Recent progress in solar wood drying: An updated review

Lamrani, Bilal; Bekkioui, Naoual; Simo-Tagne, Merlin; Ndukwu, Macmanus Chinenye

doi:10.1080/07373937.2022.2112048

Cited by 9 publications

(1 citation statement)

References 63 publications

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“…Timber drying impacts the final product quality and plays an essential role in the dimensional stability, and mechanical properties of timber, as the physical [ 1 ], elastic and viscoelastic properties of wood are moisture-dependent [ 2 ]. Additionally, timber drying facilitates coating, cutting, and remanufacturing procedures, rendering dried wood less susceptible to deformation, cracks, and decay [ 3 , 4 ]. Target moisture ( M t ) in kiln-drying should correspond to the indoor or outdoor environment where the final product will be used.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Monitoring the moisture content (MC) of wood and avoiding large MC variation is a crucial task as a large moisture spread after drying significantly devalues the product, especially in species with high green MC spread. Therefore, this research aims to optimize kiln-drying and provides a predictive approach to estimate and classify target timber moisture, using a gradient-boosting machine learning model. Inputs include three wood attributes (initial moisture, initial weight, and basic density) and three drying parameters (schedule, conditioning, and post-storage). Results show that initial weight has the highest correlation with the final moisture and possesses the highest relative importance in both predictive and classifier models. This model demonstrated a drop in training accuracy after removing schedule, conditioning, and post-storage from inputs, emphasizing that the drying parameters are significant in the robustness of the model. However, the regression-based model failed to satisfactorily predict the moisture after kiln-drying. In contrast, the classifying model is capable of classifying dried wood into acceptable, over-, and under-dried groups, which could apply to timber pre- and post-sorting. Overall, the gradient-boosting model successfully classified the moisture in kiln-dried western hemlock timber.

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Section: Introductionmentioning

confidence: 99%