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Comparative testing of two alternating current methods for determining wood moisture content in kiln conditions

Hannes Tamme

Hannes Tamme

Chair of Forest Management Planning and Wood Processing Technologies, Institute of Forestry and Rural Engineering, Estonian University of Life SciencesTartu, Estonia
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Tamme, Hannes
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Regino Kask

Regino Kask

Chair of Forest Management Planning and Wood Processing Technologies, Institute of Forestry and Rural Engineering, Estonian University of Life SciencesTartu, Estonia
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Kask, Regino
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Peeter Muiste

Peeter Muiste

Chair of Forest Management Planning and Wood Processing Technologies, Institute of Forestry and Rural Engineering, Estonian University of Life SciencesTartu, Estonia
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Muiste, Peeter
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Valdek Tamme

Valdek Tamme

Chair of Forest Management Planning and Wood Processing Technologies, Institute of Forestry and Rural Engineering, Estonian University of Life SciencesTartu, Estonia
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Tamme, Valdek
  
01 déc. 2021
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Catégorie d'article: Research paper
Publié en ligne: 01 déc. 2021
Pages: 72 - 87
Reçu: 23 août 2021
Accepté: 04 oct. 2021
DOI: https://doi.org/10.2478/fsmu-2021-0005
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© 2021 by the authors., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

(a) Circuit diagram of the measuring capacitor (MEC) and (b) photograph of the MEC prototype with the tested birch wood specimen. 1 – signal connector, 2 – capacitor plate, 3 – wood specimen, 4 – capacitor insulator, FRA – frequency response analyser, 5 – triboelectric charge collector, 6 – film insulator, 7– heating cable, 8 – heat insulator.
(a) Circuit diagram of the measuring capacitor (MEC) and (b) photograph of the MEC prototype with the tested birch wood specimen. 1 – signal connector, 2 – capacitor plate, 3 – wood specimen, 4 – capacitor insulator, FRA – frequency response analyser, 5 – triboelectric charge collector, 6 – film insulator, 7– heating cable, 8 – heat insulator.

Figure 2.

The dependence of MEC useful capacitance and parasitic capacitance caused by water vapor condensation on MEC plates on the actual average moisture content of wood in the same coordinate grid. The upper curve in the figure represents parasitic capacitance, and the lower, useful capacitance.
The dependence of MEC useful capacitance and parasitic capacitance caused by water vapor condensation on MEC plates on the actual average moisture content of wood in the same coordinate grid. The upper curve in the figure represents parasitic capacitance, and the lower, useful capacitance.

Figure 3.

Increment of parasitic capacitance due to condensation of water vapor on MEC plates at varying wood moisture levels.
Increment of parasitic capacitance due to condensation of water vapor on MEC plates at varying wood moisture levels.

Figure 4.

Impedance spectra of leakage of MEC insulators: (a) Start phase of the leakage and (b) End phase of developed leakage.
Impedance spectra of leakage of MEC insulators: (a) Start phase of the leakage and (b) End phase of developed leakage.

Figure 5.

Dynamics of the potential for static electric charges of triboelectric origin when the birch wood specimen is placed between and then removed from the MEC plates.
Dynamics of the potential for static electric charges of triboelectric origin when the birch wood specimen is placed between and then removed from the MEC plates.

Figure 6.

DECM modelling at wood moisture levels above FSP.
DECM modelling at wood moisture levels above FSP.

Figure 7.

DECM modelling at wood moisture levels below FSP.
DECM modelling at wood moisture levels below FSP.

Figure 8.

DECM modelling at wood moisture levels above FSP, multiple (k = 16) model.
DECM modelling at wood moisture levels above FSP, multiple (k = 16) model.

Figure 9.

EIS modelling at wood moisture levels above FSP.
EIS modelling at wood moisture levels above FSP.

Figure 10.

EIS modelling at wood moisture levels above FSP, multiple (k = 16) model.
EIS modelling at wood moisture levels above FSP, multiple (k = 16) model.

Figure 11.

Comparison of the tolerance intervals (TI) predicted for single measurements of different models (using formulas in Table 2) and with the Rozema quality criterion at wood moisture levels above FSP. Corresponding graphs of the predicted TI are given from top to bottom as follows: EIS, DECM, EIS multiple, Rozema quality criterion (dashed line, parallel to x-axis), and DECM multiple.
Comparison of the tolerance intervals (TI) predicted for single measurements of different models (using formulas in Table 2) and with the Rozema quality criterion at wood moisture levels above FSP. Corresponding graphs of the predicted TI are given from top to bottom as follows: EIS, DECM, EIS multiple, Rozema quality criterion (dashed line, parallel to x-axis), and DECM multiple.

The main physical reliability characteristics of the dielectric capacitance method (DECM) in kiln climate and the corresponding capacitance meter (CAM) response_

Effect, parameter, figure no. Effect range CAM response
Condensation of water vapor on MEC plates, Fig. 2, 3 Cparasite = 340 to 845 pFCparasite = 681 pF, determined using EIS method CAM reading recorded ca. 5× and moderately increasing**
Leakage of MEC insulators, Fig. 4 Cparasite = 163 to 681 pFRparallel = 61 to 0.778 kOhm Floating of CAM reading
Triboelectric charges on MEC plates, Fig. 5 Ustatic = -10.5 to 4.29 V Floating of CAM reading, CAM spoilage risk
Useful MEC capacitance,* below FSP (0.% to 30%)Useful MEC capacitance, above FSP (30% to 105%) Cuseful = 121 to 205 pFMC = 0 % to 30%C useful = 205 to 231 pFMC = 30% to 105% CAM reading stable and reliable

Modelling results of the dielectric capacitance method (DECM) and electric impedance spectrometry (EIS) method_ In regression models, the independent x-variable is the actual MC (%), and the dependent y-variable is the predicted MC (%)_ The predicted single measurement tolerance bands on the 95% confidence level, yupper and ylower, are calculated using formulas 2, 3, 4 and 5_ The SE is calculated according to formula 6_ The tolerance interval (TI) is calculated using the formula TI = yupper - ylower_ N is the number of measurements repeated under the same test conditions and k is the number of measurements averaged per series of measurements (i_e_, the averaging period)_ For models with a series of measurements (k), the identification type shall be “multiple”_

N obs., k-period Method type, Fig. no. Equations for predicting single measurement tolerance bands and TI R2 p-value and tests* SE
N = 63 DECM (above FSP), Fig. 6 yupper = 1.0131x + 5.9063ylower = 0.9406x +0.8399TI = 0.0728x +5.075 0.97 <0.01K-S 4.88
N = 42 DECM (below FSP), Fig. 7 yupper = 1.0135x + 0.2792ylower = 0.9788x – 0.1954TI = 0.0348x +0.4746 0.99 <0.01K-SS-W 0.61
N = 63k = 16 DECM (above FSP) (multiple), Fig. 8 yupper = 1.006x + 0.44ylower = 0.9929x – 0.4163TI = 0.0124x + 0.8775 0.99 <0.01K-SS-W 0.46
N = 63 EIS (above FSP), Fig. 9 yupper = 0.9448x + 11.196ylower = 0.787x + 2.41TI = 0.1622x + 8.135 0.87 <0.01K-S 5.01
N = 63k = 16 EIS (above FSP) (multiple), Fig. 10 yupper = 1.0134x +1.728ylower = 0.968x – 0.84TI = 0.0365x + 2.836 0.99 <0.01K-S 0.867
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