Method and apparatus for fluid energy milling with adjustable classification wheel

Abstract

The application discloses a fluidized bed airflow crushing control method and device with an adjustable classification wheel, relates to the material crushing technical field, and comprises the following steps: according to the initial particle size distribution of a sample of crushed material and target particle size requirements, the rotation speed of the classification wheel, airflow pressure and feeding rate are controlled; in a crushing chamber, preliminary crushing is carried out, and the particle size distribution and particle size span of the material are determined; after the target particle size requirements are met, the material enters the inner cavity of the classification wheel along with the rising airflow; otherwise, the material returns to the crushing chamber; meanwhile, the feeding rate is adjusted according to the material concentration in the fluidized bed; the collision frequency and particle residence time of the material in the crushing chamber are monitored; adaptive control parameter libraries are obtained; and the crushing parameters of the material to be crushed are configured. The application solves the technical problems that the particle size of the material to be crushed is difficult to accurately control in the prior art, the crushing efficiency is not high, and the particle classification effect is poor, and achieves the technical effects of accurately controlling the crushing of the material, improving the crushing efficiency and the particle classification.

Description

Technical Field

[0001] This invention relates to the field of material pulverization technology, specifically to a fluidized bed airflow pulverization control method and equipment with adjustable classifiers. Background Technology

[0002] In the field of fluidized bed air jet milling, traditional milling control methods have many limitations: it is difficult to accurately match key parameters such as classifier speed and airflow pressure according to the initial characteristics of the material and the target particle size, often resulting in large deviations in particle size; insufficient airflow stability during milling leads to uneven impact on the material, which not only reduces milling efficiency but also worsens particle classification and causes problems such as excessively wide particle size distribution. Furthermore, for different types or batches of materials, repeated manual parameter adjustments are required, lacking an adaptive adjustment mechanism. It is impossible to dynamically optimize the feed rate based on real-time conditions such as material concentration and collision frequency within the fluidized bed, and it is also difficult to accumulate historical data to form an effective parameter configuration library, resulting in low production efficiency and difficulty in ensuring product consistency.

[0003] Existing technologies suffer from the problem of difficulty in accurately controlling the particle size of crushed materials, resulting in low crushing efficiency and poor particle classification. Summary of the Invention

[0004] This application provides a fluidized bed airflow pulverization control method and equipment with an adjustable classifier wheel, which is used to address the technical problems in the prior art where it is difficult to accurately control the particle size of pulverized materials, resulting in low pulverization efficiency and poor particle classification effect.

[0005] In view of the above problems, this application provides a fluidized bed airflow pulverization control method and equipment with an adjustable classifier wheel.

[0006] A first aspect of this application provides a fluidized bed airflow pulverization control method with an adjustable classifier, the method comprising: Based on the initial particle size distribution and target particle size requirements of the pulverized material sample, the classifier wheel speed, airflow pressure, and feed rate are configured using fuzzy PID control. Within the pulverizing chamber, initial pulverization is performed using the classifier wheel speed, airflow pressure, and axial distance to the airflow nozzle. The particle size distribution and span of the material are determined. If the target particle size requirement is met, the material enters the classifier wheel cavity with the rising airflow; otherwise, it returns to the pulverizing chamber. Simultaneously, the feed rate is adjusted according to the material concentration in the fluidized bed. The material collision frequency and particle residence time within the pulverizing chamber are monitored. Combining the material particle size distribution and span, the classifier wheel speed, airflow pressure, and axial distance to the airflow nozzle are collected during continuous pulverizing cycles to obtain an adaptive control parameter library, which is then used to configure the pulverizing parameters for the material to be pulverized.

[0007] In a second aspect, this application provides an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the fluidized bed airflow pulverizing control method with adjustable classifier provided in this application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Based on the initial particle size distribution and target particle size requirements of the material sample, the rotational speed of the classifying wheel, the airflow pressure, and the feed rate are controlled and configured. Preliminary crushing is performed within the crushing chamber to determine the particle size distribution and span. Once the target particle size requirement is met, the material enters the classifying wheel cavity with the rising airflow; otherwise, it returns to the crushing chamber. Simultaneously, the feed rate is adjusted according to the material concentration in the fluidized bed. The material collision frequency and particle residence time within the crushing chamber are monitored. Combined with the material particle size distribution and span, the rotational speed of the classifying wheel, the airflow pressure, and the axial distance between the classifying wheel and the airflow nozzle are collected during continuous crushing cycles to obtain an adaptive control parameter library. The crushing parameters for the material to be crushed are then configured. This achieves precise control of material crushing, improving crushing efficiency and particle classification. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic flowchart of a fluidized bed airflow pulverization control method with an adjustable classifier wheel provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an electronic device provided in this application.

[0011] Explanation of reference numerals in the attached drawings: Processor 21, Memory 22, Input device 23, Output device 24. Detailed Implementation

[0012] This application provides a fluidized bed airflow pulverization control method and equipment with an adjustable classifying wheel, which is used to address the technical problems in the prior art where it is difficult to accurately control the particle size of pulverized materials, resulting in low pulverization efficiency and poor particle classification effect.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. 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.

[0014] Example 1, as Figure 1 As shown, this application provides a fluidized bed airflow pulverization control method with an adjustable classifier wheel, the method comprising: Step S100: Based on the initial particle size distribution and target particle size requirements of the crushed material sample, configure the speed of the classifier wheel, airflow pressure and feed rate using fuzzy PID control.

[0015] Specifically, based on the initial particle size distribution and target particle size requirement of the crushed material sample, fuzzy PID control is used to configure the classifier speed, airflow pressure, and feed rate. Specifically, the deviation between the initial particle size distribution and the target particle size requirement is divided into M fuzzy levels, corresponding to M fuzzy subsets of the classifier speed adjustment. A fuzzy rule matrix is ​​established based on these fuzzy subsets, and the classifier speed is configured as the associated control window corresponding to the rated speed mapping. At the same time, the airflow pressure is configured according to the material bulk density, and the feed rate is configured according to the matching degree between the classifier speed and the airflow pressure, thereby achieving accurate initial settings for the classifier speed, airflow pressure, and feed rate.

[0016] Step S200: In the grinding chamber, the material is initially ground using the speed of the classifying wheel, the airflow pressure, and the axial distance between the classifying wheel and the airflow nozzle. The particle size distribution and particle size span of the material are determined. If the target particle size requirement is met, the material enters the inner cavity of the classifying wheel with the rising airflow. Otherwise, it returns to the grinding chamber.

[0017] Specifically, in the grinding chamber, initial grinding is performed using the configured classifying wheel speed, airflow pressure, and the axial distance between the classifying wheel and the airflow nozzle, which can be adjusted within a preset control range (and the control distance value is positively correlated with the initial material particle size). During the grinding process, when the material bulk density exceeds the preset density threshold, the axial distance shortening adjustment amount is determined by combining the airflow impact kinetic energy. The distance adjustment is achieved by a servo motor. At the same time, based on the axial distance shortening adjustment amount, the impact direction of the airflow nozzle is dynamically corrected by an angle sensor, and guide vanes with the same spray angle are set at the nozzle outlet to stabilize the airflow field. After grinding, the D10, D50, and D90 characteristic values ​​are obtained based on the material particle size distribution. The particle size span is analyzed using (D90-D10) / D50. If the material particle size distribution and particle size span meet the target particle size requirements, it enters the inner cavity of the classifying wheel with the rising airflow; otherwise, it returns to the grinding chamber for further grinding.

[0018] Step S300: Simultaneously, adjust the feed rate according to the material concentration in the fluidized bed, monitor the material collision frequency and particle residence time in the grinding chamber, and collect the classifier speed, airflow pressure and axial distance to the airflow nozzle during the continuous grinding cycle in combination with the material particle size distribution and particle size span to obtain an adaptive control parameter library, and configure the grinding parameters of the material to be ground.

[0019] Specifically, during the pulverization process, the feed rate is adjusted in real time according to the material concentration in the fluidized bed, and the collision frequency and particle residence time of the material in the pulverization chamber are monitored simultaneously. Combined with the determined material particle size distribution (including D10, D50, and D90 characteristic values) and particle size span ((D90-D10) / D50), data such as the speed of the classifier wheel, airflow pressure, and axial distance between the classifier wheel and the airflow nozzle are continuously collected over multiple consecutive pulverization cycles. An adaptive control parameter library is constructed by partitioning according to material type. Each partition contains multiple sets of optimized pulverization parameters, and parameter sets with cluster center deviations less than the acceptable error range are merged and stored. For the material to be pulverized, a similarity comparison is performed based on its hardness, initial particle size, material bulk density, and target particle size requirements. If the comparison is successful, it is classified as the same type of material and the corresponding parameters are called. After small-batch trial pulverization and the pass rate exceeds the threshold, the final pulverization parameter configuration is confirmed.

[0020] In one possible implementation, step S100 further includes: Step S110: Divide the deviation between the initial particle size distribution and the target particle size requirement into M fuzzy levels, corresponding to M fuzzy subsets of the graded wheel speed adjustment.

[0021] Step S120: Based on the M fuzzy subsets of the graded wheel speed adjustment amount, establish a fuzzy rule matrix and configure the graded wheel speed as the associated control window corresponding to the rated speed mapping.

[0022] Step S130: Configure the airflow pressure according to the material bulk density; Step S140: Configure the feed rate according to the matching degree between the speed of the classifier wheel and the airflow pressure.

[0023] Specifically, the deviation between the initial particle size distribution and the target particle size requirement of the crushed material sample is divided into M fuzzy levels. Each fuzzy level corresponds to M fuzzy subsets of the classifier speed adjustment. Through this fuzzy division method, the correspondence between the degree of deviation between the initial particle size and the target particle size and the adjustment range of the classifier speed is established, laying the foundation for subsequent configuration of the classifier speed based on fuzzy rules.

[0024] Based on the M fuzzy subsets of the obtained graded wheel speed adjustment, a fuzzy rule matrix is ​​constructed. This matrix clarifies the mapping relationship between the deviation of different fuzzy levels and the corresponding speed adjustment subsets. Through this fuzzy rule matrix, the graded wheel speed is configured as an associated control window related to the rated speed. That is, the adjustment range of the graded wheel speed on the basis of the rated speed is determined according to the fuzzy rules, so that the speed adjustment can adapt to the deviation between the initial particle size distribution and the target particle size requirement, providing a rule basis for subsequent precise control of the graded wheel speed.

[0025] For the sample of material to be crushed, the bulk density of the material is detected, and the airflow pressure is configured according to the preset density-pressure correspondence. That is, when the bulk density of the material is high, a higher airflow pressure is matched to enhance the impact kinetic energy of the airflow and ensure that the high-density material can be effectively crushed; when the bulk density of the material is low, a relatively lower airflow pressure is configured to avoid the material being overly dispersed due to excessive airflow, which would affect the crushing efficiency. This makes the airflow pressure and the bulk density of the material compatible, providing suitable power conditions for the crushing process.

[0026] The feed rate is configured based on the matching degree between the configured classifier wheel speed and the airflow pressure. That is, by judging whether the classifier wheel speed and the airflow pressure are in a suitable state (such as whether the speed and pressure form a synergistic relationship that can efficiently process materials), if the matching degree is high, it indicates that the current crushing conditions are stable, and the feed rate can be appropriately increased to improve crushing efficiency. If the matching degree is low, it indicates that the crushing system may be in an unstable state, and the feed rate needs to be reduced to avoid affecting the crushing effect due to excessive material supply. In this way, the feed rate is matched with the classifier wheel speed and the airflow pressure to ensure the smoothness and efficiency of the crushing process.

[0027] In one possible implementation, step S200 further includes: Step S210: The axial distance between the classifying wheel and the airflow nozzle can be adjusted within a preset control range, and the control distance value is positively correlated with the initial material particle size.

[0028] Step S220: When the material bulk density exceeds the preset density threshold, the axial spacing shortening adjustment amount is determined by combining the airflow impact kinetic energy.

[0029] Step S230: The axial spacing shortening adjustment is driven by a servo motor. At the same time, the airflow nozzle is configured to stabilize the airflow field in the direction of airflow impact.

[0030] Specifically, the axial distance between the classifying wheel and the airflow nozzle can be adjusted within a preset range, and the value of this adjustable distance is positively correlated with the initial particle size of the material. That is, when the initial particle size of the material is large, the axial distance between the classifying wheel and the airflow nozzle is increased accordingly, and when the initial particle size of the material is small, the axial distance is decreased accordingly. In this way, by adapting to the initial particle size of the material, suitable spatial conditions are provided for subsequent preliminary crushing, ensuring the targeting and effectiveness of the crushing process.

[0031] During the crushing process, the material bulk density in the crushing chamber is monitored in real time by sensors. When the density exceeds the preset density threshold, the corresponding airflow impact kinetic energy is calculated by calling the current airflow pressure parameters (the airflow impact kinetic energy is positively correlated with the airflow pressure). Based on the preset bulk density exceedance value, the mapping relationship between airflow impact kinetic energy and axial spacing reduction (e.g., the greater the density exceedance and the relatively insufficient kinetic energy, the greater the reduction), the axial spacing reduction adjustment amount between the classifying wheel and the airflow nozzle is calculated and determined to enhance the impact effect of the airflow on the bulk material by reducing the spacing.

[0032] The axial distance between the classifying wheel and the airflow nozzle is shortened and adjusted by a servo motor to achieve precise control of the distance adjustment. At the same time, the airflow nozzle is configured to stabilize the airflow field based on the airflow impact direction. Specifically, the spray angle of the airflow nozzle is monitored in real time by an angle sensor, and the impact direction is dynamically corrected based on the axial distance shortening adjustment. Then, guide vanes are set at the outlet of the airflow nozzle, and the inclination angle of the guide vanes is consistent with the spray angle to stabilize the airflow field, ensure that the airflow can impact the material evenly, and improve the crushing efficiency.

[0033] In one possible implementation, step S230 further includes: Step S231: The angle sensor monitors the jet angle of the airflow nozzle in real time, and dynamically corrects the impact direction based on the axial spacing reduction adjustment. Then, a guide vane is set at the outlet of the airflow nozzle, and the tilt angle of the guide vane is consistent with the jet angle.

[0034] Specifically, an angle sensor is used to monitor the spray angle of the airflow nozzle in real time to obtain the actual spray direction of the nozzle. Based on the determined axial spacing reduction adjustment, the impact direction of the airflow nozzle is dynamically corrected to adapt to the airflow action requirements after the spacing adjustment. After the correction is completed, guide vanes are installed at the outlet of the airflow nozzle, and the inclination angle of the guide vanes is kept consistent with the corrected airflow nozzle spray angle. The airflow field is stabilized by the guiding effect of the guide vanes, ensuring that the airflow can impact the material uniformly in the preset direction, thereby improving the crushing efficiency.

[0035] In one possible implementation, step S231 further includes: Based on the honeycomb rectification structure, an airflow buffer section is configured.

[0036] In the airflow buffer section, CFD simulation of airflow field distribution is used. When the local airflow velocity deviation in the pulverizing chamber is detected to exceed the critical value, the pressure difference between adjacent airflow nozzles is finely adjusted.

[0037] Specifically, when configuring an airflow buffer section based on a honeycomb rectifying structure to eliminate high-frequency pulsations generated by airflow impact, a rectifying structure composed of multiple parallel honeycomb units is used, which is installed at the front end of the airflow entering the grinding chamber to form an airflow buffer section. When the airflow impacts and enters this buffer section, the honeycomb units divide and organize the airflow. Through the constraint and guidance of the airflow by the honeycomb walls, the energy concentration area in the airflow is dispersed, thereby attenuating and eliminating the high-frequency pulsations generated during the airflow impact. This allows the airflow to form a stable and uniform flow state after passing through the buffer section before entering the grinding chamber to participate in the material grinding process.

[0038] In the airflow buffer section, a mathematical model of the airflow field is constructed using CFD (Computational Fluid Dynamics) simulation tools. By inputting boundary conditions such as airflow pressure and nozzle parameters, the airflow field characteristics, such as velocity distribution and pressure distribution, are simulated, calculated, and visualized in the buffer section and pulverizing chamber. At the same time, the actual airflow velocity in each area of ​​the pulverizing chamber is monitored in real time by sensors. The monitoring data is compared with the theoretical values ​​simulated by CFD. When a local airflow velocity deviation is found to exceed the preset critical value, the pressure difference is slightly adjusted by adjusting the pressure control valve of the adjacent airflow nozzle to change the flow dynamics of the local airflow, thereby correcting the airflow velocity deviation and keeping the airflow field in the pulverizing chamber uniform and stable.

[0039] In one possible implementation, step S200 further includes: Step S240: Based on the particle size distribution of the material, obtain the D10 characteristic value, D50 characteristic value, and D90 characteristic value.

[0040] Step S250: Perform particle size span analysis using (D90-D10) / D50. When (D90-D10) / D50>2, it is determined that the particle size distribution is too wide, triggering a fine adjustment of the classifier wheel speed.

[0041] Specifically, after obtaining the particle size distribution data of the material, three key feature values ​​are extracted based on this distribution: the D10 feature value, the D50 feature value, and the D90 feature value. The D10 feature value represents that particles smaller than this value account for 10% of the total number of particles in the material, reflecting the particle size boundary of finer particles. The D50 feature value (median particle size) indicates that particles smaller than this value account for 50% of the total number of particles in the material, and is a core indicator for measuring the overall particle size. The D90 feature value means that particles smaller than this value account for 90% of the total number of particles in the material, reflecting the particle size boundary of coarser particles. By obtaining these three feature values, the particle size distribution of the material can be comprehensively characterized, providing basic data support for subsequent particle size range analysis and grinding parameter adjustment.

[0042] After obtaining the characteristic values ​​of D10, D50, and D90, particle size distribution analysis is performed by calculating the ratio (D90-D10) / D50. The numerator (D90-D10) reflects the particle size difference between coarser and finer particles in the material, while the denominator D50 is the median particle size. This ratio directly reflects the breadth of the particle size distribution. When the calculated ratio is greater than 2, it indicates a large difference in particle size, with an excessively wide particle size distribution. In this case, the system triggers a fine-tuning mechanism for the classifying wheel speed, optimizing the classification effect by adjusting the speed of the classifying wheel to make the particle size distribution of the subsequently pulverized particles more concentrated, thus meeting the target particle size requirements.

[0043] In one possible implementation, step S250 further includes: Step S251: When the fine-tuning of the grading wheel speed is triggered, the fine-tuning amount of the speed is determined based on the circumferential wind speed gradient of the grading wheel blades and the adaptation coefficient between the airflow tangential velocity and the centrifugal force of the particles in the grading area corresponding to the grading wheel blades.

[0044] Step S252: Simultaneously correct the blade tilt angle according to the speed fine-tuning amount corresponding to the classifier blade, and monitor the particle interception rate at the classifier outlet to dynamically compensate and optimize the blade tilt angle.

[0045] Specifically, the circumferential wind speed at different positions on the stager blades is collected by an anemometer, and the circumferential wind speed gradient is calculated by dividing the wind speed difference between adjacent measuring points by the corresponding radius difference. Secondly, the tangential velocity of the airflow within the stager area is monitored in real time. Based on the particle mass, current rotational speed, and particle rotation radius, the centrifugal force on the particle is calculated by multiplying the particle mass by the square of the tangential velocity and then dividing by the particle rotation radius. A fitting coefficient is then obtained through a preset mapping function. This coefficient ranges from zero to one, with a value closer to one indicating better fitting. Finally, based on the baseline wind speed gradient and the fitting coefficient, the rotational speed fine-tuning is calculated using an algorithm model that multiplies a proportional coefficient by (1 minus the fitting coefficient) and then by the baseline wind speed gradient. When the fitting coefficient is less than a threshold, the rotational speed fine-tuning is increased; conversely, it is decreased, thus achieving precise rotational speed adjustment.

[0046] The drive device fine-tunes the blade tilt angle synchronously according to the corresponding rotational speed of the classifier blades, ensuring that the blade angle matches the rotational speed change. At the same time, a particle detection device is set at the exit of the classifier to monitor the passing rate of particles that meet the target particle size (i.e., particle interception rate) in real time. The detected interception rate is compared with the preset target value. If there is a deviation, the compensation amount of the blade tilt angle is calculated according to the magnitude of the deviation. The drive device then dynamically fine-tunes the blade tilt angle until the interception rate reaches the expected target, thus achieving precise optimization of the blade tilt angle.

[0047] In one possible implementation, step S300 further includes: Step S310: The adaptive control parameter library is stored in partitions according to material type, and each partition contains multiple sets of optimized crushing parameters.

[0048] Step S320: Classify the multiple sets of optimized crushing parameters corresponding to the material to be crushed, and merge and store the optimized crushing parameters of each group whose cluster center deviation is less than the acceptable error range.

[0049] Specifically, the adaptive control parameter library adopts a storage method based on material type partitioning. That is, different storage areas are divided according to different types of materials (such as metals, non-metals, organic materials, inorganic materials, etc.), and each area is dedicated to storing the crushing parameters for the corresponding type of material. Each partition contains multiple sets of crushing parameters that have been optimized in practice. These parameters cover key indicators that affect the crushing effect, such as the speed of the classifying wheel, the airflow pressure, the axial distance between the classifying wheel and the airflow nozzle, and the feed rate, so that appropriate parameters can be quickly retrieved and configured when processing the same type of material.

[0050] Multiple sets of optimized crushing parameters corresponding to the material to be crushed are extracted from the adaptive control parameter library. These parameters include the speed of the stager wheel, the airflow pressure, and the axial distance between the stager wheel and the airflow nozzle. Then, a clustering algorithm (such as K-means algorithm) is used to classify these parameters and calculate the cluster center of each set of parameters, that is, the average or median of each index in each set of parameters. Then, an acceptable error range is set, and the cluster centers of different sets of parameters are compared. If the deviation is within the range, these sets of parameters are considered to have similar effects. Finally, the optimized crushing parameters that meet the conditions are merged into one set for storage to simplify the parameter library structure and facilitate quick subsequent retrieval.

[0051] In one possible implementation, step S300 further includes: Step S330: Based on the hardness, initial particle size, bulk density, and target particle size requirements, perform a similarity comparison on the newly added crushed material.

[0052] Step S340: If the similarity comparison is successful, the newly added crushed material is used as the same type of crushed material as the material to be crushed, and the crushing parameters of the newly added crushed material are configured.

[0053] Step S350: After configuring the crushing parameters for the newly added crushed material, conduct a small-batch trial crushing. If the pass rate of the trial crushing exceeds the pass rate threshold, then confirm the crushing parameters.

[0054] Specifically, the four parameters of the newly added crushed material—hardness, initial particle size, bulk density, and target particle size requirement—are standardized to eliminate the influence of differences in the magnitude of different parameters. Then, the corresponding parameters of various existing materials are extracted from the parameter library and standardized in the same way. Next, the Euclidean distance algorithm is used to calculate the parameter distance between the new material and various existing materials. The distance formula is the square root of the sum of the squares of the differences of the four standardized parameters. Finally, the calculated distance value is compared with a preset similarity threshold. If the distance value is less than the threshold, the similarity comparison between the new material and the existing material of that type is considered to have passed.

[0055] When the similarity comparison result between the newly added crushed material and the existing materials in the parameter library meets the preset standard (i.e., the similarity comparison passes), the newly added crushed material is classified as the same type of crushed material as the material to be crushed. Subsequently, multiple sets of optimized crushing parameters corresponding to this type of material are retrieved from the adaptive control parameter library. Combined with the specific characteristics of the newly added material (such as details of the initial particle size distribution), the most suitable set of parameters is selected as the basis. The key crushing parameters such as the speed of the classifying wheel, the airflow pressure, the axial distance between the new material and the airflow nozzle, and the feed rate are configured in a targeted manner to ensure that the parameter settings can meet the crushing requirements of the new material.

[0056] After configuring the crushing parameters for the new material, a small batch of the new material is selected for trial crushing. The equipment is run according to the configured parameters such as the classifier wheel speed, airflow pressure, and feed rate. After the trial crushing, the particle size of the product is tested, and the percentage of particles that meet the target particle size requirement is counted, i.e., the trial crushing pass rate. This pass rate is compared with a preset pass rate threshold. If the pass rate exceeds the threshold, the currently configured crushing parameters are confirmed to be suitable for the new material and can be used for subsequent large-scale crushing operations. If the pass rate does not reach the threshold, a secondary optimization process is initiated, and key parameters such as the classifier wheel speed and airflow pressure are readjusted. Trial crushing and testing are then performed again until the pass rate meets the requirements.

[0057] Example 2, Figure 2 This is a schematic diagram of the structure of an electronic device provided in Embodiment 2 of this application, and a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 2 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. Figure 2 As shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 2 Taking a processor 21 as an example, the processor 21, memory 22, input device 23, and output device 24 in an electronic device can be connected via a bus or other means. Figure 2 Taking the example of a connection between China and Israel via a bus.

[0058] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0059] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0060] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A fluidized bed airflow pulverization control method with an adjustable classifier, characterized in that... include: Based on the initial particle size distribution and target particle size requirements of the crushed material sample, the speed of the classifier wheel, airflow pressure and feed rate are configured using fuzzy PID control. In the grinding chamber, the material is initially ground by the rotational speed of the classifying wheel, the airflow pressure, and the axial distance between the classifying wheel and the airflow nozzle. The particle size distribution and particle size span of the material are determined. If the target particle size requirement is met, the material enters the inner cavity of the classifying wheel with the rising airflow. Otherwise, it returns to the grinding chamber. Meanwhile, the feed rate is adjusted according to the material concentration in the fluidized bed, the material collision frequency and particle residence time in the grinding chamber are monitored, and the particle size distribution and particle size span are combined with the classification wheel speed, air pressure and axial distance between the classification wheel and the air nozzle are collected in the continuous grinding cycle to obtain an adaptive control parameter library, and the grinding parameters of the material to be ground are configured. Based on fuzzy PID control, the configuration of the stager speed, airflow pressure, and feed rate includes: The deviation between the initial particle size distribution and the target particle size requirement is divided into M fuzzy levels, corresponding to M fuzzy subsets of the graded wheel speed adjustment amount; Based on the M fuzzy subsets of the graded wheel speed adjustment, a fuzzy rule matrix is ​​established, and the graded wheel speed is configured as the associated control window corresponding to the rated speed mapping. Configure airflow pressure according to the bulk density of the material; The feed rate is configured according to the matching degree between the speed of the classifier wheel and the airflow pressure; In the grinding chamber, preliminary grinding is performed using the classifying wheel speed, airflow pressure, and axial distance between the classifying wheel and the airflow nozzle, including: The axial distance between the classifying wheel and the airflow nozzle can be adjusted within a preset control range, and the control distance value is positively correlated with the initial material particle size; When the material bulk density exceeds the preset density threshold, the axial spacing reduction adjustment amount is determined by combining the airflow impact kinetic energy. The axial spacing reduction adjustment is driven by a servo motor, and at the same time, the airflow nozzle is configured to stabilize the airflow field in the direction of airflow impact. By collecting data on the classifier wheel speed, airflow pressure, and axial distance between the classifier wheel and the airflow nozzle during a continuous grinding cycle, an adaptive control parameter library is obtained, including: The adaptive control parameter library is stored in partitions according to material type, and each partition contains multiple sets of optimized crushing parameters. Multiple sets of optimized crushing parameters corresponding to the material to be crushed are classified, and the optimized crushing parameters of each set whose cluster center deviation is less than the acceptable error range are merged and stored. Configuring the grinding parameters for the material to be ground also includes: Based on hardness, initial particle size, bulk density, and target particle size requirements, a similarity comparison is made between the newly added pulverized materials. If the similarity comparison passes, the newly added material to be crushed will be used as the same type of material to be crushed, and the crushing parameters of the newly added material to be crushed will be configured. After the grinding parameters for the newly added material are configured, a small-batch trial grinding is conducted. If the pass rate of the trial grinding exceeds the pass rate threshold, the grinding parameters are confirmed.

2. The fluidized bed airflow pulverization control method with an adjustable classifier wheel as described in claim 1, characterized in that, The airflow field stabilization configuration of the airflow nozzle according to the airflow impact direction includes: An angle sensor monitors the jet angle of the airflow nozzle in real time, dynamically corrects the impact direction based on the axial spacing reduction adjustment, and then a guide vane is set at the outlet of the airflow nozzle with the guide vane tilt angle consistent with the jet angle.

3. The fluidized bed airflow pulverization control method with an adjustable classifier wheel as described in claim 2, characterized in that... Also includes: Based on the honeycomb rectification structure, an airflow buffer section is configured; In the airflow buffer section, CFD simulation of airflow field distribution is used. When the local airflow velocity deviation in the pulverizing chamber is detected to exceed the critical value, the pressure difference between adjacent airflow nozzles is finely adjusted.

4. The fluidized bed airflow pulverization control method with an adjustable classifier wheel as described in claim 3, characterized in that, Determining the particle size distribution and particle size range of a material includes: Based on the particle size distribution of the material, the D10 characteristic value, D50 characteristic value, and D90 characteristic value are obtained; Particle size span analysis is performed using (D90-D10) / D50. When (D90-D10) / D50>2, it is determined that the particle size distribution is too wide, triggering a fine adjustment of the classifier wheel speed.

5. The fluidized bed airflow pulverization control method with an adjustable classifier wheel as described in claim 4, characterized in that, Triggering fine-tuning of the graded wheel speed also includes: When the speed adjustment of the classifier wheel is triggered, the circumferential wind speed gradient of the classifier wheel blades is used as a reference, and the speed adjustment amount is determined according to the adaptation coefficient between the airflow tangential velocity and the centrifugal force of the particles in the classifier region corresponding to the classifier wheel blades. The blade tilt angle is synchronously corrected according to the speed adjustment of the classifier blades. At the same time, the particle interception rate at the classifier outlet is monitored to dynamically compensate and optimize the blade tilt angle.

6. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the fluidized bed airflow pulverizing control method with adjustable classifier wheel as described in any one of claims 1-5.

Images