Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte

被引：16

作者：

Azzahari, Ahmad Danial ^{[1
]}

Yusuf, Siti Nor Farhana ^{[1
]}

Selvanathan, Vidhya ^{[1
]}

Yahya, Rosiyah ^{[1
,2
]}

机构：

[1] Univ Malaya, Dept Chem, Kuala Lumpur 50603, Malaysia

[2] Univ Malaya, Ctr Ion, Kuala Lumpur 50603, Malaysia

来源：

POLYMERS | 2016年 / 8卷 / 02期

关键词：

phthaloylchitosan; ionic conductivity; gel polymer electrolyte; artificial neural network; response surface methodology; TRANSPORT-PROPERTIES; CHITOSAN; NANOCOMPOSITE; OPTIMIZATION; MEMBRANES; MIXTURES; SOLVENTS; LIQUIDS;

D O I：

10.3390/polym8020022

中图分类号：

O63 [高分子化学（高聚物）];

学科分类号：

070305 ; 080501 ; 081704 ;

摘要：

A gel polymer electrolyte system based on phthaloylchitosan was prepared. The effects of process variables, such as lithium iodide, caesium iodide, and 1-butyl-3-methylimidazolium iodide were investigated using a distance-based ternary mixture experimental design. A comparative approach was made between response surface methodology (RSM) and artificial neural network (ANN) to predict the ionic conductivity. The predictive capabilities of the two methodologies were compared in terms of coefficient of determination R-2 based on the validation data set. It was shown that the developed ANN model had better predictive outcome as compared to the RSM model.

引用

页数：18

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