Prediction of timber kiln drying rates by neural networks

Drying Technology

Volumn 24, Issue 12, 2006, Pages 1541-1545

  Wu, Hongwei ^a   Avramidis, Stavros ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Basic density; Drying; Moisture content; Neural networks; Wood

Indexed keywords

ALGORITHMS; BACKPROPAGATION; DENSITY (SPECIFIC GRAVITY); DRYING; MATHEMATICAL MODELS; MOISTURE; NEURAL NETWORKS; TESTING; TIMBER;

BACKPROPAGATION ALGORITHM; BASIC DENSITY; DRYING RATES; TIMBER KILN DRYING;

KILNS;

ALGORITHMS; BACKPROPAGATION; DENSITY (SPECIFIC GRAVITY); DRYING; KILNS; MATHEMATICAL MODELS; MOISTURE; NEURAL NETWORKS; TESTING; TIMBER;

ALGORITHMS; DENSITY; DRYING; FORESTS; KILNS; MATHEMATICAL MODELS; MOISTURE CONTENT; TESTING;

EID: 33845428996     PISSN: 07373937     EISSN: 15322300     Source Type: Journal    
DOI: 10.1080/07373930601047584     Document Type: Article

References (17)

1. Linko, P.; Zhu, Y. Neural network programming in bioprocess variable estimation and state prediction. Journal of Biotechnology, 1991, 21 (3), 253-270.
2. Ni, H.; Gunasekaran, S. Food quality prediction with neural networks. Food Technology 1998, 52 (10), 60-65.
3. Xie, G.; Xiong, R. Use of hyperbolic and neural network models in modeling quality changes of dry peas in longtime cooking. Journal of Food Engineering. 1999 41 (3/4), 151-162.
4. Chen, C.R.; Ramaswamy, H.S.; Alli, I. Prediction of quality changes during osmo-convective drying of blueberries using neural network models for process optimization. Drying Technology 2001, 19 (3&4), 507-523.
5. Myhara, R.M.; Sablani, S. Unification of fruit water sorption isotherms using artificial neural networks. Drying Technology 2001, 19 (8), 1543-1554.
6. Avramidis, S.; Iliadis L. Wood-water sorption isotherm prediction with artificial neural networks: A preliminary study. Holzforschung 2005, 59, 336-341.
7. Su, G.; Fujuda, K.; Morita, K.; Pidduck, M. Application of artificial neural network for the prediction of flow boiling curves. Journal of Nuclear Science and Technology 2002, 39 (1), 1190-1198.
8. Sablani, S.S.; Baik, O.D.; Marcotte, M. Neural networks for predicting thermal conductivity of bakery products. Journal of Food Engineering 2002, 52, 299-304.
9. Islam, M.R.; Sablani, S.S.; Mujumdar, A.S. An artificial neural network model for prediction of drying rates. Drying Technology 2003, 21 (9), 1867-1884.
10. Liu, T.Q.; Sun, X.Y.; Li, X.Q.; Wang, H.L. Neural network analysis of boiling heat transfer enhancement using additives. International Journal of Heat and Mass Transfer 2002, 45, 5083-5089.
11. Scalabrin, G.; Piazza, L. Analysis of forced convection heat transfer to supercritical carbon dioxide inside tubes using neural networks. International Journal of Heat and Mass Transfer 2003, 46, 1139-1154.
12. Scalabrin, G.; Piazza, L.; Condosta, M. Convective cooling of supercritical carbon dioxide inside tubes: Heat transfer analysis through neural networks. International Journal of Heat and Mass Transfer 2003, 46, 4413-4425.
13. Islamoglu, Y.; Kurt, A. Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels. International Journal of Heat and Mass Transfer 2004, 47, 1361-1365.
14. Sablani, S.S.; Kacimov, A.; Perret, J.; Mujumdar, A.S.; Campo, A. Non-iterative estimation of heat transfer coefficients using artificial neural network models. International Journal of Heat and Mass Transfer 2005, 48, 665-679.
15. Zhang, Y.; Oliveira, L.; Avramidis, S.Drying characteristics of hem-fir squares as affected by species and basic density presorting. Forest Products Journal 1996, 46 (2), 44-50.
16. Avramidis, S.; Mackay, J.F.G. Development of kiln schedules for 4-inch by 4-inch Pacific Coast hemlock. Forest Products Journal 1998, 38 (9), 45-48.
17. Parcheco-Vega, A.; Sen, M.; Yang, K.T.; McClain, R.L. Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data. International and Journal of Heat and Mass Transfer. 2001, 44, 763-770.