Artificial neural network and response surface methodology modeling in ionic conductivity predictions of phthaloylchitosan-based gel polymer electrolyte
Document Type
Article
Publication Date
1-1-2016
Abstract
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 R2 based on the validation data set. It was shown that the developed ANN model had better predictive outcome as compared to the RSM model.
Keywords
Phthaloylchitosan, Ionic conductivity, Gel polymer electrolyte, Artificial neural network, Response surface methodology
Divisions
CHEMISTRY
Funders
University of Malaya: University Research Grant number PG028-2014A and RP003A-13AFR,University of Malaya: Bright Spark fellowship (BSP/APP/1903/2013)
Publication Title
Polymers
Volume
8
Issue
2
Publisher
MDPI