Thin-Layer Drying Models and Artificial Neural Network for Wood Fiber in a Near-Infrared Dryer

Abstract

The purpose of this study was to fit and compare semi-empirical thin-layer drying models and an artificial neural network (ANN) model to describe the drying kinetics of wood fiber in a near-infrared (NIR) dryer. The drying kinetics of wood fiber were evaluated using 18 semi-empirical models at three temperatures (105, 120, and 135 °C), utilizing a halogen moisture analyzer. The ANN model was designed with temperature and time as input factors and moisture content as the output variable. The findings revealed that the drying process was mainly controlled by a diffusion mechanism, and all the process occurred in two falling drying rate periods. The fitness of drying curves on semi-theoretical models based on statistical parameters, including RMSE, SSE, and R² showed that there was not much difference between equations with a maximum of two constant parameters and equations with more than two constant parameters. Therefore, using a simple model can help to reduce the time of the analysis and is beneficial to avoid using complex drying models. Also, the results showed that at higher drying temperatures (120 to 135 °C), both ANN and the best-performing semi-empirical models (Page and Henderson–Pabis) produced comparable accuracy, whereas at lower temperature (105 °C), ANN performed better due to its flexibility.