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Comparison between artificial neural network and response surface methodology in the prediction of the production rate of polyacrylonitrile electrospun nanofibers

Comparison between artificial neural network and response surface methodology in the prediction... Abstract This paper focused on using response surface methodology (RSM) and artificial neural network (ANN) to analyze production rate of electrospun nanofibers. The three important electrospinning factors were studied including polymer concentration (wt %), applied voltage (kV) and the nozzle-collector distance (cm). The predicted production rates were in agreement with the experimental results in both ANN and RSM techniques. High regression coefficient between the variables and the response (R 2=0.975) indicates excellent evaluation of experimental data by second-order polynomial regression model. The regression coefficient was 0.988, which indicates that the ANN model was shows good fitting with experimental data. The obtained results indicate that the performance of ANN was better than RSM. It was concluded that applied voltage plays an important role (relative importance of 42.8 %) against production rate of electrospun nanofibers. The RSM model predicted the 2802.3 m/min value of the highest production rate at conditions of 15 wt % polymer concentration, 16 kV of the applied voltage, and 15 cm of nozzle-collector distance. The predicted value showed only 4.4 % difference with experimental results in which 2931.0 m/min at the same setting was observed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Fibers and Polymers Springer Journals

Comparison between artificial neural network and response surface methodology in the prediction of the production rate of polyacrylonitrile electrospun nanofibers

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References (23)

Publisher
Springer Journals
Copyright
2013 The Korean Fiber Society and Springer Science+Business Media Dordrecht
ISSN
1229-9197
eISSN
1875-0052
DOI
10.1007/s12221-013-1849-x
Publisher site
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Abstract

Abstract This paper focused on using response surface methodology (RSM) and artificial neural network (ANN) to analyze production rate of electrospun nanofibers. The three important electrospinning factors were studied including polymer concentration (wt %), applied voltage (kV) and the nozzle-collector distance (cm). The predicted production rates were in agreement with the experimental results in both ANN and RSM techniques. High regression coefficient between the variables and the response (R 2=0.975) indicates excellent evaluation of experimental data by second-order polynomial regression model. The regression coefficient was 0.988, which indicates that the ANN model was shows good fitting with experimental data. The obtained results indicate that the performance of ANN was better than RSM. It was concluded that applied voltage plays an important role (relative importance of 42.8 %) against production rate of electrospun nanofibers. The RSM model predicted the 2802.3 m/min value of the highest production rate at conditions of 15 wt % polymer concentration, 16 kV of the applied voltage, and 15 cm of nozzle-collector distance. The predicted value showed only 4.4 % difference with experimental results in which 2931.0 m/min at the same setting was observed.

Journal

Fibers and Polymers – Springer Journals

Published: Nov 1, 2013

Keywords: Polymer Sciences

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