Explosion-proof system and method for coal conveying belt vestibule

Abstract

The invention belongs to the technical field of coal conveying in a coal injection system, and particularly discloses a coal conveying belt vestibule explosion-proof system and method.The coal conveying belt vestibule explosion-proof system is characterized in that a thermal infrared imager is electrically connected with a PLC, the thermal infrared imager is located in a belt vestibule, a dust sensor is arranged in the belt vestibule, and the thermal infrared imager and the dust sensor are both electrically connected with the PLC; a model used for thermal image processing is integrated in the PLC, a dust remover is electrically connected to the PLC, and a spraying assembly used for dust falling is arranged on the PLC. A thermal image of an ignition source in the belt vestibule is detected in real time through the thermal infrared imager, then the thermal image and the coal dust concentration are classified through a convolutional neural network in the PLC, the coal dust concentration is monitored in real time through the dust sensor, and then the PLC controls the dust remover and the spraying assembly to remove the coal dust. Early warning and explosion-proof treatment can be carried out on the ignition source and the coal dust concentration at the same time, and the problem that in the prior art, a coal dust explosion-proof system does not carry out monitoring and explosion-proof on the ignition source is solved.

Description

Technical Field

[0001] The invention relates to the technical field of coal transportation in a coal injection system, and in particular to an explosion-proof system and method for a coal transportation belt corridor. Background Art

[0002] The coal injection system is an important part of the ironmaking production process. It involves the entire process of raw coal storage, pulverizing, conveying, injection, gas supply and coal powder metering. During the transportation process, coal is transported through belt corridors to ensure that the coal dust generated in the production process pollutes the working environment and affects the health of the workers. However, during the transportation of coal, due to the friction and vibration of the coal, a large amount of coal powder is easily generated. If the coal powder is not detected and prevented in time, after the coal powder concentration reaches a certain value, due to electrical sparks and mechanical friction sparks in the belt corridor, it is easy to ignite the coal dust and cause a coal dust explosion.

[0003] Coal dust explosion refers to the explosion phenomenon that occurs when coal dust mixes with air and encounters an ignition source. This explosion will cause a sharp increase in temperature and pressure, releasing a large amount of energy. The occurrence of a coal dust explosion requires certain conditions to be met, including the coal dust reaching a certain concentration, sufficient oxygen, and the existence of an ignition source. In this case, it is necessary to give an early warning of the coal dust concentration and ignition source. However, most of the existing coal dust explosion prevention measures are based on real-time coal dust concentration detection, different coal dust concentration graded alarms, and then corresponding disposal. However, the ignition source is not monitored or explosion-proofed. Summary of the invention

[0004] The object of the present invention is to provide a coal conveyor belt corridor explosion-proof system and method to solve the problem that the coal dust explosion-proof system in the prior art fails to monitor and prevent the ignition source.

[0005] To achieve the above-mentioned purpose, the basic solution provided by the present invention is: a coal transport belt corridor explosion-proof system, including an infrared thermal imager and a PLC controller electrically connected, the infrared thermal imager is located in the belt corridor, a dust sensor is provided in the belt corridor, the infrared thermal imager and the dust sensor are both electrically connected to the PLC controller, a model for thermal image processing is integrated on the PLC controller, a dust collector is electrically connected to the PLC controller, and a spray assembly for dust reduction is provided on the PLC controller.

[0006] The principle and beneficial effect of the present invention are: the thermal image of the belt corridor is captured in real time by an infrared thermal imager, the real-time thermal image is processed by a model in a PLC controller, the concentration of coal dust in the belt corridor is monitored in real time by a dust sensor, and then the PLC controller performs explosion-proof treatment according to the temperature of the thermal image and the concentration of the coal dust, the PLC controller controls the dust collector and the spray assembly to remove the coal dust, the infrared thermal imager can monitor the temperature of electrical sparks generated in the belt corridor in real time, the dust sensor can monitor the coal dust concentration in real time, and the coal dust concentration and ignition source can be monitored simultaneously, and the belt corridor is dusted in time, thereby reducing the probability of coal dust explosion and improving the safety of the belt corridor; the measurement temperature range of the infrared thermal imager is -50°C-1000°C, which means that it can measure various temperatures from extremely low temperatures to high temperatures.

[0007] Option 2 is the preferred option of the basic option. The spray assembly includes a liquid storage barrel, on which is provided an aspirator. The PLC controller is electrically connected to the aspirator, a water pipe is fixedly connected to the aspirator, and an atomizing nozzle is fixedly connected to the water pipe. When in use, the PLC controller controls the aspirator to start, and the water in the liquid storage barrel is extracted. The water flows from the water pipe to the atomizing nozzle, and then the atomizing nozzle sprays the water into water mist to remove dust. At the same time, the belt corridor can be cooled, thereby reducing the probability of coal dust explosion.

[0008] Solution 3, which is the basic solution, is a method for explosion-proofing a coal conveyor belt corridor, comprising the following steps:

[0009] S1. First, use an infrared thermal imager to collect thermal images of the belt corridor, and use a dust sensor to collect the concentration of coal dust in the belt corridor;

[0010] S2. The collected thermal images are formed into a data set, and then a convolutional neural network model is constructed, the convolutional neural network model is trained by the data set, and the trained convolutional neural network model is integrated into the PLC controller;

[0011] S3. Process the real-time thermal image using a convolutional neural network model to generate temperature classification labels and transmit them to the PLC controller;

[0012] S4.PLC controller combines temperature classification labels and coal dust concentration for explosion-proof treatment. The infrared thermal imager collects thermal images of locations in the belt corridor that are prone to sparks in real time. The convolutional neural network is trained to classify thermal images according to different temperatures, so that the PLC controller controls the dust collector and spray assembly to remove dust. The convolutional neural network classifies images through autonomous learning, and can detect ignition sources in the belt corridor and take explosion-proof measures in a timely manner, reducing the probability of explosion in the belt corridor.

[0013] Scheme 4 is the preferred option of Scheme 3. The specific steps in S2 are as follows:

[0014] 1) First, read the thermal image data set through the pandas library, and divide the data set into training set and test set through the train_test_split() function;

[0015] 2) The thermal images in the training set are feature scaled using StandardScaler, and a convolutional neural network model is created using PyTorch;

[0016] 3) Set key parameters, such as learning rate, number of iterations, etc., and then use the training set to train the model;

[0017] 4) After training, the trained convolutional neural network model is verified through the test set, and the parameters are adjusted according to actual needs.

[0018] Scheme 5 is the preferred option of Scheme 3. In S4:

[0019] If the thermal image shows a temperature below 610°C and the coal dust concentration is 30g / m 3 If the thermal image shows a temperature below 610°C and the coal dust concentration is 30-35g / m 3 If the thermal image shows that the temperature is below 610℃ and the coal dust concentration is 35g / m 3 When the temperature is above 610℃, the PLC controller controls the dust collector and the suction device to remove the coal dust in the belt corridor. When the temperature is below 610℃, the dust sensor monitors the different coal dust concentrations, controls the use of the dust collector and the spray assembly, and adopts different disposal methods to reduce the probability of explosion.

[0020] If the thermal image shows a temperature in the range of 610℃-700℃ and the coal dust concentration is 18g / m 3 If the temperature of the thermal image is between 610℃ and 700℃, and the coal dust concentration is between 18-30g / m 3 If the temperature of the thermal image is within the range of 610℃-700℃ and the coal dust concentration is within 30g / m 3 When the temperature is above 610℃-700℃, the dust sensor is used to monitor the concentration of different coal dust, control the use of dust collector and spray assembly, and adopt different disposal methods to reduce the probability of explosion.

[0021] If the thermal image shows a temperature above 700°C and the coal dust concentration is 15g / m 3If the temperature of the thermal image is above 700℃ and the coal dust concentration is 15-18g / m 3 If the thermal image shows that the temperature is above 700℃ and the coal dust concentration is 18g / m 3 When the temperature is above 700℃, the PLC controller will control the dust collector and the suction device to remove the coal dust in the belt corridor. When the temperature is above 700℃, the dust sensor will monitor the different coal dust concentrations, control the use of dust collector and spray components, and take different disposal methods to reduce the probability of explosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a stereoscopic diagram of an explosion-proof system and method for a coal conveyor belt corridor of the present invention;

[0023] Figure 2 This is a front view of an explosion-proof system and method for a coal conveyor belt corridor of the present invention;

[0024] Figure 3 yes Figure 2 Cross-sectional view at AA in the middle. DETAILED DESCRIPTION

[0025] The present invention is further described in detail below through specific embodiments:

[0026] The reference numerals in the drawings of the specification include: 1. infrared thermal imager, 3. belt corridor, 4. dust sensor, 5. dust collector, 6. liquid storage tank, 7. suction device, 8. water pipe, 9. atomizing nozzle.

[0027] Example 1

[0028] like Figures 1 to 3 As shown: an explosion-proof system for a coal transport belt corridor, including an infrared thermal imager 1 and a PLC controller electrically connected, the infrared thermal imager 1 is located in a belt corridor 3, a dust sensor 4 is provided in the belt corridor 3, the infrared thermal imager 1 and the dust sensor 4 are both electrically connected to the PLC controller, a model for thermal image processing is integrated on the PLC controller, a dust collector 5 is electrically connected to the PLC controller, a spray assembly for dust removal is provided on the PLC controller, the spray assembly includes a liquid storage barrel 6, a suction device 7 is provided on the liquid storage barrel 6, the PLC controller and the suction device 7 are electrically connected, a water pipe 8 is fixedly connected to the suction device 7, and an atomizing nozzle 9 is fixedly connected to the water pipe 8.

[0029] The implementation method of this embodiment is as follows: when in use, the infrared thermal imager 1 and the dust sensor 4 monitor the belt corridor 3 in real time, and transmit the measured thermal image and coal dust concentration to the PLC controller, and then the model in the PLC controller processes the thermal image, and the PLC controller determines the coal dust concentration. When the PLC controller detects that the temperature of the ignition source in the thermal image and the coal dust concentration are too high, the PLC controller controls the dust collector 5 and the suction device 7 to remove dust according to the temperature and the coal dust concentration. After the suction device 7 is started, the water in the liquid storage barrel 6 is extracted, and the water mist is sprayed from the atomizing nozzle 9 through the water pipe 8 to remove the dust from the belt corridor 3. The infrared thermal imager 1 can monitor the temperature of the electrical spark generated in the belt corridor 3 in real time, and the dust sensor 4 can monitor the coal dust concentration in real time, so as to realize the simultaneous monitoring of the coal dust concentration and the ignition source, and timely remove the dust from the belt corridor 3, thereby reducing the probability of coal dust explosion.

[0030] Example 2

[0031] A method for preventing explosion of a coal conveyor belt corridor comprises the following steps:

[0032] S1. First, use an infrared thermal imager 1 to collect thermal images of the belt corridor 3, and use a dust sensor 4 to collect the concentration of coal dust in the belt corridor 3;

[0033] S2. The collected thermal images are formed into a data set, and then a convolutional neural network model is constructed. The convolutional neural network model is trained through the data set: 1) First, the thermal image data set is read through the pandas library, and the data set is divided into a training set and a test set through the train_test_split() function; 2) The thermal images in the training set are feature scaled through StandardScaler, and a convolutional neural network model is created through PyTorch; 3) Key parameters such as learning rate, number of iterations, etc. are set, and then the model is trained using the training set; 4) After the training, the trained convolutional neural network model is verified through the test set, and the parameters are adjusted according to actual needs, and the trained convolutional neural network model is integrated into the PLC controller.

[0034] S3. Process the real-time thermal image using a convolutional neural network model to generate temperature classification labels and transmit them to the PLC controller;

[0035] S4.PLC controller combines temperature classification label and coal dust concentration for explosion-proof treatment: If the thermal image shows that the temperature is below 610℃ and the coal dust concentration is 30g / m 3 If the thermal image shows a temperature below 610°C and the coal dust concentration is 30-35g / m 3 If the thermal image shows that the temperature is below 610℃ and the coal dust concentration is 35g / m3 At the above time, the PLC controller controls the dust collector 5 and the suction device 7 to remove the coal dust in the belt corridor. The dust sensor monitors different coal dust concentrations, controls the use of the dust collector and the spray assembly, and adopts different disposal methods to reduce the probability of explosion.

[0036] If the thermal image shows a temperature in the range of 610℃-700℃ and the coal dust concentration is 18g / m 3 If the temperature of the thermal image is between 610℃ and 700℃, and the coal dust concentration is between 18-30g / m 3 If the temperature of the thermal image is within the range of 610℃-700℃ and the coal dust concentration is within 30g / m 3 At the above time, the PLC controller controls the dust collector 5 and the suction device 7 to remove the coal dust in the belt corridor. The dust sensor monitors different coal dust concentrations, controls the use of the dust collector and the spray assembly, and adopts different disposal methods to reduce the probability of explosion.

[0037] If the thermal image shows a temperature above 700°C and the coal dust concentration is 15g / m 3 If the temperature of the thermal image is above 700℃ and the coal dust concentration is 15-18g / m 3 If the thermal image shows that the temperature is above 700℃ and the coal dust concentration is 18g / m 3 When the above, the PLC controller will control the dust collector 5 and the suction device 7 to remove the coal dust in the belt corridor 3. The dust sensor monitors the different coal dust concentrations, controls the use of the dust collector and the spray assembly, and adopts different disposal methods to reduce the probability of explosion.

[0038] The implementation method of this embodiment is as follows: when in use, the infrared thermal imager 1 transmits the real-time monitored thermal image to the PLC controller, which is then processed by the convolutional neural network and the temperature classification label is then transmitted to the PLC controller. At the same time, the dust sensor 4 monitors the coal dust concentration in the belt corridor 3 in real time and transmits it to the PLC controller. The PLC controller then controls the dust collector 5 and the suction device 7 to remove dust according to the temperature and coal dust concentration. The infrared thermal imager 1 can monitor the temperature of electrical sparks generated in the belt corridor 3 in real time, and the dust sensor 4 can monitor the coal dust concentration in real time, thereby realizing simultaneous monitoring of the coal dust concentration and the ignition source, and timely removing dust from the belt corridor 3, thereby reducing the probability of coal dust explosion.

[0039] The above is only an embodiment of the present invention, and the common knowledge such as the known specific structure and characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. An explosion-proof system for a coal conveyor belt corridor, characterized in that: The invention comprises an infrared thermal imager (1) and a PLC controller which are electrically connected. The infrared thermal imager (1) is located in a belt corridor (3). A dust sensor (4) is provided in the belt corridor (3). The infrared thermal imager (1) and the dust sensor (4) are both electrically connected to the PLC controller. A model for thermal image processing is integrated in the PLC controller. A dust collector (5) is electrically connected to the PLC controller. A spray assembly for dust removal is provided on the PLC controller.

2. The explosion-proof system for coal conveyor belt corridor according to claim 1 is characterized in that The spray assembly comprises a liquid storage barrel (6), the liquid storage barrel (6) is provided with a suction device (7), the PLC controller and the suction device (7) are electrically connected, the suction device (7) is fixedly connected with a water pipe (8), and the water pipe (8) is fixedly connected with an atomizing nozzle (9).

3. A method for preventing explosion of a coal conveyor belt corridor, characterized in that , including the following steps: S1. First, use an infrared thermal imager (1) to collect thermal images of the belt corridor (3), and use a dust sensor (4) to collect the concentration of coal dust in the belt corridor (3); S2. The collected thermal images of the skin are formed into a data set, and then a convolutional neural network model is constructed, the convolutional neural network model is trained by the data set, and the trained convolutional neural network model is integrated into the PLC controller; S3. Process the real-time thermal image using a convolutional neural network model to generate temperature classification labels and transmit them to the PLC controller; S4.PLC controller combines the temperature classification label and coal dust concentration to carry out explosion-proof treatment of the belt corridor (3).

4. The explosion-proof method for a coal conveyor belt corridor according to claim 3 is characterized in that: The specific steps in S2 are as follows: 1) First, read the thermal image data set through the pandas library, and divide the data set into training set and test set through the train_test_split() function; 2) The thermal images in the training set are feature scaled using StandardScaler, and a convolutional neural network model is created using PyTorch; 3) Set key parameters, such as learning rate, number of iterations, etc., and then use the training set to train the model; 4) After training, the trained convolutional neural network model is verified through the test set, and the parameters are adjusted according to actual needs.

5. The explosion-proof method for a coal conveyor belt corridor according to claim 3 is characterized in that ,S4: If the thermal image shows a temperature below 610°C and the coal dust concentration is 30g / m 3 If the thermal image shows a temperature below 610°C and the coal dust concentration is 30-35g / m 3 If the temperature of the thermal image is below 610°C and the coal dust concentration is below 35g / m 3 In the above case, the PLC controller controls the dust collector (5) and the suction device (7) to remove the coal dust in the belt corridor (3); If the thermal image shows a temperature in the range of 610℃-700℃ and the coal dust concentration is 18g / m 3 If the temperature of the thermal image is between 610℃ and 700℃, and the coal dust concentration is between 18-30g / m 3 If the temperature of the thermal image is within the range of 610°C-700°C and the coal dust concentration is within 30g / m 3 In the above case, the PLC controller controls the dust collector (5) and the suction device (7) to remove the coal dust in the belt corridor (3); If the thermal image shows a temperature above 700°C and the coal dust concentration is 15g / m 3 If the temperature of the thermal image is above 700℃ and the coal dust concentration is 15-18g / m 3 If the temperature of the thermal image is above 700°C and the coal dust concentration is 18g / m 3 In the above case, the PLC controller will control the dust collector (5) and the suction device (7) to remove the coal dust in the belt corridor (3).

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