A kind of productivity control system and control method based on the air volume and air pressure of pulverizer system
By monitoring the air volume and air pressure of the pulverizer system, and combining data acquisition and anomaly monitoring modules, the feed rate is adjusted in real time, which solves the problem of unstable production capacity of the pulverizer system and achieves maximum production capacity and stable system operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BUHLER CHANGZHOU MASCH CO LTD
- Filing Date
- 2024-11-27
- Publication Date
- 2026-05-12
AI Technical Summary
The capacity of existing crusher systems is unstable, and they are prone to shutdown due to screen blockage. Furthermore, they rely on manual experience for control, which is inefficient and makes it difficult to maximize capacity.
By monitoring the air volume and pressure of the ventilation network, and combining data acquisition, production capacity prediction and anomaly monitoring modules, the feed rate of the crusher is adjusted in real time to avoid screen blockage and maintain minimum airflow, thereby achieving maximum motor load operation and dynamically optimizing production capacity.
This ensured the stable operation of the crusher system, prevented screen clogging, improved capacity utilization and work efficiency, and guaranteed production continuity.
Smart Images
Figure CN119702220B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of material crushing technology, specifically relating to a capacity control system and control method based on the air volume and air pressure of a crusher system. Background Technology
[0002] like Figure 1 As shown, the current material crushing system includes a buffer silo, a feeder, a hammer mill, and a blower. In the crushing process, grains or coarse particles are stored in the buffer silo above the hammer mill, discharged through the feeder, and sent into the airflow before entering the hammer mill. The material undergoes inelastic collisions with the hammers, which travel at speeds up to 110 m / s. Particles struck by the hammers burst into a cloud of dust and moisture. The impact generates destructive energy and heat, causing the air to heat up due to energy dissipation. The evaporated moisture is absorbed by the air, while fine dust and moisture are carried away by the controlled airflow. Larger particles bounce back from the screen and are struck again.
[0003] During this process, products with high fat content can cause clogging of the screen pores, increasing resistance, reducing airflow, and leading to increased temperature. Increased temperature causes the product to become more elastic, thus reducing grinding efficiency and causing the temperature to rise further.
[0004] In actual production, when the crusher's working efficiency is low, operators rely on experience to increase the amount of material entering the crushing chamber. However, due to the different characteristics of the processed materials, relying solely on manual experience results in low accuracy, which may cause screen blockage, abnormal shutdown of the crusher, and stagnation of the entire production line. This leads to problems such as decreased productivity and unstable production capacity, making it difficult to optimize production capacity in real time. Summary of the Invention
[0005] In view of the above-mentioned problems in the prior art, the purpose of the present invention is to provide a production capacity control system based on the air volume and air pressure of a crusher system. By monitoring the air volume and air pressure of the air network, the system can determine whether there is a tendency for the screen to become clogged. While ensuring that the screen does not become clogged, the system can maintain the minimum airflow required for crushing and transportation, and operate the motor at its maximum load at this time to maximize production capacity.
[0006] A capacity control system based on airflow and air pressure of a pulverizer system, comprising:
[0007] The data acquisition module is used to acquire real-time data on motor energy consumption, wind grid operation, and material production during the actual production process.
[0008] The capacity prediction module is used to calculate the predicted value of the crusher's real-time optimal capacity based on the actual data collected by the data acquisition module and the capacity prediction model.
[0009] The anomaly monitoring module includes a screen anomaly monitoring unit and an inefficient operation monitoring unit;
[0010] The screen anomaly monitoring unit is used to determine whether there is a clogging trend in the screen of the crusher based on the collected air network operation data. If there is a clogging trend, it will be fed back to the capacity adjustment module to reduce the feed rate of the crusher.
[0011] The inefficient operation monitoring unit is used to determine whether the crusher is operating inefficiently based on the collected motor energy consumption data and material production data. If the crusher is operating inefficiently, it will be fed back to the capacity adjustment module to increase the feed rate of the crusher.
[0012] The capacity adjustment module is used to dynamically adjust the feed rate of the crusher based on feedback information from the anomaly monitoring module.
[0013] The anomaly monitoring module determines whether there is a tendency for the screen of the crusher to become clogged based on the real-time data collected by the data acquisition module. While ensuring that the screen does not become clogged, it maintains the minimum airflow required for crushing and transportation, and makes the motor of the crusher run at its maximum load at this time, adjusting the feed rate of the crusher to maximize the production capacity.
[0014] Preferably, the motor energy consumption data includes motor current and motor voltage; the wind network operation data includes air volume, air pressure and time of the test section; and the material production data includes actual material fineness and actual material output.
[0015] Preferably, the screen anomaly monitoring unit compares the actual data of air pressure and air volume collected by the data acquisition module with the screen blockage anomaly threshold. If the actual data of air pressure and air volume are greater than the screen blockage anomaly threshold, it is determined that there is an abnormal trend of screen blockage in the current crusher.
[0016] Preferably, the inefficient operation monitoring unit compares the actual motor energy consumption data and actual production capacity data collected by the data acquisition module with the inefficient production anomaly threshold. If the actual motor energy consumption data and actual production capacity data are less than the inefficient production anomaly threshold, it is determined that the current crusher has an abnormal situation of inefficient operation.
[0017] Preferably, it also includes a model optimization module, which is used to record the actual data collected by the data acquisition module and compare the actual output of materials with the predicted value of the capacity prediction module, and record the difference data; and to optimize and train the capacity prediction module based on the recorded real-time data to improve the accuracy of the capacity prediction model.
[0018] Preferably, the process of building the capacity prediction model is as follows: a capacity prediction model is constructed by collecting sample data; and the capacity prediction model is calibrated under no-load conditions; the sample data includes motor current I, wind pressure P, and wind volume Q. The sample data is divided into training set data and validation set data. An initial prediction model is constructed by using the training set data, and the accuracy of the initial prediction model is verified by iterative calculation using the validation set data, thereby obtaining the final capacity prediction model.
[0019] Preferably, the capacity prediction model is built using a numerical prediction algorithm, and the specific construction process is as follows:
[0020] An initial prediction model is constructed by fitting the correspondence between motor current I, wind pressure P, wind volume Q and actual material output in the training set data using a data prediction algorithm.
[0021] The input variables of the capacity forecasting model are set as [X1,X2,X3]=[I,P,Q], and the output variable is the optimal capacity forecast value Y. The optimal capacity forecast value Y is calculated using the ReLU activation function. Where, ω i Let b be the weight and b be the bias. We continuously optimize the weights and biases to minimize the error of the fitting result.
[0022] The initial prediction model is iteratively trained using validation set data to achieve validation. The difference between the predicted value and the actual value is calculated. If the difference exceeds the set range, the model structure is adjusted or the model is retrained to obtain the validated model, until the prediction results of the capacity prediction model meet the error requirements.
[0023] Another objective of this invention is to propose a capacity control method based on the airflow and air pressure of a pulverizer system, applicable to pulverizer control systems containing screens, specifically including the following steps:
[0024] Acquire data on motor energy consumption, wind grid operation, and material production during actual production processes;
[0025] Based on the actual data obtained, the predicted value of the real-time optimal capacity of the crusher is calculated using the capacity prediction model.
[0026] In actual production, it is necessary to determine whether there is a tendency for the screen of the crusher to become clogged and whether the crusher is operating inefficiently, and adjust the feed rate of the crusher based on the judgment results;
[0027] Record motor energy consumption data, wind network operation data, and material production data during the actual production process. Compare the actual material output with the predicted value of the optimal capacity, record the difference data, and optimize and train the capacity prediction module based on the recorded real-time data to improve the accuracy of the capacity prediction model.
[0028] Preferably, the motor energy consumption data includes motor current and motor voltage; the wind network operation data includes air volume, air pressure and time of the test section; and the material production data includes actual material fineness and actual material output.
[0029] Preferably, the screen of the crusher is judged to be clogged based on the collected air network operation data. If the screen of the crusher is clogged, the feed rate of the crusher is reduced to avoid the screen being clogged due to excessive feed rate.
[0030] Based on the collected motor energy consumption data and material production data, it is determined whether the crusher is operating inefficiently. If the crusher is operating inefficiently, the feed rate of the crusher is increased to improve the working efficiency of the crusher.
[0031] The beneficial effects of this invention are: the capacity control system and method based on the air volume and air pressure of the crusher system are equipped with an anomaly monitoring module, which determines whether there is a tendency for the screen of the crusher to become clogged based on the real-time data collected by the data acquisition module. On the basis of ensuring that the screen does not become clogged, the minimum airflow required for crushing and transportation is maintained, and the motor of the crusher is operated at the maximum load at this time. The feed rate of the crusher is adjusted to maximize the capacity.
[0032] By interconnecting the anomaly monitoring module, capacity prediction module, and capacity adjustment module, the system can promptly detect the tendency for screen clogging in the crusher and reduce the feed rate in time to prevent actual screen blockage, ensuring continuous operation of the crusher and preventing abnormal shutdowns. Furthermore, it can promptly detect inefficient operation of the crusher and increase the feed rate to improve its capacity, thereby enhancing work efficiency. Attached Figure Description
[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0034] Figure 1 This is a schematic diagram of the material crushing system involved in the present invention;
[0035] Figure 2 This is a system block diagram of the present invention;
[0036] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation
[0037] Example 1
[0038] like Figure 2As shown, a capacity control system based on the airflow and air pressure of a pulverizer system is applicable to pulverizers containing screens. By monitoring the airflow and air pressure of the air network, the system determines whether the screen is prone to clogging. While ensuring that the screen does not clog, it maintains the minimum airflow required for pulverizing and conveying, and operates the motor at its maximum load at this time. The feed rate of the pulverizer is adjusted to maximize capacity.
[0039] The capacity control system includes a data acquisition module, a capacity forecasting module, an anomaly monitoring module, a capacity adjustment module, and a model optimization module.
[0040] Data acquisition module: Real-time acquisition of motor energy consumption data, ventilation network operation data, and material production data during the actual production process. Motor energy consumption data includes motor current and voltage; ventilation network operation data includes airflow, air pressure, and time in the test section; material production data includes actual material fineness and actual material output.
[0041] Capacity prediction model: Based on the actual data collected by the data acquisition module, the predicted value of the crusher's real-time optimal capacity is calculated using the capacity prediction model.
[0042] Specifically, the capacity forecasting model is built using numerical prediction algorithms, and the specific construction process is as follows:
[0043] Collect sample data: Construct a capacity prediction model using sample data. The sample data includes motor current I, wind pressure P, and wind volume Q. The sample data is divided into training set data and validation set data. An initial prediction model is constructed using the training set data, and the accuracy of the initial prediction model is verified through iterative calculations using the validation set data, thus obtaining the final capacity prediction model.
[0044] First, an initial prediction model is constructed by fitting the correspondence between motor current I, wind pressure P, wind volume Q and actual material output in the training set data using a data prediction algorithm.
[0045] The input variables for the capacity forecasting model are set as [X1, X2, X3] = [I, P, Q], and the output variable is the optimal capacity forecast value Y. Specifically, the relationship between the input and output data can be handled using the ReLU or LeakyReLU activation functions. Taking the ReLU activation function as an example, the output is the optimal capacity forecast value. Where, ω i denoted by , and b is the bias. The value of the bias b is related to parameters such as motor voltage, ventilation time, and actual material fineness. The weights and biases are continuously optimized to minimize the error of the fitting results.
[0046] Secondly, the initial prediction model is iteratively trained using validation set data to validate the model. The difference between the predicted value and the actual value is calculated. If the difference exceeds the set range, the model structure is adjusted or the model is retrained to obtain the validated model, until the prediction results of the capacity prediction model meet the error requirements.
[0047] The validated model, as a capacity prediction model, needs to be calibrated under no-load conditions. Specifically, while the crusher is running under no-load conditions, standard values of motor current I, air pressure P, and air volume Q are collected and recorded, and input into the prediction model as standard data. Validation and calibration improve the accuracy of the capacity prediction model's results.
[0048] Anomaly monitoring module: includes a screen anomaly monitoring unit and an inefficient operation monitoring unit.
[0049] Screen anomaly monitoring unit: Based on the actual data of air pressure and air volume collected by the data acquisition module, it determines whether there is a tendency for the screen to become clogged. If so, it promptly reports the clogging tendency to the crusher and makes adjustments before the screen becomes clogged, such as reducing the feed rate of the crusher.
[0050] Specifically, since the air pressure and air volume of the air network increase when the screen is clogged, the actual data of the collected air pressure and air volume are compared with the screen clogging anomaly threshold. If the actual data is greater than the anomaly threshold, it indicates that the crusher has an abnormal trend of screen clogging. The screen anomaly monitoring unit monitors the values of air pressure and air volume in real time and compares the actual data with the screen clogging anomaly threshold. It provides timely feedback before screen clogging occurs, driving the capacity adjustment module to reduce the feed rate of the crusher, thereby avoiding continuous feeding that could lead to actual screen clogging and prevent affecting the normal operation of the crusher.
[0051] Inefficient Operation Monitoring Unit: Based on the actual motor energy consumption and actual production capacity data collected by the data acquisition module, this unit determines whether the crusher is operating inefficiently. If so, it promptly reports the inefficient operation and adjusts the production capacity control accordingly, increasing the feed rate to improve capacity.
[0052] Specifically, due to the correlation between motor operation data and production capacity data, the collected actual motor energy consumption data and actual production capacity data are compared with the inefficient production anomaly threshold. If the actual production capacity data is less than the inefficient production anomaly threshold, it is reported that the crusher is inefficiently operating.
[0053] Capacity adjustment module: Dynamically adjusts the feed rate of the crusher based on feedback information from the anomaly monitoring module.
[0054] On the one hand, when there is an abnormal tendency for the screen to become clogged in the crusher, the feed rate of the crusher should be reduced to avoid actual screen clogging.
[0055] On the other hand, when the crusher experiences inefficient operation, the optimal capacity prediction is correlated with the actual material output, and the crusher's feed rate is increased to improve capacity. It is important to note that when dynamically adjusting the crusher's feed rate to increase capacity, the anomaly monitoring module must be constantly monitored to prevent excessive feed rate from clogging the screen and affecting the crusher's normal operation.
[0056] The model optimization module records motor energy consumption data, ventilation network operation data, and material production data of the crusher under different operating conditions. It compares the actual material output with the predicted values from the capacity prediction module, records the deviation data, and optimizes the capacity prediction module based on the recorded real-time data to improve the accuracy of the capacity prediction model. The specific process of the model optimization module is the same as that in existing technologies and will not be elaborated here. The main purpose of the model optimization module is to improve the accuracy of the capacity prediction model's prediction results.
[0057] Example 2
[0058] like Figure 3 As shown, the second aspect of this invention is to propose a capacity control method based on the air volume and air pressure of a pulverizer system, applicable to pulverizer control systems containing screens, specifically including the following steps:
[0059] Acquire data on motor energy consumption, ventilation network operation, and material production during actual production. Motor energy consumption data includes motor current and voltage; ventilation network operation data includes airflow, air pressure, and time in the test section; and material production data includes actual material fineness and actual material output.
[0060] Based on the actual data obtained, the predicted value of the crusher's real-time optimal capacity is calculated using a capacity prediction model.
[0061] In actual production, it is necessary to determine whether there is a tendency for the screen of the crusher to become clogged and whether the crusher is operating inefficiently, and adjust the feed rate of the crusher based on the judgment results.
[0062] On the one hand, based on the collected air network operation data, it is determined whether there is a tendency for the screen of the crusher to become clogged. If there is a tendency for the screen of the crusher to become clogged, the feed rate of the crusher is reduced to avoid the screen becoming clogged due to excessive feed rate.
[0063] On the other hand, based on the collected motor energy consumption data and material production data, it can be determined whether the crusher is operating inefficiently. If the crusher is operating inefficiently, the feed rate of the crusher can be increased to improve the working efficiency of the crusher.
[0064] Record motor energy consumption data, wind network operation data, and material production data during the actual production process. Compare the actual material output with the predicted value of the optimal capacity, record the difference data, and optimize and train the capacity prediction module based on the recorded real-time data to improve the accuracy of the capacity prediction model.
[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A capacity control system based on airflow and air pressure of a pulverizer system, characterized in that, include: The data acquisition module is used to acquire real-time data on motor energy consumption, wind grid operation, and material production during the actual production process. The capacity prediction module is used to calculate the predicted value of the crusher's real-time optimal capacity based on the actual data collected by the data acquisition module and the capacity prediction model. The anomaly monitoring module includes a screen anomaly monitoring unit and an inefficient operation monitoring unit; The screen anomaly monitoring unit is used to determine whether there is a clogging trend in the screen of the crusher based on the collected air network operation data. If there is a clogging trend, it will be fed back to the capacity adjustment module to reduce the feed rate of the crusher. The inefficient operation monitoring unit is used to determine whether the crusher is operating inefficiently based on the collected motor energy consumption data and material production data. If the crusher is operating inefficiently, it will be fed back to the capacity adjustment module to increase the feed rate of the crusher. The capacity adjustment module is used to dynamically adjust the feed rate of the crusher based on feedback information from the anomaly monitoring module. The anomaly monitoring module determines whether there is a tendency for the screen of the crusher to become clogged based on the real-time data collected by the data acquisition module. While ensuring that the screen does not become clogged, it maintains the minimum airflow required for crushing and transportation, and makes the motor of the crusher run at its maximum load at this time, adjusting the feed rate of the crusher to maximize the production capacity.
2. The capacity control system based on air volume and air pressure of a pulverizer system according to claim 1, characterized in that, The motor energy consumption data includes motor current and motor voltage; the wind network operation data includes air volume, air pressure and time of the test section; the material production data includes actual material fineness and actual material output.
3. The capacity control system based on airflow and air pressure of a pulverizer system according to claim 1 or 2, characterized in that, The screen anomaly monitoring unit compares the actual data of air pressure and air volume collected by the data acquisition module with the screen blockage anomaly threshold. If the actual data of air pressure and air volume are greater than the screen blockage anomaly threshold, it is determined that the current crusher has an abnormal trend of screen blockage.
4. The capacity control system based on air volume and air pressure of a pulverizer system according to claim 1 or 2, characterized in that, The inefficient operation monitoring unit compares the actual motor energy consumption data and actual production capacity data collected by the data acquisition module with the inefficient production anomaly threshold. If the actual motor energy consumption data and actual production capacity data are less than the inefficient production anomaly threshold, it is determined that the current crusher has an abnormal situation of inefficient operation.
5. The capacity control system based on air volume and air pressure of a pulverizer system according to claim 1, characterized in that, It also includes a model optimization module, which is used to record the actual data collected by the data acquisition module and compare the actual output of materials with the predicted value of the capacity prediction module, and record the difference data; and optimize and train the capacity prediction module based on the recorded real-time data to improve the accuracy of the capacity prediction model.
6. The capacity control system based on air volume and air pressure of a pulverizer system according to claim 1, characterized in that, The process of building the capacity prediction model is as follows: constructing the capacity prediction model using collected sample data; and calibrating the capacity prediction model under no-load conditions. The sample data includes motor current I, wind pressure P, and wind volume Q. The sample data is divided into training set data and validation set data. An initial prediction model is constructed using the training set data, and the accuracy of the initial prediction model is verified by iterative calculation using the validation set data, thereby obtaining the final production capacity prediction model.
7. The capacity control system based on air volume and air pressure of a pulverizer system according to claim 6, characterized in that, The capacity prediction model is built using a numerical prediction algorithm, and the specific construction process is as follows: An initial prediction model is constructed by fitting the correspondence between motor current I, wind pressure P, wind volume Q and actual material output in the training set data using a data prediction algorithm. The input variables of the capacity forecasting model are set as [X1,X2,X3]=[I,P,Q], and the output variable is the optimal capacity forecast value Y. The optimal capacity forecast value Y is calculated using the ReLU activation function. Where, ω i Let b be the weight and b be the bias. We continuously optimize the weights and biases to minimize the error of the fitting result. The initial prediction model is iteratively trained using validation set data to achieve validation. The difference between the predicted value and the actual value is calculated. If the difference exceeds the set range, the model structure is adjusted or the model is retrained to obtain the validated model, until the prediction results of the capacity prediction model meet the error requirements.
8. A method for controlling production capacity based on airflow and air pressure in a pulverizer system, characterized in that, This is applicable to the control system of a pulverizer that includes a screen, and specifically includes the following steps: Acquire data on motor energy consumption, wind grid operation, and material production during actual production processes; Based on the actual data obtained, the predicted value of the real-time optimal capacity of the crusher is calculated using the capacity prediction model. In actual production, it is necessary to determine whether there is a tendency for the screen of the crusher to become clogged and whether the crusher is operating inefficiently, and adjust the feed rate of the crusher based on the judgment results; Record motor energy consumption data, wind network operation data, and material production data during the actual production process. Compare the actual material output with the predicted value of the optimal capacity, record the difference data, and optimize and train the capacity prediction module based on the recorded real-time data to improve the accuracy of the capacity prediction model.
9. The capacity control method based on air volume and air pressure of a pulverizer system according to claim 8, characterized in that, The motor energy consumption data includes motor current and motor voltage; the wind network operation data includes air volume, air pressure and time of the test section; the material production data includes actual material fineness and actual material output.
10. The capacity control method based on air volume and air pressure of a pulverizer system according to claim 8, characterized in that, Based on the collected air network operation data, determine whether there is a tendency for the screen of the crusher to become clogged. If there is a tendency for the screen of the crusher to become clogged, reduce the feed rate of the crusher to avoid the screen becoming clogged due to excessive feed rate. Based on the collected motor energy consumption data and material production data, it is determined whether the crusher is operating inefficiently. If the crusher is operating inefficiently, the feed rate of the crusher is increased to improve the working efficiency of the crusher.