Micron particle grading method based on medium-speed coal mill

Abstract

The invention relates to the technical field of pulverized coal processing, and discloses a micron particle grading method based on a medium-speed coal mill, which comprises the following steps: a crushing stage: feeding coal briquettes into the medium-speed coal mill for crushing, and optimizing the crushing effect by adjusting parameters such as the rotating speed, the feeding amount and the hot air pressure of the coal mill, so that the coal briquettes are crushed; according to the micron particle grading method based on the medium-speed coal mill, by adjusting the feeding amount of coal raw materials, the coal raw materials evenly enter the medium-speed coal mill, then by adjusting the rotating speed of the coal mill, the crushing speed of coal briquettes is increased, during crushing, the coal raw materials are detected through a temperature and humidity sensor, and the pressure and temperature of hot air are adjusted, so that the coal briquettes are graded. Crushing of coal briquettes is promoted through the thermal stress effect, meanwhile, hot air passes through an air duct in the coal mill, crushed particles are blown up and brought into a separator of the medium-speed coal mill, and the effects of optimizing the crushing effect and ensuring uniform generation of micron particles are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of coal powder processing, in particular to a micron particle classification method based on a medium-speed coal mill. Background Art

[0002] A medium-speed coal mill refers to a coal mill with an operating speed of 50 to 300 r / min. It is a professional equipment for preparing and processing auxiliary materials for blast furnace ironmaking. The medium-speed coal mill can provide the blast furnace ironmaking system with suitable auxiliary materials - coal powder. The medium-speed coal mill is the most important large-scale equipment in the coal-to-oil pulverizing system. Its operating safety and economy are extremely important to the normal operation of coal-to-oil.

[0003] After crushing coal blocks, traditional medium-speed coal mills often use simple screening or air flow separation to achieve particle classification. However, this method has problems such as insufficient accuracy and low efficiency in classifying micron-level particles. Especially when dealing with different coal qualities, due to differences in coal hardness, moisture content, volatile matter and other characteristics, traditional classification methods are difficult to achieve ideal classification effects. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a micron particle classification method based on a medium-speed coal mill, so as to solve the problem proposed in the above-mentioned background technology that after the traditional medium-speed coal mill crushes the coal blocks, it often achieves particle classification through simple screening or air flow separation. However, this method has the problems of insufficient accuracy and low efficiency in the classification of micron-sized particles. In particular, when processing different coal qualities, due to the differences in coal hardness, moisture content, volatile matter and other characteristics, the traditional classification method is difficult to achieve the ideal classification effect.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: a micron particle classification method based on a medium-speed coal mill comprises the following steps: Crushing stage: The coal is fed into a medium-speed coal mill for crushing, and the crushing effect is optimized by adjusting the parameters of the coal mill such as speed, feed amount and hot air pressure; Airflow classification: adjust the airflow velocity and temperature at the outlet of the coal mill to fully mix the crushed particles with the airflow to form a gas-solid two-phase flow. By adjusting the airflow velocity distribution, flow direction, temperature and other parameters, the micron particles flow with the airflow to a specific area for collection; Monitoring and feedback: Sensors installed at the outlet of the medium-speed coal mill or in the airflow classification area monitor the particle size distribution, airflow velocity and other parameters in real time, and transmit the data to the control system; Adaptive adjustment: Automatically adjust the operating parameters of the coal mill and the airflow classification conditions according to real-time monitoring data to achieve efficient and accurate classification of micron particles; Analysis and optimization: Collect and store monitoring data during the grading process, and use data analysis technology to process and analyze the data.

[0006] Preferably, the pulverizing stage step adjusts the pulverizer speed to increase the relative movement speed between the grinding roller and the grinding disc, thereby speeding up the coal block crushing speed. Adjusting the feed amount can improve the pulverizing efficiency of the pulverizer. Adjusting the hot air pressure and temperature can accelerate the dryness of the coal blocks, allowing for better crushing.

[0007] Preferably, the airflow classification step can ensure that the coal powder particles remain suspended in the airflow by adjusting the airflow velocity and temperature, forming a stable gas-solid two-phase flow, thereby providing favorable conditions for subsequent separation and collection work.

[0008] Preferably, in the airflow classification step, the coal powder particles enter the interior of the separator by forming a stable gas-solid two-phase flow, and the coal powder particles are classified by the centrifugal force generated by the dynamic rotor blades and static guide blades installed in the separator.

[0009] Preferably, the monitoring and feedback step is performed by installing sensors such as laser particle size analyzers, wind speed meters and temperature sensors to monitor various operating parameters in real time, such as particle size distribution, air flow velocity, temperature, pressure, etc., and provide data support for subsequent process improvements and equipment upgrades.

[0010] Preferably, the adaptive adjustment step rapidly responds to changes in monitored data through the control system and automatically adjusts the grading conditions to cope with possible deviations or abnormal situations. Based on real-time monitoring data and feedback results, the control system can continuously optimize the grading process, improve grading efficiency and accuracy, and reduce energy consumption and material losses.

[0011] Preferably, the adaptive adjustment step enables the operator to intuitively understand the operating status and monitoring data of the grading process by designing a human-computer interaction interface, which facilitates the operator to perform manual intervention and debugging.

[0012] Preferably, the analysis and optimization step uses an algorithm to perform statistical analysis on the monitored data to facilitate identification of factors affecting the grading effect, provide guidance for subsequent grading operations, and improve grading efficiency and product quality.

[0013] Preferably, the analysis and optimization step uses an algorithm to perform statistical analysis on the monitored data to facilitate identification of factors affecting the grading effect, provide guidance for subsequent grading operations, and improve grading efficiency and product quality.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are: First, the present invention adjusts the feed amount of coal raw material so that the coal raw material enters the medium-speed coal mill evenly, thereby avoiding affecting the efficiency and service life of the machine due to the feed amount, and then adjusts the rotation speed of the coal mill to increase the relative movement speed between the grinding roller and the grinding disc, thereby accelerating the crushing speed of the coal blocks. During the crushing, the coal raw material is detected by a temperature and humidity sensor, and the pressure and temperature of the hot air are adjusted. Increasing the hot air pressure and temperature according to the humidity of the coal raw material can accelerate the crushing and drying of the coal blocks, and promote the crushing of the coal blocks through the thermal stress effect. At the same time, the hot air passes through the air duct inside the coal mill, blows up the crushed particles and brings them to the separator of the medium-speed coal mill, thereby optimizing the crushing effect and ensuring the uniform generation of micron particles.

[0015] Second, the present invention uses hot air to blow the crushed coal raw material into the feed pipe entering the separator, and fully mixes the coal raw material particles with the high-speed airflow, so that the coal raw material particles are suspended in the airflow to form a gas-solid two-phase flow. By adjusting the speed of the airflow, the flow direction and distribution of the airflow are adjusted to ensure that the solid particles can be evenly dispersed in the airflow. After entering the separator, the airflow speed entering the classification area is adjusted according to the particle size distribution of the crushed particles and the target classification particle size to ensure that the particles remain suspended in the airflow and have a certain separation speed difference. At the same time, by adjusting the temperature parameters of the hot air system, the airflow temperature entering the classification area is controlled to adapt to different coal quality characteristics and classification requirements, and by setting guide plates or guide blades, etc., the distribution of the airflow in the classification area is optimized, so that the airflow flows through the classification area more evenly and stably. domain, and then by providing dynamic rotor blades and static guide blades, the dynamic rotor blades are made to rotate. When the rotor speed and air flow of the dynamic rotor blades are constant, some coal powder particles in the powder selection area will be in a static state due to the equal centrifugal force and centripetal force. When the weight of the coal powder particles exceeds that of the coal powder particles, these coal powder particles will escape under the action of centrifugal force and move outward, hit the static blades and settle under the action of gravity, and return to the grinding disc through the powder return pipe for re-grinding. When the weight of the coal powder particles is less than that of the coal powder particles, these coal powder particles cannot overcome the air flow drag, pass through the rotor blades, leave the separator, and enter the boiler for combustion through the powder conveying pipeline, thereby screening out micron-particle coal powder, so that the coarse coal powder particles can be separated more completely, and the number of circulations of the fine coal powder particles in the medium-speed pulverizer can be effectively reduced, thereby improving the separation efficiency of the separator.

[0016] Third, the present invention monitors the particle size distribution, airflow velocity and other parameters of the particles in real time through sensors installed at the outlet of the medium-speed coal mill or in the airflow classification area, and transmits the data to the control system. According to the real-time monitoring data, the operating parameters of the coal mill and the airflow classification conditions are automatically adjusted to achieve efficient and accurate classification of micron particles. Through the data acquisition system, data are regularly or continuously acquired from the monitoring equipment. These data include key indicators such as particle size distribution, airflow velocity, temperature, pressure, etc. The acquired data are stored in a database or data warehouse for subsequent analysis and processing. Then, statistics, data mining, machine learning and other technologies are used to calculate the mean, standard deviation, distribution range and other statistical quantities of various indicators to understand the overall distribution of the data. Machine learning algorithms are used to detect abnormal points or abnormal patterns in the data to identify possible problems or faults in the classification process. According to the data analysis results, the operating parameters of the classification system, such as airflow velocity, temperature, pressure, etc., are adjusted to optimize the classification effect. For the potential problems or faults detected, equipment improvement plans are proposed, such as replacing worn parts, optimizing equipment structure, etc., and the performance and efficiency of the classification system are improved through continuous data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the overall steps of the present invention; Figure 2 It is a flowchart of the steps of the crushing stage of the present invention; Figure 3 It is a flowchart of the airflow classification step of the present invention; Figure 4 It is a flowchart of the monitoring and feedback steps of the present invention; Figure 5 It is a flowchart of the adaptive adjustment steps of the present invention; Figure 6 It is a flowchart of the analysis and optimization steps of the present invention; DETAILED DESCRIPTION

[0018] The specific implementation modes of the present invention will be further described in detail below with reference to the accompanying drawings.

[0019] The following is a specific implementation of the micron particle classification method based on a medium-speed coal mill.

[0020] See also Figure 1-6 The micron particle classification method based on the medium-speed coal mill comprises the following steps: 1. Crushing stage: The coal blocks are fed into a medium-speed coal mill for crushing, and the crushing effect is optimized by adjusting the parameters of the coal mill such as the speed, feed amount and hot air pressure; In the crushing stage, adjusting the speed of the coal mill can increase the relative speed between the grinding roller and the grinding disc, thereby speeding up the crushing speed of the coal blocks. Adjusting the feed rate can improve the crushing efficiency of the coal mill. Adjusting the hot air pressure and temperature can accelerate the drying of the coal blocks, making them better crushed. By adjusting the feed amount of coal raw materials, the coal raw materials can be evenly fed into the medium-speed coal mill to avoid affecting the efficiency and service life of the machine due to the feed amount. By adjusting the rotation speed of the coal mill, the relative movement speed between the grinding roller and the grinding disc is increased, thereby accelerating the crushing speed of the coal blocks. During the crushing, the coal raw materials are detected by temperature and humidity sensors, and the pressure and temperature of the hot air are adjusted. Increasing the hot air pressure and temperature according to the humidity of the coal raw materials can accelerate the crushing and drying of the coal blocks, and promote the crushing of the coal blocks through the thermal stress effect. At the same time, the hot air passes through the air duct inside the coal mill, blows up the crushed particles and brings them to the separator of the medium-speed coal mill, so as to optimize the crushing effect and ensure the uniform generation of micron particles.

[0021] 2. Airflow classification: adjust the airflow velocity and temperature at the outlet of the coal mill to fully mix the crushed particles with the airflow to form a gas-solid two-phase flow. By adjusting the airflow velocity distribution, flow direction, temperature and other parameters, the micron particles flow with the airflow to a specific area for collection; The airflow classification step can ensure that the coal powder particles remain suspended in the airflow by adjusting the airflow velocity and temperature, forming a stable gas-solid two-phase flow, providing favorable conditions for subsequent separation and collection work; The airflow classification step forms a stable gas-solid two-phase flow of coal powder particles into the separator, and the coal powder particles are classified by the centrifugal force generated by the dynamic rotor blades and static guide blades installed in the separator; The crushed coal raw material is blown into the feed pipe of the separator by hot air, and is fully mixed with the coal raw material particles by high-speed airflow, so that the coal raw material particles are suspended in the airflow to form a gas-solid two-phase flow. The airflow speed is adjusted, and then the flow direction and distribution of the airflow are adjusted to ensure that the solid particles can be evenly dispersed in the airflow. After entering the separator, the airflow speed entering the grading area is adjusted according to the particle size distribution of the crushed particles and the target classification particle size to ensure that the particles remain suspended in the airflow and have a certain separation speed difference. At the same time, the temperature parameters of the hot air system are adjusted to control the airflow temperature entering the grading area to adapt to different coal quality characteristics and grading requirements. The distribution of the airflow in the grading area is optimized by setting guide plates or guide blades, so that the airflow flows through the grading area more evenly and stably, and then Then, dynamic rotor blades and static guide blades are provided to make the dynamic rotor blades rotate. When the rotor speed and air flow of the dynamic rotor blades are constant, some coal powder particles in the powder selection area will be in a static state due to the equal centrifugal force and centripetal force. When the weight of the coal powder particles exceeds that of the coal powder particles, these coal powder particles will escape under the action of centrifugal force and move outward, hit the static blades and settle under the action of gravity, and return to the grinding disc through the powder return pipe for re-grinding. When the weight of the coal powder particles is less than that of the coal powder particles, these coal powder particles cannot overcome the air flow drag, pass through the rotor blades, leave the separator, and enter the boiler for combustion through the powder conveying pipeline, thereby screening out micron-particle coal powder, so that the coarse coal powder particles can be separated more completely, and the number of circulations of the fine coal powder particles in the medium-speed pulverizer can be effectively reduced, thereby improving the separation efficiency of the separator.

[0022] 3. Monitoring and feedback: Sensors installed at the outlet of the medium-speed coal mill or in the airflow classification area monitor the particle size distribution, airflow velocity and other parameters in real time, and transmit the data to the control system; The monitoring and feedback step uses the control system to quickly respond to changes in monitored data and automatically adjust the classification conditions to deal with possible deviations or abnormal situations. Based on real-time monitoring data and feedback results, the control system can continuously optimize the classification process, improve classification efficiency and accuracy, and reduce energy consumption and material loss; The particle size distribution of the classified particles is monitored in real time by a laser particle size analyzer to ensure that the classification effect meets the preset standards. Anemometers are installed at key locations in the classification area to monitor the airflow velocity in real time to ensure that the airflow is stable and meets the classification requirements. Temperature sensors and pressure sensors are used to monitor the temperature and pressure changes in the classification area to evaluate the operating status and classification effect of the hot air system. The control system then receives the monitoring data and automatically determines the operating status and performance of the classification process based on the received data. If an abnormal situation or fault signal (such as abnormal airflow velocity, excessive temperature, etc.) is detected, the control system will immediately trigger the alarm mechanism to remind the staff to ensure safety and stability during the classification process.

[0023] 4. Adaptive adjustment: According to real-time monitoring data, the operating parameters of the coal mill and the airflow classification conditions are automatically adjusted to achieve efficient and accurate classification of micron particles; The adaptive adjustment step is to quickly respond to the monitored data changes through the control system and automatically adjust the classification conditions to deal with possible deviations or abnormal situations. Based on real-time monitoring data and feedback results, the control system can continuously optimize the classification process, improve classification efficiency and accuracy, and reduce energy consumption and material loss; The adaptive adjustment step enables operators to intuitively understand the operating status and monitoring data of the grading process by designing a human-computer interaction interface, which is convenient for operators to perform manual intervention and debugging; By receiving monitoring data in real time and cleaning the collected raw data, removing noise, outliers and incomplete data points, ensuring the accuracy of subsequent analysis, the data from different sensors and devices are synchronized in time and aligned in space to form a complete data set. The key information that has an important impact on the classification effect is extracted through statistical analysis software. These key information include but are not limited to: by analyzing the particle size distribution data, the separation effect of particles of different sizes in the classification process can be understood, and then the classification accuracy and efficiency can be evaluated. The stability of the air flow velocity directly affects the movement trajectory and separation effect of the particles in the classification room. By monitoring the air flow velocity data, the system parameters can be discovered and adjusted in time to ensure the stability of the classification process. Then, statistical methods are used to analyze the extracted key information, and statistical indicators such as mean value, standard deviation, correlation coefficient, etc. are calculated to quantitatively evaluate the classification effect and system performance. Then, according to these parameters, the control parameters are automatically adjusted through the control system to improve the classification accuracy and stability.

[0024] 5. Analysis and Optimization: Collect and store monitoring data during the grading process, and use data analysis technology to process and analyze the data; The analysis and optimization step uses algorithms to perform statistical analysis on the monitored data to facilitate identification of factors affecting the grading effect, provide guidance for subsequent grading operations, and improve grading efficiency and product quality; The analysis and optimization step uses algorithms to perform statistical analysis on the monitored data to facilitate identification of factors affecting the grading effect, provide guidance for subsequent grading operations, and improve grading efficiency and product quality; Through the data acquisition system, data is obtained from the monitoring equipment regularly or continuously. These data include key indicators such as particle size distribution, airflow velocity, temperature, pressure, etc. The collected data is stored in a database or data warehouse for subsequent analysis and processing. Then, statistics, data mining, and machine learning are used to calculate the mean, standard deviation, distribution range and other statistical quantities of each indicator to understand the overall distribution of the data. Machine learning algorithms are used to detect abnormal points or abnormal patterns in the data and identify possible problems or faults in the grading process. According to the data analysis results, the operating parameters of the grading system, such as airflow velocity, temperature, pressure, etc., are adjusted to optimize the grading effect. For potential problems or faults detected, equipment improvement plans are proposed, such as replacing worn parts, optimizing equipment structure, etc. The performance and efficiency of the grading system are improved through continuous data analysis.

[0025] Although specific embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit thereof, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A micron particle classification method based on a medium-speed coal mill, characterized in that: The following steps are involved: Crushing stage: The coal is fed into a medium-speed coal mill for crushing, and the crushing effect is optimized by adjusting the parameters of the coal mill such as speed, feed amount and hot air pressure; Airflow classification: adjust the airflow velocity and temperature at the outlet of the coal mill to fully mix the crushed particles with the airflow to form a gas-solid two-phase flow. By adjusting the airflow velocity distribution, flow direction, temperature and other parameters, the micron particles flow with the airflow to a specific area for collection; Monitoring and feedback: Sensors installed at the outlet of the medium-speed coal mill or in the airflow classification area monitor the particle size distribution, airflow velocity and other parameters in real time, and transmit the data to the control system; Adaptive adjustment: Automatically adjust the operating parameters of the coal mill and the airflow classification conditions according to real-time monitoring data to achieve efficient and accurate classification of micron particles; Analysis and optimization: Collect and store monitoring data during the grading process, and use data analysis technology to process and analyze the data.

2. The micron particle classification method based on a medium-speed coal mill according to claim 1 is characterized in that: In the crushing stage, adjusting the speed of the coal mill can increase the relative movement speed between the grinding roller and the grinding disc, thereby speeding up the crushing speed of the coal blocks. Adjusting the feed amount can improve the crushing efficiency of the coal mill. Adjusting the hot air pressure and temperature can accelerate the drying of the coal blocks, allowing for better crushing.

3. The micron particle classification method based on a medium-speed coal mill according to claim 1 is characterized in that: The airflow classification step can ensure that the coal powder particles remain suspended in the airflow by adjusting the airflow velocity and temperature, forming a stable gas-solid two-phase flow, providing favorable conditions for subsequent separation and collection work.

4. The micron particle classification method based on a medium-speed coal mill according to claim 1 is characterized in that: In the airflow classification step, the coal powder particles enter the interior of the separator by forming a stable gas-solid two-phase flow, and the coal powder particles are classified by the centrifugal force generated by the dynamic rotor blades and static guide blades installed in the separator.

5. The micron particle classification method based on a medium-speed coal mill according to claim 1, characterized in that: The monitoring and feedback step installs sensors such as laser particle size analyzers, wind speed meters and temperature sensors to monitor various operating parameters in real time, such as particle size distribution, air flow velocity, temperature, pressure, etc., and provide data support for subsequent process improvements and equipment upgrades.

6. The micron particle classification method based on a medium-speed coal mill according to claim 1, characterized in that: The adaptive adjustment step allows the control system to quickly respond to changes in monitored data and automatically adjust the grading conditions to cope with possible deviations or abnormal situations. Based on real-time monitoring data and feedback results, the control system can continuously optimize the grading process, improve grading efficiency and accuracy, and reduce energy consumption and material losses.

7. The micron particle classification method based on a medium-speed coal mill according to claim 1, characterized in that: The adaptive adjustment step enables operators to intuitively understand the operating status and monitoring data of the grading process by designing a human-computer interaction interface, which is convenient for operators to perform manual intervention and debugging.

8. The micron particle classification method based on a medium-speed coal mill according to claim 1, characterized in that: The analysis and optimization step uses algorithms to perform statistical analysis on the monitored data to facilitate the identification of factors that affect the grading effect, provide guidance for subsequent grading operations, and improve grading efficiency and product quality.

9. The micron particle classification method based on a medium-speed coal mill according to claim 1, characterized in that: The analysis and optimization step uses algorithms to perform statistical analysis on the monitored data to facilitate the identification of factors that affect the grading effect, provide guidance for subsequent grading operations, and improve grading efficiency and product quality.

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