In-situ characterization method and device for microstructure of compost pile, and electronic equipment

Abstract

The present application provides a kind of compost heap's microstructure in-situ characterization method, device and electronic equipment, the method includes: obtaining the three-dimensional structure model of in-situ heap sample, in-situ heap sample is based on sample sampler from target composting is taken out, three-dimensional structure model is used to characterize the throat number distribution, pore size distribution and throat size distribution of in-situ heap sample;Determine the representative volume unit of three-dimensional structure model;Determine target model in three-dimensional structure model, the volume of target model is greater than or equal to representative volume unit;The heap microstructure of target composting is characterized based on target model.The volume of target model greater than or equal to representative volume unit, can effectively represent the structure of heap in the scale of microstructure and macrostructure, and the target model is three-dimensional structure model, and the structure parameter information of in-situ heap sample surface and inside can be obtained by it, and the in-situ characterization of comprehensive, fine and accurate heap microstructure can be realized based on target model.

Description

Technical Field

[0001] This invention relates to the field of organic solid waste treatment technology, and in particular to a method, apparatus and electronic equipment for in-situ characterization of the microstructure of compost piles. Background Technology

[0002] Composting generally refers to the process of composting and decomposing organic solid waste to obtain organic fertilizer. For example, organic fertilizer can be obtained by aerobic composting, mainly through aerobic microorganisms.

[0003] The compost pile is typically viewed as the result of the spatial cross-linking and arrangement of the compost matrix framework and free space at different scales. Free space, or air-filled areas, can be simply referred to as total porosity. The interaction of physical (e.g., stress settlement, fluid transport, and heat generation and dissipation), chemical (e.g., organic matter degradation), and biological (e.g., microbial metabolism and community succession) changes during aerobic composting leads to dynamic changes in the pile's pore structure, which in turn affects the microenvironment of the aerobic composting process. Aerobic composting matrices, such as livestock and poultry manure, are often mixtures of manure from different sources and crop straw, resulting in exceptionally complex chemical compositions, microbial communities, and structural morphologies. Therefore, scientific characterization of the pile structure, especially the microscopic in-situ pore structure, can provide a deeper understanding of the complex biodegradation mechanisms of aerobic composting using organic solid waste matrices such as livestock and poultry manure.

[0004] Currently, methods for characterizing compost pile structure include the free-space method, settling method, bulk density method, and air stationary region method. However, existing methods cannot identify the spatial distribution and variations of the in-situ pore morphology characteristics of heterogeneous and anisotropic compost piles, making it difficult to comprehensively and precisely characterize the pile structure. Therefore, how to comprehensively, precisely, and accurately characterize the microstructure of compost piles has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for in-situ characterization of the microstructure of compost piles, which addresses the shortcomings of existing technologies in the in-situ characterization of the microstructure of compost piles, which suffers from low comprehensiveness, precision, and accuracy. This invention aims to improve the comprehensiveness, precision, and accuracy of in-situ characterization of the microstructure of compost piles.

[0006] This invention provides an in-situ method for characterizing the microstructure of a compost pile, comprising:

[0007] A three-dimensional structural model of an in-situ compost sample is obtained. The in-situ compost sample is taken from the target compost using a sample sampler. The three-dimensional structural model is used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample.

[0008] Determine the representative volume element of the three-dimensional structural model. The representative volume element is the volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ pile sample.

[0009] A target model is determined in the three-dimensional structural model, wherein the volume of the target model is greater than or equal to the representative volume unit;

[0010] The microstructure of the target compost pile is characterized in situ based on the target model.

[0011] According to the present invention, a method for in-situ characterization of the microstructure of a compost pile includes, wherein the in-situ characterization of the microstructure of the target compost pile based on the target model comprises:

[0012] The target model is subjected to pore network modeling processing to obtain the pore network model corresponding to the target model;

[0013] Data statistics were performed on the pore network model to obtain throat number distribution data, pore size distribution data, and throat size distribution data;

[0014] The microstructure of the target compost pile is characterized in situ based on the throat number distribution data, pore size distribution data, and throat size distribution data.

[0015] According to the present invention, a method for in-situ characterization of the microstructure of a compost pile includes, in part, characterizing the microstructure of the target compost pile based on the throat number distribution data, pore size distribution data, and throat size distribution data, comprising:

[0016] Histogram statistics were performed on the throat number distribution data, the pore size distribution data, and the throat size distribution data respectively to obtain their respective histogram statistics;

[0017] For each of the aforementioned histograms, curve fitting is performed on the histograms to obtain the corresponding characterization curves;

[0018] The microstructure of the target compost pile is characterized in situ using the aforementioned characterization curves.

[0019] According to the in-situ characterization method of the microstructure of a compost pile provided by the present invention, determining the representative volume element of the three-dimensional structural model includes:

[0020] Within the three-dimensional structural model, at least one sub-model is determined with a reference point as the starting point. The reference point is a vertex or center point of the three-dimensional structural model, and the sub-model is a part of the three-dimensional structural model.

[0021] For each of the sub-models, the target parameter values ​​corresponding to the sub-models are calculated, and the target parameter values ​​include total porosity;

[0022] Based on the target parameter values ​​and the model dimensions of the corresponding sub-model, the representative volume element of the three-dimensional structural model is determined.

[0023] According to the present invention, a method for in-situ characterization of the microstructure of a compost pile includes obtaining a three-dimensional structural model of the in-situ pile sample, comprising:

[0024] A three-dimensional structural model of the in-situ pile sample is obtained from the scanning device. The three-dimensional structural model is obtained by the scanning device through three-dimensional reconstruction of at least one frame of projection image. The at least one frame of projection image is obtained by the scanning device through microcomputed tomography scanning of the in-situ pile sample.

[0025] According to the present invention, a method for in-situ characterization of the microstructure of a compost pile is provided, wherein the at least one frame of the projection image is obtained by the scanning device through the following method:

[0026] After adjusting at least one scanning parameter, perform a micro-computed tomography scan to obtain at least one test image under the scanning conditions corresponding to each scanning parameter.

[0027] For each of the test images, the test image with image quality greater than the preset image quality is determined as the target test image;

[0028] The scanning conditions corresponding to the target test image are determined as the target scanning conditions;

[0029] Under the target scanning conditions, the in-situ pile sample is subjected to microcomputed tomography to obtain at least one frame of projection image.

[0030] According to the present invention, an in-situ characterization method for the microstructure of a compost pile is provided, wherein the in-situ pile sample is obtained by the following method:

[0031] At least one of the samplers is pre-installed in the initial composting material. When the initial composting material undergoes a composting reaction and reaches the target compost, each of the samplers is removed, and the target compost in the sampler is identified as the in-situ compost sample.

[0032] The present invention also provides an in-situ characterization device for the microstructure of a compost pile, comprising:

[0033] The acquisition module is used to acquire a three-dimensional structural model of an in-situ compost sample. The in-situ compost sample is taken from the target compost using a sample sampler. The three-dimensional structural model is used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample.

[0034] The determination module is used to determine the representative volume element of the three-dimensional structural model. The representative volume element is the volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution and throat size distribution of the in-situ pile sample.

[0035] The determining module is further configured to determine a target model in the three-dimensional structural model, wherein the volume of the target model is greater than or equal to the representative volume unit;

[0036] The characterization module is used to perform in-situ characterization of the microstructure of the target compost pile based on the target model.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the in-situ characterization method of the microstructure of the compost pile as described above.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the in-situ characterization method of the microstructure of the compost pile as described above.

[0039] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the in-situ characterization method of the microstructure of compost piles as described above.

[0040] This invention provides a method, apparatus, and electronic device for in-situ characterization of the microstructure of compost piles. The method acquires a three-dimensional structural model of an in-situ compost pile sample, which is taken from the target compost using a sample sampler. Sampling using a sample sampler can reduce disturbance or damage to the in-situ compost pile sample. The three-dimensional structural model can display the surface and internal structure of the in-situ compost pile sample from a three-dimensional perspective, and can accurately characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost pile sample, thereby obtaining microscopic characteristic parameter information and improving the comprehensiveness and precision of the characterization results. The representative volume element is the volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample. The representative volume element of the three-dimensional structural model is determined, and a target model with a volume greater than or equal to the representative volume element is determined in the three-dimensional structural model. This target model can effectively represent the in-situ compost sample at both the micro and macro scales. Based on the target model, more comprehensive and accurate structural parameter information can be obtained. Therefore, it can comprehensively, finely, and accurately characterize the microstructure of the target compost pile in situ, thereby improving the comprehensiveness, fineness, and accuracy of the in-situ characterization of the compost structure. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the in-situ characterization method of the microstructure of compost piles provided in this embodiment of the invention.

[0043] Figure 2 This is a schematic diagram of the sampler provided in an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the composting reactor system provided in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of a micro-computed tomography scan provided in an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the image processing and three-dimensional reconstruction process provided in an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of the image processing effect provided in an embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram of the six-connection method provided in an embodiment of the present invention;

[0049] Figure 8 This is a schematic diagram of the three-dimensional image segmentation effect provided in an embodiment of the present invention;

[0050] Figure 9 This is a schematic diagram of in-situ compost pile sample slices at different stages provided in an embodiment of the present invention;

[0051] Figure 10 This is a flowchart of determining a representative volume unit provided in an embodiment of the present invention;

[0052] Figure 11 This is a schematic diagram of obtaining the sub-model provided in an embodiment of the present invention;

[0053] Figure 12 This is a schematic diagram of the relationship between total porosity and sample side length provided in an embodiment of the present invention;

[0054] Figure 13 This is a schematic diagram of the total porosity variation curve provided in an embodiment of the present invention;

[0055] Figure 14 This is a schematic diagram of the PNM model provided in an embodiment of the present invention;

[0056] Figure 15 This is a schematic diagram of a histogram provided in an embodiment of the present invention;

[0057] Figure 16 This is a schematic block diagram of the in-situ characterization process of the microstructure of the compost pile provided in the embodiments of the present invention;

[0058] Figure 17 This is a schematic diagram of the in-situ characterization device for the microstructure of compost piles provided in an embodiment of the present invention;

[0059] Figure 18 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0061] Existing methods for characterizing compost pile structures include free-space methods, settling methods, bulk density methods, and air stationary region methods, as well as mercury intrusion porosimetry, liquid nitrogen adsorption methods, and apparent imaging methods. Free-space methods, settling methods, bulk density methods, and air stationary region methods can only calculate the overall structure of the compost pile, obtaining overall structural characteristics, but they cannot obtain information about the internal structure, resulting in low comprehensiveness and precision of the characterization. Furthermore, the calculation of the overall pile structure can cause some degree of sample disturbance or damage, leading to lower accuracy of the characterization results. Fluid injection methods such as mercury intrusion porosimetry and liquid nitrogen adsorption can roughly obtain the pore size distribution characteristics within the compost sample, but they are difficult to refine the pore structure and may also cause damage to the internal structure of the pile due to fluid injection, resulting in lower accuracy of the characterization results. Surface imaging methods using scanning electron microscopy and optical microscopy can only present the surface morphology of small-sized pile sample matrices, making it difficult to characterize the internal three-dimensional porosity features of the pile structure non-destructively. They also suffer from low comprehensiveness, low precision, and low accuracy of the characterization results.

[0062] To address the aforementioned problems, this invention provides an in-situ method for characterizing the microstructure of a compost pile. The method includes obtaining a three-dimensional structural model of an in-situ compost sample, taken from the target compost using a sampler; the three-dimensional structural model characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample; determining a representative volume element of the three-dimensional structural model, where the representative volume element is the volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample; determining a target model within the three-dimensional structural model, where the volume of the target model is greater than or equal to the representative volume element; and performing in-situ characterization of the microstructure of the target compost pile based on the target model. The three-dimensional structural model of the in-situ compost sample can not only show the surface structure of the in-situ compost sample, but also reflect the internal structure of the in-situ compost sample in detail. The target model with a volume greater than or equal to the representative volume unit can effectively represent the in-situ compost sample at both micro and macro scales. Therefore, the structural parameter information of the surface and interior of the in-situ compost sample can be obtained through the target model. Thus, when characterizing based on the target model, the microstructure of the target compost can be characterized in-situ more comprehensively, finely, and accurately.

[0063] The following is combined with Figures 1 to 16 The in-situ characterization method of the microstructure of compost piles provided in the embodiments of the present invention is described. Figure 1 This is a flowchart illustrating the in-situ characterization method for the microstructure of compost piles provided in this embodiment of the invention. The in-situ characterization method for the microstructure of compost piles provided in this embodiment of the invention is applicable to the characterization of the microstructure of any compost substrate. The executing entity of this method can be an electronic device such as a computer, server, server cluster, or a specially designed in-situ characterization device for the microstructure of compost piles. Alternatively, it can be an in-situ characterization device for the microstructure of compost piles installed within such electronic device. This in-situ characterization device for the microstructure of compost piles can be implemented through software, hardware, or a combination of both. Figure 1 As shown, the in-situ characterization method of the microstructure of the compost pile includes steps 110 to 140.

[0064] Step 110: Obtain the three-dimensional structural model of the in-situ compost sample. The in-situ compost sample is taken from the target compost using a sample sampler. The three-dimensional structural model is used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample.

[0065] In this step, the target compost can be the compost obtained from raw materials and initial substrate through a composting reaction, and it is the original object targeted for characterizing the microstructure of the compost pile. The sampler can be a sampling tool designed and manufactured specifically for the target compost. Sampling with a sampler can reduce disturbance and damage to the target compost, and make the compost pile sample as consistent as possible with the physicochemical properties of the target compost, so that the extracted compost pile sample can more realistically reflect the physicochemical properties of the target compost.

[0066] Specifically, the three-dimensional structural model is a three-dimensional structural model, such as a digital three-dimensional model obtained through micro-computed tomography (Micro-CT). The three-dimensional structural model of the pile sample can be obtained by scanning the in-situ pile sample with a Micro-CT scanning device.

[0067] For example, a three-dimensional structural model of an in-situ pile sample reflects the actual state of the pile structure and can be used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ pile sample. The throat number distribution can include the correspondence between throat location and throat quantity. The pore size distribution can include pore radius distribution, cumulative pore volume distribution, pore coordination number distribution, and the distribution of pore radius and coordination number. The throat size distribution can include throat radius distribution and throat length distribution.

[0068] Step 120: Determine the representative volume element of the three-dimensional structural model. The representative volume element is the volume element in the three-dimensional structural model that represents the throat number distribution, pore size distribution and throat size distribution of the in-situ pile sample.

[0069] In this step, the representative volume element (RVE) is a critical volume value or range that can represent the three-dimensional structural model at both mesoscopic and macroscopic scales. It can be understood that if a sub-model with a volume smaller than the representative volume element is determined at any location within the three-dimensional structural model, the physicochemical properties obtained based on this sub-model cannot effectively represent the physicochemical properties of the entire three-dimensional structural model because its volume is less than the critical volume value. Conversely, if a sub-model with a volume greater than or equal to the representative volume element is determined at any location within the three-dimensional structural model, the physicochemical properties obtained based on this sub-model can effectively represent the physicochemical properties of the entire three-dimensional structural model because its volume is greater than or equal to the critical volume value. Therefore, the representative volume element is the volume element in the three-dimensional structural model that can characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ pile sample. It should be understood that the representative volume element of the three-dimensional structural model can also characterize other characteristic parameters that can be obtained from the three-dimensional structural model.

[0070] For example, the size quantification of RVE is affected by factors such as sample characteristics, target properties, and test conditions. RVE size can usually be estimated by statistically analyzing the convergence of target properties with changes in sample size. Methods for estimating RVE include intuitive judgment, error discrimination, coefficient of variation (CV) discrimination, hypothesis testing, curve fitting, and grey relational analysis.

[0071] Step 130: Determine the target model in the three-dimensional structural model. The volume of the target model is greater than or equal to that of the representative volume element.

[0072] In this step, after determining the representative volume element of the three-dimensional structural model, a sub-model with a volume greater than or equal to the representative volume element is determined at any location in the three-dimensional structural model, and this sub-model can be determined as the target model.

[0073] Step 140: Perform in-situ characterization of the microstructure of the target compost pile based on the target model.

[0074] In this step, based on the target model determined in the three-dimensional structural model, the throat number distribution, pore size distribution, and throat size distribution can be obtained by mathematical statistical distribution of the target model. The microstructure of the target compost pile can be characterized in situ by the throat number distribution, pore size distribution, and throat size distribution.

[0075] For example, data statistics and graphical representation can be performed based on the throat number distribution, pore size distribution, and throat size distribution to obtain pore radius distribution map, pore cumulative volume distribution map, throat radius distribution map, throat length distribution map, pore coordination number distribution map, and pore radius and coordination number distribution map, etc. Based on each distribution map, the microstructure of the target compost pile can be characterized in situ.

[0076] For example, when observing the structure of a compost pile using scanning electron microscopy or microscopy, pretreatment operations such as slicing, gold sputtering, or staining are required. These pretreatment operations damage the original structure of the sample to some extent, leading to differences between the observed structure and the original structure. Therefore, existing methods cannot achieve in-situ characterization of the compost pile structure. In-situ characterization can be understood as non-destructive or minimally destructive characterization of the sample. In-situ characterization results are more accurate and can more accurately reflect the original structure of the compost pile. Based on the method provided by this invention, the in-situ compost pile sample is not damaged during the sampling process of the target compost and the acquisition of the three-dimensional structural model of the in-situ compost pile sample. This in-situ compost pile sample can truly reflect the structure of the target compost pile. Therefore, when performing microstructural characterization based on the three-dimensional structural model of this in-situ compost pile sample, in-situ characterization can be achieved, yielding highly accurate in-situ characterization results. The present invention provides an in-situ method for characterizing the microstructure of compost piles. This method obtains a three-dimensional structural model of an in-situ compost pile sample. The in-situ compost pile sample is taken from the target compost using a sample sampler. Sampling using a sample sampler can reduce disturbance or damage to the in-situ compost pile sample. The three-dimensional structural model can display the surface and internal structure of the in-situ compost pile sample from a three-dimensional perspective. It can accurately characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost pile sample, thereby obtaining microscopic characteristic parameter information and improving the comprehensiveness and precision of the characterization results. The representative volume element is the volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample. The representative volume element of the three-dimensional structural model is determined, and a target model with a volume greater than or equal to the representative volume element is determined in the three-dimensional structural model. This target model can effectively represent the in-situ compost sample at both micro and macro scales. Based on the target model, more comprehensive and accurate structural parameter information can be obtained. Therefore, the compost structure of the target compost can be comprehensively, finely, and accurately characterized in situ, thereby improving the comprehensiveness, fineness, and accuracy of the in-situ characterization of the compost structure.

[0077] It is worth noting that the in-situ characterization method for the microstructure of compost piles provided in this invention can achieve in-situ characterization of the microstructure of the target compost pile. Compared with existing methods, the method of this invention has the advantage of being in-situ, which can be specifically reflected in sample sampling and pile characterization.

[0078] Specifically, compared to existing sampling methods, methods such as ring sampling, agglomerate sampling, and simple material mixing followed by compression sampling easily damage the original porosity structure of the compost pile, making it difficult for the samples to effectively represent the natural state of the compost pile during the composting process. Compared to existing characterization methods, methods such as free space analysis, pile settling, and pile bulk density only reflect the overall average state of the pile's porosity structure, but cannot identify the spatial distribution and changes in the morphological characteristics of heterogeneous and anisotropic pile pores. While surface imaging methods such as scanning electron microscopy and optical microscopy can reveal the surface morphology of small-sized substrates, they are difficult to characterize the three-dimensional porosity features of the compost pile non-destructively. Fluid injection methods such as mercury intrusion porosimetry and liquid nitrogen adsorption can obtain pore size distribution characteristics in samples, but are difficult to characterize the porosity structure in a refined manner. All of the above-mentioned pile characterization methods cause varying degrees of damage to the original porosity structure of the pile during sample preparation, detection, or characterization.

[0079] In practical applications, sampling methods such as ring sampler sampling, manual selection, and manual configuration are destructive, making it difficult to obtain complete compost structure samples during natural composting. To obtain high-quality in-situ compost samples, this invention employs a sampling method involving pre-embedding the sampler, thereby reducing disturbance and damage to the compost sample.

[0080] In one embodiment, the in-situ compost sample is obtained by pre-setting at least one sample sampler in the initial compost material, and when the initial compost material undergoes a composting reaction and reaches the target compost, each sample sampler is removed, and the target compost in the sample sampler is identified as the in-situ compost sample.

[0081] Specifically, the initial composting material can be the initial matrix prepared before the composting reaction. At least one sampler is pre-installed in the initial composting material, and the initial composting material is placed under conditions that meet the requirements for composting reaction. When the composting reaction time is reached, the initial composting material is transformed into the target compost. At this time, each sampler can be removed, and the target compost in the sampler can be identified as the in-situ compost sample.

[0082] For example, an aerobic composting reaction is carried out using fresh cow manure (CM) and naturally air-dried post-harvest wheat straw (WS) as the initial substrate. When WS is harvested, its roots and top branches are removed, and the middle of the stem is horizontally cut into 1.5–2.0 cm pieces to be used as filler. CM and WS are thoroughly mixed by hand at a mass ratio (wet weight) of 12.5:1.

[0083] Figure 2 This is a schematic diagram of the sampler provided in an embodiment of the present invention, as shown below. Figure 2As shown, to limit the impact on the composting process and the three-dimensional distribution of pore structure, the sampler can be designed to be lightweight, corrosion-resistant, temperature-resistant, highly porous, and easy to fill. For example, to accommodate the size of the composting reactor system, three-dimensional laser-sintered polyamide (PA12; density: 1.08 g·cm³) can be used. -3 (Heat distortion temperature HDT (1.80 MPa) = 130℃). Two samplers are pre-embedded in each compost reactor to obtain repeatable in-situ compost samples at the microscale, thereby increasing representativeness. The openwork structure helps maintain a consistent compost microenvironment inside and outside the sampler during composting, and the hexagonal truss structure makes the sampler structure more stable and lightweight.

[0084] Figure 3 This is a schematic diagram of the composting reactor system provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the composting reactor system includes four cylindrical composting reactors. For example, the inner diameter of each cylindrical composting reactor is 25 cm, the height is 30 cm, and the volume is 15 L. During the composting process, air enters the bottom pipe of the reactor and diffuses evenly upwards into the pile, while leachate flows into the bottom cavity. A PT100 temperature probe and a THJ16 temperature controller can be used to monitor the center temperature of the reactor in real time.

[0085] For example, such as Figure 3 As shown, the mixed initial matrix is ​​divided into 4 equal parts, the initial matrix being... Figure 3 The initial substrate was used. Independent aerobic composting reactions were conducted in four composting reactors labeled A, B, C, and D. Each composting reactor was filled with approximately 6 kg of initial substrate in multiple steps. Specifically, the initial substrate was first manually filled into the reactor cylinder until the initial substrate level reached 12 cm. Two samplers were placed vertically and symmetrically on the filled initial substrate, and then the remaining initial substrate was filled into the cylinder to the preset height. Aeration was performed intermittently in an on / off mode, with an aeration time set to 30 minutes on and 30 minutes off, and the oxygen supply rate could be set to approximately 0.25 L / (kg·Om). -1 ·min -1 .

[0086] A complete composting process can include different stages, such as a heating period, a high-temperature period, a cooling period, and a maturation period. All four composting reactors are used to complete one complete composting process. The four composting reactors are independently started after composting reaction times of 19 hours (reactor A, heating period), 3 days (reactor B, high-temperature period), 5 days (reactor C, cooling period), and 15 days (reactor D, maturation period), respectively, to obtain the target compost for each stage, and the microstructure of the compost pile can be characterized in situ.

[0087] For example, after recording the mass and height of the compost in the cylinder, carefully remove the compost matrix above and around the pre-buried sampler, remove the sampler, and remove any abnormal substrate with scissors. Place the sampler into a sealed transparent container, such as a transparent container made of polymethyl methacrylate material with an inner diameter of 86 mm, a height of 100 mm, and a wall thickness of 2 mm. Allow the sample temperature to drop to room temperature, and then perform a Micro-CT scan to obtain a three-dimensional structural model of the in-situ compost sample. During the removal of the sampler, disturbance to the compost matrix should be minimized as much as possible to maintain the original structural state of the compost sample and improve the accuracy of structural characterization.

[0088] In this embodiment, when obtaining in-situ compost samples, at least one sampler is pre-installed in the initial composting material. After the initial composting material undergoes a composting reaction and reaches the target compost, each sampler is removed, and the target compost within the sampler is identified as the in-situ compost sample. Based on this, high-quality compost samples can be obtained through the samplers. The obtained compost samples can more realistically and accurately characterize the original structural features of the target compost. Therefore, the three-dimensional structural model obtained based on the in-situ compost sample can realistically reflect the structural parameters of the target compost, thereby improving the accuracy of structural characterization.

[0089] In practical applications, existing methods cannot accurately obtain models reflecting the internal structure of in-situ pile samples. Therefore, they cannot obtain accurate structural information about the internal structure of in-situ pile samples at a fine scale, resulting in low comprehensiveness and precision of the characterization. To improve the comprehensiveness and precision of the characterization, this invention acquires a three-dimensional structural model of the in-situ pile sample that reflects its internal structural data. This three-dimensional structural model can be obtained through three-dimensional reconstruction using scanning equipment.

[0090] In one embodiment, when obtaining the three-dimensional structural model of the in-situ pile sample, the three-dimensional structural model of the in-situ pile sample can be obtained from a scanning device. The three-dimensional structural model is obtained by the scanning device performing three-dimensional reconstruction on at least one frame of projection image, and the at least one frame of projection image is obtained by the scanning device performing microcomputed tomography on the in-situ pile sample.

[0091] Specifically, the scanning device can be any scanning device with computed tomography (CT) capabilities, such as a Micro-CT scanner. Performing a micro-CT scan on an in-situ pile sample yields at least one frame of projected image. Based on this Micro-CT scan, three-dimensional reconstruction can be performed on each projected image to obtain a three-dimensional structural model of the in-situ pile sample. The three-dimensional reconstruction can be performed by the scanning device or by electronic devices with three-dimensional reconstruction capabilities, such as computers.

[0092] Micro-computed tomography (MCT) is a non-destructive, high-resolution three-dimensional modern physical structure analysis method. Its working principle is based on the structural analysis of differences in X-ray attenuation as X-rays pass through materials of varying densities. Micro-CT enables in-situ, three-dimensional, and visual characterization of the physical structure of the sample being tested. Since the structure of aerobic composting substrates for livestock and poultry manure is typically multi-scale, involving particles, aggregates, and the compost mass itself, a single particle or aggregate structure cannot reflect the pore morphology, topological connectivity, and contact relationships between particles and aggregates. This is crucial for a deeper understanding and revealing the interaction mechanisms between the substrate and the aerobic composting microenvironment. MCT can effectively acquire a three-dimensional structural model of the compost mass sample in situ, enabling comprehensive and refined in-situ characterization of the target compost's microstructure.

[0093] Figure 4 This is a schematic diagram of a micro-computed tomography scan provided in an embodiment of the present invention, as shown below. Figure 4 As shown, for example, a Skyscan 1275 Micro-CT scanning system can be used to obtain a three-dimensional structural model of an in-situ compost sample. Specifically, the in-situ compost sample placed in a sealed transparent container (i.e., a sealing device) is subjected to micro-computed tomography using a Micro-CT scanning system to obtain three-dimensional microscopic projection grayscale image data, that is, to obtain at least one frame of the three-dimensional reconstructed projection image.

[0094] In practical applications, to accurately reflect the pore structure inside porous media, during microcomputed tomography (CT) scanning, it is necessary to ensure that X-rays can penetrate the sample while maintaining a certain scanning accuracy to obtain sufficiently accurate information about the pore structure within the porous media. Therefore, target scanning conditions can be determined using the target test image, and based on these determined conditions, a projection image with high scanning accuracy can be obtained.

[0095] In one embodiment, at least one frame of the projected image is obtained by the scanning device through the following method:

[0096] After adjusting at least one scanning parameter, perform a microcomputed tomography scan to obtain at least one test image under the scanning conditions corresponding to each scanning parameter; for each test image, determine the test image with image quality greater than the preset image quality as the target test image; determine the scanning conditions corresponding to the target test image as the target scanning conditions; under the target scanning conditions, perform a microcomputed tomography scan on the in-situ pile sample to obtain at least one projection image.

[0097] Specifically, scanning parameters may include tube voltage, tube current, exposure time, rotation angle, step scan value, frame average value, and resolution. By adjusting at least one scanning parameter, corresponding scanning conditions can be obtained, which include the adjusted values ​​of each scanning parameter.

[0098] The test image can be a projected image obtained through scanning under defined target scanning conditions. The preset image quality can be a pre-defined value used to judge the image quality of the test image. For example, an image quality judgment can be made using a model capable of image comparison. If the image quality of the test image is greater than the preset image quality, it is determined as the target test image, and the scanning conditions corresponding to this target test image are the target scanning conditions. Under the target scanning conditions, performing microcomputed tomography on the in-situ pile sample can obtain at least one projected image with high image quality, thus meeting the scanning accuracy requirements.

[0099] For example, such as Figure 4 As shown, a sealed container containing a sampler and an in-situ pile sample is fixed on a Micro-CT stage. The in-situ pile sample is scanned using Micro-CT, and test images obtained under different scanning conditions are compared to determine the target scanning conditions. Ultimately, a projection image with high contrast and low noise can be obtained under the target scanning conditions.

[0100] Specifically, target scanning conditions can be determined by averaging the bright and dark fields of view to reduce environmental noise. For example, adjusting the tube voltage to 90kV, tube current to 110μA, and exposure time to 312ms can capture sufficient X-ray attenuation signal; setting the rotation angle to 360°, the step scan value to 0.2°, and the frame average to 3 frames improves image sharpness. A resolution of 50μm / pixel (PX) represents a trade-off between field of view and sample size. Using a 1mm thick copper filter reduces beam hardening. A random motion mode eliminates ring artifacts. For example, for an in-situ mass sample, 3616 16-bit grayscale projection images can be generated. Due to the absorption of more X-rays during scanning, the denser matrix has a higher grayscale value than air in the Micro-CT projection images.

[0101] In this embodiment, by adjusting at least one scanning parameter and performing a micro-computed tomography scan, at least one frame of test image under the scanning conditions corresponding to each scanning parameter is obtained. Then, the target scanning conditions can be determined based on the preset image quality and the test image. Under the target scanning conditions, performing a micro-computed tomography scan on the in-situ pile sample can improve the image quality of the obtained projection image, thereby enabling three-dimensional reconstruction and obtaining a three-dimensional structural model with high accuracy.

[0102] In practical applications, after scanning, multiple frames of projected images of the in-situ pile sample can be obtained, which are grayscale projected images. During the scanning process, the scanning equipment and the external environment generate a lot of noise in the generated projected images. In addition, issues such as poor contrast and unclear boundaries between the matrix and pore portions in the projected images make it unsuitable for direct 3D reconstruction. Image processing is required to improve image quality and thus enhance the 3D reconstruction effect.

[0103] For example, image processing of the acquired projected image includes: image noise reduction, image contrast enhancement, and image thresholding. The projected image after image processing is applied to the 3D reconstruction process, making the 3D digital stack structure closer to the real model, thus ensuring the accuracy of the 3D reconstruction results.

[0104] Figure 5 This is a schematic diagram of the image processing and 3D reconstruction process provided in an embodiment of the present invention. Figure 6 This is a schematic diagram illustrating the image processing effect provided by an embodiment of the present invention. For example... Figure 5 and Figure 6 As shown, the grayscale projection images obtained from Micro-CT scans can be processed and reconstructed using NRecon software. To effectively remove noise while preserving the porosity features and boundary information of the mass, a two-dimensional hybrid filtering method can be used to filter the grayscale projection images to eliminate redundant noise. The filtering parameters are adjusted through trial and error to highlight the boundary between air and the matrix, facilitating accurate segmentation later. The annular artifact correction coefficient can be set to 1, and the cup-shaped artifact correction value can be set to 30%. A Gaussian method is used, with the rectangular window side length set to 2px, to linearly smooth the projection images. The Feldkamp cone-beam reconstruction algorithm is used to generate a sequence of grayscale tomographic images. Contrast enhancement is applied to the grayscale images to strengthen the contrast between the matrix and pores. Based on the logarithmic histogram of the grayscale distribution, an effective attenuation coefficient range of 0-0.004 is extracted and mapped to a grayscale level of 0-255.

[0105] Three-dimensional reconstruction was performed by superimposing grayscale tomographic image sequences in z-axis spatial coordinate order using CTan software. To eliminate potential edge interference during sample preparation, a cylindrical region with a center size of Φ70mm×51.2mm (corresponding to a projected image size of Φ1400PX×1024PX) was cropped from the sample using the built-in cropping module as the Volume of Interest (VOI), resulting in a VOI image. The pore structure and matrix skeleton (fresh sample, hereinafter the same) were segmented and extracted from the VOI image using the Otsu3D global binarization segmentation algorithm, achieving three-dimensional threshold segmentation, thereby obtaining the matrix skeleton model and pore space model. Pore connectivity analysis was performed on the VOI image using the six-connectivity method and logical subtraction operation in CTan software to extract connected pore spaces and closed pore spaces from the total pore space.

[0106] like Figure 6 As shown, (a) represents the original projected image. Image reconstruction of (a) yields (b); (b) represents the original slice. Image enhancement of (b) yields (c); (c) represents the slice image after image enhancement. Filtering and denoising of (c) yields (d); (d) represents the slice image after denoising. VOI cropping of (d) yields (e); (e) represents the cropped VOI image. Grayscale slice extraction of (e) yields (f); (f) represents the VOI slice image. Binarization of (f) yields (g); (g) represents the binarized image. Connectivity porosity extraction of (g) yields (h); (h) represents the process of realizing connected porosity. Closed porosity extraction of (g) yields (i); (i) represents the closed porosity image.

[0107] Figure 7 This is a schematic diagram of the six-connection method provided in an embodiment of the present invention, as shown below. Figure 7 As shown, when connecting apertures, each aperture pixel can be treated as a spatial voxel, i.e. Figure 7 For the cube at the center, determine whether other spatial voxels connected to the six faces of this spatial voxel are spatial voxels corresponding to the aperture pixels. If so, the two can be connected.

[0108] Figure 8 This is a schematic diagram of the three-dimensional image segmentation effect provided in an embodiment of the present invention, such as... Figure 8 As shown, (a) represents the VOI grayscale sample, (b) represents the component configuration relationship, (c) represents the distribution of connected pores, (d) represents the matrix distribution, and (e) represents the distribution of closed pores.

[0109] Figure 9 These are schematic diagrams of in-situ compost sample slices from different stages of composting provided in this embodiment of the invention, such as... Figure 9 As shown in the figure, the white area represents the matrix, and the black area represents air (i.e., pores). Sample 1 and Sample 2 serve as parallel controls, jointly reflecting the matrix distribution in the compost pile during the initial, heating, high-temperature, cooling, and maturation stages. For example, representative binary tomographic images, such as slices of samples from different composting stages, are selected from the VOI slice images of the samples using Data Viewer software. By overlaying the threshold-segmented two-dimensional grayscale images from bottom to top, a true three-dimensional image of the pores inside the compost pile can be reconstructed. Using CTvox software, the matrix skeleton and pore space structure can be intuitively visualized in three dimensions. The established three-dimensional structural model can provide a foundation for finite element calculations of compost physical fields.

[0110] In practical applications, the volumetric dimensions of the in-situ compost sample need to meet the scanning requirements of the scanning equipment. Simultaneously, the volume of the in-situ compost sample should be sufficiently large to contain relatively complete internal structural features of the compost pile, so as to effectively represent the microstructural characteristics of the target compost pile. To ensure that the digitized three-dimensional structural model can accurately characterize the microstructural features of the compost pile, while also considering the limitations of computer storage and computing power, it is necessary to determine the representative volumetric units of the three-dimensional structural model.

[0111] In one embodiment, the representative volume element of the three-dimensional structural model is determined, which can be achieved in the following way:

[0112] Within the three-dimensional structural model, at least one sub-model is determined starting from a reference point, which is either a vertex or the center point of the three-dimensional structural model. The sub-model is a part of the three-dimensional structural model. For each sub-model, the target parameter values ​​corresponding to the sub-model are calculated. The target parameter values ​​include the total porosity. Based on the target parameter values ​​and the model dimensions of the corresponding sub-model, the representative volume element of the three-dimensional structural model is determined.

[0113] Specifically, a sub-model can be understood as a portion extracted from the three-dimensional structural model of the in-situ pile sample. Specifically, a sub-model can be understood as a portion taken from the in-situ pile sample as a sub-sample, and the corresponding three-dimensional structural model of this sub-sample. The representative volumetric unit of the in-situ pile sample can be determined based on the fluctuation curve of structural parameters with the size of the sub-sample, considering the stability trend and fault tolerance of the curve fluctuation. The reference point can be a vertex or center point in the three-dimensional structural model. At least one sub-model can be accurately and efficiently determined based on the vertex or center point. It should be understood that the representative volumetric unit of the in-situ pile sample can also be determined based on other points in the three-dimensional structural model.

[0114] The target parameter values ​​include total porosity, and may also include other parameter values, such as pore connectivity coefficient and pore connectivity density. Based on the target parameter values ​​and the model dimensions of the corresponding sub-model, representative volumetric elements of the 3D structural model can be determined.

[0115] For example, based on a comprehensive consideration of factors such as the structural characteristics and randomness of the in-situ pile sample, digital sub-samples (sub-volumes) of different sizes are obtained through the sampling window method. The digital sub-samples are also known as sub-models.

[0116] Figure 10 This is a flowchart illustrating the process of determining a representative volume unit according to an embodiment of the present invention, such as... Figure 10 As shown, when determining the representative volume unit, an initial stack unit volume can be selected first, and then sub-cube sample volume units of different sizes can be constructed to further adjust the volume size of the sub-samples. The eigenvalues ​​of each sub-sample are calculated, and the mean, standard deviation, and coefficient of variation of each eigenvalue of sub-samples of the same volume size are calculated. Then, it is determined whether the stopping criterion is met. If not, the step of adjusting the size of the sub-volume is entered. If so, the RVE, i.e. the minimum suitable sub-volume, is calculated, which can then be used to verify the representativeness of the sample and show the size dependence of the stack microstructure.

[0117] Figure 11 This is a schematic diagram of obtaining the sub-model provided in an embodiment of the present invention. For example... Figure 11 As shown, a cube can be used as the geometry of the Reverse Velocity Image (RVE), and the RVE size can be characterized by the side length of the cube. To eliminate boundary effects, the RVE can be cropped to a size of 1024×1024×1024 pixels from the center of the projected image using CTAan software. 3 (That is, 51.2×51.2×51.2mm) 3 The cubic binary matrix dataset is used as the maximum reference sample, and the maximum reference sample is as follows: Figure 11 The cubic part of the cylinder shown in (a), that is Figure 11 The cube shown in (b) is used as a reference sample. The largest reference sample is labeled V0, and its side length is denoted as ε0. A 9-point cube coverage sampling method is employed, starting from the center point E of V0 and its eight vertices (A, B, C, D, F, G, H, and I). The cube region is continuously extended from its initial position along the direction of the arrows with a constant increment until it approaches V0. As the window expands, multiple sets of cube regions of different sizes are sequentially cropped from V0 to form sub-models. Figure 11 (c) in the diagram illustrates that multiple sub-models can be determined starting from vertex G of the cube. Figure 11 (d) in the diagram illustrates that multiple sub-models can be determined starting from the center point E of the cube.

[0118] At this point, there are 9 replicates of the subsample at each size gradient, denoted as V. i-j The side length is l i (mm), where i is the size gradient, j is the position marker, and l i The selection scheme is shown in equation (1):

[0119]

[0120] For example, in the formula Let d represent the side length of V0, where d = 1 mm, and N. * For positive integers, each V0 can be truncated into 48 size gradients and 432 V values. i-j .

[0121] Using total porosity as the target parameter and the CV discriminant method to determine RVE, an example is given. For instance, CV is a dimensionless measure of the spatial dispersion of data, eliminating the influence of measurement scale and dimensions, and ensuring the comparability of the dispersion of different structural parameters. The smaller the CV value, the lower the data dispersion, and the closer the data is to the average value. When the CV is within an acceptable range, the mean of the structural characteristics of the in-situ pile sample reaches stability, and at this point, RVE... i-j If the conditions for effectively expressing structural parameters are met, then V can be used as a basis at this time. i-j The specific expressions for determining RVE are equations (2) to (5):

[0122] ε≥CV i (2)

[0123]

[0124]

[0125]

[0126] Where ε is the acceptable CV threshold (i.e., fault tolerance), %; x i-j For V i-k The structural parameter measurements; n is the number of repetitions of the measurements within the same dimensional gradient, n = 9; CV i For x i-j The coefficient of variation; σ i For x i-j Standard deviation; μ i For x i-j The average value.

[0127] For example, it is typically impossible to determine the presence of RVEs beforehand; therefore, it is necessary to ensure the presence of RVEs in the sample before analyzing the RVE size range. Using total porosity as the evaluation index for RVEs in the microstructure of the packing, based on the VOI 3D image reconstruction results, the total porosity (Vo) of the sample was analyzed using functional modules in the CTan software. Tot The total porosity (%) can be quantified using equation (6).

[0128]

[0129] in, V represents the total pore volume. VOI (mm 3 ) indicates the VOI volume.

[0130] Figure 12 This is a schematic diagram of the relationship between total porosity and sample side length provided in an embodiment of the present invention. Figure 13 This is a schematic diagram of the total porosity variation curve provided in an embodiment of the present invention, as shown below. Figure 12 and Figure 13 As shown, the total porosity μ of the in-situ pile sample can be determined. i σ i and CV i Follow l i The fluctuations in the variation are represented by the standard deviation of nine measurements at each dimensional gradient, indicating that the structure of the stack also exhibits significant size dependence at the microscale, with the total porosity increasing with l. i The increasing values ​​exhibit a similar trend, and the variability of structural features is negatively correlated with sample size, i.e., σ... i and CV i All with l i The increase in , the overall trend is a clear decrease, and it gradually approaches 0.

[0131] When the sample side length is small, for example, approximately 4-25 mm, the heterogeneity of total porosity is more sensitive to changes in sample size. In this case, the in-situ pile sample exhibits microscopic, discrete, and random structural characteristics, and the total porosity value does not represent the overall structure of the pile. With l i As x increases further, the effect of sample size variation on the total porosity of the sample gradually weakens, and the representativeness of the sample increases. i-j The dispersion gradually decreases, converging to a constant "true value," which is unaffected by the sample size and can be used as an equivalent parameter for the overall total porosity. At this point, the total porosity of the sample simultaneously considers statistical uniformity for macroscopic features and ergodicity for microscopic heterogeneity, exhibiting continuity, macroscopicity, and determinism in the total porosity of the sample. In summary, from a statistical perspective, it is demonstrated that within the sample size range, there exists an RVE representing the macroscopic response of the stack structure.

[0132] For example, when ε = 5%, the total porosity of the in-situ compost sample corresponds to a cube RVE side length of 30.3 ± 4.0 mm, which determines the porosity at 50 μm PX. -1 Under the premise of [specific conditions], the maximum reference sample size is 51.2×51.2×51.2mm. 3 This satisfies the RVE requirements for the stack structure. At this point... Dimensions (134.2×106mm) 3 The fact that the aggregates of the in-situ compost samples were larger than those of the target compost indicates that the microstructure (RVE) of the in-situ compost samples is more effective in representing the overall structure of the target compost than the compost aggregates.

[0133] In this embodiment, by determining the sub-model and based on the target parameter values ​​and the model size of the corresponding sub-model, the representative volume unit of the three-dimensional structural model can be determined. This allows the target model to be determined through the representative volume unit, thereby avoiding the defect that the determined target model cannot effectively represent the pile sample when its size is too small. Therefore, the effectiveness of the target model in representing the structural features of the pile sample can be improved, thereby improving the accuracy of structural characterization.

[0134] For example, in the target model, the image regions of the matrix skeleton and the image regions of the free space are interspersed and relatively messy. In order to more accurately distinguish and count the pores and throats in the target model, the target model can be processed into a pore network model to provide accurate throat number distribution data, pore size distribution data and throat size distribution data, thereby improving the accuracy of the pile structure characterization.

[0135] In one embodiment, the microstructure of the target compost pile is characterized in situ based on the target model, which can be achieved in the following way:

[0136] The target model is modeled into a pore network to obtain the corresponding pore network model. Data statistics are performed on the pore network model to obtain throat number distribution data, pore size distribution data, and throat size distribution data. Based on the throat number distribution data, pore size distribution data, and throat size distribution data, the microstructure of the target compost pile is characterized in situ.

[0137] Specifically, by performing pore network modeling on the target model, a corresponding pore network model, known as the PNM (Pore Network Model), can be obtained. During pore network modeling, the Euclidean distance from each pore voxel to its nearest matrix voxel is first determined based on a voxelized mesh connecting the pore space. Then, the hierarchical structure of the pore voxels is distinguished using an Euclidean distance map, thereby obtaining the parameters for pore network extraction. Next, the watershed algorithm in the Separate Objects module is used to divide the pore space into a series of connected irregular spatial blocks based on morphological differences according to the Euclidean distance map. Each spatial block represents a pore or throat, and they are distinguished by random color association. Based on 3D skeletonization, an inscribed sphere is generated on the nodes and paths along the central axis using a dilation algorithm, representing the pores and throats of the network, respectively. Simultaneously, the pore space is skeletonized using a Skeleton-Aggressive algorithm based on a hybrid of thinning and distance maps, pore centers are located, and pore throats are determined. Then, using the Generate Pore Network Model module, based on the skeletonization, and according to the size, orientation, and connection method of the spatial blocks, it further subdivides them into corresponding spherical equivalent pores (i.e., pores in the pore network model) and cylindrical equivalent throats connecting the pores (i.e., throats in the pore network model) using the maximum sphere method and the equivalent volume method. Finally, the geometric parameters of the pores are calculated, including the equivalent radius of the pores, the pore volume, the equivalent throat radius, the throat length, and the coordination number, and the PNM model is visualized in three dimensions using the PoreNetwork Model View module.

[0138] Figure 14 This is a schematic diagram of the PNM model provided in an embodiment of the present invention, as shown below. Figure 14 As shown, (a) represents the target model after separation and labeling, (b) represents the equivalent pore distribution, and (c) represents the equivalent throat distribution. Combining (b) and (c) yields (d), which represents the PNM model corresponding to the target model.

[0139] For example, the PNM model can be extracted from the microscopic interconnected pore image of the pile using Avizo software, thus enabling the pore network modeling of the target model and obtaining the PNM model. Furthermore, statistical analysis of the PNM model can yield throat number distribution data, pore size distribution data, and throat size distribution data.

[0140] In one implementation, the microstructure of the target compost pile is characterized in situ based on throat number distribution data, pore size distribution data, and throat size distribution data. This can be achieved in the following way:

[0141] Histograms were plotted on the distribution data of throat number, pore size, and throat size, respectively, to obtain their corresponding histograms. Curve fitting was performed on each histogram to obtain the corresponding characterization curve. The microstructure of the target compost pile was characterized in situ using the characterization curves.

[0142] For example, Figure 15 This is a schematic diagram of a histogram provided in an embodiment of the present invention, such as... Figure 15 As shown, histogram statistics of the relevant geometric parameters of pores and throats for the PNM model can be obtained, including histograms of pore radius distribution, cumulative pore volume distribution, throat radius distribution, throat length distribution, pore coordination number distribution, and pore radius-coordination number distribution.

[0143] The distribution pattern of pore radius is as follows Figure 15 As shown in the image corresponding to (a), the average pore diameter is 0.80 mm, and the pore radius is mainly concentrated between 0.10 and 1.70 mm, accounting for 90.87% of the cumulative frequency. The distribution pattern of the pore cumulative volume is as follows: Figure 15 As shown in the image corresponding to (b), the cumulative pore volume is mainly distributed in the pore radius range of 0.70–4.40 mm. 3 This portion accounts for 89.98% of the cumulative frequency. Although smaller pores are more prevalent in number, they contribute very little to the total pore volume.

[0144] The distribution pattern of throat radius is as follows: Figure 15 As shown in image (c), the average radius of the larynx is 0.51 mm, and the larynx radius is mainly distributed in the range of 0–1.10 mm, accounting for 90.73% of the cumulative frequency. The distribution pattern of the larynx length is as follows: Figure 15 As shown in the image corresponding to (d), the average length of the larynx is 3.16 mm, and the length of the larynx is mainly distributed in the range of 0.4 to 6.0 mm, accounting for 91.16% of the cumulative frequency.

[0145] The distribution law of pore coordination number is as follows Figure 15 As shown in the image corresponding to (e), the average coordination number is 5.914, indicating good connectivity in the spatial topology of the pores. The coordination number is mainly distributed in the range of 2–13 mm, accounting for 91.04% of the cumulative frequency. The distribution of pore radius and coordination number is as follows: Figure 15 As shown in the image corresponding to (f), it can be seen from the figure that the larger the pore volume, the larger the corresponding coordination number is, and the greater the probability of being able to connect to multiple throats.

[0146] For example, for each histogram, curve fitting can be performed on the histogram to obtain the corresponding representation curve, such as... Figure 15 The curves in each histogram are their respective characterization curves, which allow for accurate in-situ characterization of the microstructure of the target compost pile. Figure 15 As can be seen from this, the distribution law of the equivalent diameter of pores and throats is basically the same. The pore radius approximately follows a log-normal distribution as shown in equation (7), while the pore volume and coordination number approximately follow a normal distribution as shown in equation (8). Equations (7) and (8) are the corresponding characterization curves, as follows:

[0147]

[0148]

[0149] Where A is the peak area and y0 is the intercept (compensation). For equation (7), the expected value of the log-normal distribution is... variance The center of the curve is x = e μ For equation (8), the expected value of the normal distribution is E(X) = μ, and the variance is D(X) = σ. 2 .

[0150] In this embodiment, the target model is modeled into a pore network to obtain a corresponding pore network model. Data statistics are performed on the pore network model to obtain throat number distribution data, pore size distribution data, and throat size distribution data. Histogram statistics are then performed on the throat number distribution data, pore size distribution data, and throat size distribution data to obtain their respective histograms. Curve fitting is then performed on each histogram to obtain corresponding characterization curves. These characterization curves are used to perform in-situ characterization of the microstructure of the target compost. Based on this, the geometric and topological structures of the target compost can be effectively characterized using characterization curves, achieving a comprehensive, detailed, and accurate characterization of the microstructure of the target compost.

[0151] The following exemplary embodiment describes the in-situ characterization method for the microstructure of compost piles provided by the present invention.

[0152] Figure 16 This is a schematic block diagram illustrating the in-situ characterization process of the microstructure of a compost pile provided in an embodiment of the present invention, as shown below. Figure 16 As shown, taking aerobic composting as an example, the sample sampler can be designed and manufactured first. Then, composting experiments and sample preparation are carried out to obtain in-situ compost samples. The obtained in-situ compost samples are then subjected to micro-computed tomography (Micro-CT) in-situ scanning, which allows for the acquisition of complete, undisturbed compost samples.

[0153] Image processing is performed on the scanned projection images to improve image quality. Then, a microscopic digital model of the in-situ aerobic compost pile is obtained, resulting in a three-dimensional structural model. Digital three-dimensional reconstruction using Micro-CT scanning allows for the acquisition and visualization of the pore structure data of the aerobic compost pile. Subsequently, representative sample size analysis is performed to determine the representative unit volume of the three-dimensional structural model. Through image segmentation, a digital model of the connected pores in the in-situ aerobic compost pile is obtained, extracting the seepage channels. Representative microstructural models of the pile are selected, and the properties of interest are derived and calculated using samples with a pore size and a dynamic range (RVE) of at least 1. For example, by determining the target model and performing pore network modeling, a corresponding pore network model is obtained. Based on the pore network model, mathematical statistical analysis is performed on pores and throats to obtain the throat number distribution, pore size distribution, and throat size distribution, which can qualitatively and quantitatively characterize the geometric and topological features of the pore structure of the aerobic compost pile. The characteristics of pore space structure are quantitatively analyzed, and a topological pore-throat network model is established based on the real connected pore structure model. According to the pore-throat model, the geometric and topological characteristics of pore structure, such as pore equivalent radius, throat length, throat equivalent radius and coordination number, are quantitatively calculated, thereby realizing the visualization and quantitative characterization of the real microstructure of the pile.

[0154] This invention utilizes Micro-CT in-situ scanning, image processing, 3D visualization reconstruction, and pore feature analysis to study the microstructure of aerobic compost piles. The established 3D model of the microstructure of aerobic compost piles is scientifically designed, highly practical, and provides guidance for the management of aerobic composting processes. The comprehensive and accurate characterization of the pile's microstructure can serve as a research foundation for studying composting mechanisms such as the extraction of preferential gas flow paths in the pores and the creep of the pile's microstructure. It effectively addresses the practical difficulties in constructing and characterizing the microstructure in existing numerical simulations and mechanism studies of aerobic composting, contributing to research on energy-saving, emission-reduction, and efficiency-enhancing reaction mechanisms in the composting process, as well as the development of scientific and applicable engineering technologies and equipment.

[0155] This invention establishes an in-situ 3D visualization method for the microscale pore structure of compost pile units based on Micro-CT. By defining the scanning conditions and image processing algorithms, it achieves, for the first time, 3D in-situ visualization of the pore structure and dynamic changes of pile units during composting. The obtained digital model of the pile's microstructure is effectively representative. Micro-CT images and quantization results show that the pore structure is well-developed and the pore network is complex during composting. Compared with methods such as pile settling, bulk density, and free space, this invention provides a more refined characterization of the pile's microstructure. By establishing a Relative Velocity Observation (RVE), a bridge is built between the microscale and macroscale characteristics of the pile structure, laying a physical model foundation for the study of the microscale processes of aerobic composting using Micro-CT technology. This aims to provide data reference and theoretical support for multi-scale research on the microscale mechanisms and process optimization of aerobic composting.

[0156] The following describes the in-situ microstructure characterization device for compost piles provided in the embodiments of the present invention. The in-situ microstructure characterization device for compost piles described below and the in-situ microstructure characterization method for compost piles described above can be referred to in correspondence with each other.

[0157] Figure 17 This is a schematic diagram of the in-situ characterization device for the microstructure of compost piles provided in an embodiment of the present invention, with reference to... Figure 17 As shown, the in-situ microstructure characterization device 1700 for compost piles includes:

[0158] The acquisition module 1710 is used to acquire a three-dimensional structural model of the in-situ compost sample. The in-situ compost sample is taken from the target compost based on the sample sampler. The three-dimensional structural model is used to characterize the throat number distribution, pore size distribution and throat size distribution of the in-situ compost sample.

[0159] The module 1720 is used to determine the representative volume element of the three-dimensional structural model. The representative volume element is the volume element in the three-dimensional structural model that represents the throat number distribution, pore size distribution and throat size distribution of the in-situ pile sample.

[0160] The determination module 1720 is also used to determine the target model in the three-dimensional structural model, wherein the volume of the target model is greater than or equal to the representative volume element;

[0161] The characterization module 1730 is used to perform in-situ characterization of the microstructure of the target compost pile based on the target model.

[0162] In one example embodiment, the characterization module 1730 is specifically used for:

[0163] The target model is modeled into a pore network to obtain the corresponding pore network model. Data statistics are performed on the pore network model to obtain throat number distribution data, pore size distribution data, and throat size distribution data. Based on the throat number distribution data, pore size distribution data, and throat size distribution data, the microstructure of the target compost pile is characterized in situ.

[0164] In one example embodiment, the characterization module 1730 is specifically used for:

[0165] Histograms were plotted on the distribution data of throat number, pore size, and throat size, respectively, to obtain their corresponding histograms. Curve fitting was performed on each histogram to obtain the corresponding characterization curve. The microstructure of the target compost pile was characterized in situ using the characterization curves.

[0166] In one example embodiment, the determining module 1720 is specifically used for:

[0167] Within the three-dimensional structural model, at least one sub-model is determined starting from a reference point, which is either a vertex or the center point of the three-dimensional structural model. The sub-model is a part of the three-dimensional structural model. For each sub-model, the target parameter values ​​corresponding to the sub-model are calculated. The target parameter values ​​include the total porosity. Based on the target parameter values ​​and the model dimensions of the corresponding sub-model, the representative volume element of the three-dimensional structural model is determined.

[0168] In one example embodiment, the acquisition module 1710 is specifically used for:

[0169] A three-dimensional structural model of the in-situ pile sample is obtained from the scanning device. The three-dimensional structural model is obtained by the scanning device through three-dimensional reconstruction of at least one frame of projection image, which is obtained by the scanning device through microcomputed tomography scanning of the in-situ pile sample.

[0170] In one example embodiment, at least one frame of the projected image is obtained by the scanning device through the following method:

[0171] After adjusting at least one scanning parameter, perform a microcomputed tomography scan to obtain at least one test image under the scanning conditions corresponding to each scanning parameter; for each test image, determine the test image with image quality greater than the preset image quality as the target test image; determine the scanning conditions corresponding to the target test image as the target scanning conditions; under the target scanning conditions, perform a microcomputed tomography scan on the in-situ pile sample to obtain at least one projection image.

[0172] In one example embodiment, the in-situ stock sample was obtained in the following manner:

[0173] At least one sampler is pre-installed in the initial composting material. When the initial composting material undergoes a composting reaction and reaches the target compost, each sampler is removed, and the target compost in the sampler is identified as the in-situ compost sample.

[0174] The apparatus of this embodiment can be used to execute the method of any embodiment in the side embodiment of the method for in-situ characterization of the microstructure of compost piles. Its specific implementation process and technical effects are similar to those in the side embodiment of the method for in-situ characterization of the microstructure of compost piles. For details, please refer to the detailed description in the side embodiment of the method for in-situ characterization of the microstructure of compost piles, which will not be repeated here.

[0175] Figure 18 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 18 As shown, the electronic device may include: a processor 1810, a communications interface 1820, a memory 1830, and a communications bus 1840, wherein the processor 1810, the communications interface 1820, and the memory 1830 communicate with each other through the communications bus 1840. The processor 1810 can call logic instructions in the memory 1830 to execute an in-situ characterization method of the microstructure of the compost pile. This method includes: acquiring a three-dimensional structural model of an in-situ compost sample, the sample being taken from the target compost using a sampler; the three-dimensional structural model being used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample; determining a representative volume element of the three-dimensional structural model, the representative volume element being the volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample; determining a target model within the three-dimensional structural model, the target model having a volume greater than or equal to the representative volume element; and performing in-situ characterization of the microstructure of the target compost pile based on the target model.

[0176] Furthermore, the logical instructions in the aforementioned memory 1830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] On the other hand, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the in-situ characterization method for the microstructure of compost piles provided by the methods described above. The method includes: acquiring a three-dimensional structural model of an in-situ compost pile sample, wherein the in-situ compost pile sample is taken from the target compost using a sample sampler, and the three-dimensional structural model is used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost pile sample; determining a representative volume element of the three-dimensional structural model, wherein the representative volume element is a volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost pile sample; determining a target model in the three-dimensional structural model, wherein the volume of the target model is greater than or equal to the representative volume element; and performing in-situ characterization of the microstructure of the target compost pile based on the target model.

[0178] In another aspect, embodiments of the present invention also provide a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the in-situ characterization method for the microstructure of the compost pile provided by the above methods. The method includes: obtaining a three-dimensional structural model of an in-situ compost pile sample, wherein the in-situ compost pile sample is taken from the target compost using a sample sampler, and the three-dimensional structural model is used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost pile sample; determining a representative volume element of the three-dimensional structural model, wherein the representative volume element is a volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost pile sample; determining a target model in the three-dimensional structural model, wherein the volume of the target model is greater than or equal to the representative volume element; and performing in-situ characterization of the microstructure of the target compost pile based on the target model.

[0179] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0180] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for in-situ characterization of the microstructure of a compost pile, characterized in that, include: A three-dimensional structural model of an in-situ compost sample is obtained. The in-situ compost sample is taken from the target compost using a sample sampler. The three-dimensional structural model is used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample. Determining the representative volume element of the three-dimensional structural model includes: within the three-dimensional structural model, determining at least one sub-model starting from a reference point, where the reference point is a vertex or center point of the three-dimensional structural model, and the sub-model is a part of the three-dimensional structural model; for each sub-model, calculating the target parameter value corresponding to the sub-model, where the target parameter value includes total porosity; and based on the target parameter value and the model size of the corresponding sub-model, determining the representative volume element of the three-dimensional structural model, where the representative volume element is a volume element in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ pile sample. A target model is determined in the three-dimensional structural model, wherein the volume of the target model is greater than or equal to the representative volume unit; The microstructure of the target compost pile is characterized in situ based on the target model.

2. The in-situ characterization method for the microstructure of compost piles according to claim 1, characterized in that, The in-situ characterization of the microstructure of the target compost pile based on the target model includes: The target model is subjected to pore network modeling processing to obtain the pore network model corresponding to the target model; Data statistics were performed on the pore network model to obtain throat number distribution data, pore size distribution data, and throat size distribution data; The microstructure of the target compost pile is characterized in situ based on the throat number distribution data, pore size distribution data, and throat size distribution data.

3. The in-situ characterization method for the microstructure of compost piles according to claim 2, characterized in that, The in-situ characterization of the microstructure of the target compost based on the throat number distribution data, pore size distribution data, and throat size distribution data includes: Histogram statistics were performed on the throat number distribution data, the pore size distribution data, and the throat size distribution data respectively to obtain their respective histogram statistics; For each of the aforementioned histograms, curve fitting is performed on the histograms to obtain the corresponding characterization curves; The microstructure of the target compost pile is characterized in situ using the aforementioned characterization curves.

4. The in-situ characterization method for the microstructure of compost piles according to any one of claims 1-3, characterized in that, The acquisition of the three-dimensional structural model of the in-situ pile sample includes: A three-dimensional structural model of the in-situ pile sample is obtained from the scanning device. The three-dimensional structural model is obtained by the scanning device through three-dimensional reconstruction of at least one frame of projection image. The at least one frame of projection image is obtained by the scanning device through microcomputed tomography scanning of the in-situ pile sample.

5. The in-situ characterization method for the microstructure of a compost pile according to claim 4, characterized in that, The at least one frame of the projected image is obtained by the scanning device through the following method: After adjusting at least one scanning parameter, perform a micro-computed tomography scan to obtain at least one test image under the scanning conditions corresponding to each scanning parameter. For each of the test images, the test image with image quality greater than the preset image quality is determined as the target test image; The scanning conditions corresponding to the target test image are determined as the target scanning conditions; Under the target scanning conditions, the in-situ pile sample is subjected to microcomputed tomography to obtain at least one frame of projection image.

6. The in-situ characterization method for the microstructure of compost piles according to any one of claims 1-3, characterized in that, The in-situ pile sample was obtained in the following manner: At least one of the samplers is pre-installed in the initial composting material. When the initial composting material undergoes a composting reaction and reaches the target compost, each of the samplers is removed, and the target compost in the sampler is identified as the in-situ compost sample.

7. An in-situ characterization device for the microstructure of a compost pile, characterized in that, include: The acquisition module is used to acquire a three-dimensional structural model of an in-situ compost sample. The in-situ compost sample is taken from the target compost using a sample sampler. The three-dimensional structural model is used to characterize the throat number distribution, pore size distribution, and throat size distribution of the in-situ compost sample. A determination module is used to determine representative volumetric units of the three-dimensional structural model, including: determining at least one sub-model within the three-dimensional structural model, starting from a reference point, wherein the reference point is a vertex or center point of the three-dimensional structural model, and the sub-model is a part of the three-dimensional structural model; calculating target parameter values ​​corresponding to each sub-model, wherein the target parameter values ​​include total porosity; and determining representative volumetric units of the three-dimensional structural model based on the target parameter values ​​and the model dimensions of the corresponding sub-model, wherein the representative volumetric unit is a volumetric unit in the three-dimensional structural model that characterizes the throat number distribution, pore size distribution, and throat size distribution of the in-situ pile sample. The determining module is further configured to determine a target model in the three-dimensional structural model, wherein the volume of the target model is greater than or equal to the representative volume unit; The characterization module is used to perform in-situ characterization of the microstructure of the target compost pile based on the target model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the in-situ characterization method of the microstructure of the compost pile as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the in-situ characterization method of the microstructure of the compost pile as described in any one of claims 1 to 6.

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