Application of Response Surface Methodology (RSM) and Artificial Neutral Network (Ann) in Diameter Optimization of Thermo Regulating Nanofibers

B. Rezaei, M. Askari, A. Mousavi Shoushtari, Mozhdeh Ghani, A. Haji

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

This paper presents the comparative studies between Response Surface
methodology (RSM) and Artificial Neural Network (ANN) in phase change nanofiber diameter optimization. In this study, ultra fine fibers of Polyethylene glycol/Cellulose acetate (PEG/CA) composite (1:1, w/w) ,in which PEG acts as the PCM and CA acts as the polymer matrix, were successfully prepared and characterized by scanning electron microscopy (SEM). Two methods were compared for their modeling and optimization abilities. The average percentage error for ANN and RSM models were 4.1 and 0.91 and the coefficient of determination was 0.98 and 0.97, respectively. The results indicate that both of this two optimization models are in good agreement with experimental data. Moreover, the RSM model shows much lower average percentage error than the ANN model. Therefore, in this study, the efficiency of RSM was better than ANN in phase change nanofiber diameter optimization.
Original languageEnglish
Publication date2017
Number of pages10
Publication statusPublished - 2017
Externally publishedYes
Event5th Texteh International Conference - Bucharest, Romania
Duration: 18 Oct 201219 Oct 2012

Conference

Conference5th Texteh International Conference
Country/TerritoryRomania
CityBucharest
Period18/10/201219/10/2012

Keywords

  • Artificial neural network (ANN)
  • Electrospinning
  • Optimization
  • Phase change materials (PCMs)
  • Response surface methodology (RSM)

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