Prediction of Timber Kiln Drying Rates by Neural Networks

Hongwei Wu, Stavros Avramidis

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1541-1545
JournalDrying Technology : An International Journal
Issue number12
Publication statusPublished - 2006


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