Dynamic pressure control jet milling system for improving milling efficiency

Abstract

The invention discloses a dynamic pressure control airflow crushing system for improving crushing efficiency, and relates to the technical field of crushing control, the system is characterized in that the pressure change in a crushing cavity is monitored in real time through a pressure sensor, and the real-time data of the pressure in the crushing process is obtained; according to the characteristics and crushing requirements of the materials, airflow crushing cycle analysis is carried out, and initial control parameters are obtained; in the crushing control process, crushing parameters are collected and fed back; performing difference analysis according to the monitoring control parameter and the crushing monitoring parameter by taking the crushing requirement as a target, and identifying a compensation requirement parameter; and dynamically adjusting the initial control parameters to obtain self-adaptive control parameters. The technical problems that in the prior art, due to the fact that accurate pressure and airflow parameters in a smashing cavity are difficult to obtain in real time, smashing parameters cannot be flexibly adjusted, the smashing efficiency is low, and particle size distribution of products is uneven are solved, and the technical effect of improving the smashing efficiency and the uniformity of discharged particles is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to pulverization control, and in particular to a dynamic pressure controlled airflow pulverization system for improving pulverization efficiency. Background Art

[0002] In the powder processing industry, pulverization efficiency and product quality are important indicators for measuring the performance of production equipment. As an efficient and environmentally friendly pulverization equipment, the airflow pulverization system has long occupied an important position. As an important powder processing method, airflow pulverization technology has been widely used in many fields such as chemical industry, pharmaceuticals, food, and materials science. It uses high-speed airflow to impact, shear and rub the material to achieve material pulverization. Traditional airflow pulverization equipment usually includes a pulverization chamber, one or more nozzles, and a collection system. After the material is fed into the pulverization chamber, it is impacted and sheared by the high-speed airflow ejected from the nozzle, so that the material particles are gradually reduced. At the same time, the airflow in the pulverization chamber will also form eddies and turbulence, further promoting the pulverization of the material. However, due to the fixedness of the airflow speed and direction, the pulverization effect is often limited by the material characteristics and pulverization requirements. For materials of different properties, the airflow parameters need to be adjusted to obtain the best pulverization effect. Traditional airflow pulverization equipment is often difficult to achieve such flexible adjustment. Traditional equipment is prone to generate large energy consumption during the pulverization process, which has a certain impact on the environment and operators. In addition, due to the unevenness of the airflow in the pulverization chamber, the degree of pulverization of the material particles is inconsistent, affecting the particle size distribution and uniformity of the product.

[0003] Therefore, in the current pulverizing control related technologies, it is difficult to obtain accurate pressure and airflow parameters in the pulverizing chamber in real time, resulting in the inability to flexibly adjust the pulverizing parameters, resulting in low pulverizing efficiency and uneven product particle size distribution. Summary of the invention

[0004] The present application provides a dynamic pressure-controlled airflow pulverizing system for improving pulverizing efficiency, thereby solving the technical problems in the prior art that it is difficult to obtain accurate pressure and airflow parameters in the pulverizing chamber in real time, resulting in the inability to flexibly adjust the pulverizing parameters, resulting in low pulverizing efficiency and uneven product particle size distribution, thereby achieving the technical effect of improving pulverizing efficiency and uniformity of output particles.

[0005] The present application provides a dynamic pressure-controlled airflow pulverizing system for improving pulverizing efficiency, the system comprising: a pressure real-time data acquisition module, for monitoring the pressure change in the pulverizing chamber in real time through a pressure sensor, and obtaining real-time data of the pressure during the pulverizing process; an initial control parameter acquisition module, for performing airflow pulverizing cycle analysis according to the characteristics of the material and the pulverizing requirements, and obtaining initial control parameters; a feedback pulverizing parameter acquisition module, for controlling the pressure, airflow intensity, airflow velocity, and injection angle of the pulverizing environment based on the initial control parameters, and collecting feedback pulverizing parameters during the pulverizing control process, the feedback pulverizing parameters comprising monitoring control parameters and pulverizing monitoring parameters, the monitoring control parameters being the pressure, airflow intensity, airflow velocity, and injection angle monitored in the pulverizing chamber, and the pulverizing monitoring parameters being the real-time data obtained by monitoring the particle size distribution of the discharge material and the material flow rate at the discharge port; a compensation demand parameter identification module, for performing difference analysis according to the monitoring control parameters and the pulverizing monitoring parameters with the pulverizing requirements as the target, and identifying compensation demand parameters; an adaptive control parameter acquisition module, for dynamically adjusting the initial control parameters using the compensation demand parameters, and obtaining adaptive control parameters.

[0006] In a possible implementation, the compensation requirement parameter identification module also performs the following processing: according to the monitoring control parameters and the crushing monitoring parameters, the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters are obtained; a mapping relationship between the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters is established; the crushing requirement is used to perform traversal comparison with the distribution data of the crushing monitoring parameters to determine the abnormal deviation distribution; according to the abnormal deviation distribution and the mapping relationship, the abnormal control parameter distribution is determined; compensation tracing is performed according to the abnormal control parameter distribution to obtain the compensation requirement parameter.

[0007] In a possible implementation, the compensation demand parameter identification module also performs the following processing: identifying the airflow path and pressure distribution according to the distribution data of the monitoring and control parameters, and establishing a corresponding relationship between the flow pressure distribution and the distribution data of the monitoring and control parameters; performing position alignment according to the monitoring positioning range of the distribution data of the monitoring and control parameters and the distribution data of the crushing monitoring parameters, and determining the position alignment relationship between the distribution data of the monitoring and control parameters and the distribution data of the crushing monitoring parameters; based on the position alignment relationship, positionally projecting the correspondence between the flow pressure distribution and the distribution data of the monitoring and control parameters, establishing a corresponding relationship between the flow pressure distribution and the distribution data of the crushing monitoring parameters, and obtaining the mapping relationship.

[0008] In a possible implementation, the compensation demand parameter identification module also performs the following processing: constructing a computational fluid dynamics model, inputting the geometric model of the crushing chamber and the distribution data of the monitoring control parameters, simulating the flow path of the gas in the crushing chamber, and obtaining the airflow path; extracting the pressure distribution data according to the distribution data of the monitoring control parameters, and obtaining the pressure distribution.

[0009] In a possible implementation, the compensation requirement parameter identification module also performs the following processing: extracting the abnormal control parameter distribution and its corresponding abnormal deviation distribution based on the abnormal control parameter distribution; obtaining the abnormal particle size uniformity distribution and the abnormal discharge flow rate distribution based on the abnormal deviation distribution; tracing back the compensation of the abnormal control parameter distribution based on the abnormal particle size uniformity distribution to obtain the particle size uniformity compensation parameter; tracing back the compensation of the abnormal control parameter distribution based on the abnormal discharge flow rate distribution to obtain the flow uniformity compensation parameter; fusing the particle size uniformity compensation parameter and the flow uniformity compensation parameter to obtain the compensation requirement parameter.

[0010] In a possible implementation, the compensation demand parameter identification module also performs the following processing: collecting historical control sample sets, including pressure, airflow intensity, airflow velocity, injection angle, crushing particle size, and discharge flow rate; based on the historical control sample sets, taking crushing particle size and crushing particle size uniformity as input variables and control parameters as output targets, performing multiple rounds of cyclic sampling to construct multiple sets of training sample sets; based on the multiple sets of training sample sets, respectively configuring different weight ratios of the pressure, airflow intensity, airflow velocity, and injection angle, clustering the multiple sets of training sample sets according to the weight ratios, and obtaining clustering clusters; respectively performing neural network model training according to the clustering clusters to obtain multiple sub-models, testing the multiple sub-models through test sample sets, and obtaining the accuracy of each sub-model; configuring sub-model weights according to the accuracy of each sub-model, and obtaining a crushing uniformity tracing model through integrated training.

[0011] In a possible implementation, the compensation requirement parameter identification module also performs the following processing: according to the abnormal particle size uniformity distribution, the target crushing particle size and the target particle size uniformity are obtained; the target crushing particle size and the target particle size uniformity are used as input variables, and the control parameter identification and analysis is performed through the crushing uniformity traceability model to output the target control parameters; the abnormal control parameter distribution is compared with the target control parameters to determine the particle size uniformity compensation parameters.

[0012] In a possible implementation, the compensation demand parameter identification module also performs the following processing: identifying the same-direction compensation parameter and the different-direction compensation parameter according to the granularity uniformity compensation parameter and the flow uniformity compensation parameter; constructing an objective function based on the same-direction compensation parameter, taking the maximum sum of granularity uniformity and flow uniformity as the goal, and obtaining the fusion result of the same-direction compensation parameter; setting the control weights of granularity uniformity and flow uniformity; based on the control weights, using the Nash equilibrium algorithm to balance the interests of granularity uniformity and flow uniformity, and obtaining the fusion result of the different-direction compensation parameters; combining the fusion result of the same-direction compensation parameter with the fusion result of the different-direction compensation parameter to obtain the compensation demand parameter.

[0013] The dynamic pressure-controlled airflow pulverizing system proposed in this application for improving pulverizing efficiency is intended to monitor the pressure changes in the pulverizing chamber in real time through a pressure sensor, and obtain real-time data of the pressure during the pulverizing process; perform airflow pulverizing cycle analysis according to the characteristics of the material and the pulverizing requirements to obtain the initial control parameters; collect and feedback the pulverizing parameters during the pulverizing control process; take the pulverizing requirements as the goal, perform difference analysis according to the monitoring control parameters and the pulverizing monitoring parameters, and identify the compensation requirement parameters; dynamically adjust the initial control parameters to obtain adaptive control parameters. This solves the technical problem in the prior art that it is difficult to obtain accurate pressure and airflow parameters in the pulverizing chamber in real time, resulting in the inability to flexibly adjust the pulverizing parameters, resulting in low pulverizing efficiency and uneven product particle size distribution, and achieves the technical effect of improving pulverizing efficiency and uniformity of the output particles. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of a dynamic pressure-controlled airflow pulverizing system for improving pulverizing efficiency provided in an embodiment of the present application; Figure 2 A schematic diagram of the execution process of a compensation requirement parameter identification module in a dynamic pressure-controlled airflow pulverizing system for improving pulverizing efficiency provided in an embodiment of the present application.

[0016] Explanation of the reference numerals: real-time pressure data acquisition module 10 , initial control parameter acquisition module 20 , feedback crushing parameter acquisition module 30 , compensation demand parameter identification module 40 , adaptive control parameter acquisition module 50 . DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application provides a dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency, such as Figure 1 As shown, the system comprises: The pressure real-time data acquisition module 10 is used to monitor the pressure change in the crushing chamber in real time through a pressure sensor, and obtain the real-time data of the pressure during the crushing process.

[0021] Preferably, when the airflow pulverizing equipment is running, a pressure sensor is used to continuously detect the pressure inside the pulverizing chamber, and these real-time pressure values ​​are continuously transmitted and recorded to understand the actual pressure changes in the pulverizing chamber. Specifically, the real-time pressure data may include instantaneous pressure value (the pressure value in the pulverizing chamber at a certain moment, which is used to determine whether the pressure state of the current system reaches the preset working conditions), pressure fluctuation range (during the pulverizing process, the pressure in the pulverizing chamber may fluctuate due to factors such as material characteristics, pulverizing progress, and airflow intensity, which is used to analyze the stability of the pulverizing process), pressure change rate (that is, the pressure change amplitude per unit time, which can reflect the changing trend of the pressure inside the pulverizing chamber. A large change rate indicates that the difficulty of material pulverization has increased or the airflow control is unstable, so that the pulverizing parameters can be adjusted in time), and abnormal pressure alarm (by setting a reasonable pressure threshold, an alarm is triggered when the pressure exceeds a safe or stable range).

[0022] The initial control parameter acquisition module 20 is used to perform airflow pulverization cycle analysis according to the characteristics of the material and the pulverization requirements to obtain the initial control parameters.

[0023] Preferably, before the pulverization process begins, analysis and calculation are performed based on the specific properties of the processed material and the expected pulverization effect requirements to generate initial control parameters. Specifically, material property analysis refers to the analysis of hardness, brittleness, toughness, particle size, density and fluidity, among which materials with higher hardness may require higher air flow speed and intensity, brittle materials are easy to break and are suitable for lower pressure and injection angle, while tough materials require higher air flow intensity. The size of the original particle size determines the initial conditions required for pulverization. Materials with larger particle sizes may require longer pulverization cycles and higher air flow intensity. Density and fluidity affect the injection angle and speed of the air flow to ensure that the material is evenly distributed in the pulverization chamber and effectively pulverized; the setting of pulverization requirements refers to the target particle size distribution (based on The needs of downstream processes or products determine the particle size distribution after crushing) and the discharge flow rate (the discharge flow rate is set according to production needs to ensure that the crushing speed matches the production rhythm); airflow crushing cycle analysis refers to setting a complete crushing cycle according to material characteristics and crushing needs, determining the time required for each stage (for example, the initial acceleration stage, the constant crushing stage, the deceleration discharge stage), and analyzing the airflow change pattern within the crushing cycle, including the changes in airflow intensity and speed, to adapt to the crushing needs of different stages. For example, a higher airflow intensity may be required in the initial stage to quickly break up large pieces of material, while a constant airflow may be maintained in the middle and late stages to ensure uniform particle size. Finally, the initial control parameters are generated based on the analysis results, including airflow intensity, speed, pressure and injection angle.

[0024] The feedback crushing parameter acquisition module 30 is used to control the pressure, airflow intensity, airflow velocity and injection angle of the crushing environment based on the initial control parameters. During the crushing control process, the feedback crushing parameters are collected. The feedback crushing parameters include monitoring control parameters and crushing monitoring parameters. The monitoring control parameters are the pressure, airflow intensity, airflow velocity and injection angle monitored in the crushing chamber. The crushing monitoring parameters are real-time data obtained by monitoring the discharge particle size distribution and the material flow rate at the discharge port.

[0025] Preferably, the pressure, airflow intensity, airflow speed and injection angle of the pulverizing environment are controlled based on the initial control parameters, and the pulverizing parameters are collected and fed back during the pulverizing control process. This means that during the airflow pulverizing process, the pulverizing operation is started by setting the initial control parameters (such as pressure, airflow intensity, speed and injection angle), and the parameters of the pulverizing process are continuously monitored and adjusted during this process to achieve an efficient pulverizing effect. Specifically, the airflow pressure in the pulverizing chamber is controlled by the initially set pressure parameters to meet the pulverizing requirements of different materials. Too high or too low pressure will affect the pulverizing efficiency and product quality. The airflow intensity is adjusted according to the material characteristics and target particle size requirements to ensure that the airflow has sufficient impact force to achieve effective pulverization. An appropriate airflow speed can ensure that the material is quickly and evenly pulverized in the pulverizing chamber. By adjusting the injection angle, the action path of the airflow on the material is optimized so that it can hit the material more efficiently to achieve the expected pulverizing effect. The injection angle also affects the distribution of the airflow in the pulverizing chamber, thereby affecting the fluidity and pulverizing uniformity of the material. During the crushing process, feedback crushing parameters are collected to evaluate the current crushing effect and process status. The feedback crushing parameters are mainly divided into two categories: monitoring and control parameters and crushing monitoring parameters. Among them, monitoring and control parameters refer to the real-time monitored parameters in the crushing chamber, including pressure, airflow intensity, airflow velocity, injection angle, etc.; crushing monitoring parameters refer to the key parameters used to evaluate the crushing effect, including the particle size distribution of the discharge material, real-time monitoring of the particle size distribution of the discharge material after crushing, to ensure that the final product meets the required fineness and uniformity; material flow at the discharge port, monitor the material flow at the discharge port to evaluate the crushing efficiency and the system's processing capacity, to prevent blockage or material accumulation.

[0026] The compensation requirement parameter identification module 40 is used to identify the compensation requirement parameters by performing difference analysis based on the monitoring control parameters and the crushing monitoring parameters with the crushing requirement as the target.

[0027] Preferably, taking the crushing demand as the goal, performing difference analysis based on the monitoring control parameters and the crushing monitoring parameters, and identifying the compensation demand parameters, means that during the airflow crushing process, the actual crushing situation is continuously compared and analyzed with the preset crushing target to detect deviations in the actual operation and determine how to adjust the parameters to meet the crushing demand. Specifically, before starting the crushing, the crushing demand target is set according to the material characteristics and production requirements, usually including particle size distribution, discharge flow rate and production efficiency, etc. By comparing the monitoring control parameters, crushing monitoring parameters and crushing demand targets, the gap between the actual crushing effect and the preset demand is identified. For example, if the discharge particle size distribution does not meet expectations, it means that the airflow intensity or injection angle is not suitable for the current material characteristics; if the discharge flow rate is too large or too small, it means that the material is not in good condition. The air flow velocity or the pressure in the crushing chamber needs to be adjusted; by performing a difference analysis on each parameter, it is possible to evaluate which parameters cause the deviation, as well as the direction and degree of the deviation, that is, to identify the required parameters that need to be compensated and adjusted, which may include the adjustment requirements of air flow intensity, air flow velocity, pressure and injection angle. For example, when the discharge particle size is too large, it is necessary to increase the air flow intensity or adjust the injection angle to increase the crushing force; when the discharge flow rate is too small and the efficiency is low, it is necessary to increase the air flow velocity or reduce the pressure to accelerate the passage of the material; the compensation demand parameters include not only the direction of adjustment (such as increasing or decreasing a certain parameter), but also the amplitude of adjustment, to ensure that the deviation is effectively compensated, to achieve continuous optimization, to make the crushing effect more stable and consistent, and to achieve the goal of efficient and high-quality crushing.

[0028] The adaptive control parameter acquisition module 50 is used to dynamically adjust the initial control parameters using the compensation demand parameters to obtain adaptive control parameters.

[0029] Preferably, the initial control parameters are dynamically adjusted and optimized according to the compensation demand parameters, so that the pulverizing system can adapt to the changes in the pulverizing demand and ensure the best pulverizing effect. Specifically, the initially set control parameters are dynamically adjusted according to the compensation demand parameters to make up for the gap between the actual pulverizing effect and the target effect. For example, if the actual discharge particle size is coarser than the target particle size, the pulverizing force is increased by increasing the airflow intensity or adjusting the injection angle. A closed-loop control is formed through dynamic parameter adjustment, and the initial control parameters are gradually optimized and updated so that they can be adaptively adjusted as the pulverizing state changes, and can automatically adapt to different material properties and pulverizing conditions without manual intervention, ensuring that the particle size distribution and discharge flow rate of the final product meet the preset pulverizing requirements, and ensuring that the pulverizing process is always in an efficient and stable state, thereby achieving precise control of the pulverizing quality and efficiency.

[0030] The dynamic pressure-controlled airflow pulverizing system for improving pulverizing efficiency according to the embodiment of the present invention is used to solve the technical problem that it is difficult to obtain accurate pressure and airflow parameters in the pulverizing chamber in real time, which makes it impossible to flexibly adjust the pulverizing parameters, resulting in low pulverizing efficiency and uneven product particle size distribution, thereby achieving the technical effect of improving pulverizing efficiency and uniformity of discharged particles. The dynamic pressure-controlled airflow pulverizing system for improving pulverizing efficiency includes: a real-time pressure data acquisition module 10, an initial control parameter acquisition module 20, a feedback pulverizing parameter acquisition module 30, a compensation demand parameter identification module 40, and an adaptive control parameter acquisition module 50.

[0031] The specific configuration of the compensation requirement parameter identification module 40 will be described in detail below. Figure 2 As shown, the compensation demand parameter identification module 40 may further include: obtaining the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters according to the monitoring control parameters and the crushing monitoring parameters; establishing a mapping relationship between the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters; performing traversal comparison using the crushing demand and the distribution data of the crushing monitoring parameters to determine the abnormal deviation distribution; determining the abnormal control parameter distribution according to the abnormal deviation distribution and the mapping relationship; performing compensation tracing according to the abnormal control parameter distribution to obtain the compensation demand parameter.

[0032] Preferably, by continuously collecting monitoring and control parameters such as pressure, airflow intensity, airflow velocity, and injection angle in the pulverizing chamber, and statistically analyzing the distribution of these parameters at various time points during the pulverizing process, real-time data of the discharge particle size distribution and the discharge flow rate are collected and statistically analyzed to obtain the distribution of the pulverizing monitoring parameters. Based on the actual pulverizing effects of different parameter combinations, the distribution data of the monitoring and control parameters are associated with the distribution data of the pulverizing monitoring parameters, and a mapping relationship is established to display the influence of different control parameter combinations on the pulverizing results. For example, under a specific combination of airflow velocity, pressure, and injection angle, the changing trends of the discharge particle size distribution and the flow rate may form a stable mapping relationship. The target range of the pulverizing requirement is traversed and compared with the distribution data of the pulverizing monitoring parameters to determine which pulverizing monitoring parameters have not reached the required target, that is, which pulverizing monitoring parameters have not reached the required target. Through traversal comparison, it is identified which crushing monitoring parameters have abnormal distributions and the specific locations of abnormal deviations are determined, thereby determining the abnormal deviation distribution of crushing monitoring parameters and indicating which data points are significantly different from expected values. The control parameters that cause the abnormality are then traced back through the mapping relationship between the abnormal deviation distribution and the monitoring control parameters and crushing monitoring parameters. For example, if an output particle size that does not meet the requirements appears under certain pressures or airflow speeds, the pressure and airflow speed are the abnormal control parameter distributions. Finally, based on the abnormal control parameter distributions, compensation tracing is performed to determine the compensation demand parameters that need to be adjusted, including identifying the adjustment direction and amplitude of the deviation control parameters. If the abnormal deviation distribution shows that the output particle size is too coarse and the mapping relationship indicates that the situation is related to insufficient airflow intensity, the compensation demand parameters are set to increase the airflow intensity.

[0033] The specific configuration of the compensation demand parameter identification module 40 will be described in detail below. The compensation demand parameter identification module 40 may further include: identifying the airflow path and pressure distribution according to the distribution data of the monitoring control parameter, and establishing a corresponding relationship between the flow pressure distribution and the distribution data of the monitoring control parameter; performing position alignment according to the monitoring positioning range of the distribution data of the monitoring control parameter and the distribution data of the crushing monitoring parameter, and determining the position alignment relationship between the distribution data of the monitoring control parameter and the distribution data of the crushing monitoring parameter; and projecting the corresponding relationship between the flow pressure distribution and the distribution data of the monitoring control parameter according to the position alignment relationship, establishing a corresponding relationship between the flow pressure distribution and the distribution data of the crushing monitoring parameter, and obtaining the mapping relationship.

[0034] Preferably, the actual flow direction of the airflow in the pulverizing chamber, i.e., the airflow path, is identified based on the distribution data of the monitoring and control parameters (such as airflow velocity and injection angle, etc.), wherein the airflow path represents the flow direction and flow velocity changes of the airflow in the pulverizing chamber, which affects the uniformity and efficiency of the pulverizing process, and then the pressure sensor data in the pulverizing chamber is used to identify the pressure level and distribution at different positions, and the pressure distribution directly affects the strength and stability of the airflow, and thus affects the pulverizing effect of the material; then, a corresponding relationship is established with the data of the airflow path and pressure distribution and the specific monitoring and control parameters (such as airflow intensity, injection angle, pressure, etc.), indicating how the control parameters in the pulverizing chamber are distributed under different airflow paths and pressures, and then the specific position range of each parameter in the pulverizing chamber is determined based on the data distribution of the monitoring and control parameters and the pulverizing monitoring parameters, and the monitoring positioning range helps to more accurately compare the distribution of different parameters and ensure the consistency of the data in space.

[0035] Preferably, the corresponding relationship between the monitoring control parameters and the crushing monitoring parameters is determined by aligning the distribution data of the two in space, for example, the airflow path and pressure data are spatially matched with the data of the discharge particle size and flow rate, so as to obtain the crushing effects corresponding to the respective positions; according to the position alignment relationship, the airflow path and pressure distribution are projected together with the position of the crushing monitoring parameters, and the corresponding relationship between the flow pressure distribution and the crushing monitoring parameters is established. The position projection can help the system see the influence of the airflow and pressure distribution at a specific position on the crushing effect in the three-dimensional space of the crushing chamber, so as to determine the crushing effect at each position under specific airflow direction and pressure conditions. By integrating the position alignment relationship and position projection of the flow pressure distribution and the crushing monitoring parameters, the mapping relationship between the flow pressure distribution and the crushing effect (discharge particle size distribution, flow rate, etc.) is finally obtained, so as to facilitate the adjustment of the airflow path, pressure distribution and other control parameters according to the real-time feedback of the crushing effect to optimize the crushing effect.

[0036] The specific configuration of the compensation demand parameter identification module 40 will be described in detail below. The compensation demand parameter identification module 40 may further include: constructing a computational fluid dynamics model, inputting a geometric model of the pulverizing chamber and the distribution data of the monitoring control parameters, simulating the flow path of the gas in the pulverizing chamber, and obtaining the gas flow path; extracting pressure distribution data according to the distribution data of the monitoring control parameters, and obtaining the pressure distribution.

[0037] Preferably, a computational fluid dynamics (CFD) simulation environment is established using a geometric model of the pulverizing chamber, which accurately describes the physical structure of the pulverizing chamber, including the shape, size, and arrangement of the inlets and outlets of the chamber. The distribution data of the monitoring and control parameters (such as airflow velocity, airflow intensity, injection angle, inlet pressure, etc.) during the pulverizing process are input into the computational fluid dynamics model. Based on the input geometric model and monitoring and control parameters, the flow path of the gas in the pulverizing chamber is simulated by numerical calculation, including calculating the trajectory, flow direction, and velocity change of the gas particles, identifying the distribution of the airflow in the chamber, and then obtaining the airflow path. During the simulation, the CFD model calculates the pressure values ​​at different positions in the chamber based on fluid dynamics equations (such as the Navier-Stokes equations), and generates pressure distribution data in the entire pulverizing chamber, including the specific pressure values ​​at each position in the pulverizing chamber, and how the pressure changes with spatial position, providing key data support for optimizing the pulverizing environment and facilitating real-time adjustment of control parameters to meet pulverizing requirements.

[0038] The specific configuration of the compensation demand parameter identification module 40 will be described in detail below. The compensation demand parameter identification module 40 may further include: extracting the abnormal control parameter distribution and its corresponding abnormal deviation distribution according to the abnormal control parameter distribution; obtaining the abnormal distribution of uniform particle size and the abnormal distribution of discharge flow according to the abnormal deviation distribution; tracing the abnormal control parameter distribution according to the abnormal distribution of uniform particle size to obtain the compensation parameter of uniform particle size; tracing the abnormal control parameter distribution according to the abnormal distribution of discharge flow to obtain the compensation parameter of uniform flow; fusing the compensation parameter of uniform particle size and the compensation parameter of uniform flow to obtain the compensation demand parameter.

[0039] Preferably, the abnormal control parameter distribution refers to the distribution of control parameters that deviate from the normal range during the crushing process, such as abnormal airflow intensity, injection angle or pressure value. According to the specific position and deviation amplitude of the abnormal control parameter, an abnormal deviation distribution is generated to show the degree of abnormality of different positions and parameters. By analyzing the abnormal deviation distribution, the parameter abnormality that leads to uneven particle size is identified, and an abnormal distribution of uniform particle size is generated to show which positions cause the particle size distribution after crushing to deviate from the target, and the abnormal deviation that affects the discharge flow rate is identified, and an abnormal distribution of discharge flow rate is generated to show how the abnormalities at different positions affect the discharge flow consistency, that is, to obtain an abnormal distribution of uniform particle size and an abnormal distribution of discharge flow rate.

[0040] Preferably, according to the abnormal distribution of uniform particle size, the control parameters that need to be adjusted (such as airflow intensity or injection angle) are identified and the compensation amplitude is calculated to achieve uniform particle size. For example, if the abnormal distribution of particle size shows that the particle size in some areas is too coarse, the abnormal control parameters in this area are traced back to increase the airflow intensity or adjust the injection angle; according to the abnormal distribution of discharge flow rate, the abnormal control parameters are compensated and adjusted. For example, if the discharge flow rate at a certain position is low, the flow rate may be balanced by increasing the airflow speed or reducing the pressure, thereby obtaining the flow uniformity compensation parameter; the particle size uniformity compensation parameter and the flow uniformity compensation parameter are merged to generate the final compensation demand parameter, which combines the compensation requirements for particle size and flow uniformity, ensures that the crushing system meets the particle size requirements while maintaining the consistency of the discharge flow rate, and is used to adjust the control parameters in real time, automatically optimize the airflow intensity, pressure and injection angle, ensure the uniformity of particle size and flow, and improve the crushing efficiency and effect.

[0041] The specific configuration of the compensation demand parameter identification module 40 will be described in detail below. The compensation demand parameter identification module 40 may further include: collecting a historical control sample set, including pressure, airflow intensity, airflow velocity, injection angle, crushing particle size, and discharge flow rate; based on the historical control sample set, taking crushing particle size and crushing particle size uniformity as input variables and control parameters as output targets, performing multiple rounds of cyclic sampling to construct multiple sets of training sample sets; based on the multiple sets of training sample sets, respectively configuring different weight ratios of the pressure, airflow intensity, airflow velocity, and injection angle, clustering the multiple sets of training sample sets according to the weight ratios, and obtaining clustering clusters; respectively training neural network models according to the clustering clusters to obtain multiple sub-models, testing the multiple sub-models through test sample sets, and obtaining the accuracy of each sub-model; configuring sub-model weights according to the accuracy of each sub-model, and obtaining a crushing uniformity traceability model through integrated training.

[0042] Preferably, historical data of the operation of the pulverizing system are collected as a historical control sample set, including control parameters such as pressure, airflow intensity, airflow velocity, and injection angle, as well as pulverizing effect indicators (such as particle size after pulverization, particle size uniformity, discharge flow rate, etc.), with pulverized particle size and particle size uniformity as input variables (i.e., target quality indicators), and various control parameters (pressure, airflow intensity, airflow velocity, injection angle, etc.) as output targets, multiple rounds of cyclic sampling are performed to generate multiple sets of training sample sets, each set of training sample sets includes a set of input variables (pulverizing effect indicators) and corresponding control parameters; in the multiple sets of training sample sets, different weight ratios are configured for each control parameter (pressure, airflow intensity, airflow velocity, injection angle), wherein the weight ratio reflects the relative importance of the control parameter to the pulverizing effect, and then based on the configured weight ratio, a clustering algorithm ( The sample set is clustered by using a neural network (such as K-means), and similar control parameter combinations are classified into the same cluster to obtain cluster clusters. Each cluster cluster represents a class of similar control parameter settings and their crushing effects. A prediction model is constructed based on a neural network, and the data of each cluster cluster is used for training to obtain multiple neural network sub-models. Each neural network sub-model can predict the impact of different parameter combinations on the crushing effect. Each sub-model is verified using an independent test sample set to test its accuracy in predicting crushing uniformity. The accuracy of each sub-model is evaluated, and weights of corresponding sizes are configured (high-precision sub-models occupy a larger proportion). Finally, multiple sub-models are combined together through an integrated learning method (such as weighted average or stacking model) to form a crushing uniformity tracing model, which can predict and trace crushing uniformity under different parameter combinations.

[0043] The specific configuration of the compensation demand parameter identification module 40 will be described in detail below. The compensation demand parameter identification module 40 may further include: obtaining a target crushing particle size and a target particle size uniformity according to the particle size uniformity abnormal distribution; using the target crushing particle size and the target particle size uniformity as input variables, performing control parameter identification analysis through the crushing uniformity traceability model, and outputting a target control parameter; using the target control parameter to compare the abnormal control parameter distribution, and determining the particle size uniformity compensation parameter.

[0044] Preferably, the target crushing particle size and target particle size uniformity are obtained according to the particle size uniformity abnormal distribution, and the crushing uniformity tracing model is used to determine the target control parameters to optimize the control parameter settings to compensate and correct the abnormal conditions, thereby achieving the ideal crushing effect. Specifically, the target crushing particle size (ideal particle size) and target particle size uniformity (uniform distribution of particles) are obtained according to the particle size uniformity abnormal distribution to represent the expected crushing effect, and then the target crushing particle size and target particle size uniformity are used as input variables and input into the crushing uniformity tracing model. The crushing uniformity tracing model uses historical data and sub-models obtained from multiple rounds of training to predict the required time to achieve these goals through the target particle size and uniformity information. Control parameters, that is, output target control parameters (such as pressure, airflow intensity, airflow velocity and injection angle, etc.), and then compare the target control parameters output by the traceability model with the current abnormal control parameter distribution one by one, identify the deviation between the abnormal control parameters and the target value, and determine the specific gap between each abnormal control parameter and the target control parameter, including the adjusted parameters and the adjustment amplitude and direction. Finally, based on the results of the deviation comparison, generate particle size uniformity compensation parameters, including specific adjustments to the abnormal control parameters. For example, if the abnormal distribution shows that the current airflow intensity is lower than the target value, the particle size uniformity compensation parameters will recommend increasing the airflow intensity or fine-tuning the injection angle to optimize the crushing effect.

[0045] The specific configuration of the compensation demand parameter identification module 40 will be described in detail below. The compensation demand parameter identification module 40 may further include: identifying the same direction compensation parameter and the opposite direction compensation parameter according to the granularity uniformity compensation parameter and the flow uniformity compensation parameter; constructing an objective function based on the same direction compensation parameter, taking the maximum sum of granularity uniformity and flow uniformity as the goal, and obtaining the fusion result of the same direction compensation parameter; setting the control weights of granularity uniformity and flow uniformity; based on the control weights, using the Nash equilibrium algorithm to balance the interests of granularity uniformity and flow uniformity, and obtaining the fusion result of the opposite direction compensation parameter; combining the fusion result of the same direction compensation parameter with the fusion result of the opposite direction compensation parameter to obtain the compensation demand parameter.

[0046] Preferably, according to the particle size uniformity compensation parameter and the flow rate uniformity compensation parameter, the same-direction and different-direction compensation parameters are identified, and they are fused through the objective function and the Nash equilibrium algorithm to obtain the final compensation demand parameters. Specifically, the same-direction compensation parameter refers to the control parameter with the same compensation direction during the adjustment of particle size uniformity and flow rate uniformity, and the different-direction compensation parameter refers to the control parameter that has opposite effects on particle size uniformity and flow rate uniformity during the adjustment process. For example, adjusting a certain parameter may improve particle size uniformity but have a negative impact on flow rate uniformity. Based on the same-direction compensation parameter, an objective function is constructed to maximize the comprehensive score of particle size uniformity and flow rate uniformity (i.e., the sum of the two is maximized), and the objective function is optimized to obtain the fusion result of the same-direction compensation parameter, ensuring the simultaneous improvement of particle size and flow rate uniformity and achieving the best mutual synergy. The best effect is achieved; different weights are assigned to particle size uniformity and flow uniformity according to actual needs or priorities. For example, if particle size uniformity is more important, a higher weight is assigned to ensure that more emphasis is placed on particle size uniformity in the final optimization. Then, the Nash equilibrium algorithm is used to balance the interests of the anisotropic compensation parameters based on the set weights to achieve a balance between particle size uniformity and flow uniformity, and to obtain the fusion results of the anisotropic compensation parameters, wherein the Nash equilibrium algorithm ensures that the best balance between the two is found without harming either party; finally, the fusion results of the same-direction compensation parameters are integrated with the fusion results of the anisotropic compensation parameters to obtain the final compensation demand parameters, which integrate the optimization of particle size and flow uniformity to ensure a balance between the two aspects and are used to adjust the control parameters of the crushing system in real time to achieve the optimal uniformity of particle size and flow.

[0047] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0048] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency, characterized in that: The system comprises: A pressure real-time data acquisition module is used to monitor the pressure changes in the crushing chamber in real time through a pressure sensor and obtain real-time data of the pressure during the crushing process; The initial control parameter acquisition module is used to analyze the airflow crushing cycle according to the characteristics of the material and the crushing requirements to obtain the initial control parameters; A feedback pulverizing parameter acquisition module is used to control the pressure, airflow intensity, airflow velocity, and injection angle of the pulverizing environment based on the initial control parameters. In the pulverizing control process, feedback pulverizing parameters are collected. The feedback pulverizing parameters include monitoring control parameters and pulverizing monitoring parameters. The monitoring control parameters are the pressure, airflow intensity, airflow velocity, and injection angle monitored in the pulverizing chamber. The pulverizing monitoring parameters are real-time data obtained by monitoring the particle size distribution of the discharge material and the material flow rate at the discharge port. A compensation demand parameter identification module is used to identify compensation demand parameters by performing difference analysis based on the monitoring control parameters and the crushing monitoring parameters with the crushing demand as the target; The adaptive control parameter acquisition module is used to dynamically adjust the initial control parameters using the compensation demand parameters to obtain adaptive control parameters.

2. The dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency according to claim 1, characterized in that: The steps performed by the compensation requirement parameter identification module include: According to the monitoring control parameters and the crushing monitoring parameters, the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters are obtained; Establishing a mapping relationship between the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters; Using the distribution data of the crushing demand and the crushing monitoring parameter to perform traversal comparison and determine the abnormal deviation distribution; Determining abnormal control parameter distribution according to the abnormal deviation distribution and the mapping relationship; Compensation tracing is performed according to the abnormal control parameter distribution to obtain the compensation demand parameter.

3. The dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency according to claim 2, characterized in that: The steps performed by the compensation requirement parameter identification module include: According to the distribution data of the monitoring and control parameters, identifying the airflow path and pressure distribution, and establishing a corresponding relationship between the flow pressure distribution and the distribution data of the monitoring and control parameters; According to the monitoring positioning range of the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters, position alignment is performed to determine the position alignment relationship between the distribution data of the monitoring control parameters and the distribution data of the crushing monitoring parameters; According to the position alignment relationship, the corresponding relationship between the flow pressure distribution and the distribution data of the monitoring control parameter is positionally projected, and the corresponding relationship between the flow pressure distribution and the distribution data of the crushing monitoring parameter is established to obtain the mapping relationship.

4. The dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency according to claim 3, characterized in that: The steps performed by the compensation requirement parameter identification module include: Construct a computational fluid dynamics model, input the geometric model of the pulverizing chamber and the distribution data of the monitoring and control parameters, simulate the flow path of the gas in the pulverizing chamber, and obtain the airflow path; According to the distribution data of the monitoring control parameters, the pressure distribution data is extracted to obtain the pressure distribution.

5. The dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency according to claim 2, characterized in that: The steps performed by the compensation requirement parameter identification module include: According to the abnormal control parameter distribution, extracting the abnormal control parameter distribution and its corresponding abnormal deviation distribution; Obtaining uniform abnormal distribution of particle size and abnormal distribution of discharge flow rate according to the abnormal deviation distribution; According to the particle size uniform abnormal distribution, compensation tracing is performed on the abnormal control parameter distribution to obtain a particle size uniform compensation parameter; According to the abnormal distribution of the discharge flow, compensation tracing is performed on the abnormal control parameter distribution to obtain flow uniformity compensation parameters; The compensation requirement parameter is obtained by fusing the particle size uniformity compensation parameter and the flow uniformity compensation parameter.

6. The dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency according to claim 5, characterized in that: The steps performed by the compensation requirement parameter identification module include: Collect historical control sample sets, including pressure, airflow intensity, airflow velocity, injection angle, crushing particle size, and discharge flow rate; According to the historical control sample set, multiple rounds of sampling are performed with crushing particle size and crushing particle size uniformity as input variables and control parameters as output targets to construct multiple sets of training sample sets; Based on the multiple sets of training sample sets, respectively configure different weight ratios of the pressure, airflow intensity, airflow velocity, and injection angle, and cluster the multiple sets of training sample sets according to the weight ratios to obtain cluster clusters; Performing neural network model training respectively according to the clustering clusters to obtain multiple sub-models, and testing the multiple sub-models through a test sample set to obtain the accuracy of each sub-model; The sub-model weights are configured according to the accuracy of each sub-model, and a crushing uniformity tracing model is obtained through integrated training.

7. The dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency according to claim 6, characterized in that: The steps performed by the compensation requirement parameter identification module include: According to the particle size uniformity and abnormal distribution, a target crushing particle size and a target particle size uniformity are obtained; Taking the target crushing particle size and target particle size uniformity as input variables, performing control parameter identification and analysis through the crushing uniformity traceability model, and outputting target control parameters; The target control parameter is used to compare the abnormal control parameter distribution, and the particle size uniformity compensation parameter is determined.

8. The dynamic pressure controlled airflow pulverizing system for improving pulverizing efficiency according to claim 5, characterized in that: The steps performed by the compensation requirement parameter identification module include: Identify a same-direction compensation parameter and a different-direction compensation parameter according to the particle size uniformity compensation parameter and the flow rate uniformity compensation parameter; Based on the same-direction compensation parameters, an objective function is constructed, with the maximum sum of particle size uniformity and flow uniformity as the goal, to obtain a fusion result of the same-direction compensation parameters; Set the control weights for uniform particle size and uniform flow; Based on the control weight, a Nash equilibrium algorithm is used to perform a balancing process of the interests of granularity uniformity and flow uniformity, and a fusion result of the anisotropic compensation parameter is obtained; The fusion result of the in-direction compensation parameter is combined with the fusion result of the out-of-direction compensation parameter to obtain the compensation requirement parameter.

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