An experimental and theoretical examination of pine woods dried in the vacuum dryer by artificial neural network

Year 2022, Volume: 9 Issue: 1, 31 - 37, 31.03.2022

Erkan Dikmen , Arzu Şencan Şahin , Kemal Yakut

https://doi.org/10.31593/ijeat.1069080

Cited By: 1

Abstract

The drying characteristics of the pine woods were examined in the vacuum drying system under different operating conditions. Three drying temperatures (40, 50 and 60 ◦C), three operating pressures (0.6, 0.7 and 0.8 bar) and three times of exposure to vacuum (5, 10 and 15 minutes) were investigated. Experiments were carried out to obtain data from the sample moisture content. In this study, the application of Artificial Neural Network (ANN) to estimate pine woods' moisture content (output parameters for ANN modeling) was examined. Drying time, drying temperature, relative humidity, pressure and air temperature were accepted as the input parameters of the model. Training and validation were performed with great accuracy. The moisture content of woods is formulated by the ANN method. The proposed method offers more flexibility; therefore, the determination of the moisture content in pine woods is quite simpler.

Keywords

Vacuum drying, Wood, Artificial Neural Network, Modeling

Supporting Institution

Süleyman Demirel University Scientific Research Projects Unit (SDUBAP)

Project Number

1527-D-07

Thanks

Authors would like to thank Süleyman Demirel University Scientific Research Projects Unit (SDUBAP) for the financial support for the Project No: 1527-D-07.

References

• Lin, C.T. and Lee, C.S.G. Neural fuzzy systems, Prentice Hall Inc, Englewood Cliffs, New Jersey, USA, 1995, 797.
• Pedreno-Molina, J.L., Monzo-Cabrera, J., Toledo-Moreo, A. and Sánchez-Hernández, D. 2005. A novel predictive architecture for microwave-assisted drying processes based on neural Networks. International Communications in Heat and Mass Transfer, 32, 1026–1033.
• Ceylan, I. 2008. Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technology, 26, 1469–1476.
• Menlik, T., Ozdemir, M.B. and Kirmaci, V. 2010. Determination of freeze-drying behaviors of apples by artificial neural network. Expert Systems with Applications, 37, 7669–7677.
• Momenzadeh, L., Zomorodian, A. and Mowla, D. 2011. Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using artificial neural network. Food and Bioproducts Processing, 89, 15–21.
• Ceylan, I. and Aktas, M. 2008. Modeling of a hazelnut dryer assisted heat pump by using artificial neural networks. Applied Energy, 85, 841–854.
• Movagharnejad K. and Nikzad M. 2007. Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59, 78–85.
• Esteban L.G., Fernandez F.G. and Palacios P. 2009. MOE prediction in Abies pinsapo Boiss. timber: Application of an artificial neural network using non-destructive testing. Computers and Structures, 87, 1360–1365.
• Lertworasirikul S. and Tipsuwan Y. 2008. Moisture content and water activity prediction of semi-finished cassava crackers from drying process with artificial neural network. Journal of Food Engineering, 84, 65–74.
• Tripathy P.P. and Kumar S. 2009. Neural network approach for food temperature prediction during solar drying. International Journal of Thermal Sciences, 48, 1452–1459.
• Nazghelichi T., Aghbashlo M. and Kianmehr M.H. 2011. Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computers and Electronics in Agriculture, 75, 84–91.
• Topuz A. 2010. Predicting moisture content of agricultural products using artificial neural networks. Advances in Engineering Software, 41, 464–470.
• Nazghelichi T., Aghbashlo M., Kianmehr M.H. and Omid, M. 2011. Prediction of energy and exergy of carrot cubes in a fluidized bed dryer by artificial neural networks. Drying Technology, 29, 295-307.
• Wu H. and Avramidis S. 2006. Prediction of timber kiln drying rates by neural networks. Drying Technology, 24, 1541–1545.
• Cakmak G. and Yıldız C. 2011. The prediction of seedy grape drying rate using a neural network method. Computers and Electronics in Agriculture, 75, 132–138.
• Cachim P.B. 2011. Using artificial neural networks for calculation of temperatures in timber under fire loading. Construction and Building Materials, 25, 4175-4180.
• Tiryaki S. and Aydın A. 2014. An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construction and Building Materials, 62, 102-108.
• Khazaei N.B., Tavakoli T., Ghassemian H. and Banakar A. 2013. Applied machine vision and artificial neural network for modeling and controlling of the grape drying process. Computers and Electronics in Agriculture, 98, 205-213.
• Nadian M.H., Rafiee S., Aghbashlo M., Mohtasebi S.S. 2015. Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food and Bioproducts Processing, 94, 263-274.
• Dikmen E. 2010. “Design of a drying machine working at different temperatures and pressures and investigation of variable drying parameters”. Ph.D. Thesis, Süleyman Demirel University, Graduate School of Natural Sciences, Isparta, Turkey, 97 (In Turkish).
• Kalogirou S.A. 1999. Applications of artificial neural networks in energy systems A review. Energy Conversion & Management, 40, 1073-1087.
• Kalogirou S.A. 2000. Applications of artificial neural-networks for energy systems. Applied Energy, 67, 17-35.
• Sencan A., Kalogirou S.A. 2005. A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples. Energy Conversion and Management, 46, 2405–2418.