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

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 bet...

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Autores principales: Azzahari, Ahmad Danial, Yusuf, Siti Nor Farhana, Selvanathan, Vidhya, Yahya, Rosiyah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6432590/
https://www.ncbi.nlm.nih.gov/pubmed/30979129
http://dx.doi.org/10.3390/polym8020022
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author Azzahari, Ahmad Danial
Yusuf, Siti Nor Farhana
Selvanathan, Vidhya
Yahya, Rosiyah
author_facet Azzahari, Ahmad Danial
Yusuf, Siti Nor Farhana
Selvanathan, Vidhya
Yahya, Rosiyah
author_sort Azzahari, Ahmad Danial
collection PubMed
description 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.
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spelling pubmed-64325902019-04-02 Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte Azzahari, Ahmad Danial Yusuf, Siti Nor Farhana Selvanathan, Vidhya Yahya, Rosiyah Polymers (Basel) Article 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. MDPI 2016-01-29 /pmc/articles/PMC6432590/ /pubmed/30979129 http://dx.doi.org/10.3390/polym8020022 Text en © 2016 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Azzahari, Ahmad Danial
Yusuf, Siti Nor Farhana
Selvanathan, Vidhya
Yahya, Rosiyah
Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte
title Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte
title_full Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte
title_fullStr Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte
title_full_unstemmed Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte
title_short Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte
title_sort artificial neural network and response surface methodology modeling in ionic conductivity predictions of phthaloylchitosan-based gel polymer electrolyte
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6432590/
https://www.ncbi.nlm.nih.gov/pubmed/30979129
http://dx.doi.org/10.3390/polym8020022
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