institute of Chemical Engineers, 2013, 44(3): 359-364.

DOI：10.1016/j.jtice.2012.12.015

• Genetic algorithms
• Subset variable selection
• Feed forward neural networks
• Quantitative structure-viscosity relationship

In this study, a new quantitative structure-property relationship (QSPR) is presented for prediction of the liquid viscosity of pure organic compounds. The model implements eight molecular descriptors selected using the genetic algorithm-based multivariate linear regression (GA-MLR) from more than various 3000 molecular descriptors to predict the liquid viscosity. To propose a comprehensive and predictive model, 2748 pure organic compounds are investigated. Furthermore, several statistical methods are applied to evaluate the predictive capability of the model. The root mean square of error and the average absolute percent error of the model are equal to 0.34 and 7%, respectively.

• 出版日期2013-5