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Computational-based approach for predicting porosity of electrospun nanofiber mats using response surface methodology and artificial neural network methods

Hadavi Moghadam, B ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1080/00222348.2015.1090654
  3. Publisher: Taylor and Francis Inc , 2015
  4. Abstract:
  5. Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of errors. The mean relative error for the RSM and ANN models were 1.97% and 2.62% and the root mean square errors (RMSE) were 1.50 and 1.95, respectively. The superiority of the RSM-based approach is due to its high prediction accuracy and the ability to compute the combined effects of the electrospinning factors on the porosity of the nanofiber mats
  6. Keywords:
  7. Artificial neural network ; Electrospinning ; Nanofiber mat ; Errors ; Mean square error ; Nanofibers ; Neural networks ; Porosity ; Spinning (fibers) ; Surface properties ; Artificial neural network methods ; Electrospinning parameters ; Electrospun nanofibers ; Mean relative error ; Modeling and optimization ; Response surface methodology ; Root mean square errors ; Solution concentration ; Electrospinning
  8. Source: Journal of Macromolecular Science, Part B: Physics ; Volume 54, Issue 11 , 2015 , Pages 1404-1425 ; 00222348 (ISSN)
  9. URL: http://www.tandfonline.com/doi/full/10.1080/00222348.2015.1090654