Bacterial cellulose drying: diffusion mechanisms, mathematical modeling, and artificial neural network approaches

Abstract This study investigated the drying characteristics, mass transfer behavior, and predictive modeling of bacterial cellulose (BC) under different drying temperatures. The initial moisture content of BC was determined as 99.17 ± 0.07% (wet basis), and drying was carried out until a final moisture content of 0.004 g water g⁻ 1 d.m. was reached. Results showed that increasing the drying temperature accelerated the drying rate and reduced drying time, with drying completed in 60 min at 50 °C, 38 min at 60 °C, and 30 min at 70 °C. Higher temperatures also enhanced effective moisture diffusivity, mass transfer coefficients, and drying capacity, confirming that both internal and external resistances govern BC drying. Mathematical modeling of drying curves was performed using several thin-layer models. Among them, the Weibull model exhibited the best fit, with the lowest χ 2 (0.00048561–0.0132192) and RMSE (0.01958–0.108) values and the highest R 2 (0.9975–0.9992), outperforming the Page, Midilli and Kucuk, and Parabolic models. In addition, Artificial Neural Network (ANN) modeling provided superior predictive performance, achieving very low error values (RMSE: 0.001936035–0.016680348) and high correlation coefficients (R 2 : 0.9990–0.9999), without signs of overfitting. These findings highlight ANN as a reliable tool for precise prediction of drying kinetics compared to conventional mathematical models. Overall, this research demonstrates that optimizing drying temperature and employing advanced modeling techniques can significantly improve the efficiency, accuracy, and scalability of BC drying processes. The outcomes provide a scientific basis for industrial applications of BC in food, biomedical, and pharmaceutical fields, while suggesting future research on hybrid drying technologies and energy-efficient approaches.