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.

The Role of Drying Schedule and Conditioning in Moisture Uniformity in Wood: A Machine Learning Approach

“…This outcome shows that all the listed parameters considerably impact the model’s performance, though they have different RI values. It is worth mentioning that these results are moderately different from the findings by Rahimi et al [ 40 ], in which M i was the most important input. This difference may stem from different drying schedules, applying post-drying treatments, or different timber dimensions (2″ × 4″ vs. 4″ × 4″).…”

“…Kiln-drying scheduling is also covered in some studies [ 30 , 31 ] as an important factor impacting the final moisture content and drying defects [ 32 ]. In addition, previous studies focused on characterizing and modeling final moisture and its spread in air-dried [ 33 ], radio-frequency kiln-dried [ 34 , 35 , 36 ], heat treated [ 37 ], and heat-and-vent kiln-dried batches [ 38 , 39 , 40 ]. Additionally, previous studies investigated moisture prediction in kiln-dried lumber merely based on wood properties; however, the combined effects of drying conditions and wood properties on the moisture uniformity after kiln-drying still represent a knowledge gap [ 41 ].…”

Drying Behaviour of Western Hemlock with Schedules Developed for Norway Spruce and Scots Pine

“…Ward et al [9] confirmed Kozlic's results and observed a reduced presence of wet pockets in young western hemlock trees and proposed sorting the timber prior to drying according to MC, preferably determined by using electrical resistance MC meters. Modern tools such as machine learning and stochastic models are applied prior to drying to sort western hemlock timbers based on the initial MC with the goal of achieving homogeneity in the final MC [10][11][12]. Implementing post-drying equalisation of the MC may be effective in preventing non-uniformity in the MC levels by decreasing dry bulb temperature, which leads to reducing over-drying, while it prolongs drying time [13].…”