[논문]소나무 원목의 천연건조 중 함수율 변화: II. 소나무 원목의 천연건조 중 함수율 변화 예측

HAN, Yeonjung; CHANG, Yoon-Seong; EOM, Chang-Deuk; LEE, Sang-Min

doi:10.5658/wood.2019.47.6.732

소나무 원목의 천연건조 중 함수율 변화: II. 소나무 원목의 천연건조 중 함수율 변화 예측
Moisture Content Change of Korean Red Pine Logs During Air Drying: II. Prediction of Moisture Content Change of Korean Red Pine Logs under Different Air Drying Conditions 원문보기

목재공학 = Journal of the Korean wood science and technology, v.47 no.6, 2019년, pp.732 - 750

HAN, Yeonjung (Department of Forest Products, National Institute of Forest Science) , CHANG, Yoon-Seong (Department of Forest Products, National Institute of Forest Science) , EOM, Chang-Deuk (Department of Forest Products, National Institute of Forest Science) , LEE, Sang-Min (Department of Forest Products, National Institute of Forest Science)

초록
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천연건조 중 목재의 함수율 변화 예측모형을 제시하기 위하여 15본의 소나무 원목에 대한 천연건조를 수행하였다. 초기함수율이 68.7%인 6본의 소나무 원목에 대하여 여름철에 천연건조를 시작한 후 약 880일이 경과한 후의 최종함수율은 17.4%이었다. 초기함수율이 35.8%인 9본의 소나무 원목에 대하여 겨울철에 천연건조를 시작한 후 약 760일이 경과한 후의 최종함수율은 16.0%이었다. 소나무 원목의 말구지름, 온도, 상대습도, 풍속을 독립변수로 결정하고, 천연건조 중 감소한 함수율을 종속변수로 다중회귀분석을 진행한 결과, 결정계수 0.925의 회귀모형을 얻을 수 있었다. 소나무 원목의 특성인 초기함수율과 말구지름이 기상조건인 온도, 상대습도, 풍속에 비하여 천연건조 중 함수율 감소에 미치는 영향이 더 크게 나타났다. 천연건조 중 내부함수율의 분포 및 함수율 변화를 예측하기 위하여 2차원 물질전달 해석을 수행하였다. 건조일수를 서로 다르게 적용하고, 수분확산계수 및 표면방사계수를 결정하는 기상조건을 다르게 적용한 2가지의 예측모형을 제시하였다. 2가지 적용 방법의 오차는 0.1 - 0.8%의 범위였으며, 측정값과의 차이는 2.2 - 3.6%의 범위였다. 다양한 초기함수율과 말구지름의 소나무 원목에 대한 천연건조 중 내부함수율을 측정하고, 각각의 기상조건에 대한 목재 내 수분이동계수를 산출하면 예측모형의 오차를 감소시킬 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

Air drying was carried out on 15 Korean red pine logs to provide a prediction model of the moisture content (MC) change in the wood during drying. The final MC was 17.4% after 880 days since the beginning of air drying in the summer for 6 Korean red pine logs with 68.7% initial MC. The final MC was 16.0% after 760 days since the beginning of air drying in the winter for 9 Korean red pine logs with 35.8% initial MC. A regression model with R-squared of 0.925 was obtained as a result of multiple regression analyses with initial MC, top diameter, temperature, relative humidity, and wind speed as independent variable and and MC change during air drying as dependent variable. The initial MC and top diameter, which is the characteristic of Korean red pine, have greater effect on the MC decrease during air drying compared to meteorological factors such as the temperature, relative humidity, and wind speed. Two-dimensional mass transfer analysis was performed to predict the MC distribution of Korean red pine logs during air drying. Two prediction models with different air drying days and different meteorological factors for the determination of the diffusion coefficient and surface emission coefficient were presented. The error between the different two methods ranged from 0.1 to 0.8% and the difference from the measured value ranged from 2.2 to 3.6%. By measuring the internal MC during air drying of Korean pine logs with various initial MC and diameter, and calculating the moisture transfer coefficient in wood for each meteorological condition, the error of the prediction model can be reduced.

주제어

표/그림 (10)

그림 Fig. 1. Measurement of the weight variation of Korean red pine logs in air drying
표 Table 1. Physical properties of Korean red pine for air drying
그림 Fig. 2. Sketch illustrating nomenclature used in 2-dimensional numerical analysis: (a) interior node, (b) convective boundary node, (c) exterior corner node with convective boundary
그림 Fig. 3. Moisture content change of Korean red pine logs that began to air-drying in summer (a) and autumn (b).
표 Table 2. MC loss during air-drying for Korean red pine with various initial MC, diameter, temperature, relative humidity, and wind speed conditions
표 Table 3. Multiple regression analysis on initial MC, diameter, temperature relative humidity, wind speed, and MC loss during air-drying
표 Table 4. Multiple regression analysis on initial MC, diameter, temperature and MC loss during air-drying
그림 Fig. 4. Predicted moisture content change of Korean red pine log with 64% of initial MC (430 mm-diameter × 3.6 m-length) during air-drying: (a) condition 1; application of diffusion coefficient (D) and surface emission coefficient (S) calculated from monthly average temperature (T) and relative humidity (RH) during actual air-drying, (b) condition 2; application of D and S calculated from average T and RH in summer.
그림 Fig. 5. Predicted moisture content change of Korean red pine log with 35% of initial MC (300 mm-diameter × 3.6 m-length) during air-drying: (a) condition 1; application of diffusion coefficient (D) and surface emission coefficient (S) calculated from monthly average temperature (T) and relative humidity (RH) during actual air-drying, (b) condition 2; application of D and S calculated from average T and RH in summer.
그림 Fig. 6. Moisture content distribution of Korean red pine log during air-drying: (a) log with 64% of initial MC (430 mm-diameter × 3.6 m-length) during 270 days air-drying, (b) log with 35% of initial MC (300 mm-diameter × 3.6 m-length) during 750 days air-drying.

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문제 정의

The purpose of this study is to analyze the change of MC during air drying of Korean red pine logs and to suggest air drying time to reach the target. A air drying facility was set up in Seoul, and the weight of logs was measured to evaluate the changes in MC.

제안 방법

3. Two-dimensional mass transfer analysis was used to predict the MC distribution and its changes during air drying. Two different methods of analysis were used; one was to apply the actual drying days and the average weather conditions for each month, and the other to apply the effective air drying days and summer weather conditions.
During the air drying, the weight of the logs was measured using a hanging scale (W 230 mm × D 370 mm × H 474 mm; 1 ton; CAS, Korea) once every 3 to 6 months (Fig. 1).
In this study, Korean red pine logs were air-dried to analyze the changes in internal MC of wood during air drying. Multiple regression was run on air drying test results for 15 logs with weather variables.
In this study, monthly average temperature, relative humidity, and wind speed in the Seoul area where the air drying facility was installed were used to determine diffusion coefficients and surface emission coefficients. The data from the Korea Meteorological Administration (http://data.
Two methods were used to determine the drying duration, diffusion coefficient, and surface emission coefficient that are associated with prediction of MC change. The first method is to apply the actual days from January to December of the year and the average weather conditions for each month to the calculation of diffusion coefficients and surface emission coefficients. The second method is to apply the effective air drying days of each month and weather conditions in the summer (June to August) to the calculation of diffusion coefficients and surface emission coefficients.
The first method is to apply the actual days from January to December of the year and the average weather conditions for each month to the calculation of diffusion coefficients and surface emission coefficients. The second method is to apply the effective air drying days of each month and weather conditions in the summer (June to August) to the calculation of diffusion coefficients and surface emission coefficients. To predict the changes in MC, the dimension and MC of two logs, whose internal MC during air drying was measured, were applied to the moisture transfer analysis model.
A air drying facility was set up in Seoul, and the weight of logs was measured to evaluate the changes in MC. Then, wood-related data such as initial MC, top diameter, and dry weight of Korean red pine logs and weather data such as temperature, relative humidity, and wind speed were used to conduct two-dimensional moisture transfer analysis, which led to the prediction of MC distribution of wood.
5(b), the MC decreases constantly. To verify the prediction model of MC changes measured under two conditions during air drying of Korean red pine logs, the internal MC of two different condition of logs was presented. One is the log with 64% initial MC, 430 mm in top diameter and 3.
Two-dimensional mass transfer analysis was used to predict the MC distribution and its changes during air drying. Two different methods of analysis were used; one was to apply the actual drying days and the average weather conditions for each month, and the other to apply the effective air drying days and summer weather conditions. The errors in the two methods ranged from 0.
Two-dimensional mass transfer analysis was used to predict changes in MC of Korean red pine logs during air drying. Two methods were used to determine the drying duration, diffusion coefficient, and surface emission coefficient that are associated with prediction of MC change. The first method is to apply the actual days from January to December of the year and the average weather conditions for each month to the calculation of diffusion coefficients and surface emission coefficients.

대상 데이터

1. The test subjects were six Korean red pine logs for which drying started in summer and continued for about 880 days and nine logs for which drying started in autumn and continued for about 760 days. Their average final MC was 17.
Test species are Korean red pine trees (Pinus densiflora) collected from Uljin-gun, Gyeongsangbukdo and 15 logs with IV-XI age class were cut to 3.6 m in length. The initial MC of the logs measured using increment borer (L 300 mm × ø 10 mm; Haglof, Sweden) ranged from 28% to 83%, and the top diameter ranged from 310 mm to 510 mm.

이론/모형

One is the diffusion coefficient, which is the rate of moisture movement inside the wood, and the other is the surface emission coefficient, which is the rate of moisture movement from the surface of the wood to the atmosphere. Diffusion coefficients were determined using a moisture transfer model (Siau, 1995) created with a simplified cell model. Diffusion coefficient DT in the transverse direction and diffusion coefficient DL in the longitudinal direction can be expressed as Equations (12) and (13).
, 2016). In both of these studies, the finite difference method was used. In those studies, when comparing the predicted and measured values of the MC distribution during wood drying, appropriate choice of diffusion coefficients and surface emission coefficients turned out to be very important (Kamke and Vaneck, 1994).
And the results for species such as hardwoods are not exact (Simpson and Hart, 2000). In these studies, variables such as initial MC, temperature, and relative humidity were put into a regression equation to apply the results obtained in a specific area across the country (Denig and Wengert, 1982). In addition, these studies have developed a numerical analysis model using moisture diffusion coefficients obtained through experiments and suggested a method of changing each coefficient by using weather data from different regions (Hart, 1982).
The surface emission coefficient was determined by equation (14) using the method of conversion from the convective mass transfer coefficient (Yeo and Smith, 2005).
The second method is to apply the effective air drying days of each month and weather conditions in the summer (June to August) to the calculation of diffusion coefficients and surface emission coefficients. To predict the changes in MC, the dimension and MC of two logs, whose internal MC during air drying was measured, were applied to the moisture transfer analysis model.

성능/효과

As shown in Fig. 5(a), the MC of Korean red pine log was predicted to be 29.5% after 150 days of air drying, 26.7% after 300 days, 23.4% after 450 days, 22.1% after 600 days, and 19.7% after 750 days. As shown in Fig.
In Fig. 4(a), the MC of Korean red pine log was predicted to be 38.7% after 90 days of actual air drying, 34.8% after 180 days, and 33.5% after 270 days. In Fig.
2. Multiple regression was run and the dependent variable was the reduced MC during air drying, while the independent variables included the initial MC, top diameter, temperature, relative humidity and wind speed of pine logs. The regression model was “reduced MC (%) = 0.
0% below the fiber saturation point. It means that about 3% error between the measured and predicted MC change takes about 120-140 air-drying days for log with 20% MC.
The regression model is Y = 0.350 × X1 ‒ 0.030 × X2 ‒ 1.38 × X3+ 8.84 × X4 + 272 × X5 ‒ 1100, and the coefficient of determination (R-squared) is 0.925, which indicates that prediction of the dependent variable by the independent variables can be done with 92.5% accuracy.

참고문헌 (21)

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원문보기 상세보기
Han, Y., Eom, C.D., Lee, S.M., Park, Y. 2019. Moisture content change of Korean red pine logs during air drying: I. Effective air drying days in major regions in Korea. Journal of the Korean Wood Science and Technology 47(6): 721-731.

원문보기 상세보기
Hart, C.A. 1982. SIMSOR: A computer simulation of water sortpion in wood. Wood and Fiber 13(1): 46-71.
Hollman, J.P. 1989. Heat Transfer (SI metric ed.). McGraw-Hill, Sydney, Australia, pp. 676.
Jung, H.S. 1985. Study on air-drying characteristics of Taun lumber and air-drying calendar (I). Journal of the Korean Wood Science and Technology 13(3): 27-33.
Jung, H.S., Lee, N.H., Lee, J.H., Kwon, J.Y. 1997. Comparison of air-drying process in four seasons for some softwood lumbers. Journal of the Korean Wood Science and Technology 25(1): 28-36.
Jung, H.S., Lee, C.H., Kang, W., Eom, C.D. 2003. Air-drying curve and moisture content distribution of softwood square timber. Journal of the Korean Wood Science and Technology 31(1): 27-31.
Kamke, F.A., Vaneck, M. 1994. Comparison of wood drying models. Paper presented at the 4th IUFRO International Wood Drying Conference, Rotorua, New Zealand, pp. 1-21.
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원문보기 상세보기
Korea Meteorological Administration. http://data.kma.go.kr
Lee, C.J., Lee, N.H., Park, M.J., Park, J.S., Eom, C.D. 2014. Effect of reserve air-drying of Korean pine heavy timbers on high-temperature and low-humidity drying characteristics. Journal of the Korean Wood Science and Technology 42(1): 49-57.

원문보기 상세보기
Lee, H.W., Jung, H.S. 1989. The comparative analysis of drying-conditions, -rates, -defects and -yield, and heat-efficiency in solar-dehumidification-drying of oaks with those in conventional air-, semigreenhouse type solar-, and kiln-drying. Journal of the Korean Wood Science and Technology 17: 22-54.
McMillen, J.M., Wengert, E.M. 1978. Drying eastern hardwood lumber. Agric. Handb. 528. Washington, DC: U.S. Department of Agriculture, Forest Service, pp. 104.
Perre, P., Moser, M., Martin, M. 1993. Advances in transport phenomena during convective drying with superheated steam and moist air. Journal of Heat and Mass Transfer 36(11): 2725-2746.

상세보기
Ranta-Maunus, A. 1994. Computation of moisture transport and drying stresses by a 2-D FE-programme. Paper presented at the 4th IUFRO International Wood Drying Conference, Rotorua, New Zealand, pp. 187-194.
Rietz, R.C., Page, R.H. 1971. Air drying of lumber: A guide to industry practice. Agric. Handb. 402. Washington, DC: U.S. Department of Agriculture, Forest Service, pp. 110.
Simpson, W.T., Hart, C.A. 2000. Method for estimating air-drying times of lumber. Forest Products Journal 51(11/12): 56-63.
Siau, J.F. 1995. Wood: Influence of moisture on physical properties, Blacksburg, VA, Virginia Polytechnic Institute and State University, pp. 227.
Sutherland, J.W., Turner, I., Northway, R.L. 1992. A theoretical and experimental investigation of the convective drying of Australian pinus radiata timber. Paper presented at the 3rd IUFRO International Wood Drying Conference, Vienna, Austria, pp. 145-155.
Yeo, H., Smith, W.B. 2005. Development of a convective mass transfer coefficient conversion method. Wood Fiber Science 37(1): 3-13.

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