A Quantitative Study on Simultaneous Effects of Governing Parameters in Electrospinning of Nanofibers using Modified Neural Network using Genetic Algorithm

Shayan Seyedin, Shima Maghsoodloo and Vahid Mottaghitalab
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Shayan Seyedin: Institute for Frontier Materials, Deakin University, Geelong, Australia
Shima Maghsoodloo: Textile Engineering Department, Faculty of Engineering, University of Guilan, Guilan, Iran
Vahid Mottaghitalab: Textile Engineering Department, Faculty of Engineering, University of Guilan, Guilan, Iran

International Journal of Chemoinformatics and Chemical Engineering (IJCCE), 2017, vol. 6, issue 1, 20-38

Abstract: In this article, modified neural networks using genetic algorithms were employed to investigate the simultaneous effects of four of the most important parameters, namely; solution concentration (C); spinning distance (d); applied voltage (V); and volume flow rate (Q) on mean fiber diameter (MFD), as well as standard deviation of fiber diameter (StdFD) in electrospinning of polyvinyl alcohol (PVA) nanofibers. Genetic algorithm optimized neural networks (GANN) were used for modeling the electrospinning process. The results indicate better experimental conditions and more predictive ability of GANNs. Therefore, the approach of using genetic algorithms to optimize neural networks for modeling the electrospinning process has been successful. RSM could be employed when statistical analysis, quantitative study of the effects of the parameters and visualization of the response surfaces are of interest, whereas in the case of modeling the process and predicting new conditions, GANN is a more powerful tool and presents more desirable results.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcce00:v:6:y:2017:i:1:p:20-38

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