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Optimizing the pine wood drying process using a critical diffusion coefficient and a timed moistening impulse

Hannes Tamme
Hannes Tamme
, 
Peeter Muiste
Peeter Muiste
 y 
Valdek Tamme
Valdek Tamme
   | 04 jun 2022
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Article Category: Research paper
Publicado en línea: 04 jun 2022
Páginas: 150 - 165
Recibido: 17 nov 2021
Aceptado: 31 dic 2021
DOI: https://doi.org/10.2478/fsmu-2021-0017
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© 2021 Hannes Tamme et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

(a) Schematic diagram of the drying experiment and some insight into the Feutron working space of the climatic chamber (Feutron Klimasimulation GmbH, 2021).
(a) Schematic diagram of the drying experiment and some insight into the Feutron working space of the climatic chamber (Feutron Klimasimulation GmbH, 2021).

Figure 1

(b) Three specimens were used in the experiment being shown here. Sensors were attached to specimen a) to monitor the drying process; specimen b) was a reference specimen which was being used to determine the drying curve by means of weighing; and specimen c) was used to determine the moisture content of the wood at different depths by means of slicing.
(b) Three specimens were used in the experiment being shown here. Sensors were attached to specimen a) to monitor the drying process; specimen b) was a reference specimen which was being used to determine the drying curve by means of weighing; and specimen c) was used to determine the moisture content of the wood at different depths by means of slicing.

Figure 2

A cross-section linear calibration function for the calibration of resistance-type sensors into the wood's MC sensors. Points A, B, C and D are the endpoints of the line segment.
A cross-section linear calibration function for the calibration of resistance-type sensors into the wood's MC sensors. Points A, B, C and D are the endpoints of the line segment.

Figure 3

A schematic for calculating the diffusion coefficients in the first and second drying phases, using the four-point method based on experimental data.
A schematic for calculating the diffusion coefficients in the first and second drying phases, using the four-point method based on experimental data.

Figure 4

Identification of the critical RH of the drying air according to the separating line of the first and second drying phase.
Identification of the critical RH of the drying air according to the separating line of the first and second drying phase.

Figure 5

The response for uncalibrated electrical resistance sensors (at depths of 1 mm and 4 mm) upon transition from the first drying phase to the second drying phase.
The response for uncalibrated electrical resistance sensors (at depths of 1 mm and 4 mm) upon transition from the first drying phase to the second drying phase.

Figure 6

A distinction between the first and the second drying phases based on the log files of three Ahlborn thermocouples and an Ahlborn displacement sensor.
A distinction between the first and the second drying phases based on the log files of three Ahlborn thermocouples and an Ahlborn displacement sensor.

Figure 7

Effect of moistening impulse on the electrical resistance sensors.
Effect of moistening impulse on the electrical resistance sensors.

Figure 8

A comparison of drying curves under simulation and during experimentation as determined on the basis of the industrial drying schedule.
A comparison of drying curves under simulation and during experimentation as determined on the basis of the industrial drying schedule.

Figure 9

The results of simulations regarding optimized and unoptimized drying schedules.
The results of simulations regarding optimized and unoptimized drying schedules.

Figure 10

The forced drying schedules simulation graphs without the moistening impulse, and with the moistening impulse.
The forced drying schedules simulation graphs without the moistening impulse, and with the moistening impulse.

Figure 11

Dependencies of the ESCR value and the TORKSIM v5.11 simulated relative drying stresses on drying time. For a better visual comparison, the simulated relative stresses are multi plied by a factor of 3.12.
Dependencies of the ESCR value and the TORKSIM v5.11 simulated relative drying stresses on drying time. For a better visual comparison, the simulated relative stresses are multi plied by a factor of 3.12.

Figure 12

A linear model of the relationship between the electrical indicator for the surface layer and the electrical indicator for the inner layer.
A linear model of the relationship between the electrical indicator for the surface layer and the electrical indicator for the inner layer.

Figure 13

Comparison of experimental and simulated moisture profiles in pine wood: a) after 92 hours b) after 142 hours.
Comparison of experimental and simulated moisture profiles in pine wood: a) after 92 hours b) after 142 hours.

Industrial 35 mm pine wood drying schedule used in the experiment and the simulation section.

Time (h) Air temp. (°C) Air RH (%)
0 20 93
1 47 93
12 47 93
36 50 90
60 52 85
84 52 80
108 52 69
132 52 59
156 52 49
180 52 39
204 52 39

Optimized industrial pine wood drying schedule based on the definition of critical DC and critical RH.

Time (h) Air temp. (°C) Air RH (%)
0 20 60
1 47 83
113 52 81
132 52 59
156 52 49
180 52 39
204 52 39

A forced drying schedule for pine wood. Data regarding the stages of the moistening impulse are given in parenthesis in the table.

Time (h) Air temp. (°C) Air RH (%)
0 20 93
1 47 93
24 47 77
48 50 61
72 52 45
(90) (52) (40)
96 52 29
120 52 13

Calibration functions were derived from Formula (4) for electrical resistance sensors at depths of 1 mm and 4 mm from the surface of the wood in sections AB, BC, and CD.

Depth (mm) AB BC CD
1 mm y = −30.277x+1584.9 y = −0.6058x+56.458 y = −0.3867x+42.873
4 mm y = −32.895x+1691.4 y = −0.7255x+73.292 y = −1.1495+98.947
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