Intelligent detection device and method for geometric size of denitration honeycomb catalyst

Abstract

The present disclosure provides a denitration honeycomb catalyst geometry intelligent detection device and method, by integrating a sample bearing and positioning module, a visual measurement system, a length measurement system, a weighing system, and a data processing and storage system, the full-process automation and intelligent operation of the catalyst sample from code tracing, multi-directional image acquisition, automatic measurement of key geometric dimensions, quality weighing to data binding storage is realized. The device and method solve the problems of large deviation, low efficiency and poor data representativeness in manual measurement, significantly reduce the dispersion of measurement data, reduce the measurement standard deviation of key dimensions such as outer wall, inner wall and pore size by an order of magnitude, and achieve the technical effects of improving measurement accuracy and efficiency, ensuring data traceability, and providing reliable basic data for denitration catalyst performance evaluation.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, and in particular to an intelligent detection device and method for the geometric dimensions of denitrification honeycomb catalysts. Background Technology

[0002] The geometric characteristics of honeycomb catalysts include appearance, geometric dimensions, geometric specific surface area, porosity, and unit density. Geometric dimension measurement is the basis for evaluating whether the catalyst product meets the design parameters and for conducting performance testing under flue gas conditions.

[0003] The length and cross-sectional dimensions of the catalyst unit must be accurate to within 1 mm, and the outer wall thickness, pore diameter d, and pitch p must be accurate to within 0.1 mm. Currently, manual measurements are performed using tools such as steel tape measures, vernier calipers, and balances. The testing points must be distributed evenly and dispersed.

[0004] Besides the unit length and cross-sectional dimensions, the number of measurement points for other test items should be no less than 10, and the final result should be the average. Due to the large number of measurement holes, there are problems such as large measurement deviations by different personnel and poor representativeness of test data. Summary of the Invention

[0005] A first aspect of this disclosure provides an intelligent detection device for the geometric dimensions of a denitrification honeycomb catalyst, comprising: The sample carrying and positioning module is used to place and position the honeycomb catalyst sample to be tested; A visual measurement system includes at least one smart camera configured to capture images of the end face of a catalyst sample. The smart camera is connected to an image processor and is used to automatically identify and measure at least one of the following based on the end face images: cross-sectional dimensions, total number of pores, number of blocked pores, pore diameter, inner wall thickness, and outer wall thickness of the catalyst sample. A length measurement system for automatically measuring the length of the catalyst sample; A weighing system is used to obtain the mass of the catalyst sample; A data processing and storage system is used to receive and integrate measurement data from the vision measurement system, length measurement system, and weighing system, and generate an inspection report containing at least geometric dimensions and volume density information.

[0006] In conjunction with the first aspect, the sample carrying and positioning module includes: The product placement platform and product positioning mechanism are used to achieve the initial positioning of the catalyst sample before measurement.

[0007] In conjunction with the first aspect, the visual measurement system also includes a side-view camera for capturing side images of the catalyst sample to detect the sample's levelness or deformation.

[0008] In conjunction with the first aspect, the visual measurement system is configured to acquire and measure end-face images of the windward and leeward sides of the catalyst sample, respectively.

[0009] In conjunction with the first aspect, the length measurement system includes a laser sensor or a displacement sensor connected to the data processing and storage system, and acquires length data by pushing a measuring push rod to fit against the end face of the catalyst sample.

[0010] In conjunction with the first aspect, it also includes a coding and marking module, which includes: Laser marking machines are used to mark unique identification codes on the surface of catalyst samples; A barcode scanner is used to read the identification code; The data processing and storage system binds and stores the read identification code with the corresponding catalyst sample's detection data and image to achieve sample traceability. The identification code is a high-temperature resistant QR code or numerical code.

[0011] In conjunction with the first aspect, the data processing and storage system is also configured to automatically calculate the bulk density of the catalyst sample based on measured geometric dimensions and mass data.

[0012] A second aspect of this disclosure provides an intelligent detection method for the geometric dimensions of a denitrification honeycomb catalyst, comprising: S1: Place the honeycomb catalyst sample in the sample carrying and positioning module and position it accordingly; S2: Obtain an end face image of the sample through the vision measurement system, and automatically measure at least one of the following based on the image: cross-sectional dimension, total number of holes, number of plugged holes, hole diameter, inner wall thickness, and outer wall thickness; S3: The length of the sample is automatically measured by the length measurement system; S4: Obtain the mass of the sample using the weighing system; S5: Integrate the measurement data from steps S2, S3, and S4 through the data processing and storage system to generate a test report.

[0013] In conjunction with the second aspect, prior to step S1, the method further includes: marking a unique identification code on the sample surface using a laser marking machine, and recording the identification code into the data processing and storage system using a barcode scanner.

[0014] In conjunction with the second aspect, in step S2, end face image acquisition and measurement are performed on the windward and leeward sides of the sample respectively. The visual measurement system also acquires images of the side of the sample to detect its levelness or deformation. The number of measurement points for the aperture, inner wall thickness, and outer wall thickness is not less than 10, and the average value is calculated as the final result.

[0015] Beneficial Effects: This disclosure provides an intelligent detection device and method for the geometric dimensions of denitrification honeycomb catalysts. By integrating a sample carrying and positioning module, a vision measurement system, a length measurement system, a weighing system, and a data processing and storage system, it achieves fully automated and intelligent operation of the catalyst sample from coding and traceability, multi-directional image acquisition, automatic measurement of key geometric dimensions, mass weighing, to data binding and storage. This device and method solve the problems of large deviations, low efficiency, and poor data representativeness in manual measurement, significantly reducing the dispersion of measurement data. It reduces the standard deviation of key dimensions such as outer wall, inner wall, and pore size by an order of magnitude, achieving the technical effects of improving measurement accuracy and efficiency, ensuring data traceability, and providing reliable basic data for the performance evaluation of denitrification catalysts. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of an intelligent detection device for the geometric dimensions of a denitrification honeycomb catalyst according to an embodiment of this disclosure; Figure 2 This is a top view of an intelligent detection device for the geometric dimensions of a denitrification honeycomb catalyst according to an embodiment of this disclosure; Figure 3 This is a schematic flowchart of an intelligent detection method for the geometric dimensions of a denitrification honeycomb catalyst according to an embodiment of this disclosure. Detailed Implementation

[0017] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those disclosed herein.

[0018] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0019] Figure 1 This is a schematic diagram of the structure of an intelligent detection device for the geometric dimensions of a denitrification honeycomb catalyst according to an embodiment of the present disclosure, including: The sample carrying and positioning module is used to place and position the honeycomb catalyst sample to be tested; The sample carrying and positioning module includes: The product placement platform 110 and the product positioning mechanism (not shown in the figure) are used to achieve the initial positioning of the catalyst sample before measurement.

[0020] This module, comprising the product placement platform 110 and a product positioning mechanism, serves as the starting point for automated measurement. The operator places the honeycomb catalyst sample to be tested on the product placement platform, and the product positioning mechanism automatically or assisted in calibrating and initially positioning the sample. This step ensures the sample remains in a consistent and standardized position throughout all subsequent measurement stations, laying the foundation for accurate imaging by the vision system and precise contact by the length sensor, thus eliminating measurement errors introduced by the randomness of manual placement.

[0021] A visual measurement system includes at least one smart camera 124 configured to capture images of the end face of a catalyst sample. The smart camera 124 is connected to an image processor and is used to automatically identify and measure at least one of the following based on the end face images: cross-sectional dimensions, total number of pores, number of blocked pores, pore diameter, inner wall thickness, and outer wall thickness of the catalyst sample. The visual measurement system also includes a flatness detection module 122 and a side-view camera 123, used to capture side images of the catalyst sample to detect the sample's levelness or deformation.

[0022] The visual measurement system is configured to acquire and measure end-face images of the windward and leeward sides of the catalyst sample, respectively.

[0023] This module is the core for acquiring high-precision geometric parameters. It is implemented by integrating at least one high-resolution intelligent camera and image processor. In operation, the intelligent camera 124 captures clear end-face images of the windward and leeward sides of the positioned catalyst sample. The image processor uses machine vision algorithms to analyze the images, automatically identifying the edges of the honeycomb grid, thereby accurately calculating the cross-sectional dimensions (height and width), total number of pores, and number of blocked pores. It can also measure the pore diameter, inner wall thickness, and outer wall thickness in a magnified local area. The number and location of measurement points are controlled by the program to ensure uniform distribution and statistical significance. Furthermore, the added flatness detection module 122 and side-view camera 123 can be used to assess the overall levelness of the sample or whether bending deformation exists, enabling auxiliary detection of the sample's appearance quality.

[0024] Combination Figure 2 The length measuring system 130 measures the length of the catalyst sample; The length measurement system 130 is connected to a laser sensor or displacement sensor in its data processing and storage system. The length data is obtained by pushing the measuring push rod 131 to fit against the end face of the catalyst sample.

[0025] This module is responsible for the precise measurement of the sample's axial dimensions. This is achieved using a high-precision laser sensor or displacement sensor, mechanically connected to a smoothly advancing measuring push rod 131. During measurement, the control system drives the measuring push rod forward at a constant speed until its end face is in close contact with the end face of the catalyst sample. At this point, the sensor accurately records the displacement of the push rod, which is directly converted into the sample's length data. This process is fully automated, avoiding reading errors caused by inconsistent angles and tension when using a measuring tape.

[0026] Weighing system 140 is used to obtain the mass of the catalyst sample; This module is used to acquire sample mass data. It achieves this by integrating a high-precision weighing sensor (such as a strain gauge sensor) at the measurement station. When the sample is placed or transported to the weighing station, the weighing sensor converts the gravity acting on the sample into an electrical signal. After amplification and analog-to-digital conversion by a transmitter, a high-precision mass value is obtained. This data is transmitted to the data processing system in real time and is one of the key inputs for subsequent calculations of bulk density.

[0027] A data processing and storage system (not shown in the figure) is used to receive and integrate measurement data from the vision measurement system, length measurement system and weighing system, and generate an inspection report containing at least geometric dimensions and volume density information.

[0028] This module is a host computer (industrial computer) running dedicated control and data analysis software. It receives and integrates measurement data from the vision, length, and weighing subsystems in real time, and automatically performs the following key operations: First, it calculates the sample volume based on the measured geometric dimensions (cross-sectional area and length), and then automatically calculates the bulk density by combining the mass data; second, it generates a structured inspection report by combining all the above geometric dimensions, mass, and density data, along with end and side images taken during the measurement process, in a preset format (such as Excel or PDF); third, it uniformly stores all raw data and reports, establishing a complete inspection database.

[0029] Furthermore, the device also includes a coding and marking module (not shown in the figure), which includes: Laser marking machines are used to mark unique identification codes on the surface of catalyst samples; A barcode scanner is used to read the identification code; The data processing and storage system binds and stores the read identification code with the corresponding catalyst sample's detection data and image to achieve sample traceability. The identification code is a high-temperature resistant QR code or numerical code.

[0030] The data processing and storage system is also configured to automatically calculate the bulk density of the catalyst sample based on measured geometric dimensions and mass data.

[0031] This module provides technical support for achieving full lifecycle traceability of samples. It achieves this by integrating a fiber laser marking machine and a barcode scanner at the front end of the measurement line. First, the laser marking machine etches a high-temperature resistant, permanent, and unique identification code (QR code or numerical code) onto the sample surface. Then, during sample transfer, the barcode scanner automatically reads this identification code and uploads it to the data processing and storage system. The system uses this QR code as the primary key, binding and storing all subsequent testing data and image files of the sample to it. In this way, the complete testing record of the sample can be quickly retrieved at any stage using the identification code, achieving precise traceability from the physical sample to the electronic data stream.

[0032] like Figure 3 The diagram shown is a flowchart illustrating an intelligent detection method for the geometric dimensions of a denitrification honeycomb catalyst according to an embodiment of this disclosure, including: S1: Place the honeycomb catalyst sample in the sample carrying and positioning module and position it accordingly; Specifically, the operator places the honeycomb catalyst sample to be tested on the product placement stage of the sample carrying and positioning module. Subsequently, the product positioning mechanism within this module (such as a mechanical positioning block or a visual guidance mechanism) automatically activates to perform position correction and initial positioning of the sample, ensuring that its end face is parallel to the measurement reference plane and its side face is aligned with the direction of the measurement guide rail. This standardized initial positioning provides a unified spatial reference for all subsequent measurement procedures and is the primary step in ensuring measurement repeatability and accuracy.

[0033] S2: Obtain an end face image of the sample through the vision measurement system, and automatically measure at least one of the following based on the image: cross-sectional dimension, total number of holes, number of plugged holes, hole diameter, inner wall thickness, and outer wall thickness; Specifically, after positioning is completed, the vision measurement system is activated. First, a high-resolution intelligent camera moves along a preset path to acquire end-face images of the windward and leeward sides of the sample, obtaining complete images of the honeycomb grid array. Subsequently, the image processor runs advanced machine vision algorithms to perform noise reduction, enhancement, and edge detection processing on the images, automatically identifying the contour of each honeycomb hole. Based on this, the system can simultaneously measure and calculate the total dimensions (height and width), total number of holes, and number of blocked holes in a batch. Furthermore, it can accurately measure microscopic geometric parameters such as hole diameter, inner wall thickness, and outer wall thickness at multiple (usually no less than 10) evenly distributed detection points set in the program. All measurement processes are controlled by the program, completely avoiding the subjective bias of manual point selection.

[0034] S3: The length of the sample is automatically measured by the length measurement system; Specifically, after the visual measurement is completed, the length measurement system begins operation. The system controls a measuring push rod equipped with a high-precision laser displacement sensor to move forward at a constant speed until its probe gently and stably contacts one end face (usually the windward side) of the catalyst sample. The sensor records the precise displacement of the push rod from its initial position to the contact position in real time, and this displacement value is directly recorded as the length of the sample. The entire process is automated, eliminating reading errors caused by touch and parallax when using calipers or measuring tapes manually.

[0035] S4: Obtain the mass of the sample using the weighing system; Specifically, the sample is then automatically or manually transferred to the working area of ​​the weighing system. The module's integrated precision load cell senses the sample mass in real time and converts the analog signal into a digital signal, instantly obtaining accurate sample mass data. This data is then transmitted in real time to the data processing and storage system via a communication interface (such as RS-232 or Ethernet).

[0036] S5: Integrate the measurement data from steps S2, S3, and S4 through the data processing and storage system to generate a test report.

[0037] Specifically, the data processing and storage system acts as an information hub, receiving and binding all data from S2, S3, and S4. The system automatically performs the following core calculations and archiving operations: First, using the cross-sectional dimensions measured by S2 and the length measured by S3, the sample volume is automatically calculated; then, combined with the mass obtained by S4, the sample's bulk density is automatically calculated. Finally, the system integrates the original measurement data, calculated data (bulk density), and corresponding end and side images according to a preset template, automatically generating a standardized test report (in Word, Excel, or PDF format) containing complete geometric characteristics (dimensions, wall thickness, pore size, porosity, bulk density, etc.), and stores it along with the product's unique identification code (if coded) in the database, completing the entire process of this intelligent testing.

[0038] Furthermore, prior to step S1, the method further includes: marking a unique identification code on the sample surface using a laser marking machine, and then recording the identification code into the data processing and storage system using a barcode scanner.

[0039] Before the formal measurement process begins, the system performs crucial traceability preprocessing. First, a laser marking machine etches a high-temperature resistant, permanent, and unique identification code (such as a QR code or serial number) onto a designated area (usually the side) of the catalyst sample. This identification code can withstand the high-temperature treatment environments the catalyst may undergo subsequently, ensuring its identifiability throughout its entire lifecycle. Then, a scanner automatically scans this identification code and instantly uploads it to the data processing and storage system as the sample's "digital ID card." Using this identification code as an index, the system creates a unique data archive. All subsequent measurement data and images related to this sample will be automatically collected under this archive, achieving a precise one-to-one binding and end-to-end traceability from the physical sample to the electronic data stream.

[0040] Furthermore, in step S2, end face image acquisition and measurement are performed on the windward and leeward sides of the sample respectively. The visual measurement system also acquires images of the side of the sample to detect its levelness or deformation. The number of measurement points for the aperture, inner wall thickness, and outer wall thickness is not less than 10, and the average value is calculated as the final result.

[0041] The vision system controls the intelligent camera to move directly in front of the windward and leeward sides of the sample, respectively, to acquire two high-resolution end-face images. This not only comprehensively assesses the blockage status and geometric consistency of both end faces, but also detects potential axial twisting or end-face non-parallelism by comparison.

[0042] A side-view camera moves along the length of the sample to capture its side view. By analyzing the edge straightness of the side image, the system can quantitatively assess the overall degree of bending or deformation of the sample, which is an important appearance quality indicator that is difficult to quantify manually.

[0043] For critical microscopic dimensions such as aperture, inner wall thickness, and outer wall thickness, the system automatically selects no fewer than 10 representative measurement points in different regions of the end face image (such as the center, corners, and edges) according to a preset program. Subsequently, the system automatically calculates the arithmetic mean of all measurement points as the final reported value for that parameter, and simultaneously calculates the standard deviation to assess the data dispersion. This statistical method based on a large number of data points fundamentally solves the problem of poor data representativeness and repeatability caused by manually measuring a few random points.

[0044] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure 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. Such 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 this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. An intelligent detection device for the geometric dimensions of a denitrification honeycomb catalyst, characterized in that, The device includes: The sample carrying and positioning module is used to place and position the honeycomb catalyst sample to be tested; A visual measurement system includes at least one smart camera configured to capture images of the end face of a catalyst sample. The smart camera is connected to an image processor and is used to automatically identify and measure at least one of the following based on the end face images: cross-sectional dimensions, total number of pores, number of blocked pores, pore diameter, inner wall thickness, and outer wall thickness of the catalyst sample. A length measurement system for automatically measuring the length of the catalyst sample; A weighing system is used to obtain the mass of the catalyst sample; A data processing and storage system is used to receive and integrate measurement data from the vision measurement system, length measurement system, and weighing system, and generate an inspection report containing at least geometric dimensions and volume density information.

2. The detection device according to claim 1, characterized in that, The sample carrying and positioning module includes: The product placement platform and product positioning mechanism are used to achieve the initial positioning of the catalyst sample before measurement.

3. The detection device according to claim 1, characterized in that, The visual measurement system also includes a side-view camera for capturing side images of the catalyst sample to detect the sample's levelness or deformation.

4. The detection device according to claim 1, characterized in that, The visual measurement system is configured to acquire and measure end-face images of the windward and leeward sides of the catalyst sample, respectively.

5. The detection device according to claim 1, characterized in that, The length measurement system includes a laser sensor or a displacement sensor connected to the data processing and storage system, and acquires length data by pushing a measuring push rod to fit against the end face of the catalyst sample.

6. The detection device according to claim 1, characterized in that, It also includes a coding and marking module, which includes: Laser marking machines are used to mark unique identification codes on the surface of catalyst samples; A barcode scanner is used to read the identification code; The data processing and storage system binds and stores the read identification code with the corresponding catalyst sample's detection data and image to achieve sample traceability. The identification code is a high-temperature resistant QR code or numerical code.

7. The detection device according to claim 1, characterized in that, The data processing and storage system is also configured to automatically calculate the bulk density of the catalyst sample based on measured geometric dimensions and mass data.

8. A method for intelligently detecting the geometric dimensions of a denitrification honeycomb catalyst using the detection device according to any one of claims 1-9, characterized in that, The method includes the following steps: S1: Place the honeycomb catalyst sample in the sample carrying and positioning module and position it accordingly; S2: Obtain an end face image of the sample through the vision measurement system, and automatically measure at least one of the following based on the image: cross-sectional dimension, total number of holes, number of plugged holes, hole diameter, inner wall thickness, and outer wall thickness; S3: The length of the sample is automatically measured by the length measurement system; S4: Obtain the mass of the sample using the weighing system; S5: Integrate the measurement data from steps S2, S3, and S4 through the data processing and storage system to generate a test report.

9. The method according to claim 8, characterized in that, Before step S1, the method further includes: marking a unique identification code on the sample surface using a laser marking machine, and recording the identification code into the data processing and storage system using a barcode scanner.

10. The method according to claim 8, characterized in that, In step S2, end face image acquisition and measurement are performed on the windward and leeward sides of the sample respectively. The vision measurement system also acquires images of the side of the sample to detect its levelness or deformation. The number of measurement points for the aperture, inner wall thickness and outer wall thickness is not less than 10, and the average value is calculated as the final result.

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