Method for predicting regularity of catalyst carrier, regular molding method and device

Abstract

This application discloses a method for predicting the regularity of catalyst supports, a regularization forming method, and an apparatus. The prediction method is applied to catalyst support forming equipment and includes: obtaining performance evaluation parameter values ​​for catalyst support samples extruded by the equipment; these performance evaluation parameter values ​​include average extrusion speed, extrusion speed fluctuation value, diameter deviation value of the catalyst support sample, and smoothness of the catalyst support sample; and determining whether the regularity of the catalyst support sample meets the requirements based on the magnitude of the performance evaluation parameter values. This application can improve the regularity of catalyst supports produced from multiple grades of materials.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, specifically to a method for predicting the regularity of a catalyst support, a method for regularizing the catalyst support, and an apparatus. Background Technology

[0002] In modern industrial production, material formulation and molding process control have a significant impact on product quality and consistency. This is particularly true in industries such as building materials, chemicals, and ceramics, where the composition ratios, mixing uniformity, and physical properties of materials directly affect the durability and appearance of the final product. Traditionally, these production processes rely on pre-set process parameters and post-production testing to control product quality, making it impossible to pre-determine whether the raw materials meet requirements based on the proportions of key parameters in the formulation. Common testing methods include physicochemical property testing and microstructural analysis of finished products; however, these methods are mostly offline and often time-consuming, hindering real-time feedback and adjustments to the production process. Current production process monitoring methods still exhibit a degree of lag, preventing rapid assessment and adjustment of product molding quality. Summary of the Invention

[0003] The purpose of this application is to provide a method for predicting the regularity of catalyst supports, as well as a molding method, apparatus, system, and storage medium, to solve the problem that in the production of catalyst supports in the prior art, the extrusion parameters during the extrusion of catalyst supports cannot be adjusted in a timely manner, resulting in poor regularity of catalyst supports produced from multiple batches of materials of different grades.

[0004] To achieve the above objectives, the first aspect of this application provides a method for predicting the regularity of a catalyst support, a method for forming a regularity, and an apparatus, the method comprising:

[0005] The effect evaluation parameters of the catalyst carrier sample extruded by the catalyst carrier forming equipment are obtained; the effect evaluation parameters include the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst carrier sample, and the smoothness of the catalyst carrier sample.

[0006] Based on the magnitude of the performance evaluation parameter values ​​of the catalyst support sample, determine whether the regularity of the catalyst support sample meets the requirements.

[0007] In this embodiment of the application, the effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier forming equipment include: performing image recognition on the extruded catalyst carrier sample to determine the average extrusion speed, extrusion speed fluctuation value, diameter of the catalyst carrier sample, and smoothness of the catalyst carrier sample when the catalyst carrier forming equipment is extruding the catalyst carrier sample; and determining the diameter deviation value based on the diameter and the preset ideal diameter.

[0008] In this embodiment of the application, determining whether the regularity of the catalyst carrier sample meets the requirements based on the magnitude of the effect evaluation parameter value includes: inputting the effect evaluation parameter value into a pre-constructed decision tree classification model to determine whether the regularity of the current catalyst carrier meets the requirements based on the classification result of the decision tree classification model; the decision tree classification model is constructed using the regularity interval of the effect evaluation parameter as a constraint condition.

[0009] In this embodiment of the application, the method further includes: if the regularity of the catalyst support sample meets the requirements, determining the formulation data of the catalyst support sample and the extrusion parameters of the catalyst support molding equipment; and configuring the formulation data and extrusion parameters in the regularization molding configuration library.

[0010] The above technical solution is used to evaluate the performance parameters of the catalyst carrier samples extruded by the catalyst carrier forming equipment. These parameters include average extrusion speed, extrusion speed fluctuation, diameter deviation of the catalyst carrier sample, and smoothness of the catalyst carrier sample. If the regularity of the catalyst carrier sample meets the requirements, the formulation data and current extrusion parameters are configured in the regularization configuration library. This serves as a regularization reference, allowing the catalyst carrier forming equipment to quickly retrieve the optimal extrusion parameters based on the formulation data, thus ensuring that the catalyst carrier forming equipment can perform regular extrusion forming of the catalyst carrier under optimal extrusion parameters.

[0011] A second aspect of this application provides a catalyst support regularity prediction device, applied to a catalyst support forming equipment. The device includes: an effect evaluation parameter acquisition module, used to acquire effect evaluation parameter values ​​of catalyst support samples extruded by the catalyst support forming equipment; the effect evaluation parameter values ​​include average extrusion speed, extrusion speed fluctuation value, diameter deviation value of the catalyst support sample, and smoothness of the catalyst support sample; and a regularity judgment module, used to determine whether the regularity of the catalyst support sample meets the requirements based on the magnitude of the effect evaluation parameter values ​​of the catalyst support sample.

[0012] A third aspect of this application provides a method for shaping a catalyst support, the method comprising:

[0013] Obtain the current catalyst support formulation data; the current catalyst support is extruded by the catalyst support forming equipment according to the current extrusion parameters;

[0014] The formulation data is compared with the data in the structured molding configuration library to determine the optimal extrusion parameters corresponding to the formulation data; the structured molding configuration library stores the correspondence between formulation data and extrusion parameters when the structure of the catalyst support sample meets the requirements.

[0015] The optimal extrusion parameters are used as the new current extrusion parameters to control the catalyst support forming equipment.

[0016] In this embodiment of the application, the formulation data includes the moisture content and binder content in the slurry used to form the current catalyst support; obtaining the formulation data of the current catalyst support includes: identifying the moisture content in the slurry used to form the current catalyst support by infrared identification; obtaining the binder content in the slurry used to form the current catalyst support; and determining the formulation data of the current catalyst support based on the moisture content and binder content.

[0017] In this embodiment, the filling step of the structured molding configuration library includes: the configuration step of the structured molding configuration library includes: obtaining the effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier molding equipment; the effect evaluation parameter values ​​include the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst carrier sample, and the smoothness of the catalyst carrier sample; judging whether the structuredness of the catalyst carrier sample meets the requirements based on the magnitude of the effect evaluation parameter values ​​of the catalyst carrier sample; if the structuredness of the catalyst carrier sample meets the requirements, then determining the formulation data of the catalyst carrier sample and the extrusion parameters of the catalyst carrier molding equipment, for configuring the structured molding configuration library.

[0018] In this embodiment of the application, comparing the formula data with the data in the patterning configuration library to determine the optimal extrusion parameters corresponding to the formula data includes: determining the range of samples in the patterning configuration library that are closest to the formula data based on the k-nearest neighbor classification algorithm; determining the extrusion parameters corresponding to the sample range in the patterning configuration library; and using the extrusion parameters as the optimal extrusion parameters corresponding to the formula data.

[0019] By using the above technical solution, the formulation data of the catalyst carrier extruded according to the current extrusion parameters is compared with the regularized reference data in the regularized molding configuration library, thereby determining the optimal extrusion parameters corresponding to the formulation data. The optimal extrusion parameters are then used as the new current extrusion parameters, and the catalyst carrier molding equipment is controlled to extrude the catalyst carrier according to these extrusion parameters, thereby ensuring that the catalyst carrier can be formed.

[0020] The fourth aspect of this application provides a catalyst support structured forming apparatus, applied to a catalyst support forming equipment. The apparatus includes: a formula data acquisition module for acquiring the formula data of the current catalyst support; the current catalyst support is extruded by the catalyst support forming equipment according to current extrusion parameters; an extrusion parameter determination module for comparing the formula data with data in a structured forming configuration library to determine the optimal extrusion parameters corresponding to the formula data; the structured forming configuration library stores the correspondence between formula data and extrusion parameters when the structure of the catalyst support sample meets the requirements; and a control module for controlling the catalyst support forming equipment using the optimal extrusion parameters as the new current extrusion parameters.

[0021] This application provides a catalyst carrier structured forming system, including a host computer and a catalyst carrier forming device. The catalyst carrier forming device includes a formula acquisition device and an extrusion device. The extrusion device is used to extrude the current catalyst carrier according to the current extrusion parameters. The formula acquisition device is used to acquire the moisture content in the slurry used to form the current catalyst carrier and send it to the host computer. The host computer is used to obtain the formula data of the current catalyst carrier based on the moisture content. The formula data is compared with the data in the structured forming configuration library to determine the optimal extrusion parameters corresponding to the formula data. The structured forming configuration library stores the correspondence between formula data and extrusion parameters when the structure of the catalyst carrier sample meets the requirements. The optimal extrusion parameters are fed back to the extrusion device. The extrusion device is also used to use the optimal extrusion parameters as the new current extrusion parameters and perform the step of extruding the current catalyst carrier according to the current extrusion parameters.

[0022] In this embodiment, the system may further include two production lines for catalyst carrier shaping, with the two production lines sharing the same catalyst carrier shaping equipment. Each production line also includes a straightening device and a cutting device. The extrusion device of the catalyst carrier shaping equipment includes a dual-hole extrusion die, which is used to simultaneously extrude two current catalyst carriers, with each of the two current catalyst carriers corresponding to one of the two production lines. The straightening device of each production line is used to straighten the current catalyst carrier on that production line and convey it to the cutting device. The cutting device is used to cut the straightened current catalyst carrier on that production line into shape.

[0023] The sixth aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to perform the catalyst support regularity prediction method or the catalyst support regularity shaping method described above.

[0024] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0025] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0026] Figure 1 A flowchart illustrating a method for predicting the regularity of a catalyst support according to an embodiment of this application is shown schematically.

[0027] Figure 2 This schematic diagram illustrates the structure of a catalyst support regularity prediction device according to an embodiment of this application.

[0028] Figure 3 A flowchart illustrating a catalyst support shaping method according to an embodiment of this application is shown schematically.

[0029] Figure 4 This schematic diagram illustrates the structure of a catalyst support shaping apparatus according to an embodiment of this application.

[0030] Figure 5 The diagram schematically illustrates a structure of a catalyst support conditioning system according to an embodiment of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0032] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0033] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0034] Figure 1 A flowchart illustrating a method for predicting the regularity of a catalyst support according to an embodiment of this application is shown schematically. Figure 1 As shown in the embodiments of this application, a method for predicting the regularity of a catalyst support is provided, which may include the following steps.

[0035] Step 101: Obtain the effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier forming equipment; the effect evaluation parameter values ​​include the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst carrier sample, and the smoothness of the catalyst carrier sample.

[0036] In this embodiment, the average extrusion speed refers to the average speed at which the catalyst sample is extruded from the molding equipment, reflecting the stability of the extrusion process and the efficiency of the equipment. The extrusion speed fluctuation value refers to the range of fluctuation in the extrusion speed, used to assess the stability of the extrusion speed during the extrusion process. Excessive fluctuation may affect the uniformity and quality of the catalyst sample, especially when high consistency of the catalyst support is required. The diameter deviation value of the catalyst support sample is used to measure the deviation of the catalyst sample diameter from a preset ideal diameter to ensure that each catalyst sample meets specifications. Excessive diameter deviation may affect properties such as the surface area and pore structure of the catalyst sample. The smoothness of the catalyst support sample evaluates the smoothness of the catalyst sample surface, which is generally related to the extrusion die and the uniformity of the raw material. Higher smoothness can improve the hydrodynamic performance of the catalyst support in actual reactions.

[0037] Step 102: Determine whether the regularity of the catalyst support sample meets the requirements based on the magnitude of the performance evaluation parameter value.

[0038] In this embodiment, if the average extrusion speed is within the set standard range, it indicates that the catalyst sample is extruded stably during the molding process. Speeds that are too low or too high may cause changes in sample density and structure, thus affecting the regularity of the catalyst support. Smaller extrusion speed fluctuations indicate a more stable extrusion process and higher uniformity in sample shape and quality. Larger speed fluctuations may cause inconsistencies in cross-section and thickness, resulting in substandard sample regularity. Diameter deviation is a direct indicator of regularity. Smaller deviations indicate better dimensional consistency of the catalyst support. Samples with deviations exceeding the specified value may affect the arrangement and stacking structure of the catalyst due to size issues, thus affecting the reaction effect. High smoothness indicates a uniform sample surface, a smooth extrusion process, and meets the required regularity. Lower smoothness usually indicates surface defects such as unevenness, affecting the airflow and catalytic performance of the support.

[0039] In this embodiment of the application, the effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier forming equipment include: performing image recognition on the extruded catalyst carrier sample to determine the average extrusion speed, extrusion speed fluctuation value, diameter of the catalyst carrier sample, and smoothness of the catalyst carrier sample when the catalyst carrier forming equipment is extruding the catalyst carrier sample; and determining the diameter deviation value based on the diameter and the preset ideal diameter.

[0040] In this embodiment, the extrusion speed of the sample can be analyzed based on image sequences to calculate the average speed during the extrusion process. The fluctuation of the extrusion speed is calculated by analyzing the speed changes in image frames. Small fluctuations indicate a stable extrusion process, while large fluctuations suggest potential inhomogeneity. The cross-sectional diameter of the catalyst support sample is precisely measured using images to ensure the sample meets design specifications. The smoothness of the sample is evaluated by analyzing the surface texture in the image. A smooth surface reflects higher molding quality. The sample diameter measured by image recognition is compared with a preset ideal diameter to calculate the diameter deviation value. The diameter deviation reflects the dimensional consistency of the sample; a small deviation value indicates stable sample specifications. Obtaining these parameter values ​​through image recognition makes the quality monitoring of catalyst support samples more automated and precise, while reducing manual intervention. Based on these parameter values, it is possible to promptly determine whether the regularity of the sample meets the standard requirements and adjust the extrusion parameters of the equipment as necessary to maintain production consistency and efficiency.

[0041] In this embodiment of the application, determining whether the regularity of the catalyst carrier sample meets the requirements based on the magnitude of the effect evaluation parameter value includes: inputting the effect evaluation parameter value into a pre-constructed decision tree classification model to determine whether the regularity of the current catalyst carrier meets the requirements based on the classification result of the decision tree classification model; the decision tree classification model is constructed using the regularity interval of the effect evaluation parameter as a constraint condition.

[0042] In the embodiments of this application, the average extrusion speed, the extrusion speed fluctuation, the diameter deviation of the catalyst support sample, and the smoothness of the catalyst support sample each have a corresponding regularization interval. Falling into a certain interval means that the regularization condition is met.

[0043] In this embodiment of the application, the method further includes: if the regularity of the catalyst support sample meets the requirements, determining the formulation data of the catalyst support sample and the extrusion parameters of the catalyst support molding equipment; and configuring the formulation data and extrusion parameters in the regularization molding configuration library.

[0044] In this embodiment, once the regularity of the catalyst support sample meets the requirements, the corresponding formulation data (e.g., moisture content and binder content) and extrusion parameters of the molding equipment (e.g., pressure, extrusion speed, sleeve temperature, drive screw motor torque, extrusion speed, extrusion length, cut particle length, catalyst support properties, formulation characteristics, molding die and blade thickness, etc.) can be recorded. Extrusion parameters directly affect the quality and regularity of the catalyst support sample, thus having reference value in adjusting molding equipment or the production process. The aforementioned formulation data and extrusion parameters are stored in a regularized molding configuration library as reference data for future molding processes. The main function of the regularized molding configuration library is to provide a standard dataset. In future production or adjustments, production conditions can be compared and optimized based on the regularized reference data in the library, improving product consistency and production efficiency.

[0045] By using the above technical solution, the magnitude of the effect evaluation parameter values ​​is judged. If the conditions are met, it indicates that the regularity of the catalyst carrier extruded according to the current extrusion parameters meets the requirements. The catalyst carrier forming equipment can be controlled to continuously extrude the catalyst carrier according to the current extrusion parameters, or the current extrusion parameters can be modified to achieve a higher regularity of the extruded catalyst carrier. Simultaneously, a dynamically updated standard library is established. By accumulating data from qualified samples, historical data support can be provided for the production process, enabling the rapid identification of optimized parameters in actual production to stabilize the forming quality of the catalyst carrier.

[0046] Figure 2 A schematic diagram illustrating the structure of a catalyst support regularity prediction device according to an embodiment of this application is shown. Figure 2As shown in the embodiment of this application, the effect evaluation parameter acquisition module 210 is used to acquire the effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier forming equipment; the effect evaluation parameter values ​​include the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst carrier sample, and the smoothness of the catalyst carrier sample; the regularity judgment module 220 is used to determine whether the regularity of the catalyst carrier sample meets the requirements based on the magnitude of the effect evaluation parameter values ​​of the catalyst carrier sample.

[0047] In this embodiment, the effect evaluation parameter acquisition module 210 is responsible for extracting the effect evaluation parameters of the current catalyst carrier sample from the molding equipment. These parameters include the average extrusion speed, extrusion speed fluctuation value, sample diameter deviation value, and smoothness. These parameters are core indicators reflecting the molding quality of the catalyst carrier sample, and they can be used as input datasets for subsequent analysis and judgment. This data acquisition method ensures that the status of the production process is visible in real time, enabling timely monitoring of the equipment's operating status. The regularity judgment module 220 can evaluate whether the regularity of the catalyst carrier sample meets the preset requirements based on the acquired effect evaluation parameter values. By comparing the differences between parameter values ​​such as extrusion speed, diameter deviation, and smoothness and ideal values, the regularity judgment module 220 can quickly identify whether the sample meets the standard. This ensures that each sample is within the qualified range, improving the consistency of the finished product. Through the combination of the above two modules, the device can realize automated real-time monitoring and intelligent quality assessment of catalyst carrier samples, ensuring the molding quality of the samples on the production line. By accumulating data of qualified samples, historical data support can be provided for the production process, enabling the rapid identification of optimized parameters in actual production to stabilize the molding quality of the catalyst carrier.

[0048] Figure 3 A flowchart illustrating a catalyst support shaping method according to an embodiment of this application is shown schematically. Figure 3 As shown, this application also provides a method for shaping a catalyst support, which may include the following steps.

[0049] Step 301: Obtain the current catalyst support formulation data; the current catalyst support is extruded by the catalyst support forming equipment according to the current extrusion parameters;

[0050] Step 302: Compare the formulation data with the data in the structured molding configuration library to determine the optimal extrusion parameters corresponding to the formulation data; the structured molding configuration library stores the correspondence between formulation data and extrusion parameters when the structure of the catalyst support sample meets the requirements;

[0051] In this embodiment, the extrusion parameters of the molding equipment can be dynamically optimized by comparing the current catalyst support formulation data with reference data in the structured molding configuration library. The formulation data of the catalyst support molding equipment under the current extrusion parameters is recorded. This formulation data includes information such as the raw material composition and corresponding proportions of the catalyst support, which are key factors affecting the extrusion effect. The obtained current formulation data is compared with the structured reference data in the structured molding configuration library. The configuration library stores the formulations of qualified samples and their corresponding optimal extrusion parameters, i.e., the correspondence between the formulation data and the extrusion parameters of the molding equipment under ideal structured conditions. The configuration library can also store the correspondence between the ratio range of the formulation data and the extrusion parameters. For example, the formulation data may include moisture content and binder content, and the configuration library may store the optimal range of moisture content and the optimal range of binder content and their correspondence with the extrusion parameters. Based on the comparison results, the optimal extrusion parameters that best match the current catalyst support formulation data are determined. These parameters may include extrusion speed, temperature, and pressure, and are verified parameter settings that can achieve optimal structuredness under the current formulation.

[0052] Step 303: Control the catalyst support forming equipment by using the optimal extrusion parameters as the new current extrusion parameters.

[0053] The optimal extrusion parameters are then used as the new current extrusion parameters in the catalyst support molding equipment to optimize production efficiency and improve uniformity. This ensures that the molding equipment operates under optimal conditions, resulting in extruded catalyst supports of consistent quality that meet specifications.

[0054] By using the above technical solutions and dynamically adjusting the extrusion parameters, not only is production efficiency improved, but also the high consistency of the regularity of each batch of catalyst carriers is ensured, thereby effectively improving the molding quality of the catalyst carriers.

[0055] In this embodiment of the application, the formulation data may include the moisture content and binder content in the slurry used to form the current catalyst support; obtaining the formulation data of the current catalyst support includes: identifying the moisture content in the slurry used to form the current catalyst support by infrared identification; obtaining the binder content in the slurry used to form the current catalyst support; and determining the formulation data of the current catalyst support based on the moisture content and binder content.

[0056] In this embodiment, infrared technology can be used to detect the moisture content in the catalyst support sample. Excessive or insufficient moisture content can affect the flowability, viscosity, and final molding effect during catalyst support extrusion. Infrared recognition offers the advantages of speed and non-contact operation, enabling real-time and accurate measurement of sample moisture, providing a basis for subsequent parameter adjustments. The binder is a key component ensuring the molding and structural stability of the catalyst support. The binder content affects the sample's viscosity, strength, and abrasion resistance, and indirectly influences the stability and regularity during extrusion. Combining moisture and binder content data allows for more precise control of the catalyst support molding effect. By comparing this data with reference data in a regular molding configuration library, the equipment can automatically adjust extrusion parameters to achieve high-quality, stable production, ensuring the consistency of the catalyst support sample in terms of regularity.

[0057] In this embodiment of the application, the filling step of the structured molding configuration library may include: the configuration step of the structured molding configuration library includes: obtaining the effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier molding equipment; the effect evaluation parameter values ​​include the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst carrier sample, and the smoothness of the catalyst carrier sample; judging whether the structuredness of the catalyst carrier sample meets the requirements based on the magnitude of the effect evaluation parameter values ​​of the catalyst carrier sample; if the structuredness of the catalyst carrier sample meets the requirements, then determining the formulation data of the catalyst carrier sample and the extrusion parameters of the catalyst carrier molding equipment, for configuring the structured molding configuration library.

[0058] In this embodiment, the configuration library records the proportioning ranges of different formulation data and their corresponding extrusion parameters. By storing a large amount of qualified sample data, an experienced molding configuration database is gradually accumulated, which can provide optimal extrusion parameter guidance for similar formulations in the future. Among the recorded formulation data, the optimal ranges for moisture content and binder content can be determined based on the specific formulation and materials.

[0059] In this embodiment, the optimal extrusion parameters corresponding to the formula data can be determined by comparing the formula data with the data in the patterning configuration library. This includes: determining the range of samples in the patterning configuration library that are closest to the formula data based on the k-nearest neighbor classification algorithm; determining the extrusion parameters corresponding to the range of samples in the patterning configuration library; and using the extrusion parameters as the optimal extrusion parameters corresponding to the formula data.

[0060] In this embodiment, the effect evaluation parameter values ​​are compared with a set standard to confirm whether the sample meets the required regularity. The set standard can be a range, and effect evaluation parameter values ​​within this range indicate that the extruded catalyst carrier sample can meet the required regularity requirements. Once the catalyst carrier sample regularity meets the requirements, the current formulation data (such as component ratios, moisture, and binder content) and the extrusion parameters of the molding equipment (such as extrusion speed and temperature) are recorded. These parameters constitute a key dataset that is well correlated with the sample regularity. The formulation data and extrusion parameters that meet the standard are stored in the regularity molding configuration library as a reference for future production. This data will serve as the basis for future debugging and optimization of production conditions, helping the molding equipment to replicate samples with qualified regularity based on known optimal parameter conditions. Through the above technical means, the configuration library gradually accumulates optimal configuration data, enabling the molding equipment to quickly identify the optimal parameter combinations under different formulations, thereby stably generating high-quality catalyst carrier samples and significantly improving production efficiency and product consistency.

[0061] In this embodiment of the application, the method may further include: adjusting the current extrusion parameters when the classification result is that the catalyst carrier is not formed; controlling the catalyst carrier forming equipment to extrude the catalyst carrier sample according to the adjusted extrusion parameters; and determining whether the regularity of the catalyst carrier sample meets the requirements.

[0062] In this embodiment, if the classification result is "unformable," the current extrusion parameters can be adjusted, and the molding equipment can be controlled to extrude the catalyst carrier according to the adjusted extrusion parameters. Simultaneously, the catalyst carrier is reclassified to determine if its regularity meets the requirements. If it does, the corresponding formulation data and extrusion parameters are stored in the regularized molding configuration library to accumulate formable data. If it does not meet the requirements, the extrusion parameters are adjusted again, and the above process is repeated.

[0063] In this embodiment of the application, determining the classification result and the ratio range may include: using the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst support sample, and the smoothness of the catalyst support sample as feature values; using multiple feature values ​​as a dataset; determining the empirical entropy of the dataset and the empirical conditional entropy of the feature values ​​on the dataset; and determining the gain value corresponding to the effect evaluation parameter based on the empirical entropy and the empirical conditional entropy.

[0064] In this embodiment, the feature values ​​of each sample are combined into a dataset for subsequent information entropy analysis. The dataset contains a set of feature values ​​from multiple samples, which helps extract the impact of different feature values ​​on the molding effect. Empirical entropy is used to measure the diversity of sample features in the dataset, i.e., the distribution of sample quality. A higher entropy value usually indicates greater diversity of feature values, while a lower entropy value may mean that the sample features in the dataset are relatively concentrated or consistent. In the dataset, the empirical conditional entropy is calculated under each feature value (average extrusion speed, speed fluctuation value, diameter deviation value, and smoothness). This reveals the classification contribution of each feature value to the molding effect in the dataset. The gain value of each feature value is determined by calculating the difference between the empirical entropy and the empirical conditional entropy (information gain). The higher the gain value, the greater the contribution of the feature value to the classification of the dataset, and the more significant its impact on molding quality. The gain value helps determine the relative importance of each effect evaluation parameter, thereby optimizing the control strategy of the molding equipment. For example, parameters with high gain values ​​can be prioritized for parameter adjustment to improve the molding effect of the catalyst support. This helps to scientifically optimize the extrusion process, improve production efficiency, and enhance sample consistency.

[0065] In this embodiment of the application, determining the empirical entropy of the dataset may include formula (1):

[0066]

[0067] Where H(D) is the empirical entropy of the dataset, |C k | represents the number of samples in class k, |D| represents the number of datasets, D represents the number of datasets, and k represents the number of classes in the dataset.

[0068] In this embodiment of the application, determining the empirical conditional entropy of the feature values ​​on the dataset may include formula (2):

[0069]

[0070] Where H(D|A) is the empirical conditional entropy, |D| is the number of data sets, and D i Let A represent the number of samples in the dataset, A be the feature value, and D be the dataset.

[0071] In this embodiment of the application, determining the gain value corresponding to the effect evaluation parameter may include formula (3):

[0072] g(D, A) = H(D) - H(D | A) (3)

[0073] Where g(D, A) is the gain value, H(D|A) is the empirical conditional entropy, H(D) is the empirical entropy, A is the feature value, and D is the dataset.

[0074] In this embodiment, the empirical entropy H(D) reflects the diversity of sample features in the entire dataset. For example, in the dataset of catalyst support samples, the quality of the samples may have different classification results (e.g., qualified, unqualified). By calculating H(D), the uncertainty of the current dataset can be quantified, that is, the distribution of sample quality under existing conditions. The empirical conditional entropy H(D|A) is used to measure the uncertainty of dataset D given feature value A. Assuming feature value A is a feature value such as average extrusion speed or smoothness, by calculating H(D|A), the influence of a specific feature on the sample classification result can be evaluated. A lower conditional entropy indicates that the feature can better explain the sample classification. The information gain g(D,A) is obtained by calculating H(D)-H(D|A) and is used to determine the contribution of feature value A to the classification. For example, if the gain value of smoothness is the highest, it indicates that smoothness has the most significant impact on the quality of catalyst support samples. This feature can be preferentially used for quality control in the production process.

[0075] In this embodiment of the application, the adhesive content in the formulation data can also be determined by formula (4):

[0076] Z11=PI*X11 (4)

[0077] Wherein, Z11 is the binder content, PI is the boehmite solubility index, and X11 is the percentage content of boehmite on a dry basis. The values ​​of Z11 binder content and moisture content range from (0, 1).

[0078] In one embodiment, the binder content and moisture content mentioned above can also be predicted and optimized using a k-nearest neighbor classification algorithm. Thus, it can be determined whether the catalyst support sample can be formed under the given formulation conditions by inputting the binder content and moisture content values.

[0079] By comparing the formulation data of the catalyst carrier extruded according to the current extrusion parameters with the regularized reference data in the regularized molding configuration library, the optimal extrusion parameters corresponding to the current formulation data can be determined. The optimal extrusion parameters are then used as the new current extrusion parameters, and the catalyst carrier molding equipment is controlled to extrude the catalyst carrier according to the new current extrusion parameters, thereby ensuring that the catalyst carrier can be formed.

[0080] Figure 4 The diagram schematically illustrates the structure of a catalyst support shaping apparatus according to an embodiment of this application. Figure 4As shown, this application also provides a catalyst support structured forming apparatus, applied to a catalyst support forming equipment. The apparatus includes: a formula data acquisition module 410, used to acquire the formula data of the current catalyst support; the current catalyst support is extruded by the catalyst support forming equipment according to the current extrusion parameters; an extrusion parameter determination module 420, used to compare the formula data with data in a structured forming configuration library to determine the optimal extrusion parameters corresponding to the formula data; the structured forming configuration library stores the correspondence between formula data and extrusion parameters when the structure of the catalyst support sample meets the requirements; and a control module 430, used to control the catalyst support forming equipment by using the optimal extrusion parameters as the new current extrusion parameters.

[0081] In this embodiment, the formulation data acquisition module 410 can acquire the formulation data of the current catalyst carrier from the molding equipment, including the proportions of important components (such as moisture content and binder content). This provides a foundation for further optimization and comparison. By continuously recording formulation data, the correlation analysis between the formulation and extrusion parameters can be performed more accurately. After acquiring the formulation data, the extrusion parameter determination module 420 compares this data with historical data in the structured molding configuration library to determine the optimal extrusion parameters for the current formulation. The structured molding configuration library stores parameter records of high-quality samples formed under different formulations and equipment settings, thus providing optimal parameter values ​​to achieve the ideal molding effect. This also reflects the self-learning function of the above scheme; through accumulation and comparison, the molding device can adjust parameters more and more accurately to adapt to the needs of different formulations. Once the optimal extrusion parameters are determined, the control module 430 sets them as the current extrusion parameters and uses them to guide the operation of the equipment. In this way, the molding equipment can be dynamically adjusted to adapt to the formulation requirements of different batches. Through the feedback and execution of the control module 430, the molding process can always remain in an optimal state. This ensures the equipment's real-time adjustment capability, allowing adjustments to be made based on the latest parameter requirements, ensuring that each batch of products meets consistent quality standards. Through the aforementioned device, the catalyst carrier forming equipment can be adjusted based on real-time formula data and optimal parameters, effectively avoiding quality fluctuations caused by changes in external variables, thereby improving the uniformity of catalyst carriers produced from multiple grades and batches of materials.

[0082] Figure 5 A schematic diagram illustrates the structure of a catalyst support configuration system according to an embodiment of this application. Figure 5As shown, this application also provides a catalyst carrier structured forming system, which may include a host computer 900 and a catalyst carrier forming device. The catalyst carrier forming device includes a formula acquisition device and an extrusion device. The extrusion device is used to extrude the current catalyst carrier according to the current extrusion parameters. The formula acquisition device is used to acquire the moisture content in the mud material used to form the current catalyst carrier and send it to the host computer 900. The host computer 900 is used to obtain the formula data of the current catalyst carrier according to the moisture content. The formula data is compared with the data in the structured forming configuration library to determine the optimal extrusion parameters corresponding to the formula data. The structured forming configuration library stores the correspondence between formula data and extrusion parameters when the structuredness of the catalyst carrier sample meets the requirements. The optimal extrusion parameters are fed back to the extrusion device. The extrusion device is also used to use the optimal extrusion parameters as the new current extrusion parameters and perform the step of extruding the current catalyst carrier according to the current extrusion parameters.

[0083] In this embodiment, the system may further include: two production lines for shaping catalyst supports, the two production lines sharing the same catalyst support forming equipment, each production line further including a straightening device and a cutting device; the extrusion device of the catalyst support forming equipment includes a dual-hole extrusion die, the dual-hole extrusion die being used to simultaneously extrude two current catalyst supports, the two current catalyst supports corresponding one-to-one with the two production lines; the straightening device of each production line is used to straighten the current catalyst support on the production line and convey it to the cutting device; the cutting device is used to cut the straightened current catalyst support on the production line into shape.

[0084] In this embodiment, the steps of the catalyst carrier forming equipment when extruding the catalyst carrier sample may include: a) Feeding: The catalyst carrier sample enters through the feeding funnel (1-3), and the loading rate is precisely controlled by adjusting the frequency and speed of the feeding screw. Controlling the feeding rate can affect the subsequent average extrusion speed and speed fluctuation value, thereby affecting the forming quality of the sample. b) Extrusion: Driven by a motor (1-1), the single-screw extruder (1-4) is driven by a reducer (1-2), and the catalyst carrier sample is extruded into strips through a double-hole extrusion die (2). The extrusion speed is also controlled by the frequency and speed of the feeding screw. The extrusion speed and stability (fluctuation value) in this process are key performance evaluation parameters, which are related to the calculated gain value mentioned above and can help determine the optimal extrusion parameters. c) Forming: The sample is formed through two sets of forming dies (3-11 and 3-12). The quality of the forming process (such as diameter and smoothness) will directly affect the regularity of the final catalyst carrier sample. At this stage, image recognition technology can be used to monitor the sample characteristics in real time to ensure that the sample forming meets the standards. d. Straightening: The shaped samples pass through two straightening devices (4-11 and 4-12) to ensure their straightness. This helps control the consistency of sample shape, thereby reducing diameter deviation. e. Fixed-length cutting: The samples are conveyed via two conveyor belts (6-11 and 6-12), inspected by two corresponding laser sensors (5-21 and 5-22), and then cut by fixed-length cutting equipment (5-11 and 5-12) to ensure consistent sample length, which helps in overall quality control. Two strip conveying devices (7-11 and 7-12) are used to transfer the fixed-length cut catalyst carrier strips from the cutting station to the next station or to a designated storage location. f. Arrangement cutting: Finally, two arrangement cutting devices (8-01 and 8-02) complete the arrangement of the cut samples. Through the cooperation of these devices, the samples reach the required regularity conditions and enter the subsequent processes.

[0085] In one embodiment, an online monitoring data acquisition module (000) may be included for automatically acquiring grade and formulation data, as well as effect evaluation parameter values. An automated control coordination module, i.e., a host computer (900), is used to integrate the above-mentioned technical solutions, thereby enabling the monitoring and optimization of the catalyst support structure forming scheme.

[0086] This application also provides a machine-readable storage medium storing instructions for causing a machine to perform the above-described catalyst support regularity prediction method or the above-described catalyst support regularity shaping method.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0092] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0093] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0094] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0095] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting the regularity of a catalyst support, applied to a catalyst support forming equipment, characterized in that, The method includes: The effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier forming equipment are obtained; the effect evaluation parameter values ​​include the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst carrier sample, and the smoothness of the catalyst carrier sample. Based on the magnitude of the effect evaluation parameter value of the catalyst support sample, determine whether the regularity of the catalyst support sample meets the requirements.

2. The method according to claim 1, characterized in that, The evaluation parameters for obtaining the catalyst carrier sample extruded by the catalyst carrier molding equipment include: Image recognition is performed on the catalyst carrier sample extruded by the catalyst carrier forming equipment to determine the average extrusion speed, the extrusion speed fluctuation value, the diameter, and the smoothness of the catalyst carrier sample; The diameter deviation value of the catalyst support sample is determined based on the diameter of the catalyst support sample and the preset ideal diameter.

3. The method according to claim 1, characterized in that, The step of determining whether the regularity of the catalyst support sample meets the requirements based on the magnitude of the effect evaluation parameter value includes: The effect evaluation parameter values ​​are input into a pre-constructed decision tree classification model to determine whether the regularity of the current catalyst support meets the requirements based on the classification results of the decision tree classification model. The decision tree classification model is constructed using the regularized interval of the effect evaluation parameters as a constraint.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: If the regularity of the catalyst support sample meets the requirements, the formulation data of the catalyst support sample and the extrusion parameters of the catalyst support molding equipment are determined. The formula data and the extrusion parameters are configured in the patterning configuration library.

5. A catalyst support regularity prediction device, applied to catalyst support forming equipment, characterized in that, The device includes: The effect evaluation parameter acquisition module is used to acquire effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier forming equipment; the effect evaluation parameter values ​​include average extrusion speed, extrusion speed fluctuation value, diameter deviation value of the catalyst carrier sample, and smoothness of the catalyst carrier sample; The regularity judgment module is used to determine whether the regularity of the catalyst support sample meets the requirements based on the magnitude of the effect evaluation parameter value.

6. A method for shaping a catalyst support, characterized in that, The method includes: Obtain the current catalyst support formulation data; the current catalyst support is extruded by a catalyst support forming device according to the current extrusion parameters; The formulation data is compared with the data in the structured molding configuration library to determine the optimal extrusion parameters corresponding to the formulation data; the structured molding configuration library stores the correspondence between formulation data and extrusion parameters when the structure of the catalyst carrier sample meets the requirements; The optimal extrusion parameters are used as the new current extrusion parameters to control the catalyst support forming equipment.

7. The method according to claim 6, characterized in that, The formulation data includes the moisture content and binder content of the slurry used to form the current catalyst support; The process of obtaining the current catalyst support formulation data includes: The moisture content in the slurry used to form the current catalyst support is identified by infrared detection. Obtain the binder content in the slurry used to form the current catalyst support; The formulation data of the current catalyst support are determined based on the moisture content and the binder content.

8. The method according to claim 6, characterized in that, The configuration steps for the regularized configuration library include: The effect evaluation parameter values ​​of the catalyst carrier sample extruded by the catalyst carrier forming equipment are obtained; the effect evaluation parameter values ​​include the average extrusion speed, the extrusion speed fluctuation value, the diameter deviation value of the catalyst carrier sample, and the smoothness of the catalyst carrier sample. Based on the magnitude of the effect evaluation parameter value of the catalyst support sample, determine whether the regularity of the catalyst support sample meets the requirements; If the regularity of the catalyst support sample meets the requirements, the formulation data of the catalyst support sample and the extrusion parameters of the catalyst support molding equipment are determined to configure the regularity molding configuration library.

9. The method according to claim 8, characterized in that, The step of comparing the formula data with data in the structured molding configuration library to determine the optimal extrusion parameters corresponding to the formula data includes: Based on the k-nearest neighbor classification algorithm, the range of samples in the regularized molding configuration library that are nearest neighbors to the formula data is determined; Determine the extrusion parameters corresponding to the sample range in the well-formed configuration library; The extrusion parameters are used as the optimal extrusion parameters corresponding to the formulation data.

10. A catalyst support shaping and forming apparatus, applied to catalyst support forming equipment, characterized in that, The device includes: The formulation data acquisition module is used to acquire the formulation data of the current catalyst support; the current catalyst support is extruded by the catalyst support forming equipment according to the current extrusion parameters. The extrusion parameter determination module is used to compare the formulation data with the data in the structured molding configuration library to determine the optimal extrusion parameters corresponding to the formulation data; the structured molding configuration library stores the correspondence between formulation data and extrusion parameters when the structure of the catalyst carrier sample meets the requirements. The control module is used to control the catalyst support forming equipment by using the optimal extrusion parameters as the new current extrusion parameters.

11. A catalyst support shaping system, characterized in that, It includes a host computer and a catalyst carrier forming equipment, wherein the catalyst carrier forming equipment includes a formula acquisition device and an extrusion device; The extrusion device is used to extrude the current catalyst support according to the current extrusion parameters; The formula acquisition device is used to acquire the moisture content in the mud material used to form the current catalyst carrier and send it to the host computer. The host computer is used to obtain the formulation data of the current catalyst carrier based on the moisture content; compare the formulation data with the data in the structured molding configuration library to determine the optimal extrusion parameters corresponding to the formulation data; the structured molding configuration library stores the correspondence between formulation data and extrusion parameters when the structuredness of the catalyst carrier sample meets the requirements; and feeds back the optimal extrusion parameters to the extrusion device. The extrusion device is also used to take the optimal extrusion parameters as the new current extrusion parameters and perform the step of extruding the current catalyst support according to the current extrusion parameters.

12. The system according to claim 11, characterized in that, The system includes two production lines for the formation of catalyst supports, the two production lines share the same catalyst support forming equipment, and each production line also includes a straightening device and a cutting device. The extrusion device of the catalyst carrier forming equipment includes a dual-hole extrusion die, which is used to extrude two current catalyst carriers simultaneously, and the two current catalyst carriers correspond one-to-one with the two production lines. The straightening device on each of the production lines is used to straighten the current catalyst carrier on that production line and transfer it to the cutting device; The cutting device is used to cut the current catalyst carrier, which has been straightened on the production line, into different shapes.

13. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the catalyst support regularity prediction method according to any one of claims 1 to 4, or the catalyst support regularity shaping method according to any one of claims 6 to 9.

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