Name
Title | ||
|---|---|---|
A Quantitative Study on Simultaneous Effects of Governing Parameters in Electrospinning of Nanofibers using Modified Neural Network using Genetic Algorithm |
Abstract | ||
|---|---|---|
AbstractIn 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJCCE.2017010102 | Periodicals |
DocType | Volume | Issue |
Journal | 6 | 1 |
ISSN | Citations | PageRank |
2155-4110 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shayan Seyedin | 1 | 0 | 0.34 |
| Shima Maghsoodloo | 2 | 0 | 0.34 |
| Vahid Mottaghitalab | 3 | 0 | 0.34 |