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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
MDPI
2016
|
| Subjects: | |
| Online Access: | http://eprints.um.edu.my/18421/ https://doi.org/10.3390/polym8020022 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1831449811330531328 |
|---|---|
| author | Azzahari, A.D. Yusuf, S.N.F. Selvanathan, V. Yahya, R. |
| author_facet | Azzahari, A.D. Yusuf, S.N.F. Selvanathan, V. Yahya, R. |
| author_sort | Azzahari, A.D. |
| building | UM Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaya |
| content_source | UM Research Repository |
| continent | Asia |
| country | Malaysia |
| 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 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. |
| format | Article |
| id | my.um.eprints-18421 |
| institution | Universiti Malaya |
| publishDate | 2016 |
| publisher | MDPI |
| record_format | eprints |
| spelling | my.um.eprints-184212017-12-04T06:03:52Z http://eprints.um.edu.my/18421/ Artificial neural network and response surface methodology modeling in ionic conductivity predictions of phthaloylchitosan-based gel polymer electrolyte Azzahari, A.D. Yusuf, S.N.F. Selvanathan, V. Yahya, R. Q Science (General) QD Chemistry 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. MDPI 2016 Article PeerReviewed 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 <https://doi.org/10.3390/polym8020022>. https://doi.org/10.3390/polym8020022 doi:10.3390/polym8020022 |
| spellingShingle | Q Science (General) QD Chemistry Azzahari, A.D. Yusuf, S.N.F. Selvanathan, V. Yahya, R. 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 | Q Science (General) QD Chemistry |
| url | http://eprints.um.edu.my/18421/ https://doi.org/10.3390/polym8020022 |
| url_provider | http://eprints.um.edu.my/ |