Prediction by artificial neural network of insulation performance of eco-treated cork stoppers: Experimental measurement, modeling and optimization - Kermezli, Tayeb; Announ, Mohamed; Boukrida, Aboubakr; Douani, Mustapha (Edelweiss Applied Science and Technology, 2025)
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°<D200°<D350° ≈ D450° and the protocol type: DA<DB<DC. The results revealed a positive correlation between temperature and the diffusion phenomenon, with a significant influence observed up to 350 °C. Furthermore, to enhance the accuracy of Dapp, the Bat Algorithm optimization method was applied, achieving a precision of the order of 10⁻⁵. An experimental database, composed of 3864 points, was previously optimized to be integrated into an artificial neural network (ANN) model with a specific architecture (5-5-6-1). The model thus developed demonstrated remarkable reliability, displaying a coefficient of correlation R² of 0.9997 and an extremely low root mean square error (RMSE), evaluated at 7.38 × 10⁻¹⁴. These performances underline the robustness and accuracy of the proposed model for the prediction of the studied phenomenon.