Prediction of Wood Drying Process Based on Artificial Neural Network

Abstract

Taking the conventional drying process of Pinus sylvestris square wood with pith as the research material, based on the Back Propagation (BP) neural network algorithm, a model was constructed using the real-time online-measurement data. Softening treatment time and temperature, variable treatment time and temperature, initial moisture content of wood, and position of wood core and sapwood were used as model inputs. Wood drying rate and longitudinal cracking degree were used as outputs to indicate wood drying quality. The results showed that with a suitable model structure of 6-9-2 (input layer-hidden layer-output layer), the coefficient of determination R² and mean square error of the test samples were 0.96, 0.99, and 0.00605, respectively, indicating that the neural network model has good generalization ability. Compared with the experimental value, the predicted value basically conforms to the change law and size of the experimental value, and the error distribution is approximately 2%. This shows that the BP neural network model can simulate the drying rate and longitudinal cracking degree in the drying process and realize the prediction of the drying process.