Predicting the Wood Mean Moisture Content in a Conventional Kiln-based Drying Process: A Data-driven Approach

The quality of the production process is the biggest concern of a company to retain their clients and be competitive on the market. In the wood production industry, the wood moisture content is one of the most important criteria to define the final quality, price, and reliability of the lumbers. After the trees have been sawn into lumbers, the latter are dried using a conventional kiln to decrease the percentage of humidity in the wood. Thus, to control the quality of the process, the moisture content should be monitored all along the drying so it can be stopped at the right moisture content. Our approach consists of using machine learning techniques to predict the mean moisture content in the kiln throughout the drying process with a lag of ten hours. Using this lag, we will be able to know exactly when to stop the drying while giving more time for the logistics preparations. The data of real time sensor's measurements, the drying conditions and some other key performance indicators were used as inputs to predict the mean moisture content in the kiln within ten hours for every five minutes. After the feature engineering, the final inputs are selected using a hybrid Forward-Backward Stepwise Selection, and then fed to a Convolutional Bidirectional LSTM recurrent neural network which has been chosen after evaluating multiple machine learning models. The final model choice is based on its theoretical performance with an R2 of 95.24% and an MAE of 3.61% on the test dataset, and several discussions with the experts of the domain to reflect the operational perspective.