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Prediction by artificial neural network of insulation performance of eco-treated cork stoppers: Experimental measurement, modeling and optimization

Author

Listed:
  • Tayeb Kermezli
  • Mohamed Announ
  • Aboubakr Boukrida
  • Mustapha Douani

Abstract

This study aims to predict by artificial neural networks (ANN) the improvement in mass insulation of cork stoppers treated by high temperature thermal (HTT) and/or boiling. Experimental tests have shown that the desorption kinetics are more favorable for smaller molecules DKCl < DNaCl. The results validated the developed mathematical model, which accounted for the actual cylindrical shape of the stopper, and quantified the improvement in apparent diffusion coefficients as a function of the maximum temperature of the treatment cycle: D105°

Suggested Citation

  • Tayeb Kermezli & Mohamed Announ & Aboubakr Boukrida & Mustapha Douani, 2025. "Prediction by artificial neural network of insulation performance of eco-treated cork stoppers: Experimental measurement, modeling and optimization," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(4), pages 3082-3093.
    • Handle: RePEc:ajp:edwast:v:9:y:2025:i:4:p:3082-3093:id:6738
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