A method for distinguishing pores and bubbles in ultra-stable froth based on fc and spherical degree analysis

Abstract

The application discloses a method for distinguishing pores and bubbles in ultra-stable foam based on FC and sphericity analysis, belongs to the field of solid-liquid separation of flotation clean coal, and comprises the following steps: obtaining a three-dimensional gray image of the foam through Micro-CT, performing three-dimensional reconstruction and extracting a gap structure according to a filtered back projection algorithm; determining a representative sub-volume based on porosity change to ensure statistical reliability; calculating geometric characteristic parameters of the gap and performing morphological classification by adopting a Feret Caliper method; determining a distinguishing threshold TH in combination with a relationship between sphericity and bubble volume fraction; then, realizing automatic identification and distinction of the bubbles and the pores according to the threshold; and finally, verifying the result by using an optical microscope. The method realizes non-destructive identification and quantitative analysis of the internal structure of the ultra-stable foam, has high precision and repeatability, and provides a reliable image quantitative means for optimization of a coal dewatering process.

Description

Technical Field

[0001] This invention belongs to the field of solid-liquid separation of flotation clean coal, and particularly relates to a method for distinguishing pores and bubbles in ultrastable foam based on the combined use of FC and sphericity analysis. Background Technology

[0002] With the increasing mechanization of coal mining, the content of fine coal particles has significantly increased. Coupled with the widespread use of flotation reagents, this has led to enhanced foam stability in flotation clean coal, forming ultra-stable three-phase foam (gas-liquid-solid) that is difficult to break naturally. This type of foam severely hinders water removal, reduces dewatering efficiency, increases the moisture content of the clean coal, and consequently increases transportation costs and affects coal quality. The bubble and pore structure within the foam are key factors affecting solid-liquid separation performance. Bubbles hinder water migration by forming gas nuclei, while pores provide drainage channels. Therefore, accurately distinguishing and quantifying the morphological characteristics of bubbles and pores (such as quantity ratio, average diameter, porosity, specific surface area, etc.) is of great significance for optimizing the dewatering process.

[0003] Currently, the characterization of foam structures mainly relies on image analysis techniques, such as scanning electron microscopy (SEM), two-dimensional surface imaging, and conventional image processing algorithms (such as the watershed algorithm). However, these methods have significant limitations: SEM is a destructive detection method and can only provide local information; two-dimensional imaging cannot reveal the internal structure; and traditional image algorithms are prone to oversegmentation or undersegmentation when processing ultrastable foams with low grayscale contrast, blurred boundaries, or adherent structures, making it difficult to accurately distinguish between bubbles sharing a wall and open pores. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for distinguishing pores and bubbles in ultrastable foam based on FC and sphericity analysis, comprising:

[0005] A three-dimensional grayscale image of the ultra-stable foam is obtained by Micro-CT scanning, and three-dimensional reconstruction is performed based on the grayscale image to extract the void region;

[0006] Representative sub-volumes are determined based on the characteristics of void volume variation, and void shapes are classified according to the Feret Caliper method.

[0007] The distinction threshold is determined based on the relationship between sphericity and volume fraction. The bubble structure and the pore structure are then distinguished and verified based on the threshold.

[0008] Optionally, the Micro-CT scan is based on the foam structure obtained from sample preparation, wherein the foam stability is adjusted according to the surfactant concentration, and median filtering and threshold segmentation are performed according to the grayscale distribution range to obtain clear void boundaries.

[0009] Optionally, during image reconstruction, three-dimensional voxel data are generated according to a filtering back projection algorithm; porous phase and solid phase are extracted based on voxel grayscale differences; isolated noise volumes are screened out based on voxel connectivity to obtain a continuous void network.

[0010] Optionally, determining the representative sub-volume includes: calculating porosity based on different sampling volumes; determining the minimum sub-volume size based on the stable range of porosity variation; and verifying the representativeness of the statistical characteristics of the overall foam structure based on this sub-volume.

[0011] Optionally, the Feret Caliper method includes: calculating the maximum Feret diameter, minimum width, and maximum vertical length based on the three-dimensional void boundary;

[0012] Based on the above parameter ratios, the voids are classified into five categories: rod-shaped, sheet-shaped, cuboid-shaped, plate-shaped, and cubic-shaped; and the candidate bubble types are determined based on the volume distribution.

[0013] Optionally, the determination of sphericity includes: calculating sphericity based on void volume and surface area;

[0014] Matching is performed based on the bubble volume fraction measured from the surfactant concentration;

[0015] The sphericity threshold TH is determined based on the cumulative volume fraction curve.

[0016] Optionally, the distinction between bubbles and pores includes: classifying pores as bubbles based on their sphericity being higher than the threshold TH;

[0017] Voids below the threshold TH or non-cubic are identified as pores; and the ratio of bubbles to pores is calculated based on their respective volume fractions.

[0018] Optionally, the threshold TH is dynamically adjusted based on the surfactant concentration. When the concentration increases, TH is reduced according to the rightward shift of the sphericity distribution curve to maintain the bubble volume fraction consistent with the experimentally measured value.

[0019] On the other hand, the present invention also provides an electronic device including a memory, a processor, and a computing program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computing program.

[0020] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.

[0021] Compared with the prior art, the present invention has the following advantages and technical effects:

[0022] This invention overcomes the limitations of traditional two-dimensional image analysis in boundary recognition, morphological differentiation, and grayscale transition processing by synergistically combining Feret Caliper shape classification and sphericity calculation. It enables precise differentiation of bubbles and pores in complex three-phase foam systems. Complete three-dimensional structural information is obtained through Micro-CT reconstruction, avoiding morphological damage caused by sample slicing. The determination of representative subvolumes improves statistical stability and computational efficiency, ensuring the results accurately reflect the overall foam structure characteristics. FC classification systematically divides pores of different shapes, providing a classification basis for subsequent sphericity calculation. The dynamic determination mechanism of the sphericity threshold allows the algorithm to adaptively adjust according to different foam stability, achieving cross-sample universality and consistency. Comparative verification using optical microscopy data further improves the reliability of the results. Overall, this invention constructs a multi-feature fusion pore identification framework, providing a scalable quantitative analysis platform for the study of flotation clean coal dewatering mechanisms, foam structure characterization, and process parameter optimization. Attached Figure Description

[0023] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of the FC method and its main parameters, and a classification standard diagram of the FC method for gap shape, according to an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram illustrating the quantification of foam volume according to an embodiment of the present invention;

[0027] Figure 4 This is a graph showing the variation of porosity with sub-volume side length in REV analysis according to an embodiment of the present invention.

[0028] Figure 5 This is a statistical distribution diagram of pore shape categories under different SDBS concentrations according to an embodiment of the present invention;

[0029] Figure 6 This is a volume distribution diagram of various types of voids in an embodiment of the present invention;

[0030] Figure 7 This is a diagram showing the results of distinguishing between bubbles and pores based on sphericity in an embodiment of the present invention.

[0031] Figure 8 This is an image of foam under an optical microscope and its preprocessing and segmentation results, as shown in this embodiment of the invention.

[0032] Figure 9 This is a comparison diagram of the average diameter of bubbles measured by Micro-CT and microscope according to an embodiment of the present invention. Detailed Implementation

[0033] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0034] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0035] Example 1

[0036] like Figure 1 As shown, this embodiment provides a method for distinguishing pores and bubbles in ultrastable foam based on FC and sphericity analysis, including:

[0037] Using coal samples from New South Wales, Australia as raw material, the material was ground to a P80 of 38 μm. Foam was generated in a brine medium simulating industrial water quality, and 0 mmol / L, 0.5 mmol / L, and 0.75 mmol / L SDBS were added to regulate the foam structure. Three-dimensional images were acquired using Micro-CT scanning, and REV analysis determined the representative volume to be a cube with a side length of 3016 μm. The FC method was used to classify pores, and then sphericity thresholds (TH values ​​of 0.64, 0.57, and 0.63) were used to distinguish between bubbles and pores. Finally, optical microscopy was used for verification. The results showed that the bubble diameter measured by Micro-CT was slightly smaller than that obtained by microscopy (due to differences in measurement principles), but the trend was consistent, proving the reliability and effectiveness of this method. The specific process is as follows:

[0038] A three-dimensional grayscale image of the ultra-stable foam is obtained by Micro-CT scanning, and three-dimensional reconstruction is performed based on the grayscale image to extract the void region;

[0039] Representative sub-volumes are determined based on the characteristics of void volume variation, and void shapes are classified according to the Feret Caliper method.

[0040] The distinction threshold is determined based on the relationship between sphericity and volume fraction. The bubble structure and the pore structure are then distinguished and verified based on the threshold.

[0041] 1. Sample preparation and Micro-CT scanning:

[0042] Using fine-grained coal as raw material, ultrastable foam was generated in a brine medium simulating industrial water quality. Then, different concentrations of surfactant (sodium dodecylbenzenesulfonate, SDBS) were added to obtain foam structures with different stability levels. The foam samples with different stability levels were non-destructively scanned using an X-ray microtomography system to obtain high-resolution grayscale images. Finally, the three-dimensional structure was reconstructed by a filtered back projection algorithm, and median filtering and interactive threshold segmentation were performed to extract pores and solid phases.

[0043] 2. Determination of representative subvolume (REV):

[0044] By gradually increasing the sampling volume, the stability of porosity as a function of volume is calculated, and the smallest subvolume (REV) that can represent the overall structural characteristics is determined, ensuring the statistical representativeness of subsequent analyses.

[0045] 3. FC Shape Classification:

[0046] The Feret Caliper method was used to measure the maximum Feret diameter (L), minimum width (S), and maximum diameter (l) perpendicular to S in three-dimensional space for each void. Based on the ratio of S / l to l / L, the voids were divided into five categories: Class 1 (rod-shaped), Class 2 (plate-shaped), Class 3 (cubic-piezoelectric), Class 4 (plate-shaped), and Class 5 (cubic-shaped). The number and volume distribution of each type of void were statistically analyzed to preliminarily identify possible bubble candidate regions (mainly concentrated in Class 5).

[0047] 4. Sphericity analysis and threshold determination:

[0048] First, according to the formula Calculate the sphericity (ψ) of each Class 5 void, where V is the void volume and A is the surface area; then, determine the bubble volume fraction (denoted as Rbubble) at different SDBS concentrations using the foam degassing method; sort the Class 5 voids in descending order starting from 1, and accumulate the volume fractions until they equal Rbubble. The sphericity at this point is the threshold (TH) for distinguishing between bubbles and pores; voids in Class 5 with a sphericity higher than TH are classified as bubbles, and the remaining Class 5 voids and all non-Class 5 voids are classified as pores.

[0049] 5. Verification and Comparative Analysis:

[0050] The same foam sample was characterized in two dimensions using an optical microscope and ImageJ image processing software to quantify the average diameter of the bubbles. The bubble size measured by Micro-CT and the microscope was compared to verify the reliability and consistency of the proposed method.

[0051] By combining the FC method with sphericity analysis, this embodiment overcomes the limitations of a single method in terms of boundary irregularities and morphological diversity, and achieves accurate differentiation between bubbles and pores.

[0052] Micro-CT-based non-destructive 3D imaging can completely preserve the internal structure of foam and avoid sampling bias.

[0053] REV analysis ensures statistical representativeness and improves the reliability and repeatability of results;

[0054] It can be used to verify traditional methods such as optical microscopy, forming a multi-scale, multi-modal analysis system;

[0055] This provides a reliable quantitative tool and theoretical basis for the study of the formation mechanism of ultrastable foam and the optimization of dehydration process.

[0056] Example 2

[0057] This embodiment provides a method for distinguishing pores and bubbles in ultrastable foam based on FC and sphericity analysis, including:

[0058] a) Sample preparation and Micro-CT 3D scanning;

[0059] b) Image reconstruction and gap extraction;

[0060] c) REV determination and representative volume selection;

[0061] d) FC shape classification;

[0062] e) Sphericity calculation and threshold determination;

[0063] f) Distinguishing and verifying bubbles and pores.

[0064] The specific process is as follows: a three-dimensional grayscale image of the ultra-stable foam is obtained based on Micro-CT scanning, and three-dimensional reconstruction is performed based on the grayscale image to extract the void region;

[0065] Representative sub-volumes are determined based on the characteristics of void volume variation, and void shapes are classified according to the Feret Caliper method.

[0066] The distinction threshold is determined based on the relationship between sphericity and volume fraction. The bubble structure and the pore structure are then distinguished and verified based on the threshold.

[0067] Furthermore, the Micro-CT scan is based on the foam structure obtained from sample preparation, wherein the foam stability is adjusted according to the surfactant concentration, and median filtering and threshold segmentation are performed according to the grayscale distribution range to obtain clear void boundaries.

[0068] Furthermore, during image reconstruction, three-dimensional voxel data is generated based on a filtering back projection algorithm; porous phase and solid phase are extracted based on voxel grayscale differences; isolated noise volumes are screened out based on voxel connectivity to obtain a continuous void network.

[0069] Furthermore, the determination of the representative sub-volume includes: calculating porosity based on different sampling volumes; determining the minimum sub-volume size based on the stable range of porosity variation; and verifying the representativeness of the statistical characteristics of the overall foam structure based on this sub-volume.

[0070] Furthermore, the Feret Caliper method includes: calculating the maximum Feret diameter, minimum width, and maximum vertical length based on the three-dimensional void boundary;

[0071] Based on the above parameter ratios, the voids are classified into five categories: rod-shaped, sheet-shaped, cuboid-shaped, plate-shaped, and cubic-shaped; and the candidate bubble types are determined based on the volume distribution.

[0072] Furthermore, the determination of sphericity includes: calculating sphericity based on the void volume and surface area;

[0073] Matching is performed based on the bubble volume fraction measured from the surfactant concentration;

[0074] The sphericity threshold TH is determined based on the cumulative volume fraction curve.

[0075] Furthermore, the distinction between bubbles and pores includes: classifying pores with a sphericity higher than the threshold TH as bubbles;

[0076] Voids below the threshold TH or non-cubic are identified as pores; and the ratio of bubbles to pores is calculated based on their respective volume fractions.

[0077] Furthermore, the threshold TH is determined by dynamically adjusting the surfactant concentration. When the concentration increases, TH is reduced according to the rightward shift of the sphericity distribution curve to maintain the bubble volume fraction consistent with the experimentally measured value.

[0078] The surfactant mentioned in step a) is sodium dodecylbenzenesulfonate (SDBS) with a concentration range of 0–3 mmol / L.

[0079] The FC classification described in step d) divides the voids into five categories, among which Class 5 is cubic, with the highest sphericity, and is the main candidate category for bubbles.

[0080] The sphericity threshold TH mentioned in step e) is determined by the cumulative volume fraction method and is dynamically adjusted as the SDBS concentration changes.

[0081] In step f), the results are verified using an optical microscope and ImageJ image processing.

[0082] This embodiment discloses a method for distinguishing pores and bubbles in ultrastable foam based on synergistic FC and sphericity analysis, belonging to the field of solid-liquid separation technology for flotation clean coal. This method acquires the three-dimensional structure of the foam using Micro-CT, classifies its shape using the FC method, dynamically distinguishes bubbles from pores by combining sphericity thresholding, and finally verifies the results using an optical microscope. This invention overcomes the limitations of traditional image processing methods in ultrastable foam structure analysis, achieving non-destructive, accurate, and repeatable quantification of bubbles and pores, providing reliable technical support for optimizing dewatering processes.

[0083] Example 3

[0084] This embodiment provides a method for distinguishing pores and bubbles in ultrastable foam based on FC and sphericity analysis, including:

[0085] Figure 2 The diagram shows the FC method and its main parameters according to an embodiment of the present invention (left image) and the classification standard diagram of the FC method's void shape (right image). Figure 2 As shown in the left-middle figure, the pore dimensions are derived in a global coordinate system (X, Y, Z axes). The FC method measures the maximum Feret diameter (L), minimum width (S) in the same plane, and maximum diameter (l) perpendicular to S for each pore in 100 orientations. The S / l ratio is defined as the flattening or elongation of the pore along its minor and intermediate axes. The closer this value is to 1, the closer the dimensions of S and l are to equiaxed dimensions; while a lower value indicates that the shape gradually becomes flatter or more elongated. Similarly, the l / L ratio is defined as the elongation along the intermediate and major axes. When this ratio is close to 1, the pore exhibits equiaxed dimensions in the l and L dimensions; while a decreasing ratio reflects a significant elongation along the major axis. Therefore, when both S / l and l / L values ​​are close to 1, the shape of the pore is closer to spherical. Thus, Class 5 represents a bubble.

[0086] Figure 3 This is a schematic diagram illustrating the quantification of foam volume according to an embodiment of the present invention. Figure 3 As shown, the volume characterization of bubbles in the flotation system is achieved through system analysis of foam. The total volume of foam collected from the flotation cell is denoted as V0. Different amounts of sodium dodecyl sulfate (SDBS) are added to V0, and the resulting foam volume is recorded as V1. Subsequently, sufficient SDBS is added until the foam is completely eliminated, and the remaining volume of the corresponding liquid-solid slurry component is recorded as V2. The reduction in the volume of air entrained in the bubbles is denoted as V3. Considering that this method cannot directly measure the liquid volume within the pores, the pore volume will not be calculated separately in this invention. The quantification of bubble volume follows the following basic relationship:

[0087] V3 = V1 - V2;

[0088] Given that the thickness of the liquid film is typically on the nanometer scale, the volume occupied by the bubble film is negligible. Therefore, the volume fraction of bubbles in the foam can be calculated as V3 / V1, and this key ratio is called R. bubble It can be used as a quantitative criterion for distinguishing between bubbles and stable foam in subsequent Micro-CT data analysis.

[0089] Figure 4 This is a graph showing the variation of porosity with sub-volume side length in REV analysis according to an embodiment of the present invention. Figure 4 As shown, the process of determining the representative unit volume (REV) begins with the analysis of a small initial region (134 μm on each side, corresponding to 10 pixels). The sampling size is then progressively increased up to 4289 μm (320 pixels), and the evolution of porosity with increasing size is assessed and presented by the change in the percentage of total porosity. Total porosity represents the combined volume fraction of bubbles and pores in the total sample volume. Figure 4 The data show that at smaller sampling scales, the total porosity fluctuates significantly, reflecting the influence of local structural heterogeneity. Once the critical side length is exceeded, the total porosity tends to stabilize within a narrow range, indicating that the REV state has been reached. For the 0 mmol / L SDBS system, the total porosity decreases with increasing sampling size, stabilizing at around 62% when the size exceeds 3000 μm. For the 0.5 mmol / L SDBS system, the total porosity increases with increasing sampling size, stabilizing at approximately 51% when the side length exceeds 2000 μm. For the 0.75 mmol / L SDBS system, the total porosity decreases with increasing sampling size, reaching a stable value of approximately 38% when the side length exceeds 1000 μm.

[0090] In the SDBS-modified system, the initial decrease in porosity observed at larger scales is because small sampling areas may accidentally predominantly cover the pore space, leading to an artificially high initial porosity value (approaching 100% in extreme cases). Based on this REV analysis, a cubic sample with a side length of 3016 μm (225 pixels) was finally selected, and the volume of this cubic sample (denoted as V) was calculated according to the following formula. sample For use in subsequent research:

[0091] V sample = L³ = 3016³ μm³;

[0092] Figure 5 This is a statistical distribution diagram of pore shape categories under different SDBS concentrations in an embodiment of the present invention. Figure 6 This is a volume distribution diagram of various types of voids in an embodiment of the present invention. Figure 5 (ac) in the figure shows the morphological distribution of pores (including voids and bubbles) at different SDBS concentrations, and the total number of pores at each shape level is as follows: Figure 5 (d) is shown in the middle. Figure 6 This reveals the pore volume at various shape levels. The combined number of pore types 3, 4, and 5 is the highest, accounting for over 98% of the total, while types 1 and 2 account for less than 2%. This distribution characteristic is consistent with... Figure 5 The cumulative number of pores in category d is consistent with the statistical results. Although categories 3, 4, and 5 constitute the majority of the pore count, it is important to note that bubbles are mainly distributed in category 5, not categories 3 and 4. The reasons are as follows: Figure 5 As shown in d, the addition of 0.5 mmol / L SDBS had minimal effect on the number of pores in categories 1, 2, 3, and 4 (change rates of -14%, -12%, -4%, and -3%, respectively, corresponding to an absolute decrease in number from 42 to 36, 73 to 64, 1146 to 1104, and 1496 to 1456), but significantly reduced the number of category 5 pores by 12% (from 4052 to 3556). This significant reduction in selectivity for category 5 pores is highly consistent with the decrease in foam volume after the addition of 0.5 mmol / L SDBS in Table 2. The decrease in foam volume is mainly due to bubble bursting, therefore it can be inferred that bubbles are mainly present in category 5. When the SDBS concentration increased to 0.75 mmol / L, the cumulative number of pores ( Figure 5 d) and the fifth type of pore volume ( Figure 6 All showed a further decrease, with the number of Category 5 bubbles decreasing by another 35% (from 3556 to 2299), further confirming that bubbles are mainly concentrated in Category 5. Therefore, in the coal-derived foam system, bubbles are mainly enriched in Category 5 shapes.

[0093] Figure 7 This is a diagram showing the results of distinguishing between bubbles and pores based on sphericity in an embodiment of the present invention. Figure 7 This paper demonstrates the results of distinguishing air bubbles in foam using critical sphericity values ​​calculated based on the following method. First, the individual volumes and sphericity values ​​of all data points for Class 5 pores were obtained using Excel software, and the data points were sorted by volume in descending order of sphericity value. Data points with perfect sphericity (ψ=1) were selected, and their total volume was calculated. This volume was then compared with the previously obtained V... sample Normalization is performed to obtain the ratio. This ratio is then accumulated in descending order of sphericity, until the accumulated value reaches the corresponding R mentioned earlier. bubble When the value is set, the corresponding sphericity is the threshold (TH value). Data points with sphericity between 1 and TH in category 5 are classified as bubbles, while the remaining data points (including all data points not in category 5) are classified as pores. In this experiment, the TH values ​​corresponding to the 0 mmol / L, 0.5 mmol / L, and 0.75 mmol / L SDBS addition concentrations were determined to be 0.64, 0.57, and 0.63, respectively.

[0094] Figure 8This is a foam image under an optical microscope according to an embodiment of the present invention, along with its preprocessing and segmentation results. Figure 9 This is a comparison diagram of the average diameter of bubbles measured by Micro-CT and microscope according to an embodiment of the present invention. Figure 8 The images show raw microscopic images (a), preprocessed images (b), and segmented images (c) of foams after the addition of 0, 0.5, and 0.75 mmol / L SDBS. Figure 8 (a) After image preprocessing and segmentation, the following results are obtained: Figure 8 (b) and Figure 8 The result is shown in (c). Based on the data obtained from the segmented image, a plot is drawn. Figure 9 This study aims to demonstrate the bubble size distribution and cumulative number of bubbles at different SDBS concentrations. The bubble size distribution in the foam showed a significant correlation with the SDBS concentration. At 0 mmol / L SDBS, the bubble size exhibited a unimodal distribution with a wide main peak range (65-95 μm), and the frequency of three consecutive adjacent size ranges exceeded 10%. When the SDBS concentration increased to 0.5 mmol / L, the distribution shifted to a distinct bimodal pattern: the main peak was located in the 60-69.9 μm range (10.12%), and the secondary peak was located in the 80.0-89.9 μm range (10.39%). When the SDBS concentration reached 0.75 mmol / L, the distribution curve flattened, the peak intensity weakened, and the frequency of larger bubbles increased. A significant decrease in the cumulative number of bubbles was also observed at the highest SDBS concentration.

[0095] On the other hand, this embodiment also provides an electronic device, including a memory, a processor, and a computing program stored in the memory and executable on the processor, wherein the processor implements the method when executing the computing program.

[0096] On the other hand, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.

[0097] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for distinguishing pores and bubbles in ultrastable foam based on FC and sphericity analysis, characterized in that, include: A three-dimensional grayscale image of the ultra-stable foam is obtained by Micro-CT scanning, and three-dimensional reconstruction is performed based on the grayscale image to extract the void region; Representative sub-volumes are determined based on the characteristics of void volume variation, and void shapes are classified according to the Feret Caliper method. A distinction threshold is determined based on the relationship between sphericity and volume fraction. Bubbles and pore structures are then distinguished and verified based on the threshold. The determination of the representative sub-volume includes: calculating porosity based on different sampling volumes; determining the minimum sub-volume size based on the stable range of porosity variation; and verifying the representativeness of the statistical characteristics of the overall foam structure based on this sub-volume. The Feret Caliper method includes: calculating the maximum Feret diameter, minimum width, and maximum vertical length based on the three-dimensional void boundary; Based on parameter ratios, voids are classified into five categories: rod-shaped, sheet-shaped, cuboid-shaped, plate-shaped, and cubic-shaped; and candidate bubble types are determined based on volume distribution. Determining sphericity includes: calculating sphericity based on void volume and surface area; Matching is performed based on the bubble volume fraction measured from the surfactant concentration; The sphericity threshold TH is determined based on the cumulative volume fraction curve. The distinction between bubbles and pores includes: pores with a sphericity higher than the threshold TH are identified as bubbles; Voids below the threshold TH or non-cubic are identified as pores; and the ratio of bubbles to pores is calculated based on their respective volume fractions.

2. The method according to claim 1, characterized in that, The Micro-CT scan is based on the foam structure obtained from the sample preparation. The foam stability is adjusted according to the surfactant concentration, and then median filtering and threshold segmentation are performed according to the grayscale distribution range to obtain clear void boundaries.

3. The method according to claim 1, characterized in that, In the image reconstruction process, three-dimensional voxel data are generated according to the filtering back projection algorithm; the porous phase and solid phase are extracted according to the voxel gray-level differences; and isolated noise volumes are screened out according to the voxel connectivity to obtain a continuous void network.

4. The method according to claim 1, characterized in that, The threshold TH is determined by dynamically adjusting the surfactant concentration. When the concentration increases, TH is reduced according to the rightward shift of the sphericity distribution curve to maintain the bubble volume fraction consistent with the experimentally measured value.

5. An electronic device comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that, When the processor executes the computing program, it implements the method according to any one of claims 1-4.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-4.

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