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

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.

SUBMITTER: Azzahari AD

PROVIDER: S-EPMC6432590 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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

Azzahari Ahmad Danial AD Yusuf Siti Nor Farhana SNF Yusuf Siti Nor Farhana SNF Selvanathan Vidhya V Yahya Rosiyah R

Polymers 20160129 2

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 coefficien ...[more]

PMID: 30979129

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