FLORAM receives Impact Factor

We are pleased to announce that FLORAM has received its first impact factor rating in the 2022 Journal Citation Reports (JCR).

Now FLORAM has the highest impact factor among Brazilian Forest Sciences journals.

Floresta e Ambiente
Floresta e Ambiente
Original Article Wood Science and Technology

Neuro-fuzzy Hybrid System for Monitoring Wood Moisture Content During Drying

Antônio José Vinha Zanuncio; Amélia Guimarães Carvalho; Carlos Alberto Araújo Júnior; Maíra Reis de Assis; Liniker Fernandes da Silva

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ABSTRACT: The heterogeneous behavior of wood during drying is a process difficult to control. The objective of this study was to evaluate the accuracy of the neuro-fuzzy hybrid system for monitoring wood moisture during drying. Eucalyptus urophylla x Eucalyptus grandis samples (2 x 2 x 4 cm) were saturated and dried in climatic chamber for 15 days. Basic density was determined by the dry mass/saturated volume ratio. Two neuro-fuzzy systems were developed to monitor wood moisture, the first based on the genetic material and drying period and the second based on basic density and drying period. The drying rate of wood samples was higher at the initial period and all reached equilibrium moisture content after 15 days. Density showed relationship with wood moisture during the study period. Both systems have the potential to monitor moisture, however, neuro-fuzzy system based on basic density and drying period showed better results and is therefore more suitable.


air drying, basic density, Eucalyptus


Aliabadi M, Golmohammadi R, Khotanlou H, Mansoorizadeh M, Salarpour A. Artificial neural networks and advanced fuzzy techniques for predicting noise level in the industrial embroidery workrooms. Applied Artificial Intelligence 2015; 29(8): 766-785. http://dx.doi.org/10.1080/08839514.2015.1071090.

Araújo CA Jr, Silva LF, Silva ML, Leite HG, Valdetaro EB, Donato DA et al. Modelling and forecast of charcoal prices using a neurofuzzy system. Revista Cerne 2016; 22(2): 151-158.

Associação Brasileira de Normas Técnicas – ABNT. ABNT: NBR 11941:2003 - Madeira - Determinação da densidade básica . Rio de Janeiro: ABNT; 2003. 6 p.

Bedane AH, Afzal MT, Sokhansanj S. Simulation of temperature and moisture changes during storage of woody biomass owing to weather variability. Biomass and Bioenergy 2011; 35(7): 3147-3151. http://dx.doi.org/10.1016/j.biombioe.2011.04.008.

Brand MA, Muñiz GIB, Quirino WF, Brito JO. Storage as a tool to improve wood fuel quality. BiomassandBioenergy 2011; 35(7): 2581-2588.

Cavalcante AA, Naveiro RM, Costa SS. Secagem da madeira de louro preto (Nectandracuspidata ) em estufa de micro-ondas. Floresta e Ambiente 2016; 23(3): 427-434. http://dx.doi.org/10.1590/2179-8087.012412.

Costa VE, Rezende MA, Rodrigues VA. Conversion between basic density and apparent density at any moisture content in Eucalyptus grandis. Holzforschung 2014; 68(8): 981-986. http://dx.doi.org/10.1515/hf-2013-0211.

Engelund ET, Thygesen LG, Svensson S, Hill CAS. A critical discussion of the physics of wood–water interactions. Wood Science and Technology 2013; 47(1): 141-161. http://dx.doi.org/10.1007/s00226-012-0514-7.

Fullér R. Neural fuzzy systems. Abo: Abo Akademi University; 1995.

Khaki M, Yusoff I, Islami N. Application of the artificial neural network and neuro-fuzzy system for assessment of groundwater quality. Clean – soil, air. Water (Basel) 2015; 43(4): 551-560.

Kollmann FFP, Côté WA. Principles of wood science and technology: solid wood. New York: Springer, 1968. 592 p. http://dx.doi.org/10.1007/978-3-642-87928-9.

Korkut S, Ünsal O, Kocaefe D, Aytin A, Gökyar A. Evaluation of kiln-drying schedules for wild cherry wood (Cerasus avium). Maderas. Ciencia y Tecnología 2013; 15(3): 281-292.

Mesiarová-Zemánková A, Ahmad K. T-norms in subtractive clustering and backpropagation. International Journal of Intelligent Systems 2010; 25: 909-924.

Moya R, Tenorio C, Meyer I. Influence of wood anatomy on moisture content, shrinkage and during defects in Vochysiaguatemalensis Donn Sm. Scientia Forestalis 2012; 40(94): 249-258.

Naji S, Shamshirband S, Basser H, Keivani A, Alengaram UJ, Jumaat MZ et al. Application of adaptive neuro-fuzzy methodology for estimating building energy consumption. Renewable & Sustainable Energy Reviews 2016; 53(1): 1520-1528. http://dx.doi.org/10.1016/j.rser.2015.09.062.

Skaar C. Wood-water relations. New York: Springer-Verlag; 1988. 263 p. http://dx.doi.org/10.1007/978-3-642-73683-4.

Swithenbank J, Chen Q, Zhang X, Sharifi V, Pourkashanian M. Wood would burn. Biomass and Bioenergy 2011; 35(3): 999-1007. http://dx.doi.org/10.1016/j.biombioe.2010.12.026.

Vieira FHA, Affonso C, Alves MCS. Application of neuro-fuzzy inference system on wood identification. Applied Mechanics and Materials 2014; 590: 667-671. http://dx.doi.org/10.4028/www.scientific.net/AMM.590.667.

Watanabe K, Kobayashi I, Kuroda N. Investigation of wood properties that influence the final moisture content of air-dried sugi (Cryptomeria japonica) using principal component regression analysis. Journal of Wood Science 2012; 58(6): 487-492. http://dx.doi.org/10.1007/s10086-012-1283-5.

Zadeh LA. Fuzzy sets. Information and Control 1965; 8(3): 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X.

Zanuncio AJV, Carvalho AG, Silva LF, Lima JT, Trugilho PF, Silva JRM. Predicting moisture content frombasic density and diameter during air drying of Eucalyptus and Corymbia logs. Maderas. Ciencia y Tecnología 2015; 17(2): 335-344. http://dx.doi.org/10.4067/S0718-221X2015005000031.

Zanuncio AJV, Carvalho AG, Silva MG, Lima JT. Importance of wood dry in gtothe forest transport and pulp production. Revista Ceres 2017; 23(2): 147-152.

Zanuncio AJV, Monteiro TC, Lima JT, Andrade HB, Carvalho AG. Drying biomass for energy use of Eucalyptus urophylla and Corymbia citriodora logs. Bio Resources 2013; 8(4): 5159-5168. http://dx.doi.org/10.15376/biores.8.4.5159-5168.

Zanuncio AJV, Lima JT, Monteiro TC, Trugilho PF, Lima FS. Curva característica de secagem da madeira de Tectona grandis e Acacia mangium ao ar livre. Floresta e Ambiente 2014; 22(1): 117-123. http://dx.doi.org/10.1590/2179-8087.037913.

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