Prediction of Timber Kiln Drying Rates by Neural Networks

Wu, Hongwei and Avramidis, Stavros (2006) Prediction of Timber Kiln Drying Rates by Neural Networks. Drying Technology - An International Journal, 24 (12). pp. 1541-1545. ISSN 0737-3937

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The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.

Item Type: Article
Uncontrolled Keywords: Basic density, Drying, Moisture content, Neural networks, Wood
Subjects: D500 Forestry
F200 Materials Science
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Hongwei Wu
Date Deposited: 20 Nov 2015 15:29
Last Modified: 12 Oct 2019 23:11

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