Benchmarking moisture prediction in kiln-dried Pacific Coast hemlock wood

Abstract

The uniformity of final moisture content within a drying timber batch is crucial. Lack of such uniformity leads to producing large percentages of over-dried and under-dried timber, resulting in significant quality degradation and value downgrade. This study aims to predict kiln-dried timber moisture content using its initial moisture value, timber weight, and density. The distribution of wood properties in different drying runs was analyzed, and the difference in their means was statistically assessed. Various machine learning models were used for moisture prediction. The performance of the group method of data handling network was compared with the adaptive neuro-fuzzy inference system, support vector regression, decision tree, and random forest method. The best performance was achieved using random forest with the initial moisture content and weight of the wood as input parameters. Finally, the models’ performances were compared and practical recommendations for employing the adopted methodology in industrial settings were provided.