Stavros Avramidis

RADIO FREQUENCY VACUUM DRYING OF WOOD. I. MATHEMATICAL MODEL

A one-dimensional mathematical model to describe the transport phenomena during continuous radio frequency/vacuum (RF/V) drying of thick lumber was developed from general conservation equations. When drying at temperatures near the boiling point, as in RF/V drying, the effect of the gas phase pressure gradient on moisture transfer within the solid can be very important. The controlling resistances and transport mechanisms are discussed in detail. In addition, capillary transport in RF/V drying is discussed and its effect is compared with convective drying. The model provides a relatively fast and efficient way to simulate vacuum drying behavior assisted by dielectric heating. As an example, the governing heat and mass transfer equations, including consideration of internal heat generation and the effect of gas phase pressure gradient, are derived and solved in a one-dimensional system using a finite volume method. The effect of changes of the most important parameters on the predictions of the model is also presented.

RADIO FREQUENCY VACUUM DRYING OF WOOD. II. EXPERIMENTAL MODEL EVALUATION

Thick specimens of two softwoods were dried in a laboratory radio frequency/vacuum (RF/V) dryer to investigate the evolution of internal moisture flow patterns. The total average moisture content and local temperatures and pressures were monitored as a function of time. A number of runs were carried out for different conditions of voltage, power density, ambient pressure, core temperature, and initial moisture content. The results obtained were compared with the ones predicted by a 1D simplified model. Despite the simplicity of the model, good agreement was obtained for a variety of drying conditions, especially in predicting the average moisture evolution and the drying time.

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.

Wood-water sorption isotherm prediction with artificial neural networks: A preliminary study

Abstract This is a preliminary study that proposes an original prototype artificial neural network to be used in addition to the two classic sorption isotherm modeling methods, Hailwood-Horrobin (HH) and Guggenheim-Anderson-deBoer (GAB), in predicting the equilibrium moisture content in wood at three different temperatures (30, 45 and 60°C) for softwood (lodgepole pine) sapwood and heartwood specimens. Contrary to the HH and GAB equations, which use physical data for modeling, the predictive power of the artificial neural network is based on both physical and chemical data for the specific wood types. The results prove the potential efficient use of neural networks in predicting moisture content based not only on the ambient conditions, but also on taking into consideration the chemical composition of wood.

Behaviour of solid wood and bound water as a function of moisture content : a proton magnetic resonance study

Nuclear magnetic resonance lineshape second moments of the solid wood protons and spin-spin relaxation times, ?2, of the bound water in lodgepole pine heartwood have been measured at 30°C for a ränge of moisture

Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.)

Abstract The stiffness and strength, modulus of elasticity (MOE) and modulus of rupture (MOR), as well as density, moisture content, microfibril angle and diffraction pattern coefficient of variation of azimuthal intensity profile (ICV) was determined for 259 small clear specimens. These samples represent 38 old- and second-growth western hemlock (Tsuga heterophylla) trees harvested from several sites in coastal British Columbia, Canada. The data were analyzed by classic statistical regression techniques to reveal interrelations among the mechanical properties and the inherent wood properties. Simultaneously, the predictive power of artificial neural networks was evaluated with the same data set by employing several optimization techniques. Regression analysis of wood density and the flexural strength properties resulted in R2 of 0.172 and 0.332 for MOE and MOR, respectively. The most efficient network model proved to be far superior demonstrating correlation coefficients with models for MOE ranging between 0.693 and 0.750, and the corresponding MOR models ranging between 0.438 and 0.561 in all testing phases. It is apparent that neural networks have the potential and capacity to self-train and become powerful adaptive systems that can predict the strength and stiffness of wood samples. The neural network analysis also revealed the importance level of each independent variable on both MOE and MOR properties.

Analysis of the wood sorption isotherm using clustering theory

The Zimm-Lundberg cluster theory was used to characterise water adsorption and desorption for wood isotherms at 10, 25, 40 and 55°C.The analysis showed that clustering of water molecules begin to form at approximately 20% moisture content for adsorption and begin to break up at 25% moisture content for desorption. The isotherm can be divided into three regions describing the adsorption process and at least two regions describing the desorption process. Water in wood has a different behavior in adsorption to desorption processes.

Application of near-infrared spectroscopy for moisture-based sorting of green hem-fir timber

A rapid, non-destructive, in-line method suitable for sorting green hem-fir timbers (115-mm square) based on moisture content was established by near-infrared (NIR) spectroscopy. The accuracy of NIR sorting was compared with a commercial capacitance-type moisture meter. Mixedspecies samples consisting of three moisture classes were assessed in this study. The NIR-based prediction model showed positive correlation with the actual calculated values as determined by oven-drying, regardless of knots, surface roughness, and the mix of two wood species. NIR proved to be capable of detecting the moisture content between all pairs of the three moisture groups, whereas the capacitance-type moisture meter failed to establish a significant difference between middle- and high-moisture groups. These findings clearly demonstrate that NIR spectroscopy has a potential to estimate average moisture of green timber indirectly, although it inherently gives only surface moisture content values, as it is limited by scan depth.

Non-destructive Measurement of Moisture Distribution in Wood during Drying Using Digital X-ray Microscopy

The objective of this study was to develop a nondestructive method by which moisture distribution in wood during drying could be predicted. A newly developed digital X-ray microscope was used to measure the moisture content of wood and its accuracy and resolution was evaluated compared to the classic oven-dry method. Small green wood specimens of Sugi (Cryptomeria japonica D. Don) were cut and dried under constant temperature and humidity. As the weight was decreasing during drying, X-ray microscope images of cross section were obtained. From these digital images and specimen weight, the moisture content during drying was measured by the two methods. After the shrinkage of the specimen was canceled, the standard error achieved finally was about 1% moisture content within the experimental range. As the image was divided into small subimages, the clear moisture distribution can be seen. It was found that the image divided into 32 × 32 subimages in each size of 0.625 × 0.625 mm might be valid to determine the moisture distribution, and that the drying rate in early wood is larger than in late wood.

On The Permeability of Main Wood Species in China

Summary This research dealt with the longitudinal air permeability of 40 important Chinese woods. The air permeabilities measured at about 10% moisture content for softwoods ranged between 0.00759 and 1.87130 darcy, while for hardwoods ranged between 0.00182 and 13.49867 darcy. The sapwood permeability of both softwoods and hardwoods was generally much higher than that of heartwood with 3 to 144 and 1 to 1302 times, respectively. The permeability values of wood were not affected by density between and within species. In all species studied, the permeability of heartwood of all softwoods, about 70% ring-porous and about 30% diffuse-porous or semi-diffuse porous hardwoods were below 0.1 darcy. The number of low permeability softwoods was higher than that of hardwoods. The number of low permeability ring-porous hardwoods was higher than that of diffuse-porous or semi-diffuse porous hardwoods. Finally, all the studied species were classified into five categories by their longitudinal permeabilities.