Moisture content measurement method of lignocellulosic biomass

Abstract

The present invention discloses a method for measuring the moisture content of lignocellulosic biomass and a sample compressor for measuring the same. The method for measuring the moisture content of biomass according to the present invention comprises: (a) a step of supplying a sample containing lignocellulosic biomass; (b) a step of acquiring a near-infrared spectrum of the sample; (c) a step of mathematically preprocessing the near-infrared spectrum; (d) a step of constructing a moisture content prediction model by performing partial least squares regression analysis (PLSR) on the mathematically preprocessed near-infrared spectrum; and (e) a step of obtaining the moisture content of the sample using the moisture content prediction model. The method has the effect of preventing data variation according to the density of the sample, preventing a reduction in the sample volume by not collecting a sample for density measurement, and enabling non-destructive and rapid measurement of the moisture content.

Description

Technology Field

[0001] The present invention relates to a method for measuring the moisture content of woody biomass and a sample compressor for measuring the same. Background Technology

[0002] As of 2022, the supply of unused forest biomass in Korea reached 1.18 million tons, and the utilization amounted to 1.17 million tons (Supply and utilization of unused forest biomass, Korea Forest Service, 2023). Climate change and carbon neutrality are the greatest concerns of humanity today, and in Korea's '2050 Carbon Neutrality Scenario,' fuel switching in the energy supply and industrial sectors accounts for the majority of emission reductions. The supply and utilization of unused forest biomass are rapidly increasing as utilizing discarded resources as energy sources enables the efficient use of forest resources and the realization of a circular economy; furthermore, the demand for unused forest biomass is expected to continue to rise as environmental issues emerge.

[0003] The Korea Forest Service is promoting the 'Unused Forest Biomass' system to facilitate the energy utilization of forest biomass (Regulations on the Use, Supply, and Promotion of Forest Biomass Energy, Korea Forest Service). The market size of wood pellets based on unused forest biomass more than doubled from 240,000 tons in 2019 to 510,000 tons in 2021, accounting for 14% of the total domestic market (Current Status and Implications of Unused Forest Biomass Utilization, National Institute of Forest Science). As such, the size of the unused forest biomass market continues to grow.

[0004] In the utilization of unused forest biomass and sweet sorghum straw, controlling moisture content is the most important factor for the storage of unused forest biomass and sweet sorghum straw, which have the characteristic of easily spoiling.

[0005] Therefore, there is a need for a non-destructive, rapid, and accurate method for measuring the moisture content of woody biomass, such as unused forest biomass and sweet sorghum straw, which is inexpensive to build and can be measured through a simple process. Prior art literature

[0006] Korean Patent Publication No. 10-2019-0098558 (August 22, 2019) The problem to be solved

[0007] The objective of the present invention is to solve the aforementioned problems by providing a method for rapidly and accurately measuring the moisture content of woody biomass using a near-infrared spectrum.

[0008] Another objective of the present invention is to provide a sample compressor for measuring the moisture content of woody biomass through a non-destructive and simple process that can acquire a near-infrared spectrum by directly attaching to and compressing the sample without the need to collect a separate sample. means of solving the problem

[0009] According to one aspect of the present invention, a method for measuring the moisture content of woody biomass is provided, comprising: (a) supplying a sample containing woody biomass; (b) acquiring a near-infrared spectrum of the sample; (c) mathematically preprocessing the near-infrared spectrum; (d) constructing a moisture content prediction model by performing partial least squares regression analysis (PLSR) on the mathematically preprocessed near-infrared spectrum; and (e) obtaining the moisture content of the sample using the moisture content prediction model.

[0010] In addition, the above-mentioned woody biomass may include fragmented woody biomass.

[0011] In addition, the above woody biomass may include one or more selected from the group consisting of logging residue and sweet sorghum straw.

[0012] Additionally, the above step (c) may include (c-1) a step of obtaining second derivative spectrum data by applying a second derivative method to the near-infrared spectrum.

[0013] Additionally, the above step (c) may further include, after step (c-1), (c-2) a step of smoothing the near-infrared spectrum by setting the measurement wavelength gap (gap, nm) of the near-infrared spectrum to one of 1 to 10 nm.

[0014] In addition, the above smoothing can be performed using the moving average method with 11 points.

[0015] Additionally, the above step (d) may include: (d-1) a step of obtaining a moisture content prediction model by performing partial least squares regression analysis (PLSR) on the mathematically preprocessed near-infrared spectrum; and (d-2) a step of verifying the moisture content prediction model to construct a verified moisture content prediction model.

[0016] In addition, the verification of the above step (d-2) can be performed by K-fold cross validation.

[0017] In addition, the number of folds in step (e) may be 2 to 6, preferably 4 or 6.

[0018] In addition, the wavelength range of the acquired spectrum data may be 1250 to 2300 nm.

[0019] In addition, the method for measuring the moisture content of the above-mentioned lignocellulosic biomass may further include, prior to step (a), a step of (a') compressing the lignocellulosic biomass to produce compressed lignocellulosic biomass having a predetermined density.

[0020] In addition, the above-mentioned predetermined density is 0.1 to 0.5 g / cm³ 3 , preferably 0.2 to 0.5 g / cm² 3 , more preferably 0.3 to 0.5 g / cm² 3 It could be.

[0021] In addition, the method for measuring the moisture content of the above-described woody biomass may further include, after step (b), step (b') of obtaining modified spectrum data by deleting outliers from the above-described near-infrared spectrum data.

[0022] Additionally, the outlier in step (b') above may be data that does not belong to a cluster and exists outside of it in the principal component analysis (PCA).

[0023] In addition, the above outlier may be due to incomplete contact where the near-infrared probe of the near-infrared detector and the sample did not come into contact with each other.

[0024] According to another aspect of the present invention, a sample compressor for measuring the moisture content of woody biomass is provided, comprising: a compression part (100) including a body (120) having a cylindrical shape and a hollow (110) oriented in the longitudinal direction; a detection part (200) oriented in the longitudinal direction at the center of the body and including a near-infrared probe (NIR probe, 210); and a plurality of sample fixing parts (300) located inside the hollow and oriented in the longitudinal direction around the detection part.

[0025] Additionally, the body may include a hole (130) penetrating the hollow and the outside, and the sample fixing part may include a piston (310), a spring (320), and a fixing pin (330).

[0026] Additionally, the piston may include a protrusion (311), and the spring may be compressed when the piston moves toward a sample containing the woody biomass, and the protrusion may be inserted into the hole and fixed.

[0027] Additionally, when the above-mentioned projection is discharged from the hole and the lock is removed, the sample fixing part may move in the opposite direction to the direction of the sample due to the elastic force of the spring.

[0028] In addition, the fixing pin may be connected to one end of the piston and positioned longitudinally within the internal hollow of the spring.

[0029] In addition, the spring may be embedded in the woody biomass upon compression to fix the woody biomass.

[0030] Additionally, the above near-infrared probe may include a light source fiber (211) and a light-absorbing fiber (212) that absorbs light reflected from the sample.

[0031] In addition, the compression unit and the detection unit may compress woody biomass. Effects of the invention

[0032] The method for measuring the moisture content of biomass according to the present invention has the effect of preventing data variation depending on the density of the sample, preventing a reduction in the amount of sample by not collecting a sample for density measurement, and enabling non-destructive and rapid measurement of moisture content. Brief explanation of the drawing

[0033] These drawings are for reference to explain exemplary embodiments of the present invention, and therefore, the technical concept of the present invention should not be interpreted as being limited to the attached drawings. Figure 1 is a flowchart illustrating a method for measuring the moisture content of woody biomass according to Example 3 of the present invention. FIG. 2 is a schematic diagram showing the configuration of a sample compressor for measuring the moisture content of woody biomass according to Example 3 of the present invention. FIG. 3a is an image of a logging residue sample for measuring moisture content according to the present invention, and FIG. 3b is an image of a sweet sorghum straw sample for measuring moisture content according to the present invention. FIG. 4a is a flowchart illustrating a method for measuring the moisture content of woody biomass using a commercial wood moisture meter according to Example 1 of the present invention, FIG. 4b is a flowchart illustrating a method for measuring the moisture content of woody biomass using an electric resistance method according to Example 2 of the present invention, and FIG. 4c is a flowchart illustrating a method for measuring the moisture content of woody biomass using a near-infrared spectrum according to Example 3 of the present invention. FIG. 5 is a schematic diagram of k-fold cross-validation for constructing a model for measuring the moisture content of woody biomass materials according to Example 3 of the present invention. FIG. 6a is a graph showing the moisture content of woody biomass measured by the oven drying method according to Example 1 of the present invention, FIG. 6b is a graph showing the root mean square error of the moisture content of woody biomass measured by a moisture content meter compared to the moisture content of woody biomass measured by the oven drying method according to Example 1 of the present invention, FIG. 6c is a graph comparing the moisture content of logging residue measured by the oven drying method according to Example 1 of the present invention and a moisture content meter, and FIG. 6d is a graph comparing the moisture content of sorghum measured by the oven drying method and a moisture content meter. FIG. 7a is a graph showing the moisture content of logging residue through oven drying, the moisture content through a moisture content meter, and the moisture content through a moisture content meter with a correction factor applied, according to Example 1 of the present invention, and FIG. 7b is a graph showing the moisture content of sweet sorghum straw through oven drying and a bulk density of 0.32 g / cm³ according to Example 1 of the present invention. 3 This is a graph showing the moisture content through a moisture content meter and the correction coefficient of the moisture content through a moisture content meter. FIG. 8a is a graph showing the electrical resistance of logging residue at 10°C according to Example 2 of the present invention, FIG. 8b is a graph showing the electrical resistance of sweet sorghum straw at 10°C according to Example 2 of the present invention, FIG. 8c is a graph showing the change in electrical resistance of logging residue according to an increase in bulk density and the relationship between moisture content and electrical resistance according to Example 2 of the present invention, FIG. 8d is a graph showing the change in electrical resistance of sweet sorghum straw according to an increase in bulk density and the relationship between moisture content and electrical resistance according to Example 2 of the present invention. FIG. 9a shows 0.32 g / cm³ at 20 ℃ in the region of 1250 to 2300 nm according to Example 3 of the present invention. 3 This is a graph showing the near-infrared spectrum of a sample of logging residue compressed to a bulk density, and FIG. 9b is 0.32 g / cm³ at 20 °C in the 1250 to 2300 nm region according to Example 3 of the present invention. 3Figure 9c is a graph showing the near-infrared spectrum of a sample of sweet sorghum compressed to a bulk density, Figure 9d is a graph showing the Euclidean distance between near-infrared spectra measured three times at each density of logging residue according to Example 3 of the present invention, and Figure 9d is a graph showing the Euclidean distance between near-infrared spectra measured three times at each density of sweet sorghum according to Example 3 of the present invention. FIG. 10a is a graph showing a pair plot of principal component (PC) scores clustered by DBSCAN for near-infrared data with outliers of logging residue according to Example 3 of the present invention, and FIG. 10b is a graph showing a pair plot of principal component (PC) scores clustered by DBSCAN for near-infrared data with outliers of sweet sorghum straw according to Example 3 of the present invention. In FIG. 10a and FIG. 10b, the percentage values ​​in parentheses of the axis titles represent the variance of the PCs. FIG. 11a is a graph showing the near-infrared (NIR) spectrum of logging residue according to Example 3 of the present invention and a plot of the principal component analysis (PCA) scores of the two PCs at the loading of the first PC for the two materials, FIG. 11b is a graph showing the near-infrared (NIR) spectrum of sweet sorghum straw according to Example 3 of the present invention and a plot of the principal component analysis (PCA) scores of the two PCs at the loading of the first PC for the two materials, FIG. 11c is a graph showing the loading of the first PC according to the near-infrared wavelength of logging residue and sweet sorghum straw according to Example 3 of the present invention. FIG. 12a is a graph showing a scatter plot of moisture content measured by oven drying at 10°C for a sweet sorghum moisture prediction model constructed with near-infrared data according to Example 3 of the present invention, and FIG. 12b is a graph showing a scatter plot of moisture content predicted by a Partial Least Squares Regression (PLSR) model at 10°C for no outliers according to Example 3 of the present invention. For reference, R2C is the coefficient of determination for correction, RMSEC is the root mean square error of correction, R2P is the coefficient of determination for prediction, RMSEP is the root mean square error for prediction, and an outlier is a statistical observation that is clearly distinguishable from other subjects in the sample. Specific details for implementing the invention

[0034] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present invention.

[0035] However, the following description is not intended to limit the present invention to specific embodiments, and detailed descriptions of related prior art are omitted if it is determined that such detailed descriptions could obscure the essence of the present invention.

[0036] The terms used herein are merely for describing specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, or combinations thereof.

[0037] Additionally, terms including ordinal numbers, such as "first," "second," etc., used below may be used to describe various components, but said components are not limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0038] Furthermore, when it is stated that a component is "formed" or "laminated" on another component, it should be understood that while it may be formed or laminated by being directly attached to the entire surface or one surface of the other component, there may also be other components present in between.

[0039] Hereinafter, a method for measuring the moisture content of woody biomass and a sample compressor for measuring the same will be described in detail. However, this is presented as an example and is not intended to limit the present invention, and the present invention is defined only by the scope of the claims set forth below.

[0040] According to one aspect of the present invention, a method for measuring the moisture content of woody biomass is provided, comprising: (a) supplying a sample containing woody biomass; (b) acquiring a near-infrared spectrum of the sample; (c) mathematically preprocessing the near-infrared spectrum; (d) constructing a moisture content prediction model by performing partial least squares regression analysis (PLSR) on the mathematically preprocessed near-infrared spectrum; and (e) obtaining the moisture content of the sample using the moisture content prediction model.

[0041] In addition, the above-mentioned woody biomass may include fragmented woody biomass.

[0042] In addition, the above woody biomass may include one or more selected from the group consisting of logging residue and sweet sorghum straw.

[0043] Additionally, the above step (c) may include (c-1) a step of obtaining second derivative spectrum data by applying a second derivative method to the near-infrared spectrum.

[0044] Additionally, the above step (c) may further include, after step (c-1), (c-2) a step of smoothing the near-infrared spectrum by setting the measurement wavelength gap (gap, nm) of the near-infrared spectrum to one of 1 to 10 nm.

[0045] In addition, the above smoothing can be performed using the moving average method with 11 points.

[0046] Additionally, the above step (d) may include: (d-1) a step of obtaining a moisture content prediction model by performing partial least squares regression analysis (PLSR) on the mathematically preprocessed near-infrared spectrum; and (d-2) a step of verifying the moisture content prediction model to construct a verified moisture content prediction model.

[0047] In addition, the verification of the above step (d-2) can be performed by K-fold cross validation.

[0048] In addition, the number of folds in step (e) may be 2 to 6, preferably 4 or 6. Here, if the number of folds is less than 2, it is undesirable because the number of data is not secured and statistics are impossible, and if it exceeds 6, it is undesirable because the number of data becomes unnecessarily large and the increase in accuracy is negligible.

[0049] In addition, the wavelength range of the acquired spectrum data may be 1250 to 2300 nm.

[0050] In addition, the method for measuring the moisture content of the above-mentioned lignocellulosic biomass may further include, prior to step (a), a step of (a') compressing the lignocellulosic biomass to produce compressed lignocellulosic biomass having a predetermined density.

[0051] In addition, the above-mentioned predetermined density is 0.1 to 0.5 g / cm³ 3 , preferably 0.2 to 0.5 g / cm² 3 , more preferably 0.3 to 0.5 g / cm² 3 It may be. Here, the above-mentioned predetermined density is 0.1 g / cm³ 3 If it is less than, it is undesirable because it is difficult to obtain the near-infrared spectrum due to the large amount of void space in the woody biomass, and 0.5 g / cm³ 3 If it exceeds this limit, it is undesirable because compression is difficult due to the air layers present outside and inside the cell walls constituting the lignocellulosic biomass.

[0052] In addition, the method for measuring the moisture content of the above-described woody biomass may further include, after step (b), step (b') of obtaining modified spectrum data by deleting outliers from the above-described near-infrared spectrum data.

[0053] Additionally, the outlier in step (b') above may be data that does not belong to a cluster and exists outside of it in the principal component analysis (PCA).

[0054] In addition, the above outlier may be due to incomplete contact where the near-infrared probe of the near-infrared detector and the sample did not come into contact with each other.

[0055] According to another aspect of the present invention, a sample compressor for measuring the moisture content of woody biomass is provided, comprising: a compression part (100) including a body (120) having a cylindrical shape and a hollow (110) oriented in the longitudinal direction; a detection part (200) oriented in the longitudinal direction at the center of the body and including a near-infrared probe (NIR probe, 210); and a plurality of sample fixing parts (300) located inside the hollow and oriented in the longitudinal direction around the detection part.

[0056] Additionally, the body may include a hole (130) penetrating the hollow and the outside, and the sample fixing part may include a piston (310), a spring (320), and a fixing pin (330).

[0057] Additionally, the piston may include a protrusion (311), and the spring may be compressed when the piston moves toward a sample containing the woody biomass, and the protrusion may be inserted into the hole and fixed.

[0058] Additionally, when the above-mentioned projection is discharged from the hole and the lock is removed, the sample fixing part may move in the opposite direction to the direction of the sample due to the elastic force of the spring.

[0059] In addition, the fixing pin may be connected to one end of the piston and positioned longitudinally within the internal hollow of the spring.

[0060] In addition, the spring may be embedded in the woody biomass upon compression to fix the woody biomass.

[0061] Additionally, the above near-infrared probe may include a light source fiber (211) and a light-absorbing fiber (212) that absorbs light reflected from the sample.

[0062] In addition, the compression unit and the detection unit may compress woody biomass.

[0063] [Example]

[0064] Hereinafter, preferred embodiments of the present invention will be described. However, this is for illustrative purposes only and does not limit the scope of the present invention.

[0065] Preparation Example 1: Samples and Humidification

[0066] Figure 3a is an image of a logging residue sample for moisture content measurement according to the present invention, Figure 3b is an image of a sweet sorghum straw sample for moisture content measurement according to the present invention, and Table 1 is a table showing the details of the tested woody biomass samples. Referring to Figures 3a, 3b, and Table 1, logging residue and sweet sorghum (Sorghum bicolor var. dulciusculum) straw were used as woody biomass materials for moisture content measurement. A 10 g sample of each material was used in all experiments. The logging residue consists of crushed amorphous wood fragments and residues left on-site after timber harvesting operations. The bulk density of both materials was lower than reported values ​​due to their particle size and long origin, and they exhibited a low Woody Biomass Characteristics Index (BCI).

[0067] biomass Particle size (mm) Bulk density (g / cm³) 3 ) Moisture content (MC, %) Biomass Characteristics Index (BCI) logging residue 4.8 to 103.9 0.125 11.6 11,050 sweet sorghum 22.2 to 131.7 0.103 11.0 9,167

[0068] Table 2 below shows the environmental conditions and corresponding equilibrium moisture content of the lignocellulosic biomass of the present invention. Referring to Table 2, to control moisture levels, samples were gradually adjusted in a climate chamber (HB-105MP, Hanbaek Scientific Co., Bucheon-si, Korea) to predefined temperature and relative humidity (RH) values ​​as listed in Table 2. Tests were conducted at equilibrium moisture content (EMC) ranges from 5.2% to 24.3% under these climate conditions, and after all humidification cycles were completed, the moisture content of the samples was determined using an oven drying method. The oven drying method was used as the standard method for measuring moisture content.

[0069] Equilibrium moisture content (EMC, %) Relative Humidity (RH, %) Temperature (°C) 10 20 30 25 5.5 5.4 5.2 40 7.9 7.7 7.5 60 11.2 11.0 10.6 80 16.4 16.0 15.5 95 24.3 23.9 23.4

[0070] Preparation Example 2: Sample compression for moisture content data collection

[0071] Logging residue and sweet sorghum samples consist of loosely aggregated, narrow, elongated fragments; due to incomplete charge transport pathways, this form induces unstable electrical resistance. Therefore, the moisture content of the logging residue and sweet sorghum samples was measured by compressing them with a cylinder. A plunger was used to compress 10 g of the sample into a 45 mm diameter cylinder using a high-density polyethylene plate. The moisture data were obtained when the bulk densities of the samples were 0.09, 0.11, 0.13, 0.16, 0.21, and 0.32 g / cm³. 3 It was obtained using a wood moisture meter, an insulation resistance meter, and a near-infrared (NIR) spectrometer.

[0072] Referring to Table 1 above, in the case of logging residue, the bulk density of the raw material is 0.125 g / cm³ 3 Therefore, 0.13 to 0.32 g / cm³ 3Compression was performed within the range. In the stepwise compression of the two materials, the bulk density in the first step is the uncompressed state. Data was collected by drilling a hole at the end of the compression cylinder and inserting an electrode and a near-infrared (NIR) probe.

[0073] All measurements were performed in a climate chamber to minimize changes in sample moisture. Commercial wood moisture meters, electrical resistance, and NIRS were used to measure the moisture of the biomass materials, as shown in Figures 4a through 4c. Sample moisture data were obtained from compressed materials using all moisture measurement methods when the samples reached a constant weight under each climate condition.

[0074] Example 1: Measurement of moisture content (moisture) using a moisture meter

[0075] FIG. 4a is a flowchart illustrating a method for measuring the moisture content of woody biomass using a commercial wood moisture meter according to Example 1 of the present invention. Referring to FIG. 4a, an electric resistance-based wood moisture meter (MC-460; Exotek Instruments, Fichtenberg, Germany) equipped with a general-purpose 2-pin probe was used for measuring moisture content (MC). The moisture meter is designed for measuring the moisture content of wood, boards, chips, cardboard, and pellets, with a moisture content range of 3 to 140% and manual temperature compensation available. The moisture content correction factor for logging residue and sorghum measured by the moisture meter was calculated by comparing the moisture content measured by the moisture meter with the moisture content measured by the oven drying method.

[0076] Example 2: Measurement of water content based on electrical resistance

[0077] FIG. 4b is a flowchart illustrating a method for measuring the moisture content of lignocellulosic biomass using an electrical resistance method according to Example 2 of the present invention. Referring to FIG. 4b, the electrical resistance of the lignocellulosic biomass sample was measured using two electrodes insulated with polytetrafluoroethylene, excluding the ends. The distance between the electrodes was 25 mm, and the electrodes were fixed to a probe (26-ES, Delmhorst Instrument Co., Towaco, NJ) to maintain the distance of 25 mm. The electrode penetration depth was adjusted for each bulk density to measure the moisture content at the center of the compressed lignocellulosic biomass sample inside the cylinder. To measure the electrical resistance of the compressed lignocellulosic biomass sample according to Preparation Example 2, a super megohm meter (SM-8220, HIOKI EE Corp., Nagano, Japan) was used as an insulation resistance meter. The insulation resistance meter measured the current by flowing a constant DC voltage through the sample and calculated the electrical resistance from the relationship between voltage, current, and resistance. The regression equation for constructing the moisture content prediction model was calculated for each temperature condition tested using simple linear regression for moisture content measurements based on electrical resistance and five-minute drying. Additionally, to construct the moisture content prediction model, an electrical resistance-based Ordinary Least Squares Regression (OLSR) model using the relationship between moisture content, temperature, and electrical resistance was built using Python 3.8 with an open source library to measure the moisture content of lignocellulosic biomass samples based on electrical resistance.

[0078] Example 3: Near-infrared (NIR)-based water content measurement

[0079] Sample compressor for near-infrared-based moisture content measurement

[0080] FIG. 2 is a schematic diagram showing the configuration of a sample compressor for measuring the moisture content of woody biomass according to Example 3 of the present invention. Referring to FIG. 2, a sample compressor having a ballpoint pen-type sample fixing structure was devised as a method for manufacturing the sample compressor. A sample compressor for measuring moisture content based on near-infrared radiation was manufactured by applying a method in which a ballpoint pen-type sample fixer is operated at a location where the moisture content is to be measured after pushing a near-infrared probe into the sample to fix the position of the surrounding sample and to bring the probe closer to the sample to reduce the gap between the probe and the sample.

[0081] Spectrum dataset

[0082] FIG. 1 is a flowchart illustrating a method for measuring the moisture content of lignocellulosic biomass according to Example 3 of the present invention, and FIG. 4c is a flowchart illustrating a method for measuring the moisture content of lignocellulosic biomass using a near-infrared spectrum according to Example 3 of the present invention. Referring to FIG. 1 and FIG. 4c, a near-infrared (NIR) spectrum was acquired from a compressed lignocellulosic biomass sample according to Preparation Example 3 using a near-infrared (NIR) spectrometer (NIR Quest, Ocean Insight, Orlando, FL, USA) equipped with a near-infrared probe having a scan diameter of 5 mm in reflection mode. The near-infrared spectrum has wavelengths ranging from 870 to 2500 nm with a spectral resolution of 6.6 nm and was prepared as the average value of 16 scans. Since near-infrared characterizes shallow points on the surface of the material, the spectrum was measured three times at different points for each bulk density of the compressed lignocellulosic biomass sample. Consequently, 180 spectra for the logging residue dataset and 270 spectra for the single sorghum dataset were collected for all climate conditions, acquiring a database consisting of 450 near-infrared spectra. To ensure that all spectra within the entire wavelength range of 870 to 2500 nm contained wavelengths within the 1250 to 2300 nm range, the noise regions of wavelengths in the 870 to 1249 nm and 2301 to 2500 nm ranges were removed. Subsequently, to improve model performance by increasing data precision, the spectra were converted into second-order derivatives using a Savitzky-Golay filter (Savitzky and Golay 1964) in the 1250 to 2300 nm range (polynomial order 5, smoothing point 11).

[0083] Clustering

[0084] Principal Component Analysis (PCA) was performed to analyze the spectral changes of logging residue and sweet sorghum straw according to changes in moisture and bulk density. PCA involves converting the near-infrared (NIR) spectrum from 1250 to 2300 nm into six principal components (6-dimensional vectors) in a 165-dimensional spectral vector, and data variability due to changes in moisture content was analyzed using PC score plots and loadings. Density-based spatial clustering of noisy applications (DBSCAN) (Ester et al. 1996; Zhang et al. 2004) was used to detect outliers in data points projected onto the PC orthogonal coordinate system. The DBSCAN clustering parameters epsilon (esp) and the minimum number of samples (min_samples) were empirically selected as 0.1 and 3, respectively. The parameter 'esp' is the influence distance of a data point for determining valid neighbors, and the minimum number of samples (min_samples) is the minimum number of data points required to create a cluster. Three or more consecutive points within a distance of 0.1 from the data point are considered as a cluster.

[0085] Partial Least Squares Regression (PLSR) Model

[0086] FIG. 5 is a schematic diagram of k-fold cross-validation for constructing a model for measuring the moisture content of woody biomass materials according to Example 3 of the present invention. Referring to FIG. 5, a Partial Least Squares Regression (PLSR) model (Abdi 2010) was constructed to predict the moisture content (MC) of woody biomass materials. The Partial Least Squares Regression (PLSR) model used a 165-dimensional near-infrared (NIR) spectrum as an input variable and moisture content (MC) as an output variable, and the Partial Least Squares Regression (PLSR) model was validated using k-fold cross-validation. Data folds were generated for each bulk density, resulting in 4-fold data for logging residue and 6-fold data for sweet sorghum straw. That is, the dataset was split into correction and prediction sets in a ratio of 1:3 for logging residue and 1:5 for sweet sorghum straw. This data splitting was intended to independently generate correction and prediction sets by bulk density, and the coefficient of determination (R²) according to Equation 1 below and the root mean square error (RMSE) according to Equation 2 below were used as performance indicators for the Partial Least Squares Regression (PLSR) model. The following and represents the measurement and prediction function rates of the i-th observation, respectively, parameter μ is the overall mean, and n is the total number of observations.

[0087] [Equation 1]

[0088] )

[0089] [Equation 2]

[0090]

[0091] [Test Example]

[0092] Test Example 1: Comparison of moisture content according to moisture content meter and oven drying

[0093] Test Example 1-1: Analysis of moisture content without applied correction factor

[0094] FIG. 6a is a graph showing the moisture content of woody biomass measured by the oven drying method according to Example 1 of the present invention; FIG. 6b is a graph showing the root mean square error of the moisture content of woody biomass measured by a moisture content meter relative to the moisture content of woody biomass measured by the oven drying method according to Example 1 of the present invention; FIG. 6c is a graph comparing the moisture content of logging residue measured by the oven drying method according to Example 1 of the present invention and the moisture content measured by a moisture content meter; and FIG. 6d is a graph comparing the moisture content of sorghum measured by the oven drying method and the moisture content measured by a moisture content meter. Referring to FIG. 6a, it can be seen that the relationship between the moisture content of woody biomass measured using the oven drying method and the equilibrium moisture content (EMC) of the climate chamber varies depending on the type of woody biomass. The moisture content of the logging residue is slightly lower than the equilibrium moisture content (EMC) inside the chamber and exhibited a linear relationship. In contrast, the moisture content of sweet sorghum was higher than the equilibrium moisture content (EMC) of the chamber and exhibited an exponential regression line by absorbing excess moisture above the fiber saturation point (FSP) during humidification at 95% relative humidity (RH). The high moisture content of sweet sorghum is due to the high proportion of hydrophilic components, and sweet sorghum has a lower lignin (hydrophobic component) content and a higher hemicellulose (hydrophilic component) content compared to wood (Fajardo et al. 2015). Additionally, referring to Figure 6b, increasing the bulk density of the woody biomass reduces the root mean square error (RMSE) between the moisture content and the oven-dried moisture content, indicating that woody biomass with high bulk density improves the accuracy of moisture content measurements.In addition, referring to Figure 6c, the importance of compressing the woody biomass sample was confirmed by the fact that the moisture content measured by the moisture content meter at higher bulk densities approaches the moisture content achieved through oven drying. Since loose compression of the woody biomass sample interferes with the continuity of charge transfer paths, it is desirable to increase the bulk density by compressing the woody biomass sample to ensure accurate moisture content measurement when measuring the moisture content of the woody biomass using the electrical resistance method.

[0095] Test Example 1-2: Analysis of moisture content with applied correction factor

[0096] FIG. 7a is a graph showing the moisture content of logging residue through oven drying, the moisture content through a moisture content meter, and the moisture content through a moisture content meter with a correction factor applied, according to Example 1 of the present invention, and FIG. 7b is a graph showing the moisture content of sweet sorghum straw through oven drying and a bulk density of 0.32 g / cm³ according to Example 1 of the present invention. 3 [Image] is a graph showing the moisture content measured by a moisture content meter and the correction coefficient of the moisture content measured by the moisture content meter, and Table 3 shows the moisture content and bulk density of 0.32 g / cm³ obtained through oven drying according to Example 1 of the present invention. 3 This is a table showing the correction coefficient and root mean square error (RMSE) data of the moisture content measured by a moisture content meter. Referring to Figures 7a and 7b and Table 3, it was confirmed that the root mean square error (RMSE) of the correction coefficient was 1.46 MC - 0.51 and 1.19 MC - 1.42 for logging residue, and decreased from 4.64 to 1.58 and from 5.33 to 3.96 for sweet sorghum straw. It was confirmed that by applying the correction coefficient to the moisture content measured by the moisture content meter, the trend line of the corrected moisture content for the logging residue and sweet sorghum straw shifted closer to the line of the reference moisture content obtained by oven drying.

[0097] biomass Correction Factor RMSE Original MC Corrected MC logging residue 1.46 MC - 0.51 4.64 1.58 sweet sorghum 1.19 MC - 1.42 5.33 3.96

[0098] In Table 3, RMSE is the Root Mean Square Error, and MC is the Moisture Content.

[0099] Test Example 2: Electrical Resistance Analysis

[0100] Test Example 2-1: Analysis of the Relationship Between Moisture Content and Electrical Resistance

[0101] FIG. 8a is a graph showing the electrical resistance of logging residue at 10°C according to Example 2 of the present invention, FIG. 8b is a graph showing the electrical resistance of sweet sorghum straw at 10°C according to Example 2 of the present invention, FIG. 8c is a graph showing the change in electrical resistance of logging residue according to an increase in bulk density according to Example 2 of the present invention and the relationship between moisture content and electrical resistance, FIG. 8d is a graph showing the change in electrical resistance of sweet sorghum straw according to an increase in bulk density according to Example 2 of the present invention and the relationship between moisture content and electrical resistance, and Table 4 below is a table showing a simple linear regression equation between moisture content and electrical resistance below the fiber saturation point.

[0102] Referring to Figures 8a and 8b, it can be seen that an increase in water content causes a decrease in electrical resistance, and an increase in bulk density at a specific water content also causes a decrease in electrical resistance, and similar to the water content measurement, an increase in bulk density contributed to the creation of a continuous path for charge transfer.

[0103] In addition, referring to Figures 8c and 8d and Table 4, the relationship between electrical resistance and moisture content on a logarithmic scale showed a linear relationship with a high coefficient of determination, but the linearity was valid only below the Fiber Saturation Point (FSP). However, sweet sorghum straw showed a moisture content higher than the Fiber Saturation Point (FSP) due to excessive moisture absorption at 95% relative humidity. Therefore, it was found that a linear relationship could not be established, and separate models above and below the Fiber Saturation Point (FSP) are required for precise moisture content measurements based on electrical resistance.

[0104] biomass Temperature (°C) regression equation R 2 Logging residue 10 0.984 20 0.962 30 0.989 Sweet sorghum straw 10 0.986 20 0.965 30 0.956

[0105] In Table 4, R is electrical resistance, M is moisture content (%), and R 2 is the coefficient of determination.

[0106] Test Example 2-2: Least Squares Regression Model Prediction Analysis

[0107] Table 5 below shows the prediction results of a general least squares regression model regarding the relationship between electrical resistance, moisture content, and temperature according to Example 2 of the present invention. Referring to Table 5, the model prediction for logging residue is R 2 While high performance was achieved with an R² of 0.933 and an RMSE of 0.505, the model prediction for sweet sorghum straw R² 2 It appeared inferior with an R² of 0.483 and an RMSE of 1.657. However, within a limited moisture content range below the Fiber Saturation Point (FSP), the model for sweet sorghum showed an R² 2The performance was significantly improved with RMSE values ​​of 0.833 and 0.891, respectively, which indicated that controlling the bulk density and moisture content range of the material is essential for accurately predicting the moisture content of woody biomass materials.

[0108] biomass Moisture content range (%) regression equation Correction value Predicted value R 2 RMSE R 2 RMSE Logging residue 5.4 to 22.0 logR(MΩ) = 8.202 - 0.334M - 0.012T 0.941 0.460 0.933 0.505 Sweet sorghum straw 7.2 to 62.5 logR(MΩ) = 4.340 - 0.092M + 0.005T 0.522 1.578 0.483 1.657 7.2 to 22.1 logR(MΩ) = 8.737 - 0.390M - 0.035T 0.902 0.669 0.833 0.891

[0109] In Table 5, R is electrical resistance, M is moisture content (%), T is temperature (°C), and R 2 is the coefficient of determination and RMSE is the Root Mean Square Error.

[0110] Test Example 3: Multivariate Analysis of Near-Infrared (NIR) Spectrum Data

[0111] Test Example 3-1: Analysis of Near-Infrared (NIR) Spectral Characteristics

[0112] FIG. 9a shows 0.32 g / cm³ at 20 ℃ in the region of 1250 to 2300 nm according to Example 3 of the present invention. 3 This is a graph showing the near-infrared spectrum of a sample of logging residue compressed to a bulk density, and FIG. 9b is 0.32 g / cm³ at 20 °C in the 1250 to 2300 nm region according to Example 3 of the present invention. 3 Figure 9c is a graph showing the near-infrared spectrum of a sample of sweet sorghum compressed to a bulk density, Figure 9d is a graph showing the Euclidean distance between near-infrared spectra measured three times at each density of logging residue according to Example 3 of the present invention, and Figure 9d is a graph showing the Euclidean distance between near-infrared spectra measured three times at each density of sweet sorghum according to Example 3 of the present invention.

[0113] Referring to Figures 9a and 9b, the 1437 and 1927 nm band regions show two peaks representing moisture, which are most prominent in logging residue and sweet sorghum straw. High relative humidity (RH) shifts the water peak to the low-wavelength band region, and this shift in the band region is attributed to changes in the mobility and binding strength of water molecules due to changes in moisture content. The spectral band at 1437 nm is rarely used for qualitative analysis but can be helpful for quantitative analysis purposes.

[0114] Furthermore, referring to Figures 9c and 9d, it can be observed that an increase in bulk density reduces the Euclidean distance between the triple-measured spectra. Thus, 0.21 g / cm³ 3 Measuring near-infrared spectra at the above bulk densities is desirable for obtaining consistent near-infrared data for logging residues and sweet sorghum straw, and the optimal lignocellulosic biomass density for improving the accuracy of lignocellulosic biomass moisture content predictions is approximately 0.3 g / cm³ 3 It could be confirmed that it was

[0115] Test Example 3-2: Principal Component Analysis (PCA) and Outlier Analysis

[0116] FIG. 10a is a graph showing a pair plot of principal component (PC) scores clustered by DBSCAN for near-infrared data with outliers of logging residue according to Example 3 of the present invention, and FIG. 10b is a graph showing a pair plot of principal component (PC) scores clustered by DBSCAN for near-infrared data with outliers of sweet sorghum straw according to Example 3 of the present invention. In FIG. 10a and FIG. 10b, the percentage values ​​in parentheses of the axis titles represent the dispersion of the PCs. Referring to FIG. 10a and FIG. 10b, the clustering results of DBSCAN for the secondary derived near-infrared (NIR) spectra of lignocellulosic biomass materials are projected onto the PC score plot. DBSCAN identified four data points as outliers in the near-infrared (NIR) spectrum of logging residues measured at 20 °C and two data points in sweet sorghum straw at 10 °C; these outliers were generated by scan failures due to incomplete contact between the near-infrared probe and the sample at low bulk densities. In the score plot, outliers did not belong to any clusters and appeared separated from other clusters. Although not exactly matching predefined relative humidity (RH) conditions, clusters were formed based on moisture levels. The efficiency of DBSCAN for outlier detection in near-infrared (NIR) data was verified by comparing the performance of models built with and without outliers for moisture content prediction, a comparison of which is described in the subsection of the prediction model.

[0117] FIG. 11a is a graph showing the near-infrared (NIR) spectrum of logging residue according to Example 3 of the present invention and a plot of the principal component analysis (PCA) scores of the two PCs at the loading of the first PC for the two materials, FIG. 11b is a graph showing the near-infrared (NIR) spectrum of sweet sorghum straw according to Example 3 of the present invention and a plot of the principal component analysis (PCA) scores of the two PCs at the loading of the first PC for the two materials, FIG. 11c is a graph showing the loading of the first PC according to the near-infrared wavelength of logging residue and sweet sorghum straw according to Example 3 of the present invention. In FIG. 11a and FIG. 11b, the percentages in parentheses represent the dispersion rate of each PC. Referring to FIG. 11a to FIG. 11c, the data points in the score plots were aligned along PC1 for each relative humidity (RH) condition for both materials, and it was found that the PC1 score increased as the moisture content increased. In the sweet sorghum scores, data points were grouped by temperature at a specific relative humidity (RH). Loading plots for the first PCs of logging residue and sweet sorghum showed that the 1437 and 1927 nm bands indicated the moisture levels of the materials. The 2087 nm band, representing the OH stretching vibrations of cellulose and hemicellulose, also contributed to some extent.

[0118] Test Example 3-3: Prediction Model for Near-Infrared (NIR) Data

[0119] FIG. 12a is a scatter plot of moisture content measured by oven drying at 10°C for a sweet sorghum moisture prediction model constructed with near-infrared data according to Example 3 of the present invention, and FIG. 12b is a scatter plot of moisture content predicted by a Partial Least Squares Regression (PLSR) model at 10°C for no outliers according to Example 3 of the present invention. For reference, R 2 Cε is the coefficient of determination for correction, RMSEC is the root mean square error of the correction, and R 2 P ε is the coefficient of determination, RMSEP is the root mean square error for prediction, and outliers are statistical observations that are distinctly different from other subjects in the sample. Referring to Figures 12a and 12b, a PLSR model constructed using the near-infrared (NIR) spectra of logging residue and sweet sorghum straw was built for moisture content (MC) prediction. Outliers in the dataset significantly degraded the model's predictive ability. In the MC predictions of the model built with outlier near-infrared (NIR) data, several data points (i.e., outliers) were far from the correction line. In contrast, the predictions of the model built with outlier-free data showed R 2 The results were similar to the prediction of the correction with a high RMSE value and a low RMSE value. These results indicate that data preprocessing, such as outlier removal, is necessary when building a prediction model using near-infrared (NIR) data, and that DBSCAN is an effective technique for detecting outliers in the near-infrared (NIR) spectrum.

[0120] Test Example 3-4: Performance Analysis of PLS ​​Regression Model

[0121] Table 6 below shows the performance of a Partial Least Squares Regression (PLSR) model constructed using near-infrared spectra for predicting the moisture content of lignocellulosic biomass according to Example 3 of the present invention. Referring to Table 6, spectral data processing using second-order derivative transform improved the prediction of the Partial Least Squares Regression (PLSR) model. In all tested cases, the model constructed using near-infrared (NIR) spectra exhibited higher R values ​​with PLS coefficients equal to or lower than the original model constructed using near-infrared (NIR) spectra. 2and lower RMSE values ​​were achieved. The best performance in predicting the water content (MC) was achieved by a model built from the near-infrared (NIR) spectra measured for both materials.

[0122] Models constructed using total near-infrared (NIR) data measured at all temperatures demonstrated excellent predictive performance for both materials. Models built with total NIR data achieved a score of 0.9 or higher for both logging residue and sweet sorghum straw. These results suggest that partial least squares regression (PLSR) models can predict the moisture content (MC) of lignocellulosic biomass materials with high precision within a temperature range of 10 to 30 °C, regardless of band shifts due to temperature fluctuations. Constructing PLSR models using NIR spectra is a promising approach for determining the moisture content (MC) of logging residue and sweet sorghum straw, regardless of changes in moisture status within the tested temperature variations. Models were established using k-fold cross-validation with datasets separated by the bulk density of the materials. This implies that, unlike electrical resistance-based models, it is possible to predict moisture content (MC) regardless of the bulk density of the materials. Near-infrared (NIR)-based methods that do not require material grinding are more promising for industrial applications as they enable online or inline measurements without disrupting process flow. Since the predictive model determines the local moisture content (MC), multi-point measurements are desirable for a more reliable evaluation, and the model prediction is valid within the range of moisture content (MC) tested in this study. Therefore, it was found that data and model updates must be performed prior to determining the moisture content (MC) outside this range.

[0123] biomass Temperature (°C) Near-infrared spectrum PLS Calibration Prediction R 2 RMSE R 2 RMSE Logging residue Total Original 8 0.94 1.32 0.91 1.63 2 nd Derivative 7 0.95 1.23 0.93 1.47 10 Original 4 0.94 1.22 0.92 1.42 2 nd Derivative 4 0.94 1.25 0.85 1.96 20 Original 4 0.96 1.11 0.92 1.49 2 nd Derivative 3 0.96 1.14 0.94 1.34 30 Original 6 0.97 0.95 0.94 1.42 2 nd Derivative 4 0.97 1.07 0.93 1.56 Sweet sorghum straw Total Original 6 0.97 3.06 9.65 3.36 2 nd Derivative 6 0.97 2.99 0.97 3.33 10 Original 5 0.98 1.85 0.97 2.40 2 nd Derivative 3 0.93 3.37 0.91 3.86 20 Original 7 0.99 1.71 0.98 2.51 2 nd Derivative 4 0.99 2.14 0.97 3.04 30 Original 6 0.98 2.58 0.97 3.49 2 nd Derivative 6 0.98 2.60 0.98 3.18

[0124] In Table 6, PLS is partial least squares, R 2is the coefficient of determination and RMSE is the Root Mean Square Error.

[0125] As shown above, since loose aggregation of woody biomass pieces hinders the continuity of charge transport paths, it was found that when using the electrical resistance method according to Example 1 of the present invention, it is desirable to increase bulk density through material compression for accurate moisture measurement. The calculated correction factor reduced the root mean square error (RMSE) of commercial moisture meters for logging residue and sweet sorghum straw. The electrical resistance-based general least squares regression (OLSR) model according to Example 1 of the present invention achieved better predictions for logging residue than for sweet sorghum straw, and the performance of the model for both materials was valid below the fiber saturation point (FSP).

[0126] In addition, the near-infrared (NIR) spectrum according to Example 2 of the present invention was stabilized at relatively loose aggregation of sample pieces, and the near-infrared (NIR) spectrum-based model was able to predict the water content (MC) regardless of the bulk density of the material. Data preprocessing through second derivative transform and outlier removal on the near-infrared (NIR) spectrum data improved the predictive performance of the model. Explanation of the symbols

[0127] 10: Sample compressor 100: Compression section 110: Communist China 120: Torso 130: Hall 200: Detector 210: Near-infrared Provoo 211: Light source fiber 212: Light-absorbing fiber 300: Sample fixing part 310: Piston 311: Protrusion 320: Spring 330: Fixing pin

Claims

Claim 1 (a') a step of compressing woody biomass using a sample compressor to produce compressed woody biomass having a predetermined density; (a) a step of supplying a sample containing the compressed woody biomass having the predetermined density; (b) a step of acquiring a near-infrared spectrum of the sample; (c) a step of mathematically preprocessing the near-infrared spectrum; (d) a step of constructing a moisture content prediction model by performing partial least squares regression analysis (PLSR) on the mathematically preprocessed near-infrared spectrum; and (e) a step of obtaining the moisture content of the sample using the moisture content prediction model; wherein the sample compressor comprises a compression part including a body having a cylindrical shape and a hollow oriented in the longitudinal direction; and a detection part oriented in the longitudinal direction at the center of the body and including a near-infrared probe. A method for measuring the moisture content of woody biomass, comprising: a plurality of sample fixing parts located inside the hollow and oriented longitudinally around the detection part; and for measuring the moisture content of woody biomass. Claim 2 A method for measuring the moisture content of woody biomass according to claim 1, characterized in that the woody biomass includes fragmented woody biomass. Claim 3 A method for measuring the moisture content of woody biomass according to claim 1, characterized in that the woody biomass comprises one or more types selected from the group consisting of logging residue and sweet sorghum straw. Claim 4 A method for measuring the moisture content of woody biomass according to claim 1, wherein step (c) comprises (c-1) a step of obtaining second derivative spectrum data by applying a second derivative method to the near-infrared spectrum. Claim 5 A method for measuring the moisture content of woody biomass according to claim 4, wherein step (c) further comprises, after step (c-1), (c-2) setting the measurement wavelength gap (gap, nm) of the near-infrared spectrum to one of 1 to 10 nm and smoothing the near-infrared spectrum. Claim 6 A method for measuring the moisture content of woody biomass according to claim 5, characterized in that the smoothing is performed using a moving average method with 11 points. Claim 7 A method for measuring the moisture content of woody biomass according to claim 1, wherein step (d) comprises: (d-1) a step of obtaining a moisture content prediction model by performing regression analysis (PLSR) on the mathematically preprocessed near-infrared spectrum using the partial least squares method; and (d-2) a step of verifying the moisture content prediction model to construct a verified moisture content prediction model. Claim 8 A method for measuring the moisture content of woody biomass, characterized in that the verification of step (d-2) in claim 7 is performed by K-fold cross validation. Claim 9 A method for measuring the moisture content of woody biomass according to claim 8, characterized in that the number of folds in step (d-2) is 2 to 6. Claim 10 delete Claim 11 In claim 1, the predetermined density is 0.1 to 0.5 g / cm³ 3 A method for measuring the moisture content of woody biomass characterized by the following. Claim 12 A method for measuring the moisture content of woody biomass according to claim 1, characterized in that, after step (b), the method for measuring the moisture content of woody biomass further comprises the step (b') of obtaining modified spectrum data by deleting outliers from the near-infrared spectrum data. Claim 13 A method for measuring the moisture content of lignocellulosic biomass according to claim 12, characterized in that the outlier in step (b') is data that does not belong to a cluster in principal component analysis (PCA) and exists outside. Claim 14 delete Claim 15 delete Claim 16 delete Claim 17 delete Claim 18 delete Claim 19 delete Claim 20 delete

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