Mining main ventilator monitoring system

Abstract

The invention relates to the field of ventilator monitoring systems, in particular to a mining main ventilator monitoring system which is used for solving the problems that the wind speed of an existing main ventilator monitoring system is kept unchanged all the time, on one hand, the ventilation effect is not ideal, and reasonable main ventilator rotating speed and blade angles cannot be designated according to environment requirements; and meanwhile, the problem that potential faults and hidden dangers of equipment cannot be found in time through the air volume loss value due to the fact that monitoring of a traditional system on the state of the ventilator is weak is solved. The main ventilator monitoring system comprises a sensor group, an upper computer and a PLC (Programmable Logic Controller), according to the main ventilator monitoring system, the rotating speed and the blade angle of the main ventilator are adjusted in real time according to the comprehensive indexes of environmental factors, the purposes of energy conservation and environmental protection are achieved while the ventilation requirement is met, and meanwhile possible faults and to-be-maintained reasons of the main ventilator are judged by calculating the air volume loss value and combining historical data.

Description

Technical Field

[0001] The invention relates to the field of ventilator monitoring systems, and in particular to a main ventilator monitoring system for a mine. Background Art

[0002] A ventilator is a device installed in a specific environment to increase air flow rate and improve air replacement rate. It mainly relies on an electric drive motor to drive the blades to generate wind. Different installation environments of ventilators require different controls, so a control monitoring system is required for monitoring and control.

[0003] Traditional fan monitoring systems lack real-time and accurate response to the complex and changeable underground environment. Traditional systems find it difficult to flexibly adjust ventilation parameters according to the unique risk characteristics of different mine types. In terms of fan control, traditional systems rely more on manual experience or simple timing regulation. They are unable to dynamically adjust the fan speed and blade angle in real time based on the comprehensive changes in multiple environmental parameters such as gas, carbon monoxide, carbon dioxide, dust, temperature, and humidity. They usually adjust the fan's fixed operating frequency at a fixed time or space, and the wind speed remains unchanged. This will lead to unsatisfactory ventilation effects and make it impossible to specify a reasonable main fan speed and blade angle according to environmental requirements. On the other hand, it will not achieve energy-saving and environmental protection effects. Blindly high-speed ventilation will result in a large amount of electrical energy waste and affect the service life of the fan. At the same time, it is impossible to promptly discover potential equipment failures through air volume loss values. Summary of the invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a main ventilation fan monitoring system for mines: through the main ventilation fan monitoring system, the main ventilation fan can adjust the rotation speed and blade angle in real time according to the comprehensive indicators of environmental factors, so as to ensure the ventilation demand while achieving the purpose of energy saving and environmental protection. At the same time, by calculating the air volume loss value and combining historical data to judge the possible faults and reasons for maintenance of the main ventilation fan, the existing main ventilation fan monitoring system solves the problem that the wind speed always remains unchanged. On the one hand, it will lead to unsatisfactory ventilation effect and it is impossible to specify a reasonable main ventilation fan rotation speed and blade angle according to environmental requirements. On the other hand, it cannot achieve the effect of energy saving and environmental protection. Blindly high-speed ventilation will result in a large amount of electric energy waste, which will also affect the service life of the fan. At the same time, the traditional system is also relatively weak in monitoring the status of the fan itself, and it is impossible to timely discover potential equipment failures through the air volume loss value.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A mine main ventilation fan monitoring system, comprising: a sensor group, a host computer and a PLC, wherein the sensor group comprises a gas concentration sensor, a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a dust concentration sensor, a temperature sensor and a humidity sensor, the host computer is installed with wind flow simulation software, and the PLC is installed with an adaptive control system; The sensor group is used to monitor the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature and humidity in the ventilation environment where the main fan is to be installed in real time, and transmit the detection data to the PLC; The host computer is used to run the wind flow simulation software. The software has a complete 3D model in the built-in environment, covering detailed information on the direction of the lane, cross-section changes, and obstacle distribution. The real dynamics of the wind flow are simulated according to real-time ventilation parameters, where the ventilation parameters include the speed of the main fan and its blade angle; The PLC is used to execute an adaptive control system, which includes weights of data such as gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the storage environment. A comprehensive index β is calculated based on the weights and real-time monitoring data. An adaptive control strategy for the main fan speed and blade angle is formulated based on the comprehensive index β and wind flow simulation software. The PLC controls the fan speed and blade angle of the main fan through the adaptive control strategy.

[0006] As a further solution of the present invention: the specific process of the adaptive control system presetting the weight of each data is as follows: According to the environmental type and industry regulations, weights are set for gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity respectively, and the weights are set to K1, K2, K3, K4, K5, and K6 in sequence. Different weight coefficients are set artificially according to different mine types. The sum of the six weights is marked as K total, and K total = 1.

[0007] According to the environment type and industry regulations, weights are set for gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity respectively, and the weights are set to K1, K2, K3, K4, K5, and K6 in sequence. The sum of the six weights is marked as Ktotal, and Ktotal=1.

[0008] As a further solution of the present invention: the specific calculation formula for calculating the comprehensive index β according to each weight and real-time monitoring data is as follows: ; in, is the maximum allowable value of gas concentration, usually 1%±0.5%. The maximum permissible value of carbon monoxide concentration is usually 24ppm±2. is the maximum allowable value of carbon dioxide concentration, usually 1.5%±0.5%. The maximum allowable value of dust concentration is usually 10 mg / m 3 ±2, The suitable temperature is usually 22℃±2. The maximum allowable temperature is usually 26℃±2. For suitable humidity, 50%±5% is usually taken. The maximum allowable humidity is usually 60% ± 5%, which is set in advance according to the environment type. Monitoring data for gas concentration sensors, The carbon monoxide concentration sensor monitoring data, The carbon dioxide concentration sensor monitors the data. is the monitoring data of the dust concentration sensor, T is the monitoring data of the temperature sensor, and H is the monitoring data of the humidity sensor.

[0009] As a further solution of the present invention: the main fan speed adaptive control strategy is formulated as follows: The β value is divided into four stages, corresponding to the excellent environmental condition β≤0.2, the general fluctuation condition 0.2<β≤0.5, the deterioration trend condition 0.5<β≤0.8, and the emergency and dangerous condition β>0.8; When β≤0.2, the PLC controls the main fans to operate at the lowest operating frequency, and sets this frequency as the main fan base frequency; When 0.2<β≤0.5, based on the basic frequency, the main fan operating frequency is adaptively adjusted with the strategy that the frequency increases by 10% when the comprehensive index β increases by 0.1. In this stage, β=0.5 is the highest frequency of the main fan in this stage. When 0.5<β≤0.8, the frequency corresponding to β=0.5 in the previous stage is set as the basic frequency of this stage, and the operating frequency of the main fan is adaptively adjusted with the strategy that the frequency increases by 15% when the comprehensive index β increases by 0.1; When β>0.8, the main fan is adjusted to the highest frequency; Through real-time monitoring of gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity data, the comprehensive index β is calculated in real time. The PLC uses this adaptive control strategy to adaptively control the main fan operating frequency in real time, and then adaptively control the main fan speed.

[0010] As a further solution of the present invention: the main fan blade angle adaptive adjustment strategy is formulated as follows: Based on four cases: β≤0.2, 0.2<β≤0.5, 0.5<β≤0.8, β>0.8; When β≤0.2, the actual output air volume of the main fan is collected through the air volume sensor , the wind pressure sensor collects the output wind pressure P of the main fan, and then uses the formula Calculate the shaft power W, where Represents the real-time efficiency of the main fan, , usually between 0.719-0.8, Represents the mechanical transmission efficiency of the main fan, usually between 70% and 90%. According to the factory parameter setting of the main fan, and the roadway resistance coefficient R under the main fan installation environment is measured in advance, the actual output air volume , the main fan output pressure P, shaft power W, and tunnel resistance coefficient R parameters are introduced into the wind flow simulation software, the wind flow dynamics are simulated through the built-in environmental 3D model of the software, and then the blade angle core adjustment coefficient is set , initially reset to zero, according to the formula: ; Calculation, including These are the corresponding ideal values ​​set in advance. is the maximum allowed value, It is a weight coefficient customized according to the fan performance and the actual situation of the mine, and , calculate Then, adjust the angle according to = The main fan blade angle is adjusted by the formula, and the fine-tuning range is limited to ±2°, and the angle is set as the initial setting angle. Therefore, on the basis of energy saving and noise reduction, the blade angle is fully optimized according to the real-time operation of the fan and the underground environment, so that the airflow is uniform and efficient, and the ventilation effect is greatly improved; When 0.2<β≤0.5, the real-time efficiency of the main fan is synchronously introduced. , surge margin S, according to the formula ,in The real-time efficiency weight coefficient of the main fan, ranging from 0 to 1. is the surge margin weight coefficient, which takes a value between 0 and 1. These are the corresponding ideal values ​​set in advance. The blade angle compensation coefficient G is calculated, and the final blade angle is adjusted to , ensuring that the fan can operate efficiently and stably when responding to changes in working conditions, avoiding unstable phenomena such as surge; When 0.5<β≤0.8, recalculate the real-time efficiency of the main fan , surge margin S, introduction of tunnel temperature gradient , air specific heat capacity C, set blade optimization angle coefficient , according to the formula work out Value, where It indicates the weight coefficient of the influence of temperature gradient on ventilation demand, and the value is between 0 and 1. Indicates the weight coefficient of air specific heat capacity difference, with a value between 0 and 1. The real-time efficiency weight coefficient of the main fan, with a value between 0 and 1. is the surge margin weight coefficient, which takes a value between 0 and 1. These are the corresponding ideal values ​​set in advance. are the maximum values ​​allowed, and the After that, the blade angle is adjusted to , adapt to the dynamic underground thermal environment and changes in fan working conditions, and deepen the cooling and ventilation efficiency; When β>0.8, the smoke concentration gradient is introduced , smoke particle size distribution D, set the compensation coefficient to , according to the formula work out Value, where They represent the weight coefficient of smoke concentration on ventilation demand and the weight coefficient of smoke particle size distribution on ventilation demand, respectively, and both have values ​​between 0 and 1. These are the corresponding ideal values ​​set in advance. are the maximum values ​​allowed, and the After that, the blade angle is anchored , ensuring that wind flow disperses smoke efficiently and clears escape corridors.

[0011] As a further solution of the present invention: the inherent parameters of the main fan, including the impeller diameter, blade shape, and hub ratio, are stored in the PLC, and the speed and blade angle of the main fan are controlled based on the adaptive control system to calculate the current theoretical air volume of the main fan. , the specific formula is as follows: ,in are the structural parameters of the fan, including blade shape and hub ratio. They are fixed parameter values ​​obtained by the fan manufacturer after testing and are between 0.05 and 0.15. n is the actual speed of the current main fan. is the impeller diameter, is the current blade angle obtained; get Then, bring it into , calculate the air volume loss value .

[0012] As a further solution of the present invention: based on the calculation of the air volume loss value , different levels of loss value thresholds are stored in PLC, respectively ,in , , ; The PLC continuously collects historical data of the fan's long-term operation, including the fan speed n and blade angle under four different working conditions. , Theoretical air volume , Actual air volume , the environmental sensor data and the corresponding timestamp information, classify and organize these data, store them according to the working condition category and time series, build a database, use the decision tree algorithm, train the model in the built-in data analysis module of the PLC, and convert the air volume loss value As the target variable, the fan operating parameters, environmental parameters and the treatment measures taken at the corresponding time in history are taken as characteristic variables. Through repeated learning of historical data, the decision tree model gradually constructs the decision relationship between various influencing factors under different air volume loss levels, forming a diagnosis and decision model. During the actual operation of the fan, the PLC collects the current operating parameters and environmental parameters in real time to calculate the air volume loss value. , and input it into the trained model. The model determines the level of air loss and the corresponding cause based on the input parameter information and the learned decision rules.

[0013] As a further solution of the present invention: the specific processing steps of the system are as follows: S1. Install several gas concentration sensors, carbon monoxide concentration sensors, carbon dioxide concentration sensors, dust concentration sensors, temperature sensors, and humidity sensors in the environment where the main ventilation fan is to be installed. The data of these sensors are collected, stored, and processed by PLC; S2, calculating the comprehensive index β through the weight strategy based on the processed sensor data, and specifying the main fan adaptive wind speed adjustment strategy and blade angle adjustment strategy based on the comprehensive index β; S3, PLC adaptively adjusts the main ventilation speed and blade angle based on the main fan adaptive speed adjustment strategy and blade angle adjustment strategy combined with the wind flow simulation software in the host computer; S4. Based on the adjusted main fan speed and blade angle, various factory parameters of the main fan and the resistance coefficient R of the roadway to be installed are introduced to calculate the current theoretical air volume of the main fan ; S5. Theoretical air volume based on calculation The actual air volume obtained by monitoring Calculate and obtain the air loss Based on the lost air volume combined with historical data and the decision tree model, the level of air volume loss and the corresponding reasons are determined.

[0014] Beneficial effects of the present invention: By setting up an adaptive control system, the adaptive control system can calculate the comprehensive index of the fan installation environment through the real-time collected sensor data, and cooperate with the specified adaptive ventilation control strategy to adaptively adjust the main fan speed with the value of the comprehensive index. On the one hand, it can always maintain a good ventilation environment in the environment without problems of insufficient ventilation or excessive ventilation. On the other hand, it avoids the waste of electricity and damage to the life of the main fan caused by long-term excessive ventilation. In addition, because it adopts a weight calculation method, people can preset weights according to the environment type and needs, so that the system can be applied to the main fan monitoring work in various environments; By setting the blade adaptive control function, the main fan blade angle can be adjusted in time within different β value ranges according to the main fan speed controlled by the adaptive control system and the main fan fixed parameters, so that the main fan blade can be at a suitable angle in coordination with its speed and environmental requirements. The real-time fan air volume, air pressure, shaft power and other parameters are deeply collected. Combined with the tunnel specification parameters and wind flow simulation software, the air volume matching, air pressure matching, tunnel resistance, fan efficiency, surge margin and other factors are comprehensively considered to accurately adjust the blade angle. For example, in energy-saving oriented mines, the fan efficiency can be improved by adjusting the blade angle to reduce Energy consumption. In mines where ventilation resistance fluctuations are sensitive, the blade angle is adjusted in time according to changes in tunnel resistance to ensure stable ventilation. Under different working conditions, such as general fluctuating working conditions, the blade angle is accurately adjusted according to factors such as gas concentration and fire hazards, taking into account ventilation, energy saving, noise reduction and various safety hazard prevention and control. Through precise blade angle adjustment, the airflow is distributed more evenly in the underground tunnels, reducing ventilation dead corners and turbulence. With the assistance of wind flow simulation software, the blade angle is optimized according to information such as tunnel direction, cross-section changes, and obstacle distribution to ensure stable airflow transmission, effectively improve ventilation effects, and ensure the safety of the underground working environment. By setting the loss value calculation function, the main fan speed, blade angle and some fixed parameters of the main fan are adjusted by the adaptive control system to calculate the theoretical air volume of the main fan. The loss value is obtained by the difference between the theoretical air volume and the actual air volume. An intelligent processing model is built in the PLC based on historical data. According to the air volume loss value and other operating parameters, the cause of the fault can be quickly determined. If the model determines that moderate air volume loss may be caused by increased duct resistance, it can automatically issue an instruction to check the duct, or start related equipment for preliminary investigation and cleaning. At the same time, a detailed maintenance work order is generated to improve the efficiency of fault handling and ensure the continuous and stable operation of the fan. In summary, the main fan monitoring system can realize real-time adjustment of the speed and blade angle of the main fan according to the comprehensive indicators of environmental factors, so as to ensure the ventilation demand while achieving the purpose of energy saving and environmental protection. At the same time, it can judge the possible faults and reasons for maintenance of the main fan by calculating the air volume loss value and combining historical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 It is a principle block diagram of a mine main ventilation fan monitoring system in the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1:

[0019] See also Figure 1 As shown, this embodiment is a monitoring system for a main ventilation fan for a mine, comprising: a sensor group, a host computer and a PLC, the sensor group comprises a gas concentration sensor, a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a dust concentration sensor, a temperature sensor, and a humidity sensor, the host computer is installed with a wind flow simulation software, and the PLC is installed with an adaptive control system; The sensor group is used to monitor the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature and humidity in the ventilation environment where the main fan is to be installed in real time, and transmit the detection data to the PLC; The host computer is used to run the wind flow simulation software. The software has a complete 3D model in the built-in environment, covering detailed information on the direction of the tunnel, cross-section changes, and obstacle distribution. It simulates the real dynamics of the wind flow based on real-time ventilation parameters, including the speed of the main fan and its blade angle. PLC is used to execute the adaptive control system. The adaptive control system includes the weights of the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the storage environment. The comprehensive index β is calculated based on the weights and real-time monitoring data. The main fan speed and blade angle adaptive control strategy is formulated based on the comprehensive index β and wind flow simulation software. The PLC controls the fan speed and blade angle of the main fan through the adaptive control strategy.

[0020] Preferably, the specific process of presetting the weight of each data in the adaptive control system is as follows: According to the environment type and industry regulations, weights are set for gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity, and the weights are set to K1, K2, K3, K4, K5, and K6 respectively. The weight of gas concentration K1 in the environment is set to 0.4, the weight of carbon monoxide concentration K2 is set to 0.2, the weight of carbon dioxide concentration K3 is set to 0.1, the weight of dust concentration K4 is set to 0.1, the weight of temperature K5 is set to 0.1, and the weight of humidity K6 is set to 0.1. The sum of the six weights is marked as Ktotal, and Ktotal=1.

[0021] It should be noted that different weights should be set to different values ​​for different types of environments, as shown in Table 1: Table 1: Weight table of some types of mines

[0022] Preferably, the specific calculation formula for calculating the comprehensive index β according to each weight and real-time monitoring data is as follows: ; in, is the maximum allowable value of gas concentration, usually 1%. The maximum permissible concentration of carbon monoxide is usually 24 ppm. is the maximum allowable value of carbon dioxide concentration, usually 1.5%. The maximum allowable value of dust concentration is usually 10 mg / m 3 , The suitable temperature is usually 22℃. The maximum allowable temperature is usually 26°C. For suitable humidity, 50% is usually taken. The maximum allowable humidity is usually 60%, which is set in advance according to the environment type. Monitoring data for gas concentration sensors, The carbon monoxide concentration sensor monitoring data, The carbon dioxide concentration sensor monitors the data. is the monitoring data of the dust concentration sensor, T is the monitoring data of the temperature sensor, and H is the monitoring data of the humidity sensor.

[0023] Preferably, the main fan speed adaptive control strategy is formulated as follows: The β value is divided into four stages, corresponding to the excellent environmental condition β≤0.2, the general fluctuation condition 0.2<β≤0.5, the deterioration trend condition 0.5<β≤0.8, and the emergency and dangerous condition β>0.8; When β≤0.2, the PLC controls the main fans to run at the lowest operating frequency, and sets this frequency as the main fan base frequency. At this time, the fans run smoothly and all performance indicators are good. When 0.2<β≤0.5, based on the basic frequency, the main fan operating frequency is adaptively adjusted with the strategy that the frequency increases by 10% when the comprehensive index β increases by 0.1. In this stage, β=0.5 is the highest frequency of the main fan in this stage. At this time, the fan speed increases and the air volume and air pressure will also change accordingly. When 0.5<β≤0.8, the frequency corresponding to β=0.5 in the previous stage is set as the basic frequency of this stage, and the operating frequency of the main fan is adaptively adjusted with the strategy that the frequency increases by 15% when the comprehensive index β increases by 0.1; When β>0.8, the main fan is adjusted to the highest frequency. At this time, the fan outputs air volume at the maximum capacity to ensure rapid dilution of harmful gases underground, creating conditions for personnel escape and emergency rescue; Through real-time monitoring of gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity data, the comprehensive index β is calculated in real time. The PLC uses this adaptive control strategy to adaptively control the main fan operating frequency in real time, and then adaptively control the main fan speed.

[0024] Preferably, the main fan blade angle adaptive adjustment strategy is formulated as follows: Based on four cases: β≤0.2, 0.2<β≤0.5, 0.5<β≤0.8, β>0.8; When β≤0.2, the actual output air volume of the main fan is collected through the air volume sensor , the wind pressure sensor collects the output wind pressure P of the main fan, and then uses the formula Calculate the shaft power W, where Represents the real-time efficiency of the main fan, , usually between 0.719-0.8, Represents the mechanical transmission efficiency of the main fan, usually between 70% and 90%. According to the factory parameter setting of the main fan, and the roadway resistance coefficient R under the main fan installation environment is measured in advance, the actual output air volume , the main fan output pressure P, shaft power W, and tunnel resistance coefficient R parameters are introduced into the wind flow simulation software, the wind flow dynamics are simulated through the built-in environmental 3D model of the software, and then the blade angle core adjustment coefficient is set , initially reset to zero, according to the formula: ; Calculation, including These are the corresponding ideal values ​​set in advance. is the maximum allowed value, It is a weight coefficient customized according to the fan performance and the actual situation of the mine, and , calculate Then, adjust the angle according to = The main fan blade angle is adjusted by the formula, and the fine-tuning range is limited to ±2°, and the angle is set as the initial setting angle. Therefore, on the basis of energy saving and noise reduction, the blade angle is fully optimized according to the real-time operation of the fan and the underground environment, so that the airflow is uniform and efficient, and the ventilation effect is greatly improved; It should be noted that the wind flow simulation software used is: "Mine Three-Dimensional Ventilation Simulation System" launched by Jinma Software (Beijing) Co., Ltd.; It is the air volume matching influence coefficient, reflecting the actual air volume of the current fan With ideal air volume The influence of the deviation on the blade angle adjustment is used to ensure that the air volume provided by the ventilator meets the actual needs of the mine. If the air volume deviates greatly from the ideal value, the blade angle needs to be adjusted significantly. For example, when the actual air volume is much less than the ideal air volume, If the value is 0.6, it will cause the blade angle to be adjusted more significantly to increase the air volume; It is the wind pressure matching influence coefficient, reflecting the real-time wind pressure P of the fan and the ideal wind pressure The difference in the blade angle adjustment weight is used to ensure that the wind pressure generated by the fan can overcome the roadway resistance and maintain a stable wind flow. If the wind pressure does not meet the standard, it will affect the ventilation effect. For example, in a long-distance roadway with large ventilation resistance, if the wind pressure is insufficient, Set it to 0.5, the blade angle can be adjusted to increase the wind pressure to meet ventilation needs; is the influence coefficient of the roadway resistance. Since changes in roadway resistance will affect the operation of the fan, the blade angle is adjusted to adapt to different resistance conditions. Represents the effect of the roadway resistance coefficient R on the blade angle adjustment. For example, when the roadway resistance increases due to local collapse, If it is 0.7, the system will quickly adjust the blade angle based on this coefficient to ensure that the fan overcomes the increased resistance and maintains normal ventilation; is the shaft power matching influence coefficient, which represents the difference between the fan shaft power W and the ideal shaft power The influence of the deviation on the blade angle adjustment is used to avoid the fan shaft power being too large or too small, ensure the stable operation of the equipment, and prevent overload or inefficient operation. For example, when the fan shaft power W exceeds the ideal value, it may cause equipment damage. If it is 0.6, the blade angle will be adjusted to reduce shaft power and ensure safe operation of the fan.

[0025] When 0.2<β≤0.5, the real-time efficiency of the main fan is synchronously introduced. , surge margin S, according to the formula ,in The real-time efficiency weight coefficient of the main fan, ranging from 0 to 1. is the surge margin weight coefficient, which takes a value between 0 and 1. These are the corresponding ideal values ​​set in advance. The blade angle compensation coefficient G is calculated, and the final blade angle is adjusted to , ensuring that the fan can operate efficiently and stably when responding to changes in working conditions, avoiding unstable phenomena such as surge; It should be noted that It is a measure of the real-time efficiency of the ventilator. The degree of influence on the blade angle adjustment is used to improve the operating efficiency of the fan and reduce energy consumption. When the efficiency is low, the operation is optimized by adjusting the blade angle. For example, in a mine with high energy-saving requirements, if the fan efficiency is lower than the ideal value, Taking 0.6 can promote blade angle adjustment, improve efficiency and reduce unnecessary energy consumption; Reflects the weight of the fan surge margin S on the blade angle adjustment, which is used to prevent the fan from entering the surge state and ensure the stability of equipment operation. For example, in mines where the ventilation system is prone to unstable conditions, in order to prevent surge, Take 0.5. When the surge margin is small, the blade angle will be adjusted to increase the surge margin and ensure smooth operation of the fan.

[0026] When 0.5<β≤0.8, recalculate the real-time efficiency of the main fan , surge margin S, introduction of tunnel temperature gradient , air specific heat capacity C, set blade optimization angle coefficient , according to the formula work out Value, where It indicates the weight coefficient of the influence of temperature gradient on ventilation demand, and the value is between 0 and 1. Indicates the weight coefficient of air specific heat capacity difference, with a value between 0 and 1. The real-time efficiency weight coefficient of the main fan, with a value between 0 and 1. is the surge margin weight coefficient, which takes a value between 0 and 1. These are the corresponding ideal values ​​set in advance. are the maximum values ​​allowed, and the After that, the blade angle is adjusted to , adapt to the dynamic underground thermal environment and changes in fan working conditions, and deepen the cooling and ventilation efficiency; It should be noted that Reflects the weight of the influence of the tunnel temperature gradient on the blade angle adjustment. It is used in mines with obvious temperature changes. The temperature gradient affects the airflow distribution. The blade angle needs to be adjusted to optimize the ventilation and cooling effect. For example, in mines with heat damage problems, when the temperature gradient is large, Set to 0.5, the system adjusts the blade angle according to this coefficient to enhance ventilation and heat dissipation and improve the working environment; It reflects the effect of air specific heat C on blade angle adjustment. It is used in different areas where air specific heat is different, affecting ventilation effect. The blade angle is adjusted to adapt to the difference in air thermal properties. For example, in a mine, the specific heat changes due to differences in air composition. Set to 0.4, the system adjusts the blade angle according to this coefficient to optimize the ventilation effect; The specific expression is the same.

[0027] When β>0.8, the smoke concentration gradient is introduced , smoke particle size distribution D, set the compensation coefficient to , according to the formula work out Value, where They represent the weight coefficient of smoke concentration on ventilation demand and the weight coefficient of smoke particle size distribution on ventilation demand, respectively, and both have values ​​between 0 and 1. These are the corresponding ideal values ​​set in advance. are the maximum values ​​allowed, and the After that, the blade angle is anchored , ensuring that wind flow can effectively disperse smoke and clear escape corridors; It should be noted that Reflecting the smoke concentration gradient The effect of adjusting the blade angle, for example, when a fire occurs in a mine and the smoke concentration gradient is large, If it is 0.7, the blades are adjusted to guide the wind flow to disperse the smoke, creating conditions for people to escape; Reflects the influence of smoke particle size distribution D on blade angle adjustment. For example, when the smoke particles generated by the fire are large in size and difficult to diffuse, If it is 0.5, the wind flow state can be changed by adjusting the blades to improve the smoke dissipation efficiency.

[0028] Embodiment 2:

[0029] Preferably, the inherent parameters of the main fan, including the impeller diameter, blade shape, and hub ratio, are stored in the PLC, and the speed and blade angle of the main fan are controlled based on the adaptive control system to calculate the current theoretical air volume of the main fan. , the specific formula is as follows: ,in are the structural parameters of the fan, including blade shape and hub ratio. They are fixed parameter values ​​obtained by the fan manufacturer after testing and are between 0.05 and 0.15. n is the actual speed of the current main fan. is the impeller diameter, is the current blade angle obtained; get Then, bring it into , calculate the air volume loss value .

[0030] It should be noted that It is a comprehensive coefficient related to the specific model and structure of the fan. It integrates a variety of internal structural factors that affect the performance of the fan. These factors include blade shape (such as whether the blade is forward curved, backward curved or radial, the degree of blade bending and torsion law, etc.), number of blades, blade installation method and hub ratio (ratio of hub diameter to impeller diameter), etc. Different fan designs have different structural parameters, resulting in different flow characteristics of the airflow inside the fan. It is a comprehensive quantitative reflection of these differences; and, It is obtained through experimental calibration. After the fan is produced, the manufacturer will conduct a series of performance test experiments on it. Under different speeds, blade angles and other working conditions, the actual air volume output of the fan is measured, and compared with the theoretical calculated value. Through a large amount of experimental data fitting and optimization, the more accurate value of this model of fan under different working conditions is finally determined. This value is usually recorded in the product technical data of the fan for reference in subsequent use and maintenance, usually between 0.05-0.15.

[0031] Preferably, the air volume loss value is calculated based on , different levels of loss value thresholds are stored in PLC, respectively ,in , , ; The PLC continuously collects historical data of the fan's long-term operation, including the fan speed n and blade angle under four different working conditions. , Theoretical air volume , Actual air volume , the environmental sensor data and the corresponding timestamp information, classify and organize these data, store them according to the working condition category and time series, build a database, use the decision tree algorithm, train the model in the built-in data analysis module of the PLC, and convert the air volume loss value As the target variable, the fan operating parameters, environmental parameters and the treatment measures taken at the corresponding time in history are taken as characteristic variables. Through repeated learning of historical data, the decision tree model gradually constructs the decision relationship between various influencing factors under different air volume loss levels, forming a diagnosis and decision model. During the actual operation of the fan, the PLC collects the current operating parameters and environmental parameters in real time to calculate the air volume loss value. , and input it into the trained model. The model determines the level of air loss and the corresponding cause based on the input parameter information and the learned decision rules.

[0032] Preferably, the specific processing steps of the system are as follows: S1. Install several gas concentration sensors, carbon monoxide concentration sensors, carbon dioxide concentration sensors, dust concentration sensors, temperature sensors, and humidity sensors in the environment where the main ventilation fan is to be installed. The data of these sensors are collected, stored, and processed by PLC; S2, calculating the comprehensive index β through the weight strategy based on the processed sensor data, and specifying the main fan adaptive wind speed adjustment strategy and blade angle adjustment strategy based on the comprehensive index β; S3, PLC adaptively adjusts the main ventilation speed and blade angle based on the main fan adaptive speed adjustment strategy and blade angle adjustment strategy combined with the wind flow simulation software in the host computer; S4. Based on the adjusted main fan speed and blade angle, various factory parameters of the main fan and the resistance coefficient R of the roadway to be installed are introduced to calculate the current theoretical air volume of the main fan ; S5. Theoretical air volume based on calculation The actual air volume obtained by monitoring Calculate and obtain the air loss Based on the lost air volume combined with historical data and the decision tree model, the level of air volume loss and the corresponding reasons are determined.

[0033] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0034] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A mine main ventilation fan monitoring system, comprising: A sensor group, a host computer and a PLC, wherein the sensor group includes a gas concentration sensor, a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a dust concentration sensor, a temperature sensor, and a humidity sensor; the host computer is installed with a wind flow simulation software; and the PLC is installed with an adaptive control system; The sensor group is used to monitor the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature and humidity in the ventilation environment where the main fan is to be installed in real time, and transmit the detection data to the PLC; The host computer is used to run the wind flow simulation software, which has a complete 3D model in the built-in environment and simulates the real dynamics of the wind flow according to the real-time ventilation parameters, wherein the ventilation parameters include the speed of the main fan and the angle of its blades; The PLC is used to execute an adaptive control system, which includes weights of data such as gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the storage environment. A comprehensive index β is calculated based on the weights and real-time monitoring data. An adaptive control strategy for the main fan speed and blade angle is formulated based on the comprehensive index β and wind flow simulation software. The PLC controls the fan speed and blade angle of the main fan through the adaptive control strategy.

2. The mine main ventilator monitoring system according to claim 1 is characterized in that: The specific process of presetting the weight of each data in the adaptive control system is as follows: According to the environment type and industry regulations, weights are set for gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity respectively, and the weights are set to K1, K2, K3, K4, K5, and K6 in sequence. The sum of the six weights is marked as Ktotal, and Ktotal=1.

3. The mine main ventilator monitoring system according to claim 1 is characterized in that: The specific calculation formula for calculating the comprehensive index β based on each weight and real-time monitoring data is as follows: ; in, is the maximum allowable value of gas concentration, is the maximum permissible concentration of carbon monoxide, is the maximum permissible value of carbon dioxide concentration, is the maximum allowable value of dust concentration, For the appropriate temperature, is the maximum allowable temperature, For suitable humidity, The maximum allowable humidity is set in advance according to the environment type. Monitoring data for gas concentration sensor, The carbon monoxide concentration sensor monitoring data, The carbon dioxide concentration sensor monitors the data. is the monitoring data of the dust concentration sensor, T is the monitoring data of the temperature sensor, and H is the monitoring data of the humidity sensor.

4. The mine main ventilator monitoring system according to claim 3 is characterized in that: The main fan speed adaptive control strategy is specifically formulated as follows: The β value is divided into four stages, corresponding to the excellent environmental condition β≤0.2, the normal fluctuation condition 0.2<β≤0.5, the deterioration trend condition 0.5<β≤0.8, and the emergency and dangerous condition β>0.8; When β≤0.2, the PLC controls the main fans to operate at the lowest operating frequency, and sets this frequency as the main fan base frequency; When 0.2<β≤0.5, based on the basic frequency, the main fan operating frequency is adaptively adjusted with the strategy that the frequency increases by 10% for every 0.1 increase in the comprehensive index β. In this stage, β=0.5 is the highest frequency of the main fan in this stage. When 0.5<β≤0.8, the frequency corresponding to β=0.5 in the previous stage is set as the basic frequency of this stage, and the operating frequency of the main fan is adaptively adjusted with the strategy of increasing the frequency by 15% for every 0.1 increase in the comprehensive index β; When β>0.8, the main fan is adjusted to the highest frequency; Through real-time monitoring of gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity data, the comprehensive index β is calculated in real time. The PLC uses this adaptive control strategy to adaptively control the main fan operating frequency in real time, and then adaptively control the main fan speed.

5. The mine main ventilator monitoring system according to claim 1 is characterized in that: The main fan blade angle adaptive adjustment strategy is formulated as follows: Based on four cases: β≤0.2, 0.2<β≤0.5, 0.5<β≤0.8, β>0.8; When β≤0.2, the actual output air volume of the main fan is collected through the air volume sensor , the wind pressure sensor collects the output wind pressure P of the main fan, calculates the shaft power W, and determines the value of the factory parameters of the main fan, and obtains the roadway resistance coefficient R under the installation environment of the main fan in advance, and calculates the actual output air volume , the main fan output pressure P, shaft power W, and tunnel resistance coefficient R parameters are introduced into the wind flow simulation software, the wind flow dynamics are simulated through the built-in environmental 3D model of the software, and then the blade angle core adjustment coefficient is set , calculate Then, adjust the angle according to = The main fan blade angle is adjusted by the formula, and the fine-tuning range is limited to ±2°, and the angle is set as the initial setting angle; When 0.2<β≤0.5, set the compensation coefficient to G and adjust the blade angle to ; When 0.5<β≤0.8, the compensation coefficient is set to , the blade angle is adjusted to ; When β>0.8, the compensation coefficient is set to , blade angle anchoring .

6. The mine main ventilator monitoring system according to claim 1, characterized in that: The PLC stores the inherent parameters of the main fan, including the impeller diameter, blade shape, and hub ratio, and controls the speed and blade angle of the main fan based on the adaptive control system to calculate the current theoretical air volume of the main fan. ,get Then, bring it into , calculate the air volume loss value .

7. The mine main ventilator monitoring system according to claim 6, characterized in that: Based on the calculation, the air loss value is obtained , different levels of loss value thresholds are stored in PLC, respectively ,in , , ; The PLC continuously collects historical data of the fan's long-term operation, including the fan speed n and blade angle under four different working conditions. , Theoretical air volume , Actual air volume , the environmental sensor data and the corresponding timestamp information, classify and organize these data, store them according to the working condition category and time series, build a database, use the decision tree algorithm, train the model in the built-in data analysis module of the PLC, and convert the air volume loss value As the target variable, the fan operating parameters, environmental parameters and the treatment measures taken at the corresponding time in history are taken as characteristic variables. Through repeated learning of historical data, the decision tree model gradually constructs the decision relationship between various influencing factors under different air volume loss levels, forming a diagnosis and decision model. During the actual operation of the fan, the PLC collects the current operating parameters and environmental parameters in real time to calculate the air volume loss value. , and input it into the trained model. The model determines the level of air loss and the corresponding cause based on the input parameter information and the learned decision rules.

8. The main ventilation fan monitoring system for a mine according to any one of claims 1 to 7, characterized in that: The specific processing steps of the system are as follows: S1. Install several gas concentration sensors, carbon monoxide concentration sensors, carbon dioxide concentration sensors, dust concentration sensors, temperature sensors, and humidity sensors in the environment where the main ventilation fan is to be installed. The data of these sensors are collected, stored, and processed by PLC; S2, calculating the comprehensive index β through the weight strategy based on the processed sensor data, and specifying the main fan adaptive wind speed adjustment strategy and blade angle adjustment strategy based on the comprehensive index β; S3, PLC adaptively adjusts the main ventilation speed and blade angle based on the main fan adaptive speed adjustment strategy and blade angle adjustment strategy combined with the wind flow simulation software in the host computer; S4. Based on the adjusted main fan speed and blade angle, various factory parameters of the main fan and the resistance coefficient R of the roadway to be installed are introduced to calculate the current theoretical air volume of the main fan ; S5. Theoretical air volume based on calculation The actual air volume obtained by monitoring Calculate and obtain the air loss Based on the lost air volume combined with historical data and the decision tree model, the level of air volume loss and the corresponding reasons are determined.

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