Method for determining catalyst pore defects

Abstract

The application discloses a method for measuring catalyst pore defects, which comprises the following steps: mixing molten paraffin with a catalyst, stirring and ultrasonic dispersing simultaneously; rapidly cooling the mixture of the paraffin and the catalyst by using liquid nitrogen; analyzing the three-dimensional structure of the catalyst by using X-ray computed tomography; processing the three-dimensional scanning original data by using data processing software, and the program is sequentially median filtering, threshold segmentation, boundary particle removal, filling the catalyst image holes by using a morphological hole filling algorithm, particle marking and screening, taking an image difference set, and statistically calculating data, and finally obtaining the catalyst pore rate. Compared with the traditional measurement method, the method can more accurately observe the catalyst structure from the three-dimensional angle, and provides data for the manufacture and evaluation of the catalyst.

Description

Technical Field

[0001] This invention relates to a method for determining pore defects in catalysts, which can be used to detect product qualification in catalyst production. Background Technology

[0002] Catalytic cracking is a core process in oil refining. Catalytic cracking catalysts are fine particles with a size distribution of 20–100 μm, primarily prepared via spray drying. The sphericity and particle size distribution of the catalyst are crucial physical properties affecting its quality; ideally, the catalyst consists of spherical particles with a normally distributed diameter. Due to limitations in domestic spray forming technology, the shape and size of the catalyst are very difficult to control. After spray drying, the catalyst particles are uneven in size, and many catalyst spheres form pits, cavities, and irregular shapes, which significantly impact the catalyst's fluidization performance, reactivity, and product distribution. However, characterization methods for defects such as pores in catalytic cracking catalysts are currently limited.

[0003] Zhang Yi et al. used scanning electron microscopy to observe the pore defects of catalyst particles in their paper "Sphericity and Particle Size Distribution Control Method of FCC Catalyst" (Petroleum Refining and Chemical Engineering, 2022, 53(3):31-36). The observation of scanning electron microscopy is based on two-dimensional image analysis, which limits the number of particles that can be analyzed and cannot observe the type of catalyst particles that are hollow inside and not connected to the outside.

[0004] In her paper "Establishment of Morphology Analysis Methods for Catalytic Cracking Catalysts" (Industrial Catalysis, 2020, 28(3):73-77), Guo Yaoqing used dynamic digital imaging, static imaging, and microscopy to analyze the morphology of catalytic cracking catalysts. These methods are all based on two-dimensional projection image analysis and cannot observe the pores inside the catalyst particles. This technique is mainly used to analyze the diameter distribution of catalyst particles.

[0005] Both GB / T 21649.1 "Particle size analysis - Image analysis method - Part 1: Static image analysis method" and GB / T 38879 "Particle size analysis - Color image analysis method" use two-dimensional image analysis technology to analyze the shape characteristics of particles, but cannot observe the pores inside the catalyst particles.

[0006] "Three-dimensional reconstruction of porous media by X-CT images" (Advances in permeation mechanics, 2014, 4(4): 59-64), technical description: The three-dimensional structure of foam was reconstructed using ImageJ software and X-ray computed tomography (X-CT); the defects of this technology or the shortcomings of this invention: this method is only applicable to foam materials and not to catalytic cracking catalysts. Summary of the Invention

[0007] The purpose of this invention is to provide a method for determining pore defects in catalysts, which refer to pits or cavities formed on the surface or inside the catalyst during the production process. This invention utilizes three-dimensional data to calculate pore defects in the catalyst, solving the problem that two-dimensional image analysis cannot observe internal catalyst defects; and by mixing paraffin with the catalyst, it solves the problem of large computational load and inaccuracies caused by catalyst particles adhering to each other.

[0008] To achieve the above objectives, the present invention provides a method for determining the porosity defects of a catalyst, the method comprising the following steps:

[0009] ① Heat and melt paraffin wax with a mass of m1, mix it with catalyst with a mass of m2, and simultaneously stir and ultrasonically disperse it;

[0010] ②Use liquid nitrogen to rapidly cool the mixture of paraffin and catalyst;

[0011] ③ Use an X-ray computed tomography scanner to perform an all-round scan of the mixture to obtain the raw three-dimensional scan data of the catalyst;

[0012] ④ Use data processing software to process the raw 3D scan data. The program consists of median filtering, threshold segmentation, and boundary particle removal. The data after boundary particle removal is denoted as A_ts.

[0013] ⑤ The holes in the catalyst image are filled using a morphological hole-filling algorithm, and the processed data is denoted as B_fh;

[0014] ⑥ Use the watershed algorithm to segment the data B_fh and label each particle to obtain the catalyst particle image data after the catalyst particles are separated, and the data is denoted as B_fh2; filter the data B_fh2, count according to particle size, remove broken particles in the catalyst, and relabel each particle, and the data is denoted as C.

[0015] ⑦ Take the difference between the data B_fh filled with holes and the data A_ts that are not filled to obtain the filled image data. Use the watershed algorithm to segment the difference data, filter out holes with too few pixels in the data, and denote the remaining data as F.

[0016] ⑧ In data C and data F, set an interval distance d, and take an image slice at each interval distance d to obtain the selected image slice data C_d and F_d respectively; according to the selected image slices, count the number of particles in each slice of C_d and F_d respectively, and the ratio of the total number of particles in each slice of F_d to the total number of particles in each slice of C_d is the porosity Cc.

[0017] In the method for determining catalyst pore defects of the present invention, the number of image slices for C_d and F_d is the same, both being m, and the formula for calculating the porosity Cc is: a i b represents the total number of all particles in the selected i-th image slice in the F_d data. j This represents the total number of all particles in the j-th image slice selected in the C_d data.

[0018] In the method for determining catalyst pore defects of the present invention, the ratio of m1 to m2 is in the range of 2-20.

[0019] The method for determining catalyst pore defects of the present invention involves ultrasonic dispersion with an ultrasonic power of 60 watts to 120 watts, slow addition of the catalyst to molten paraffin while continuously stirring, and immediate cooling with liquid nitrogen after completion.

[0020] In the method for determining catalyst pore defects of the present invention, in step ①, the temperature at which the paraffin is melted is 50-70℃.

[0021] The method for determining catalyst pore defects of the present invention uses an X-ray computed tomography scanner with a minimum spatial resolution of 1 micrometer and a minimum pixel resolution of 500 nanometers.

[0022] The method for determining catalyst pore defects of the present invention, wherein the morphological pore filling algorithm is: X k =(X k-1 ⊕B)∩A c k = 1, 2, 3..., where the symbol ⊕ represents dilation, the superscript C represents the complement, A is the set of image pixels, B is a 4-connected structural unit, and k is the pixel subscript. The pixels are iteratively processed to identify holes.

[0023] The method for determining catalyst pore defects in this invention involves filtering data B_fh2 by: statistically analyzing fragmented particles with a diameter less than 10 micrometers and a volume less than 1000 cubic micrometers, and deleting image information of these particles from the data.

[0024] The method for determining catalyst pore defects of the present invention, wherein the value of the interval distance d is greater than or equal to 100 micrometers.

[0025] The method for determining catalyst pore defects of the present invention includes the following processing procedures for the raw three-dimensional scanning data: median filtering, threshold segmentation, boundary particle removal, pore filling, particle marking and screening, image difference extraction, interval image extraction, and statistical calculation.

[0026] The method for determining pore defects in a catalyst according to the present invention includes, but is not limited to, at least one of a fresh catalyst, a regenerated catalyst, a spent catalyst, and a waste catalyst.

[0027] The method for determining catalyst pore defects of the present invention does not particularly limit the type of catalyst. The catalyst to be measured can be selected according to the method of the present invention, and the catalyst can be, for example, but not limited to, a catalytic cracking catalyst or a catalytic pyrolysis catalyst.

[0028] This invention first uses paraffin wax to completely disperse the catalyst, avoiding the problems of large computational load and inaccurate pattern cutting caused by catalyst particles sticking together. Then, X-ray computed tomography is used to measure the three-dimensional data of the catalyst. Finally, the pore defects of the catalyst are calculated through data processing. Compared with traditional measurement methods, the method of this invention can observe the catalyst structure more accurately from a three-dimensional perspective, providing data for catalyst manufacturing and evaluation.

[0029] The method of this invention utilizes X-ray computed tomography to analyze the three-dimensional structure of catalysts, overcoming the limitation of traditional analysis which can only observe the surface morphology of catalysts. By mixing paraffin with the catalyst, it solves the problem of inaccurate calculations caused by catalyst particles adhering to each other. The watershed algorithm is used to segment the particles, preventing a small number of adhered particles from being mistaken for single particles in image recognition. A morphological hole-filling algorithm is employed to fill holes in the catalyst image, offering simplicity and high efficiency. Attached Figure Description

[0030] Figure 1a This is a three-dimensional distribution diagram of the catalytic cracking catalyst-1 in Example 1.

[0031] Figure 1b This is the 900th image slice of data A_ts-1 from Example 1.

[0032] Figure 1c This is a slice image of the 900th image from data B_fh-1 in Example 1.

[0033] Figure 1d This is a slice of the 757th image from data C-1 in Example 1.

[0034] Figure 1e This is an image showing the effect of the filled holes in Example 1 overlapping with the original data.

[0035] Figure 2a This is a slice image of the 925th image from data A_ts-2 in Example 1.

[0036] Figure 2b This is a slice image of the 925th image from data B_fh-2 in Example 1.

[0037] Figure 2c This is a slice of the 925th image from data C-2 in Example 1.

[0038] Figure 2d This is an image showing the effect of the filled holes in Example 1 overlapping with the original data.

[0039] Figure 3a This is a diagram showing the original data effect of Example 3.

[0040] Figure 3b This is a slice of the 1001st image from data B_fh-3 in Example 3.

[0041] Figure 3c This is a diagram showing the hole recognition effect of the data in Example 3.

[0042] Figure 3d This is a three-dimensional view of the hole distribution in Example 3.

[0043] Figure 4a This is a scanning electron microscope image of Comparative Example 1. Detailed Implementation

[0044] The present invention will be further described in detail below with reference to specific embodiments, but the present invention is not limited to the following embodiments. Any modifications that do not depart from the concept and scope of the present invention are within the scope of the present invention.

[0045] X-ray computed tomography scanner with a maximum spatial resolution of 500 nanometers and a maximum pixel resolution of 300 nanometers.

[0046] Ultrasonic cleaner, maximum power 150 watts, maximum heating temperature 100℃.

[0047] Catalyst-1: Catalytic cracking catalyst-1, PetroChina Petroleum & Chemical Research Institute.

[0048] Catalyst-2: Catalytic cracking catalyst-2, PetroChina Petroleum & Chemical Research Institute.

[0049] Catalyst-3: Catalytic cracking catalyst-3, PetroChina Petroleum & Chemical Research Institute.

[0050] Example 1:

[0051] The ultrasonic cleaner was set to a power of 120 watts and a heating temperature of 70°C. 10 grams of paraffin wax was heated and melted. Using a plastic dropper, 5 grams of catalytic cracking catalyst-1 were slowly added to the paraffin wax in multiple batches, while simultaneously stirring and ultrasonically dispersing.

[0052] After the catalyst is added, the mixture of paraffin and catalytic cracking catalyst-1 is immediately cooled rapidly with liquid nitrogen.

[0053] The X-ray computed tomography (CT) scanner was configured with a spatial resolution better than 1 micrometer and a pixel resolution better than 500 nanometers. The mixture was scanned from all directions using the X-ray CT scanner to obtain the raw three-dimensional scanning data of the catalyst. The three-dimensional distribution results of the catalyst are shown below. Figure 1a As shown, there are a total of 2199 slices.

[0054] The raw 3D scan data was processed using data processing software. The program sequentially performed median filtering, threshold segmentation, and boundary particle removal. The data after boundary particle removal is denoted as A_ts-1. Data from the 900th image slice of A_ts-1 was extracted, and the result is as follows. Figure 1b As shown. A morphological hole-filling algorithm was used to fill the holes in the catalyst image. The processed data is denoted as B_fh-1. Data from the 900th image slice of B_fh-1 was extracted, and the result is shown below. Figure 1c As shown. The morphological hole-filling algorithm is as follows, X k =(X k-1 ⊕B)∩A c Let k = 1, 2, 3..., where the symbol ⊕ represents dilation, the superscript C represents the complement, A is the set of image pixels, B is a 4-connected structural unit, and k is the pixel subscript. Iterative processing is performed on the pixels to identify holes. The above formula is substituted into 2199 slices, and iterative calculations are performed on each image.

[0055] The watershed algorithm was used to segment the data B_fh-1 and label each particle, resulting in image data of the separated catalyst particles, denoted as B_fh-2. Data B_fh-2 was then filtered, and fragmented particles with a diameter less than 10 micrometers and a volume less than 1000 cubic micrometers were removed from the data to eliminate fragmented particles from the catalyst. Each particle was relabeled, denoted as C-1, and the 757th image slice was selected for display. The results are as follows. Figure 1d As shown, different particles are distinguished by color.

[0056] The difference between the data B_fh-1 filled with holes and the unfilled data A_ts-1 is taken to obtain the filled image data. The watershed algorithm is then used to segment the difference data, filtering out holes with too few pixels. The remaining data is denoted as F-1. The result is as follows: Figure 1e As shown, bright blue represents filled pores, and dark blue represents the original particles.

[0057] In data C-1 and data F-1, the interval distance d is set to 100 micrometers. An image slice is taken at every 100-micrometer interval, resulting in the selected image slice data C_d-1 and F_d-1, respectively. Both datasets contain the same number of slices: 1020 slices. The formula for calculating the porosity Cc is: Where m = 1020; a i This represents the total number of particles in the i-th slice image of the F_d-1 data, and is statistically significant. b j This represents the total number of particles in the j-th slice image of the C_d-1 data, statistically... The calculated value is Cc = 10.94%.

[0058] Example 2:

[0059] The ultrasonic cleaner was set to a power of 60 watts and a heating temperature of 50°C. 80 grams of paraffin wax was heated and melted. Using a plastic dropper, 4 grams of catalytic cracking catalyst-2 were slowly added to the paraffin wax in multiple batches, while simultaneously stirring and ultrasonically dispersing.

[0060] After the catalyst was added, the mixture of paraffin and catalytic cracking catalyst-2 was immediately cooled rapidly with liquid nitrogen.

[0061] The X-ray computed tomography scanner was configured with a spatial resolution better than 500 nanometers and a pixel resolution better than 400 nanometers. The mixture was scanned from all directions using the X-ray computed tomography scanner to obtain the raw three-dimensional scanning data of the catalyst.

[0062] The raw 3D scan data was processed using data processing software. The program sequentially performed median filtering, threshold segmentation, and boundary particle removal. The data after boundary particle removal is denoted as A_ts-2. Data from the 925th image slice of data A_ts-2 was extracted, and the result is as follows. Figure 2a As shown. A morphological hole-filling algorithm was used to fill the holes in the catalyst image. The processed data is denoted as B_fh-2. Data from the 925th image slice of data B_fh-2 was extracted, and the result is shown below. Figure 2b As shown.

[0063] The watershed algorithm was used to segment the data B_fh-2, and each particle was labeled to obtain the image data of the separated catalyst particles, denoted as B_fh2-2. Data B_fh2-2 was then filtered, and fragmented particles with a diameter less than 10 micrometers and a volume less than 1000 cubic micrometers were removed from the data. Each particle was relabeled, denoted as C-2, and the 925th image slice was selected. The result is as follows. Figure 2c As shown, different particles are distinguished by color.

[0064] The difference between the hole-filled data B_fh-2 and the unfilled data A_ts-2 is taken to obtain the filled image data. The watershed algorithm is used to segment the difference data, filtering out holes with too few pixels. The remaining data is denoted as F-2. The 801st slice of the F-2 data is overlaid with data A_ts-2. Bright blue represents the filled holes, and dark blue represents the original particles. The result is as follows. Figure 2d As shown.

[0065] In data C-2 and data F-2, the interval distance d is set to 120 micrometers. An image slice is taken at every 120-micrometer interval, resulting in the selected image slice data C_d-2 and F_d-2, respectively. Both datasets contain the same number of slices: 951 slices. The formula for calculating the porosity Cc is: Where m = 951; a i This represents the total number of particles in the i-th slice image of the F_d-2 data, and is statistically significant. b j This represents the total number of particles in the j-th slice image of the C_d-2 dataset, statistically... The calculated value is Cc = 9.33%.

[0066] Example 3:

[0067] The ultrasonic cleaner was set to a power of 90 watts and a heating temperature of 65°C. 16 grams of paraffin wax was heated and melted. Using a plastic dropper, 4.5 grams of catalytic cracking catalyst-3 were slowly added to the paraffin wax in multiple batches, while stirring and ultrasonically dispersing were performed simultaneously.

[0068] After the catalyst was added, the mixture of paraffin and catalytic cracking catalyst-3 was immediately cooled rapidly with liquid nitrogen.

[0069] The X-ray computed tomography (CT) scanner was set to a spatial resolution of 800 nm and a pixel resolution of 400 nm. The mixture was scanned from all directions using the CT scanner to obtain the raw three-dimensional scan data of the catalyst, totaling 1975 slices. Slice number 781 is shown below. Figure 3a As shown.

[0070] The raw 3D scan data was processed using data processing software. The program sequentially performed median filtering, threshold segmentation, and boundary particle removal. The data after boundary particle removal is denoted as A_ts-3. A morphological hole-filling algorithm was then used to fill the holes in the catalyst image, and the processed data is denoted as B_fh-3. The morphological hole-filling algorithm is as follows: X k =(X k-1 ⊕B)∩A ck = 1, 2, 3..., where the symbol ⊕ represents dilation, the superscript C represents the complement, A is the set of image pixels, B is a 4-connected structural unit, and k is the pixel subscript. Iterative processing is performed on the pixels to identify holes. Data from the 1001st image slice of data B_fh-3 is extracted, and the result is as follows... Figure 3b As shown.

[0071] The watershed algorithm was used to segment the data B_fh-3, and each particle was labeled to obtain the catalyst particle image data after particle separation, denoted as B_fh-3-2. Data B_fh-3-2 was then filtered, and fragmented particles with a diameter less than 10 micrometers and a volume less than 1000 cubic micrometers were removed from the data. Each particle was then relabeled, and the data was denoted as C-3.

[0072] The difference between the data B_fh-3 filled with holes and the data A_ts-3 without holes is taken to obtain the filled image data, as shown in the figure. Figure 3c As shown, holes are marked with color. The watershed algorithm is used to segment the difference set data, filtering out holes with too few pixels after segmentation; the remaining data is denoted as F-3. The three-dimensional distribution of data F-3 is shown below. Figure 3d As shown.

[0073] In data C-3 and data F-3, the interval distance d is set to 110 micrometers. An image slice is taken at every 110-micrometer interval, resulting in the selected image slice data C_d-3 and F_d-3, respectively. Both datasets contain the same number of slices: 877 slices. The formula for calculating the porosity Cc is: Where m = 877; a i This represents the total number of particles in the i-th slice image of the F_d-3 data, statistically... b j This represents the total number of particles in the j-th slice image of the C_d-3 data, statistically... The calculated value is Cc = 8.60%.

[0074] Comparative Example 1:

[0075] The surface morphology of catalytic cracking catalyst-1 was observed using scanning electron microscopy (SEM). Catalyst particles were adhered to a conductive adhesive. The SEM was set to 85x magnification, using a secondary electron signal in low-magnification mode. The acquired images are shown below. Figure 4a As shown.

[0076] As can be seen from the image, the catalyst spheres have many holes in their center, resembling an apple shape. Because scanning electron microscopy (SEM) images are two-dimensional, the distribution of these holes is random, making it impossible to identify holes located at the bottom of the spheres during statistical analysis. Furthermore, some holes are located inside the catalyst particles and are not connected to the outside; these holes also cannot be identified using two-dimensional images. In summary, identifying holes using two-dimensional images has a high error rate.

[0077] from Figure 4a As can be seen, many catalyst particles are stuck together or overlap. These stuck particles will be identified as the same particle when watershed segmentation is performed, which will also increase the statistical error.

[0078] In summary, the results of Example 1 and Comparative Example 1 show that, compared with the traditional scanning electron microscopy measurement method, the method of the present invention can more accurately observe the catalyst structure from a three-dimensional perspective, providing data for the manufacture and evaluation of catalysts.

[0079] Of course, the present invention may have other embodiments and variations. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and variations according to the present invention, but these corresponding changes and variations should all fall within the protection scope of the claims of the present invention.

Claims

1. A method for determining pore defects in a catalyst, characterized in that, Includes the following steps: ① Heat and melt paraffin wax with a mass of m1, mix it with catalyst with a mass of m2, and simultaneously stir and ultrasonically disperse it; ②Use liquid nitrogen to rapidly cool the mixture of paraffin and catalyst; ③ Use an X-ray computed tomography scanner to perform an all-round scan of the mixture to obtain the raw three-dimensional scan data of the catalyst; ④ Use data processing software to process the raw 3D scan data. The program consists of median filtering, threshold segmentation, and boundary particle removal. The data after boundary particle removal is denoted as A_ts. ⑤ The holes in the catalyst image are filled using a morphological hole-filling algorithm, and the processed data is denoted as B_fh; ⑥ Use the watershed algorithm to segment the data B_fh and label each particle to obtain the catalyst particle image data after the catalyst particles are separated, and the data is denoted as B_fh2; filter the data B_fh2, count according to particle size, remove broken particles in the catalyst, and relabel each particle, and the data is denoted as C. ⑦ Take the difference between the data B_fh filled with holes and the data A_ts that are not filled to obtain the filled image data. Use the watershed algorithm to segment the difference data, filter out holes with too few pixels in the data, and denote the remaining data as F. ⑧ In data C and data F, set an interval distance d, and take an image slice at each interval distance d to obtain the selected image slice data C_d and F_d respectively; according to the selected image slices, count the number of particles in each slice of C_d and F_d respectively, and the ratio of the total number of particles in each slice of F_d to the total number of particles in each slice of C_d is the porosity Cc.

2. The method for determining catalyst pore defects according to claim 1, characterized in that, The number of image slices for C_d and F_d is the same, m. The formula for calculating the porosity Cc is: a i b represents the total number of all particles in the selected i-th image slice in the F_d data. j This represents the total number of all particles in the j-th image slice selected in the C_d data.

3. The method for determining catalyst pore defects according to claim 1, characterized in that, The ratio of m1 to m2 ranges from 2 to 20.

4. The method for determining catalyst pore defects according to claim 1, characterized in that, The ultrasonic dispersion is performed with an ultrasonic power of 60-120 watts. The catalyst is slowly added to the molten paraffin while continuously stirring, and then immediately cooled with liquid nitrogen after completion.

5. The method for determining catalyst pore defects according to claim 1, characterized in that, In step ①, the temperature at which the paraffin wax melts is 50-70℃.

6. The method for determining catalyst pore defects according to claim 1, characterized in that, The X-ray computed tomography scanner has a minimum spatial resolution of 1 micrometer and a minimum pixel resolution of 500 nanometers.

7. The method for determining catalyst pore defects according to claim 1, characterized in that, The morphological hole-filling algorithm is: X k =(X k-1 ⊕B)∩A c k = 1, 2, 3..., where the symbol ⊕ represents dilation, the superscript C represents the complement, A is the set of image pixels, B is a 4-connected structural unit, and k is the pixel subscript. The pixels are iteratively processed to identify holes.

8. The method for determining catalyst pore defects according to claim 1, characterized in that, The method for filtering data B_fh2 is as follows: count fragmented particles with a diameter of less than 10 micrometers and a volume of less than 1000 cubic micrometers, and delete the image information of these particles from the data.

9. The method for determining catalyst pore defects according to claim 1, characterized in that, The value of the interval distance d is greater than or equal to 100 micrometers.

10. The method for determining catalyst pore defects according to claim 1, characterized in that, The processing procedure for the raw 3D scan data is as follows: median filtering, threshold segmentation, boundary particle removal, hole filling, particle marking and filtering, image difference extraction, interval image extraction, and statistical calculation.

11. The method for determining catalyst pore defects according to claim 1, characterized in that, The catalyst includes at least one of fresh catalyst, regenerator, recycled catalyst, and spent catalyst.

12. The method for determining catalyst pore defects according to claim 1, characterized in that, The catalyst includes a catalytic cracking catalyst or a catalytic pyrolysis catalyst.

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