Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process
Artificial Neural Network Modeling for Predicting Wood Moisture Content in High Frequency Vacuum Drying Process
复制标题
人工神经网络模型预测高频真空干燥过程中木材含水量
DOI：
10.3390/f10010016
复制
发表时间：
2019-01-01
期刊：
影响因子：
2.9
通讯作者：
Zhao, Jingyao
中科院分区：
文献类型：
--
作者：
Chai, Haojie;Chen, Xianming;Zhao, Jingyao
木材干燥过程中，含水率(MC)控制至关重要。研究了基于BP神经网络算法的木材高频真空干燥过程中含水率变化的预测方法。利用实时在线测量数据建立模型，将木材干燥时间、测点位置、木材内部温度和压力作为BP神经网络模型的输入。模型结构为4-6-1，训练样本的决策系数R2和均方误差分别为0.974和0.07355，表明神经网络模型具有很强的泛化能力。与实验值比较，预测值符合实验值的变化规律和大小，误差在2%左右，测点沿厚度方向的MC预测值误差在2%以内。因此，BP神经网络模型能够成功地模拟和预测木材在高频干燥过程中含水率的变化。
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.