Abstract
The moisture content (MC) control is vital in the wood drying process. The study was based on BP (Back Propagation) neural network algorithm to predict the change of wood MC during the drying process of a high frequency vacuum. The data of real-time online measurement were used to construct the model, the drying time, position of measuring point, and internal temperature and pressure of wood as inputs of BP neural network model. The model structure was 4-6-1 and the decision coefficient R2 and Mean squared error (Mse) of the training sample were 0.974 and 0.07355, respectively, indicating that the neural network model had superb generalization ability. Compared with the experimental measurements, the predicted values conformed to the variation law and size of experimental values, and the error was about 2% and the MC prediction error of measurement points along thickness direction was within 2%. Hence, the BP neural network model could successfully simulate and predict the change of wood MC during the high frequency drying process.
Highlights
Wood moisture content (MC) is one of the crucial indicators in the drying process as it has a direct impact on the stability of wood drying quality, and a reasonable control of MC can help in meeting the various quality requirements of actual wood products [1]
The research on the prediction model of wood MC is of great significance in the high frequency drying process
The artificial neural networks are widely used in the study of conventional drying characteristics, stress monitoring, and MC prediction of wood [19]; the use of neural networks to predict changes in the wood MC during high frequency drying has been rarely studied
Summary
Wood MC (moisture content) is one of the crucial indicators in the drying process as it has a direct impact on the stability of wood drying quality, and a reasonable control of MC can help in meeting the various quality requirements of actual wood products [1]. The high frequency vacuum drying of wood is a non-linear, complex drying process, which is difficult to accurately express, control, or implement by using general mathematical methods [5]. The artificial neural networks are widely used in the study of conventional drying characteristics, stress monitoring, and MC prediction of wood [19]; the use of neural networks to predict changes in the wood MC during high frequency drying has been rarely studied. In order to provide a predictive model for the control of wood MC during high frequency drying, based on the BP neural network algorithm and using the real-time online measurement data, drying time, location of measuring point, and internal temperature and pressure of wood as the input to neural network model, the changes in the wood MC can be predicted. The feasibility and prediction accuracy of the model was analyzed
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