Azzahari, A.D. and Yusuf, S.N.F. and Selvanathan, V. and Yahya, R. (2016) Artificial neural network and response surface methodology modeling in ionic conductivity predictions of phthaloylchitosan-based gel polymer electrolyte. Polymers, 8 (2). p. 22. ISSN 2073-4360, DOI https://doi.org/10.3390/polym8020022.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya: University Research Grant number PG028-2014A and RP003A-13AFR, University of Malaya: Bright Spark fellowship (BSP/APP/1903/2013) |
| Uncontrolled Keywords: | Phthaloylchitosan; Ionic conductivity; Gel polymer electrolyte; Artificial neural network; Response surface methodology |
| Subjects: | Q Science > Q Science (General) Q Science > QD Chemistry |
| Divisions: | Faculty of Science > Department of Chemistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 04 Dec 2017 06:03 |
| Last Modified: | 04 Dec 2017 06:03 |
| URI: | http://eprints.um.edu.my/id/eprint/18421 |
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