Artificial neural network modeling for prediction of binary surface tension containing ionic liquid

被引：17
|
作者：
Lashkarbolooki, Mostafa [1 ]
机构：
[1] Babol Noshirvani Univ Technol, Sch Chem Engn, Babol Sar 4714871167, Iran
来源：
SEPARATION SCIENCE AND TECHNOLOGY | 2017年 / 52卷 / 08期
关键词：
Artificial neural network; binary mixture; ionic liquids; surface tension; PRESSURE PHASE-BEHAVIOR; SUPPORT VECTOR MACHINES; PHYSICOCHEMICAL PROPERTIES; PHYSICAL-PROPERTIES; EXCESS PROPERTIES; HECK REACTION; PLUS ETHANOL; MIXTURES; DENSITY; WATER;
D O I：
10.1080/01496395.2017.1288137
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
In this study, a feed-forward multilayer perceptron neural network is applied to predict the surface tension of 32 binary ionic liquids (ILs)/non-ILs systems using melting point (Tm), molecular weight (Mw) and mole fraction of ILs as well as Tm and Mw of non-IL components. The data are divided into two different subsets, namely training and testing subsets, to obtain the optimum parameters of the used network and to evaluate the correlative capability of the trained network. The results of the test stage show excellent capability of the proposed network to predict/correlate the binary surface tension of ILs/non-ILs systems (AARD%: 0.93, MSE: 6.67 x 10(-7) and R-2: 0.9950).
引用
收藏
页码：1454 / 1467
页数：14
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