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

Tayeb Kermezli (), Mohamed Announ (), Aboubakr Boukrida () and Mustapha Douani ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 4, 3082-3093

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°Keywords: ANN; Cork; Mass diffusion; Modeling; Optimization; THT. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:4:p:3082-3093:id:6738

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