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Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks

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Abstract

In the present work, two artificial neural network (ANN) models were developed for modeling the effects of conditions of heat treatment process such as exposure period and temperature at equilibrium moisture content (EMC) and specific gravity (SG) at different relative humidity levels of heat treated Uludag fir (Abies bornmülleriana Mattf.) and hornbeam (Carpinus betulus L.) wood. A custom MATLAB application created with MATLAB codes and functions related to neural networks was used for the development of feed forward and back propagation multilayer ANN models. The prediction models having the best prediction performance were determined by means of statistical and graphical comparisons. The results show that the prediction models are practical, reliable and quite effective tools for predicting the EMC and SG characteristics of heat treated wood. Thus, this study presents a novel and alternative approach to the literature to optimize conditions of heat treatment process.

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Acknowledgements

The authors are thankful to Dr. Deniz Aydemir, Department of Forest Industry Engineering, Forestry Faculty, Bartin University, Bartin, Turkey, for providing the database used in the paper.

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Authors and Affiliations

  1. Department of Industrial Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Sukru Ozsahin & Mirac Murat

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  1. Sukru Ozsahin
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  2. Mirac Murat
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Correspondence to Sukru Ozsahin.

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Ozsahin, S., Murat, M. Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks. Eur. J. Wood Prod. 76, 563–572 (2018). https://doi.org/10.1007/s00107-017-1219-2

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  • DOI: https://doi.org/10.1007/s00107-017-1219-2

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