Modeling and On-line Measurement of Drying Stress of Pinus massoniana Board

A drying stress model was established by considering that the total shrinkage of wood is the sum of free shrinkage, instantaneous strain, viscoelastic strain, and mechanosorptive strain. From the stress model, the stress can be calculated once the actual wood shrinkage and moisture content gradient are known. Based on this theory, on-line measurement of the drying stress has been realized by measuring the moisture content (MC) gradient between the surface and the core layers, and the actual shrinkage of the board for Pinus massoniana.

A sensor for measuring wood shrinkage was developed based on electric resistance and strain relationship in a selected element material within the sensor. A resistance type of MC sensor was used for the MC gradient measurement. These sensors are reliable and can meet the requirement of the measurements in practical drying. The technique reported in this article for detecting drying stress from the on-line measurements of board shrinkage and MC gradient can be applied to develop optimized drying schedule in commercial drying.     

Moisture Diffusion Coefficients for Modeling the First and Second Drying Sections of Green Bricks

A model has been developed that describes the dependence of the moisture diffusion coefficient on the water fraction. Until the end of shrinkage has been achieved, the moisture diffusion coefficient is proportional to the second power of the water fraction. Due to shrinkage, the relevant capillary spaces available for water transport become smaller. Consequently, the moisture diffusion coefficient decreases continually. After the end of shrinkage, the flow resistance to the water moving toward the surface increases sharply due to penetrating air. This leads to a steep drop of the moisture diffusion coefficient by several powers of ten. Measurements were carried out with specimens of defined geometry to determine the moisture diffusion coefficient. On the basis of a specified limiting value, the model is capable of calculating the moisture diffusion for all initially specified raw materials moistures. The moisture can also be determined if the degree of drying shrinkage is known. Using the determined moisture diffusion coefficient, the first and the second drying section can be located. Drying tests were carried out in a laboratory dryer and the experimental results obtained were compared to the simulation results. The simulation results are in good agreement with the experimental results.     

Modeling Shrinkage Response to Tensile Stresses in Wood Drying: I. Shrinkage-Moisture Interaction in Stress-Free Specimens

This article reports on the wood shrinkage during drying in relationship with the temperature and moisture content. All tests were performed perpendicular to the grain on small clear wood specimens of green Western hemlock while drying at 40, 60, and 80°C to 17, 11, and 5% final moisture contents. Overall, wood dimensional changes and moisture loss phenomena were successfully analyzed and interpolated. The shrinkage strain followed a nonlinear pattern with the moisture loss being the driving force and exhibited good correlation with the square value of moisture content in tangential, and linear moisture values could be used to describe shrinkage in radial direction. Both shrinkage intersection points and end of capillary water values increased with temperature; the distinction between the two values could not be made at all times. A nonlinear function containing two regression coefficients (α and β) was found to be a good interpolation of the moisture loss experimental data. Further analyses revealed that β is independent of both target moisture content and temperature, whereas α appears to be influenced by both variables. The correlation between shrinkage and moisture loss rate is intended to be used as a stress prediction tool.     

Mathematical Modeling of Kiln Drying of Softwood Timber: Model Development, Validation, and Practical Application

Mathematical modeling of wood drying is a powerful tool to better understand and quantify the effects of wood properties as well as the effects of drying and post-drying treatment conditions on drying and thus the wood drying models can be used to improve drying quality. The models that have been developed can be divided into three categories: models for drying a single board, models for drying a kiln-wide stack, and models for drying stress and deformation. The single-board drying model employs comprehensive heat and moisture mass transfer equations and can be used to investigate the influence of wood variability. The kiln-wide drying model, which is based on the transfer processes between wood and the drying medium, is able to examine the influence of drying schedules and wood properties. The stress model can predict stress development in drying and stress relief in final steam conditioning and post-kiln treatment. An integrated model can be used to optimize drying schedules and develop strategies for high-quality dried timber.     

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.     

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.     

High-Frequency Energy-Assisted Vacuum Drying of Fresh Eucalyptus globulus

Worldwide, eucalyptus tree plantations have been established in appropriate climates because of fast growth and wood qualities suitable mainly for pulp. A potential exists of converting eucalyptus trees into lumber that may be of higher value than pulp. Conventional drying of lumber of Eucalyptus globulus is often difficult because of the occurrence of drying stresses, leading to collapse and checking. The special method of vacuum drying while heating the wood with high-frequency energy (75-77 mbar, 46-51°C) was used to obtain short drying times (5-13 days from green state to 10% final moisture content) and low crack amount.     

Modeling Drying Kinetics of Pistachio Nuts with Multilayer Feed-Forward Neural Network

Drying kinetics of pistachio nuts (Akbari v.) was simulated using a multilayer feed-forward neural network (MFNN). Experiments were performed at five drying air temperatures (ranging from 40 to 80°C) and four input air flow velocities (ranging from 0.5 to 2 m/s) with three replicates in a thin-layer dryer. Initial moisture content in all experiments was held at about 0.3 kg/kg d.b. To find the optimum model, various multilayer perceptron (MLP) topologies, having one and/or two hidden layers of neurons, were investigated and their prediction performances were evaluated. The (3-8-5-1)-MLP, namely, a network having eight neurons in the first hidden layer and five neurons in the second hidden layer resulted in the best-suited model estimating the moisture content of the pistachio nuts at all drying runs. For this topology, R² and MSE values were 0.9989 and 4.20E-06, respectively. A comparative study among MFNN and empirical models was also carried out. Among the empirical models, the logarithmic model, with MSE = 7.29E-6 and R² = 0.9982, gave better predictions than the others. However, the MFNN model performed better than the Lewis, Henderson and Pabis, two-term, and Page models and was marginally better than the logarithmic model.     

Simulation of Temperature and Moisture Content Profiles in a Pinus radiata Board during High-Temperature Drying

A two-dimensional drying model has been simulated for industrial drying schedules to assess sensitivity of the model parameters. The predicted drying times and rates were similar to the results of experimental data. The influence of wood properties on the predicted drying rate was assessed. It was found that the effects of specific heat capacity and thermal conductivity on predicted overall drying rate of board were small. However, the effect of changes in the diffusion coefficient on drying rate below fiber saturation point (moisture content at 30%) was found to be significant.     

Simulation of Temperature and Moisture Content Profiles in a Pinus radiata Board during High-Temperature Drying

A two-dimensional drying model has been simulated for industrial drying schedules to assess sensitivity of the model parameters. The predicted drying times and rates were similar to the results of experimental data. The influence of wood properties on the predicted drying rate was assessed. It was found that the effects of specific heat capacity and thermal conductivity on predicted overall drying rate of board were small. However, the effect of changes in the diffusion coefficient on drying rate below fiber saturation point (moisture content at 30%) was found to be significant.     

Simulation of Drying Using a Kiln Model

A mathematical model was developed for simulating a convective batch lumber drying process. The model incorporates mass and heat transfer relationships within the lumber stack, as well as thermodynamic properties of the wood and drying air. It takes into account the change of air properties along the stack and its effect on the mass and heat transfer parameters. The model relies on a drying rate function that is an empirical correlation based on single-board tests. A drying rate function for western hemlock (Tsuga heterophylla) lumber was developed. The drying rate function was obtained based on experiment results from 500 small boards dried over a range of conditions used in commercial practice. The model was first validated against data available in the literature and then against large batches of hemlock dried in a laboratory kiln. In both cases, the model output was in good agreement with the average moisture content, the drying rates, and the temperatures measured in the larger batches.     

Variation in Drying Behavior and Final Moisture Content of Wood during Conventional Low Temperature Drying and Vacuum Drying of Betula pendula Timber

Conventional and vacuum drying experiments were conducted on Betula pendula timber, which was sawn from trees felled during three different seasons. The influence of the wood procurement season on drying behavior differed, on the one hand, between the drying phases above and below 30% moisture content in the conventional drying, and, on the other hand, between the conventional and vacuum drying methods. During the first steps of the conventional drying process, relative humidity in the kiln, as well as drying time and drying rate, varied according to the felling season. Variations in environmental conditions outside the kiln and the seasonal variation in the physical properties of the wood were presumed to be the reasons for differences in drying behavior. The difference in moisture content gradient, i.e., the difference in final moisture content between the inner wood and the surface layer of boards, was greater in conventionally dried timber than in vacuum-dried timber. In conventionally dried timber there was a clear seasonal variation in the gradient of final moisture content, which was greatest for winter-felled wood. The premature drying of the surface layer during the first steps of the conventional drying process of winter-felled wood was the reason for the higher gradient of moisture content. Storage of wood as logs decreased the standard deviation of the final moisture content.     

Variation in Drying Behavior and Final Moisture Content of Wood during Conventional Low Temperature Drying and Vacuum Drying of Betula pendula Timber

Conventional and vacuum drying experiments were conducted on Betula pendula timber, which was sawn from trees felled during three different seasons. The influence of the wood procurement season on drying behavior differed, on the one hand, between the drying phases above and below 30% moisture content in the conventional drying, and, on the other hand, between the conventional and vacuum drying methods. During the first steps of the conventional drying process, relative humidity in the kiln, as well as drying time and drying rate, varied according to the felling season. Variations in environmental conditions outside the kiln and the seasonal variation in the physical properties of the wood were presumed to be the reasons for differences in drying behavior. The difference in moisture content gradient, i.e., the difference in final moisture content between the inner wood and the surface layer of boards, was greater in conventionally dried timber than in vacuum-dried timber. In conventionally dried timber there was a clear seasonal variation in the gradient of final moisture content, which was greatest for winter-felled wood. The premature drying of the surface layer during the first steps of the conventional drying process of winter-felled wood was the reason for the higher gradient of moisture content. Storage of wood as logs decreased the standard deviation of the final moisture content.     

A Simulation Tool for the Optimization of Lumber Drying Schedules

Abstract

A two-dimensional wood drying model based on the water potential concept is used to simulate the convection batch drying of lumber at conventional temperature. The model computes the average drying curve, the internal temperature and moisture content profiles, and the maximum effective moisture content gradient through board thickness. Various scenarios of conventional kiln-drying schedules are tested and their effects on drying time, maximum effective moisture content gradient, final moisture content distribution within and between boards, and energy consumption are analyzed. Simulations are performed for two softwood species, black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). The simulation results indicate that the predictive model can be a very useful tool to optimize kiln schedules in terms of drying time, energy consumption, and wood quality. Such a model could be readily combined with intelligent adaptive kiln controllers for on-line optimization of the drying schedules.     

A Simulation Tool for the Optimization of Lumber Drying Schedules

A two-dimensional wood drying model based on the water potential concept is used to simulate the convection batch drying of lumber at conventional temperature. The model computes the average drying curve, the internal temperature and moisture content profiles, and the maximum effective moisture content gradient through board thickness. Various scenarios of conventional kiln-drying schedules are tested and their effects on drying time, maximum effective moisture content gradient, final moisture content distribution within and between boards, and energy consumption are analyzed. Simulations are performed for two softwood species, black spruce (Picea mariana (Mill.) B.S.P.) and balsam fir (Abies balsamea (L.) Mill.). The simulation results indicate that the predictive model can be a very useful tool to optimize kiln schedules in terms of drying time, energy consumption, and wood quality. Such a model could be readily combined with intelligent adaptive kiln controllers for on-line optimization of the drying schedules.     

Modeling and Stress Analysis During Drying of a Deformable and Saturated Porous Medium

This article addresses how to express the behaviors that develop stresses within a porous media during convective drying processes. The work is focused on the coupling of the thermal (temperature distribution), hygroscopic (moisture, humidity), and mechanical (strains and stresses) aspects shown during the drying process of a saturated porous medium. Natural clay plate samples were used as a model material. Using two different mechanical behaviors (elastic and viscoelastic), the strain–stress equations were studied and discussed through the simulation results. Obtaining almost the same parameters of the main modeling variables (temperature, liquid pressure, and moisture content), a significant difference was observed between the results obtained for the stresses assuming the two behaviors, particularly depending on the viscoelastic parameters deduced from an experimental study. The simulation highlights a response of the medium supposed viscoelastic different to that of elastic case in intensity and response time.     

Model Predicted Effect of Process Variables on Kiln Drying of Pinus radiata Boards

The effects of kiln-drying process variables on drying time and final moisture content (MC) variability were assessed using a mathematical drying model (a kiln-wide model) developed earlier. Drying time was predicted to decrease by using higher air velocities and temperatures and, to a less extent, by increasing fan reversal frequencies. The drying time extended as board thickness or stack width increased. Increase in air velocity from 5 to 8 m/s tended to minimize the final MC variability. At least three reversals in the early stages of drying were required to reduce final moisture contents variation. The final MC variability increased with increasing stack width and with increasing temperature.     

Model Predicted Effect of Process Variables on Kiln Drying of Pinus radiata Boards

The effects of kiln-drying process variables on drying time and final moisture content (MC) variability were assessed using a mathematical drying model (a kiln-wide model) developed earlier. Drying time was predicted to decrease by using higher air velocities and temperatures and, to a less extent, by increasing fan reversal frequencies. The drying time extended as board thickness or stack width increased. Increase in air velocity from 5 to 8 m/s tended to minimize the final MC variability. At least three reversals in the early stages of drying were required to reduce final moisture contents variation. The final MC variability increased with increasing stack width and with increasing temperature.