Skip to main content
SpringerLink
Log in

Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood

Bestimmung der nicht isothermischen Feuchtediffusion in Holz mittels eines neuronalen Netzwerks im Vergleich zu einer mathematischen Modellierung

  • ORIGINALARBEITEN ORIGINALS
  • Published:
Holz als Roh- und Werkstoff Aims and scope Submit manuscript

Abstract

The objective of this study was to develop an optimum artificial neural network (ANN) capable of predicting the direction and magnitude of the moisture flux through wood under nonisothermal steady-state diffusion. A comparison between experimental measurements and the predicted values of three mathematical models reported in the literature and of the proposed neural network is presented and discussed. When developing the ANN model, several configurations were evaluated. The optimal ANN model was found to be a network with six neurons in one hidden layer. This well-trained network correlated the forecasted to the experimental data with low-level errors compared to previously developed models and also predicted the flux-reversal phenomenon thus confirming that ANN modeling has a much better predictive performance. It was also shown that the numbers of the training data were linked to the performance of the network during estimation. However, the powerful predictive capacity of this modeling method was still supported although a limited experimental data set was trained.

Zusammenfassung

Ziel dieser Arbeit war die Entwicklung eines optimalen neuronalen Netzwerks (ANN) zur Bestimmung der Richtung und der Grössenordnung der Feuchtebewegung in Holz bei nicht isothermischer stationärer Diffusion. In der vorliegenden Arbeit werden die empirischen Werte mit den Ergebnissen von drei der Literatur entnommenen Rechenmodellen sowie mit dem entwickelten neuronalen Netzwerk verglichen und diskutiert. Bei der Entwicklung des ANN-Modells wurden verschiedene Konfigurationen untersucht. Dabei hat sich ein Netzwerk mit einer inneren Schicht mit sechs Neuronen als optimal erwiesen. Verglichen mit bisher entwickelten Modellen führte dieses gut trainierte Netzwerk zu einer besseren Übereinstimmung mit den Versuchsdaten. Es erklärte auch die “Umkehrdiffusion”. Damit wurde die bessere Vorhersagegenauigkeit der ANN-Modellierung bestätigt. Darüber hinaus konnte gezeigt werden, wie die Anzahl der Trainingsdaten das Ergebnis der Netzwerkmodellierung beeinflusst. Trotz der begrenzten Anzahl der für das Trainieren verwendeten Versuchsdaten wurde die sehr gute Vorhersageleistung dieser Methode bestätigt.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Avramidis S (1986) Nonisothermal diffusion of moisture in wood and sorption studies. Ph.D. Thesis. SUNY – College of Environmental Science and Forestry, Syracuse, NY

  2. Avramidis S, Hatzikiriakos SG (1995) Convective heat and mass transfer in nonisothermal moisture desorption. Holzforschung 49:163–167

    Article  Google Scholar 

  3. Avramidis S, Hatzikiriakos SG, Siau JF (1994) An irreversible thermodynamics model for unsteady-state nonisothermal moisture diffusion in wood. Wood Sci Technol 28:349–358

    Article  CAS  Google Scholar 

  4. Avramidis S, Iliadis L (2005) Wood-water sorption isotherm prediction with artificial neural networks: A preliminary study. Holzforschung 59:336–341

    Article  CAS  Google Scholar 

  5. Avramidis S, Kuroda N, Siau JF (1987) Experiments in nonisothermal diffusion of moisture in wood. Part II. Wood Fiber Sci 19(4):407–413

    Google Scholar 

  6. Avramidis S, Siau JF (1987) An investigation of the external and internal resistance to moisture diffusion in wood. Wood Sci Technol 21:249–256

    Article  Google Scholar 

  7. Bishop MC (1994) Neural network and their applications. Rev Sci Instrum 64(6):1803–1831

    Article  Google Scholar 

  8. Genel K, Ozbek I, Kurt A, Bindal C (2002) Boriding response of AISI W1 steel and use of artificial neural network for prediction of borided layer properties. Surf Coat Technol 160(1):38–43

    Article  CAS  Google Scholar 

  9. Chen CR, Ramaswamy HS, Alli I (2001) Prediction of quality changes during osmo-convective drying of blueberries using neural network models for process optimization. Dry Technol 19(3/4):507–523

    Article  Google Scholar 

  10. Hornik K, Stinchombe M, White H (1989) Multilayer feedforward network are universal approximator. Neural Network 2:359–366

    Article  Google Scholar 

  11. Islam MR, Sablani SS, Mujumdar AS (2003) An artificial neural network model for prediction of drying rates. Dry Technol 21(9):1867–1884

    Article  Google Scholar 

  12. Islamoglu Y, Kurt A (2004) Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels. Int J Heat Mass Transf 47:1361–1365

    Article  CAS  Google Scholar 

  13. Lin TY, Tseng C (2000) Optimum design for artificial neural networks: an example in a bicycle derailleur system. Eng Appl Art Int 13:3–14

    Article  Google Scholar 

  14. Liu TQ, Sun XY, Li XQ, Wang HL (2002) Neural network analysis of boiling heat transfer enhancement using additives. Int J Heat Mass Transf 45:5083–5089

    Article  CAS  Google Scholar 

  15. Myhara RM, Sablani S (2001) Unification of fruit water sorption isotherms using artificial neural networks. Dry Technol 19(8):1543–1554

    Article  CAS  Google Scholar 

  16. Ni H, Gunasekaran S (1998) Food quality prediction with neural networks. Food Technol 52(10):60–65

    Google Scholar 

  17. Sablani SS, Baik OD, Marcotte M (2002) Neural networks for predicting thermal conductivity of bakery products. J Food Eng 52:299–304

    Article  Google Scholar 

  18. Sablani SS, Kacimov A, Perret J, Mujumdar AS, Campo A (2005) Non-iterative estimation of heat transfer coefficients using artificial neural network models. Int J Heat Mass Transf 48:665–679

    Article  Google Scholar 

  19. Scalabrin G, Piazza L (2003) Analysis of forced convection heat transfer to supercritical carbon dioxide inside tubes using neural networks. Int J Heat Mass Transf 46:1139–1154

    Article  CAS  Google Scholar 

  20. Scalabrin G, Piazza L, Condosta M (2003) Convective cooling of supercritical carbon dioxide inside tubes: heat transfer analysis through neural networks. Int J Heat Mass Transf 46:4413–4425

    Article  CAS  Google Scholar 

  21. Siau JF (1983a) A proposed theory for nonisothermal unsteady-state transport of moisture in wood. Wood Sci Technol 17:75–77

    Article  Google Scholar 

  22. Siau JF (1983b) Chemical potential as a driving force for nonisothermal moisture movement in wood. Wood Sci Technol 17:101–105

    Article  CAS  Google Scholar 

  23. Siau JF (1984) Chemical potential and nonisothermal diffusion. Letter to the editor. Wood Fiber Sci 16(4):628–629

    Google Scholar 

  24. Siau JF (1992) Nonisothermal diffusion model based on irreversible thermodynamics. Wood Sci Technol 26(5):325–328

    Article  CAS  Google Scholar 

  25. Siau JF, Avramidis S (1993) Application of a thermodynamic model to nonisothermal diffusion experiments. Wood Sci Technol 27(2):131–136

    Article  CAS  Google Scholar 

  26. Siau JF, Bao F, Avramidis S (1986) Experiments in nonisothermal diffusion of moisture in wood. Wood Fiber Sci 18(1):84–89

    Google Scholar 

  27. Su G, Fujuda K, Morita K, Pidduck M (2002) Application of artificial neural network for the prediction of flow boiling curves. J Nucl Sci Technol 39(1):1190–1198

    CAS  Google Scholar 

  28. Xie G, Xiong R (1999) Use of hyperbolic and neural network models in modeling quality changes of dry peas in longtime cooking. J Food Eng 41(3–4):151–162

    Article  Google Scholar 

  29. Zeng P (1998) Neural computing in mechanics. Appl Mech Rev 51(2):173–197

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Wood Science, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    Stavros Avramidis & Hongwei Wu

Authors
  1. Stavros Avramidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongwei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stavros Avramidis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Avramidis, S., Wu, H. Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood. Holz Roh Werkst 65, 89–93 (2007). https://doi.org/10.1007/s00107-006-0113-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00107-006-0113-0

Keywords

Search

Navigation