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




Artificial Neural Network and Response Surface Methodology Modeling in Ionic Conductivity Predictions of Phthaloylchitosan-Based Gel Polymer Electrolyte - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1

Department of Chemistry, University of Malaya, Kuala Lumpur 50603, Malaysia

2

Centre of Ionics, University of Malaya, Kuala Lumpur 50603, Malaysia



These authors contributed equally to this work.





*

Author to whom correspondence should be addressed.



Academic Editor: Martin Kröger

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. View Full-Text

Keywords: phthaloylchitosan; ionic conductivity; gel polymer electrolyte; artificial neural network; response surface methodology phthaloylchitosan; ionic conductivity; gel polymer electrolyte; artificial neural network; response surface methodology





Autor: Ahmad Danial Azzahari 1,†, Siti Nor Farhana Yusuf 1,†, Vidhya Selvanathan 1,† and Rosiyah Yahya 1,2,*

Fuente: http://mdpi.com/



DESCARGAR PDF




Documentos relacionados