Biomass component detection method based on particle size and shape

Abstract

The invention provides a biomass component detection method based on particle size and shape, which comprises the following steps: step S1, drying and grinding a biomass sample to obtain crushed biomass particles, and then uniformly dispersing the crushed biomass particles by using a PVP solution to obtain a sample suspension; s2, dropwise adding the sample suspension onto a glass slide, standing for 5 minutes to form single-layer distribution, and then loading the glass slide on an objective table of an optical device; s3, detecting the particle size and the shape of the biomass particles through an optical device, adjusting the magnification factor and the focal length until an image is clear, and then generating a biomass image through an imaging device; s4, analyzing and extracting outlines of the particles, and obtaining length-width ratios and roundness of the particles according to the outlines; and S5, inputting the length-width ratio and the roundness of the particles into the component prediction model to obtain the lignin percentage content. The method has the beneficial effects that the lignin content in the biomass sample can be rapidly and accurately measured at low cost, and the applicability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomass component detection methods, and in particular to a biomass component detection method based on particle size and shape. Background Art

[0002] As a core resource of renewable energy, the efficient utilization of biomass depends on the accurate analysis of its composition (such as cellulose, hemicellulose, and lignin) and physical properties (such as particle size and shape). Currently, biomass composition detection technology is mainly divided into two categories: chemical analysis and physical characterization.

[0003] Chemical analysis methods include wet chemical methods (such as the Klason method for determining lignin) and spectroscopic methods (such as near-infrared spectroscopy and Raman spectroscopy). These methods require complex pretreatment (such as acid hydrolysis and high-temperature ashing), take up to several days, and destroy the sample structure, making it impossible to obtain physical property data simultaneously.

[0004] Physical characterization methods: such as laser particle size analyzers and scanning electron microscopes (SEMs) can measure particle size and morphology, but the equipment is expensive, the operation is complicated, and it cannot directly correlate with composition information.

[0005] In summary, the existing biomass composition detection technology has the following defects: Data fragmentation: Composition analysis and physical property testing must be performed independently, making data integration difficult and making it difficult to fully evaluate the process suitability of biomass, which affects measurement efficiency. Inefficiency: Traditional methods are cumbersome and require more than 24 hours for a single test. High cost: High-end equipment (such as SEM) costs over one million yuan per unit and has high maintenance costs; Limited applicability: Existing biomass composition detection technology has stringent requirements on the sample particle size range and dispersion uniformity, and its actual application scenarios are limited. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to quickly, accurately and at low cost determine the lignin content in biomass samples and improve applicability. In order to overcome the defects of the above-mentioned existing technologies (or related technologies), the present invention provides a biomass component detection method based on particle size and shape.

[0007] The present invention provides a method for detecting biomass components based on particle size and shape, comprising the following steps: Step S1, drying the biomass sample in a blast drying oven, grinding the biomass sample to obtain biomass crumbs, and then uniformly dispersing the biomass crumbs in a 0.25% PVP solution to obtain a sample suspension; Step S2, dropwise adding the sample suspension onto a hydrophilic glass slide, allowing it to stand for 5 minutes to form a monolayer distribution, and then placing the slide on a stage of an optical device; Step S3, detecting the particle size and shape of the biomass particles on the hydrophilic glass slide by the optical device and continuously adjusting the magnification and focal length until the image is clear, and then generating a biomass image by an imaging device; Step S4, performing edge detection, binarization, and morphological analysis on the biomass image based on the OpenCV library to extract the outline of the particles, and obtaining the aspect ratio and roundness of the particles according to the outline; Step S5: input the aspect ratio and the roundness of the particles into a pre-built component prediction model to obtain the lignin content percentage.

[0008] Compared with the existing technology, the biomass composition detection method based on particle size and shape in this application has the following advantages: In the present application, step S1 is used to dry and grind the biomass sample and prepare and obtain the sample suspension, step S2 is used to statically mount the sample suspension, step S3 is used to detect the particle size and shape of the biomass particles and obtain biomass imaging, step S4 is used to detect the particle contour, and step S5 is used to analyze the lignin percentage content. In the entire measurement process, only a blast drying oven, an optical device, and an imaging device are used. There is no need to use high-precision high-end equipment, which can greatly reduce costs. The component analysis step and the physical property detection step are performed simultaneously, which is convenient for data integration and can effectively improve the analysis efficiency and accuracy. The subsequent lignin percentage content analysis can be carried out by detecting the particle size and shape of the biomass particles and using a PVP solution with a concentration of 0.25% to evenly disperse the biomass particles. There are no requirements for the particle size range and dispersion uniformity, which can improve applicability.

[0009] In a possible embodiment, in step S1, the biomass sample is dried in the blast drying oven at an ambient temperature of 60-80° C. for 24 hours.

[0010] Compared with the existing technology, the above technical solution can prevent the thermal decomposition of biomass products by maintaining a specific temperature range of 60-80°C, ensuring the original component structure. The 24-hour drying time can ensure that moisture interference is fully removed while avoiding embrittlement caused by excessive drying.

[0011] In one possible embodiment, in step S1, the biomass sample after drying is ground using a planetary ball mill with a rotation speed of 300 rpm and a grinding medium of zirconia balls to obtain the biomass crumbs, which are then filtered through a 230-mesh sieve to obtain the biomass crumbs with a diameter of less than 230 μm.

[0012] Compared with the existing technology, the above technical solution can achieve efficient and uniform crushing through a planetary ball mill combined with zirconia media, maintaining the original shape of the crushed particles, while 230-mesh sieve filtration can ensure particle size consistency and eliminate the interference of large particles on optical detection. The rotation speed of 300 rpm can balance crushing efficiency and energy consumption control.

[0013] In a possible implementation, in step S2, the stage is mounted on an XYZ three-axis translation stage and the loading area of the stage is 10×10 cm².

[0014] Compared with the existing technology, the above technical solution can achieve precise positioning through the XYZ three-axis translation stage and support continuous detection of large sample volumes.

[0015] In a possible implementation, in step S3, an LED cold light source array with a wavelength of 450-650 nm is used as the light source of the optical device.

[0016] Compared with the existing technology, the above technical solution can provide stable and uniform illumination through the LED cold light source array, avoiding sample deformation caused by thermal effects. At the same time, the 450-650nm wide spectrum can cover and enhance the optical contrast of different components.

[0017] In a possible implementation, in step S3, a continuous zoom lens with a focal length range of 0.7-4.5 times is used as the lens group of the optical device for detection.

[0018] In a possible implementation, in step S3, a CCD camera with a resolution of 1920×1080, a pixel size of 4.0 μm², and a frame rate of 30 fps is used as the imaging device.

[0019] Compared with existing technologies, the above technical solution can ensure the ability to resolve submicron morphological features through a 4.0μm² pixel size, support real-time feedback adjustment of the dynamic focusing process at a 30fps frame rate, and meet industrial-grade detection accuracy requirements with full HD resolution.

[0020] In a possible implementation, in step S5, the component prediction model is expressed as follows: L=−0.7020+2.8382·AR+8.1197·R in, L represents the percentage content of lignin; AR represents the aspect ratio; R represents the circularity. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0022] First, those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the embodiments of the present application and are not intended to limit the scope of protection of the embodiments of the present application. Those skilled in the art may adjust them as needed to suit specific application scenarios.

[0023] The present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] See also Figure 1 The present invention discloses a method for detecting biomass components based on particle size and shape, comprising: Step S1, using a blast drying oven to dry and grind the biomass sample to obtain biomass crumbs, and then using a 0.25% PVP solution to evenly disperse the biomass crumbs to obtain a sample suspension; Step S2, dropwise adding the sample suspension onto a hydrophilic glass slide, allowing it to stand for 5 minutes to form a monolayer distribution, and then placing the slide on the stage of an optical device; Step S3, detecting the particle size and shape of the biomass particles on the hydrophilic glass slide by an optical device and continuously adjusting the magnification and focal length until the image is clear, and then generating a biomass image by an imaging device; Step S4, performing edge detection, binarization, and morphological analysis on the biomass image based on the OpenCV library to extract the particle outline, and obtaining the aspect ratio and roundness of the particle based on the outline; Step S5: input the aspect ratio and roundness of the particles into a pre-built component prediction model to obtain the lignin percentage content.

[0025] In the embodiment of the present application, the light source of the optical device adopts a high-brightness LED array (wavelength 450-650nm), which covers the entire visible light band and ensures uniform illumination of biomass particles of different colors (such as dark lignin and light cellulose). The brightness of the light source can be continuously adjusted from 0 to 100% through PWM (pulse width modulation) technology to adapt to biomass samples with different transmittances.

[0026] In the embodiment of the present application, the optical device has a built-in aluminum heat sink and a micro fan to ensure that the temperature is ≤40°C during long-term operation, thereby preventing thermal radiation from interfering with the stability of the sample.

[0027] In the embodiment of the present application, a linear polarizer (extinction ratio > 1000:1) is integrated in the optical device, which can effectively suppress reflections on the surface of the slide and improve the imaging contrast. At the same time, the lens group is equipped with a 0.7-4.5x continuous zoom lens, which supports clear imaging of particles in the range of 5-230μm. The continuous zoom lens adopts an electric zoom design with a focal length range of 0.7-4.5x. It is equipped with a high-precision stepper motor (step angle 0.9°) to achieve nanometer-level focusing accuracy. The lens is coated with a broadband anti-reflection film (400-700nm) to achieve a transmittance of ≥98%.

[0028] In the embodiment of the present application, the stage has an integrated temperature control function (±0.5°C accuracy) to ensure the stability of the biomass sample. It has a built-in semiconductor refrigeration chip (TEC) and PID temperature control module with a temperature control range of 15-35°C and an accuracy of ±0.5°C. It is suitable for heat-sensitive biomass samples (such as energy crops containing oil). It is also equipped with an XYZ three-axis translation stage (repeat positioning accuracy of ±1μm), supports automatic scanning mode, and can cover a biomass sample area of 10×10cm². An air flotation vibration isolation device (natural frequency <2Hz) is installed at the bottom of the stage, which can effectively isolate environmental vibrations (such as common ground vibrations in laboratories).

[0029] In the embodiment of the present application, the imaging device uses a CCD camera with a resolution of 1920×1080, a pixel size of 4.0 μm², and a frame rate of 30fps, which supports dynamic capture of particle motion trajectories. Through the multi-focus superposition algorithm (Focus Stacking), images of different focal planes are fused in a single shot to solve the problem of insufficient depth of field at high magnification, ensuring clear imaging of particles with a particle size of 5-230μm. A Sony IMX series CMOS sensor (1 / 1.8 inch) is used with a quantum efficiency of >60%, a dynamic range of 72dB, and support for 14-bit RAW format output to ensure that high signal-to-noise ratio images can still be captured in low-light environments. The global shutter technology (frame rate 30 fps) is used to dynamically capture the particle sedimentation process, and combined with a motion blur compensation algorithm, image smear caused by high-speed motion is eliminated.

[0030] In the embodiment of the present application, step S4 is implemented using an analysis system of a host computer. The host computer adopts the USB 3.2 Gen 1 protocol (transmission rate 5 Gbps), supports real-time transmission of uncompressed image data, and has a delay of <10 ms. The imaging device transmits data to the host computer in real time through the USB 3.0 interface.

[0031] In the embodiment of the present application, the optical device is equipped with a high-precision lifting screw (resolution 1μm) and a translation guide rail to achieve multi-angle scanning. The high-precision lifting screw adopts a ball screw structure (lead 0.5 mm) and is equipped with a closed-loop stepper motor (resolution 0.1μm), supporting manual fine-tuning and programmed control.

[0032] In an embodiment of the present application, in step S4, edge detection, binarization and morphological analysis are implemented based on the OpenCV library to extract the contours of the particles, and a quantitative relationship between the lignin percentage content and the shape parameters is established through multivariate linear regression. Among them, edge detection adopts an improved Canny algorithm (Gaussian kernel σ=1.5, double thresholds T_low=30, T_high=90), combined with a morphological closing operation (3×3 rectangular kernel) to fill small holes; particle segmentation is based on the watershed algorithm (Watershed) to solve the segmentation problem of adhered particles, and high-precision contour extraction is achieved through distance transformation and label control; during feature extraction, the aspect ratio (AR=major axis / minor axis) and roundness (R=4π·area / perimeter²) of each particle are calculated, and AR and R are selected as core input variables through Pearson correlation analysis to eliminate redundant features (such as convexity).

[0033] In the examples of the present application, a forced air drying oven (60-80°C) was used for drying for 24 hours to reduce the moisture content to <5%. The biomass sample was then crushed using a planetary ball mill (rotation speed 300 rpm, grinding medium zirconia balls) and passed through a 230-mesh sieve (pore size 63 μm) to ensure uniform particle size distribution. An orthogonal experiment determined that 0.25% PVP (polyvinyl pyrrolidone) was the optimal concentration, which could reduce the surface tension to 40 mN / m and prevent particle agglomeration.

[0034] In the embodiment of the present application, the host computer automatically generates a particle size distribution histogram (bin width 1 μm), an AR-R scatter plot, and a lignin percentage content heat map and then outputs a report. The report supports PDF / Excel format and contains raw data, analysis results, and confidence intervals (95% confidence level). The host computer can upload the data to the cloud platform through the RESTful API, supporting multi-terminal access and team collaboration.

[0035] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "in the present embodiment", "specific example", or "some examples" means that the specific features, mechanisms, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0036] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for detecting biomass components based on particle size and shape, characterized in that: The following steps are involved: Step S1, using a blast drying oven to dry and grind the biomass sample to obtain biomass crumbs, and then using a 0.25% PVP solution to evenly disperse the biomass crumbs to obtain a sample suspension; Step S2, dropwise adding the sample suspension onto a hydrophilic glass slide, allowing it to stand for 5 minutes to form a monolayer distribution, and then placing the slide on a stage of an optical device; Step S3, detecting the particle size and shape of the biomass particles on the hydrophilic glass slide by the optical device and continuously adjusting the magnification and focal length until the image is clear, and then generating a biomass image by an imaging device; Step S4, performing edge detection, binarization, and morphological analysis on the biomass image based on the OpenCV library to extract the outline of the particles, and obtaining the aspect ratio and roundness of the particles according to the outline; Step S5: input the aspect ratio and the roundness of the particles into a pre-built component prediction model to obtain the lignin content percentage.

2. The biomass component detection method according to claim 1, characterized in that: In the step S1, the biomass sample is dried in the blast drying oven at an ambient temperature of 60-80° C. for 24 hours.

3. The biomass component detection method according to claim 1, characterized in that: In the step S1, the biomass sample after drying is ground using a planetary ball mill with a rotation speed of 300 rpm and a grinding medium of zirconia balls to obtain the biomass crumbs, which are then filtered through a 230-mesh sieve to obtain the biomass crumbs with a diameter of less than 230 μm.

4. The biomass component detection method according to claim 1, characterized in that: In step S2, the stage is mounted on an XYZ three-axis translation stage and the stage has a loading area of 10×10 cm².

5. The biomass component detection method according to claim 1, characterized in that: In the step S3, an LED cold light source array with a wavelength of 450-650 nm is used as the light source of the optical device.

6. The biomass component detection method according to claim 1, characterized in that: In step S3, a continuous zoom lens with a focal length range of 0.7-4.5 times is used as the lens group of the optical device for detection.

7. The biomass component detection method according to claim 1, characterized in that: In step S3, a CCD camera with a resolution of 1920×1080, a pixel size of 4.0 μm², and a frame rate of 30 fps is used as the imaging device.

8. The biomass component detection method according to claim 1, characterized in that: In step S5, the expression of the component prediction model is as follows: L=−0.7020+2.8382·AR+8.1197·R in, L represents the percentage content of lignin; AR represents the aspect ratio; R represents the circularity.

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