Muon-based blast furnace refractory material layer analysis system and method

Abstract

The invention relates to a muon-based blast furnace refractory material layer analysis system and method, and belongs to the technical field of muon detection. The system comprises three groups of first muon detectors which are arranged at intervals of 120 degrees along the circumferential direction of the blast furnace; the included angle between the detection direction of each group of first muon detectors and the horizontal plane is alpha; 15 degrees < = alpha < = 45 degrees; the three groups of second muon detectors are arranged at intervals of 120 degrees along the circumferential direction of the blast furnace; the included angle between the detection direction of each group of second muon detectors and the horizontal plane is beta; 45 DEG < = beta < = 75 DEG; and the data processing module is used for carrying out three-dimensional reconstruction based on the muon detection data and carrying out blast furnace refractory material layer analysis based on a three-dimensional imaging result. By means of the mode, the method can be accurately focused on key areas where blast furnace stress is concentrated and damage is most likely to happen, accurate coverage of blast furnace refractory material layer damage high-risk areas is achieved, and non-contact, non-stop and 24-hour continuous monitoring can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of muon detection, and in particular to a blast furnace refractory material layer analysis system and method based on muons. BACKGROUND

[0002] As the core equipment of steel metallurgy, the refractory lining of the hearth and the bottom of the blast furnace is prone to damage such as thickness thinning, erosion or cracking under the action of long-term high temperature, high pressure and chemical corrosion. If not detected in time, it may lead to molten iron leakage, even the furnace body burns through, explosion and other serious safety accidents, causing huge economic losses and production interruption. Therefore, it is of great engineering significance to analyze the refractory material layer of the blast furnace.

[0003] It is found that the current analysis and detection of the blast furnace mainly rely on traditional monitoring methods, including thermocouple temperature monitoring, cooling wall heat flow monitoring, ultrasonic flaw detection, etc. Among them, the thermocouple and cooling wall heat flow monitoring belong to indirect monitoring method, which can only reflect the temperature or heat flow change of the outside of the furnace lining, and cannot directly show the position, shape and scale of the damage in the furnace; the contact type detection method such as ultrasonic flaw detection is limited by the extreme working conditions of high temperature, high pressure and sealing of the blast furnace, and it is difficult to realize online detection of the furnace area, and the detection process often needs to stop the furnace, causing huge production and economic losses. SUMMARY

[0004] To solve the above technical problems, the present application provides a blast furnace refractory material layer analysis system and method based on muons.

[0005] In a first aspect, the present application provides a blast furnace refractory material layer analysis system based on muons, comprising: three groups of first muon detectors distributed outside the hearth area of the blast furnace and arranged at intervals of 120° along the circumferential direction of the blast furnace; wherein the detection direction of each group of first muon detectors is towards the blast furnace, and the included angle with the horizontal plane is α; 15°≤α≤45°; three groups of second muon detectors distributed outside the bottom area of the blast furnace and arranged at intervals of 120° along the circumferential direction of the blast furnace; the detection direction of each group of second muon detectors is towards the blast furnace, and the included angle with the horizontal plane is β; 45°≤β≤75°; the detection area of each group of first muon detectors intersects with the detection area of the corresponding group of second muon detectors at the junction area of the hearth and the bottom; a data processing module connected with the first muon detector and the second muon detector respectively; the data processing module is used for three-dimensional reconstruction based on the muon detection data of the first muon detector and the muon detection data of the second muon detector, and blast furnace refractory material layer analysis based on the three-dimensional imaging result.

[0006] Optionally, each of the first muon detectors comprises 6 muon detection units; and each of the second muon detectors comprises 6 muon detection units.

[0007] Optionally, an angle a between a detection direction of each of the first muon detectors and a horizontal plane ranges from 25° to 35°; a projection of the detection direction of each of the first muon detectors on the horizontal plane points to a central axis of the blast furnace; an angle β between a detection direction of each of the second muon detectors and the horizontal plane ranges from 55° to 65°; a projection of the detection direction of each of the second muon detectors on the horizontal plane points to the central axis of the blast furnace.

[0008] Optionally, the data processing module comprises a data acquisition and screening unit; the data acquisition and screening unit is configured to receive muon detection data of the first muon detectors and muon detection data of the second muon detectors, and eliminate interference signals from the muon detection data of the first muon detectors and the muon detection data of the second muon detectors by analyzing signal shape differences, and extract target muon data.

[0009] Optionally, the data acquisition and screening unit is further configured to construct a three-dimensional feature vector from a rise time, a pulse width and an energy deposition parameter of the extracted muon detection data; the energy deposition parameter comprises a signal peak amplitude or an integral charge amount; and the data acquisition and screening unit is further configured to screen signals with a similarity satisfying a preset threshold value as the target muon signals by calculating a similarity between the three-dimensional feature vector and a standard muon signal feature vector.

[0010] Optionally, the data processing module further comprises a three-dimensional imaging unit; the three-dimensional imaging unit is configured to extract muon transmission attenuation information and muon scattering angle information from the muon detection data; and the three-dimensional imaging unit is configured to generate the three-dimensional imaging result by integrating the muon transmission attenuation information and the muon scattering angle information through a data fusion algorithm.

[0011] Optionally, the three-dimensional imaging unit is further configured to extract muon transmission attenuation information from the muon detection data, generate a three-dimensional density distribution map through iterative reconstruction, extract muon scattering angle information from the muon detection data, generate a three-dimensional atomic number sensitive distribution map through scatter tomographic inversion, and generate the three-dimensional imaging result by data fusion of the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map; and different dynamic weights of the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map are set during data fusion for different regions of the hearth.

[0012] Optionally, the data processing module further comprises a multi-source information evaluation unit; the multi-source information evaluation unit is configured to acquire the three-dimensional imaging result and auxiliary measured data, and perform joint analysis based on a preset comprehensive evaluation rule to output a blast furnace damage diagnosis result; wherein the auxiliary measured data comprises at least one of the following: blast furnace thermocouple detection data, and cooling wall heat flow data.

[0013] Optionally, the preset comprehensive evaluation rule at least comprises: if the three-dimensional imaging result shows that the density of a first region decreases, and the thermocouple temperature corresponding to the first region continuously increases or the cooling wall heat flow data corresponding to the first region continuously increases, then it is evaluated that the first region has intensified erosion of the blast furnace lining; and if the three-dimensional imaging result shows that the density of a second region decreases, and the thermocouple temperature corresponding to the second region remains unchanged or decreases, or the cooling wall heat flow data corresponding to the second region decreases, then it is evaluated that the second region forms an air gap.

[0014] In a second aspect, the application provides a blast furnace refractory layer analysis method based on muons, which is applied to the data processing module of the blast furnace refractory layer analysis system based on muons in any one of the above first aspect, and the method comprises the following steps: receiving muon detection data of the first muon detector and muon detection data of the second muon detector; performing three-dimensional reconstruction on the muon detection data of the first muon detector and the muon detection data of the second muon detector; and performing blast furnace refractory layer analysis based on the three-dimensional imaging result.

[0015] The application has the following beneficial effects: considering that muon detection can completely passively receive cosmic rays existing in nature without any artificial radioactive source, the application is absolutely safe without radiation protection concerns, and the production of the blast furnace does not need to be interrupted, and cosmic rays have strong penetrating properties, therefore, the application provides a blast furnace refractory layer analysis method based on muons. The existing muon detection scheme is mostly arranged at a single angle or a single layer, and for a blast furnace with a three-dimensional complex structure, especially for the key region at the junction of the hearth and the bottom, there are problems of incomplete spatial coverage and insufficient three-dimensional structure reconstruction accuracy, and it is difficult to accurately identify the damage position and degree. Based on this, the application sets 3 groups of first muon detectors and 3 groups of second muon detectors for the two high-damage regions of the hearth and the bottom, and the two types of detectors are arranged at an interval of 120° along the circumferential direction of the blast furnace, which conforms to the cylindrical symmetry structure characteristics of the blast furnace, ensures the uniformity of the radial and circumferential detection, and realizes full coverage detection in the circumferential direction of the blast furnace. At the same time, by setting the detection angle α of the first muon detector to 15°-45° and the detection angle β of the second muon detector to 45°-75°, the detection regions of the two types of detectors intersect at the junction region of the hearth and the bottom, accurately focus on the key region where stress concentration of the blast furnace refractory layer and damage are most likely to occur, and realize accurate coverage of the high-risk region of the blast furnace refractory layer damage.

[0016] In addition, since the detectors are arranged outside the blast furnace body and can detect the work in real time, the non-contact, uninterrupted and 24-hour continuous monitoring in the true sense is realized, which can meet the industrial properties of continuous operation of the blast furnace. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A module block diagram of a blast furnace refractory material layer analysis system based on muons provided by the embodiment of the present application; Figure 2 A muon detector distribution effect diagram provided by the embodiment of the present application; Figure 3 Another muon detector distribution effect diagram provided by the embodiment of the present application; Figure 4 A module block diagram of a data processing module provided by the embodiment of the present application; Figure 5 A step flow chart of a blast furnace refractory material layer analysis method based on muons provided by the embodiment of the present application. DETAILED DESCRIPTION

[0018] In the following description, specific details are set forth such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0019] In addition, in the description of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for differentiation in description and cannot be understood as indicating or implying relative importance.

[0020] It is found that the current analysis and detection of the blast furnace mainly rely on traditional monitoring methods, including thermocouple temperature monitoring, cooling wall heat flow monitoring, ultrasonic flaw detection, etc. Among them, the thermocouple and cooling wall heat flow monitoring belong to indirect monitoring methods, which can only reflect the temperature or heat flow changes outside the furnace lining, and cannot directly show the position, shape and scale of the damage in the furnace; the contact detection methods such as ultrasonic flaw detection are limited by the extreme working conditions of high temperature, high pressure and sealing of the blast furnace, and it is difficult to realize online detection of the furnace area, and the detection process often needs to stop the furnace, causing huge production capacity and economic loss.

[0021] In view of the above problems, the present application proposes the following embodiments to solve the above technical problems.

[0022] Please refer to Figures 1-3 The present application provides a blast furnace refractory material layer analysis system 100 based on muons, comprising: 3 groups of first muon detectors 10 are distributed outside the hearth area of the blast furnace and are arranged at intervals of 120° along the circumferential direction of the blast furnace; wherein the detection direction of each group of first muon detectors is directed towards the blast furnace, and the included angle with the horizontal plane is a; 15°≤a≤45°.

[0023] Muons are natural cosmic ray particles with extremely strong penetrating power, and are not affected by the high temperature, closed, multi-layer refractory material structure of the blast furnace. Therefore, the application provides a blast furnace refractory material layer analysis based on cosmic muons.

[0024] It should be noted that the hearth is the core area of the blast furnace to withstand the scouring of high-temperature molten iron and chemical corrosion, and is also a high-incidence site of damage such as cracks and corrosion. Focusing the first muon detector 10 on this area can achieve targeted detection of the core damage area.

[0025] At the same time, the 3 groups of first muon detectors 10 are arranged at intervals of 120° along the circumferential direction of the blast furnace, and are arranged in a regular triangle. This arrangement can achieve full-range coverage of the circumferential direction of the blast furnace hearth under the premise of using the least number of detectors, avoiding the blind area of the circumferential direction caused by insufficient number of detectors. And the detection angle a of the first muon detector 10 is set to 15°~45°, which can ensure that the muon beam penetrates the core thickness area of the hearth side wall at an optimal path and accurately captures the structural changes inside the hearth.

[0026] 3 groups of second muon detectors 20 are distributed outside the hearth area of the blast furnace and are arranged at intervals of 120° along the circumferential direction of the blast furnace; the detection direction of each group of second muon detectors is directed towards the blast furnace, and the included angle with the horizontal plane is β; 45°≤β≤75°; the detection area of each group of first muon detectors intersects with the detection area of the corresponding group of second muon detectors at the junction area of the hearth and the hearth bottom.

[0027] It should be noted that the hearth bottom bears the overall weight of the blast furnace and the static pressure of high-temperature molten iron, and is prone to damage such as hearth bottom corrosion and refractory layer shedding, which is a key part to ensure the safe operation of the blast furnace. The deployment of the second muon detector 20 can achieve targeted detection of the damage in the hearth bottom area.

[0028] Similarly, the 3 groups of second muon detectors 20 are arranged at intervals of 120° along the circumferential direction of the blast furnace, and are arranged in a regular triangle. And the detection angle β of the second muon detector 20 is set to 45°~75°, which can make the muon beam penetrate the refractory layer, base and other key structures of the hearth bottom at a large angle, and fully obtain the medium distribution information inside the hearth bottom.

[0029] In the application, the refractory material layer is mainly for the refractory lining of the hearth and the hearth bottom.

[0030] In addition, the detection area of each group of first muon detectors 10 intersects with the detection area of a corresponding group of second muon detectors 20 at the junction area of the hearth and the bottom. The junction area of the hearth and the bottom is an extreme area of stress concentration of the refractory layer of the blast furnace, and is also a position where damage to the refractory layer is most likely to occur and most concealed. The intersection of the detection areas of the two types of detectors can achieve double coverage and cross verification of the high-risk junction area.

[0031] The data processing module 30 is connected with the first muon detector 10 and the second muon detector 20, respectively. Specifically, the connection can be achieved through a signal transmission line, or a communication connection can be established through wireless transmission.

[0032] The data processing module 30 is configured to perform three-dimensional reconstruction based on the muon detection data of the first muon detector and the muon detection data of the second muon detector, and perform analysis of the refractory layer of the blast furnace based on the three-dimensional imaging result.

[0033] The muon detection data described above can include, but is not limited to, muon flux, propagation trajectory, and attenuation degree.

[0034] Specifically, the data processing module 30 is configured to receive, fuse, and process the muon detection data from the six groups of detectors, and then invert the muon information at multiple angles and positions into a three-dimensional reconstruction map of the interior of the blast furnace (especially the hearth and the bottom), and further perform analysis of the refractory layer of the blast furnace through the three-dimensional reconstruction map, such as automatically identifying, locating, and evaluating the damage (such as erosion, thinning, or hollowing) to the refractory layer of the blast furnace.

[0035] In summary, the blast furnace refractory layer analysis system 100 provided by the embodiments of the present application has the following beneficial effects: Considering that muon detection can be completely passive to receive cosmic rays existing in nature, without any artificial radioactive source, absolute safety, no radiation protection concerns, and no need to interrupt blast furnace production, and cosmic rays have strong penetration, the embodiment of the application proposes a blast furnace refractory layer analysis method based on muons. The existing muon detection scheme mostly adopts single-angle or single-layer arrangement. For the three-dimensional complex structure of the blast furnace, especially the key area of the junction of the hearth and the bottom, there are problems of incomplete spatial coverage and insufficient three-dimensional structure reconstruction accuracy, making it difficult to accurately identify the damage location and degree. Based on this, the embodiment of the application sets 3 groups of first muon detectors 10 and 3 groups of second muon detectors 20 for the two high-damage areas of the hearth and the bottom, and both types of detectors are arranged at intervals of 120° along the circumferential direction of the blast furnace, which conforms to the cylindrical symmetry structure characteristics of the blast furnace, ensures the uniformity of radial and circumferential detection, and realizes full coverage detection in the circumferential direction of the blast furnace. At the same time, by setting the detection angle α of the first muon detector 10 to 15°~45° and the detection angle β of the second muon detector 20 to 45°~75°, the detection areas of the two types of detectors intersect in the junction area of the hearth and the bottom, and are accurately focused on the key area where stress concentration of the blast furnace refractory layer and damage are most likely to occur, realizing accurate coverage of the high-risk area of the blast furnace refractory layer damage.

[0036] In addition, since the detectors are arranged outside the blast furnace body and can detect in real time, they realize true non-contact, non-stop production, and 24-hour continuous monitoring, which can meet the industrial properties of continuous operation of the blast furnace.

[0037] Optionally, each group of first muon detectors 10 includes 6 muon detection units; and each group of second muon detectors 20 includes 6 muon detection units.

[0038] It should be noted that each muon detection unit is a functionally complete minimum detection entity. Six muon detection units form a group of first muon detectors, which can realize cooperative detection, amplify the equivalent detection area in this direction, and collect more muons in the same time.

[0039] For each muon detection unit, the embodiment of the application sets four layers of staggered scintillator strips.

[0040] Specifically, the two layers of scintillators arranged in the first layer and the third layer are parallel in direction. The two layers of scintillators arranged in the second layer and the fourth layer are parallel in direction, and the two layers of scintillators arranged in the first layer and the second layer are orthogonal in direction. Each layer of scintillators is formed by closely arranging a plurality of scintillator strips, thereby forming a three-dimensional detection matrix. It should be noted that the conventional scintillator arrangement is mostly 2 layers of orthogonal arrangement, which is insufficient for capturing the damage track in the blast furnace scene (the blast furnace scene is accompanied by high temperature, vibration and multiple radiation working conditions), and is prone to cause missed judgment due to insufficient sampling points. The four-layer staggered arrangement enables the muon to be captured by at least 3-4 layers of scintillator strips when penetrating, thereby forming a high-density sampling point matrix and improving the spatial resolution.

[0041] Moreover, considering that there are interference particles such as gamma photons and electrons in the blast furnace scene, the interaction of the interference particles with the scintillators has discreteness, and usually only 1-2 layers of scintillator strips can be triggered, and the triggered position has no continuous track feature. As a heavy charged particle, the muon forms a continuous track when penetrating, and triggers at least 3 layers of scintillator strips. In this way, the discrimination ability of the interference signal can be strengthened, and the blast furnace multi-radiation interference environment can be adapted.

[0042] In summary, the muon detector provided in the embodiment of the present application includes 6 muon detection units and four layers of scintillators, and high precision and high resolution of detection are achieved, and the deficiencies of the existing detector in the blast furnace detection are solved.

[0043] In addition, in an embodiment, the two layers of spaced scintillator strips can also be arranged in a staggered manner, for example, the third layer of scintillator strips is staggered by 5-10 mm relative to the first layer of scintillator strips, and the fourth layer of scintillator strips is staggered by 5-10 mm relative to the second layer of scintillator strips. In this way, the two layers of spaced scintillator strips form an interlaced sampling network, forming a more dense sampling matrix, and further improving the spatial resolution of the muon reconstructed track.

[0044] Optionally, the angle a between the detection direction of each group of first muon detectors and the horizontal plane is in the range of 25°≤a≤35°, and the projection of the detection direction of each group of first muon detectors on the horizontal plane points to the central axis of the blast furnace. The angle β between the detection direction of each group of second muon detectors and the horizontal plane is in the range of 55°≤β≤65°, and the projection of the detection direction of each group of second muon detectors on the horizontal plane points to the central axis of the blast furnace. By narrowing the angle, the invalid path of the muon beam when penetrating the furnace body is reduced, and the data collected is more focused on the core damage area, further improving the detection accuracy.

[0045] Please refer to Figure 4 Optionally, the data processing module 30 includes a data acquisition and screening unit 301.

[0046] The data acquisition and screening unit 301 is configured to receive muon detection data of the first muon detector 10 and muon detection data of the second muon detector 20, and to remove interference signals from the muon detection data of the first muon detector and the muon detection data of the second muon detector by analyzing signal shape differences, and to extract target muon data.

[0047] That is, the embodiment of the present application utilizes the different deposition rules of different particles in the interaction with the scintillator, which mainly manifests as differences in signal shapes, to distinguish target muons from interference particles (mainly gamma photons and electrons).

[0048] For example, the target muon mainly loses energy by ionization in the interaction with the scintillator, and the energy deposition is concentrated and the interaction time is short, so the generated electrical signal has the characteristics of fast rise time, narrow pulse width, and stable peak amplitude. The interference particles (such as gamma photons and electrons): The gamma photons generate electrons through Compton scattering, and the energy deposition in the interaction of the electrons with the scintillator is dispersed and the interaction time is long, so the generated electrical signal has the characteristics of slow rise time, wide pulse width, and large fluctuation in peak amplitude. Therefore, by analyzing the signal shape differences, the embodiment of the present application can effectively remove the interference signals such as gamma photons and electrons, and improve the accuracy and resolution of subsequent damage detection.

[0049] Optionally, the data acquisition and screening unit 301 is further configured to construct a three-dimensional feature vector of the extracted rise time, pulse width, and energy deposition parameters of the muon detection data, the energy deposition parameters include the signal peak amplitude or the integral charge amount, and to screen signals with a similarity satisfying a preset threshold as target muon signals by calculating the similarity between the three-dimensional feature vector and a standard muon signal feature vector.

[0050] That is, first, the feature vector is selected, including: the rise time, the pulse width, and two of the signal peak amplitude or the integral charge amount. Then, a three-dimensional feature vector is constructed, and each detection event is quantized as a three-dimensional space point.

[0051] The standard muon signal feature vector is a preset reference vector representing an ideal or typical muon event.

[0052] The similarity calculation can use distance or similarity measurement algorithms, such as Euclidean distance, Mahalanobis distance, and cosine similarity. Then, the similarity between the three-dimensional feature vector and the standard muon signal feature vector is calculated. When the similarity between the two satisfies the threshold condition (for example, the distance is less than a preset threshold, or the similarity is higher than a preset threshold), the event is determined as a target muon signal; otherwise, it is removed as an interference signal.

[0053] As can be seen, the embodiment of the present application uses three-dimensional joint features for analysis, utilizes the behavior differences of different types of particles in multiple dimensions for statistical distinction, and thus realizes efficient and accurate real-time signal discrimination.

[0054] Optionally, the data processing module 30 further comprises a three-dimensional imaging unit 302.

[0055] The three-dimensional imaging unit 302 is configured to extract muon transmission attenuation information and muon scattering angle information from the muon detection data, and integrate the muon transmission attenuation information and the muon scattering angle information through a data fusion algorithm to generate a three-dimensional imaging result.

[0056] That is, in the three-dimensional reconstruction mode, the muon transmission attenuation information and the muon scattering angle information are fused.

[0057] The muon transmission attenuation information refers to the degree of flux attenuation of muons due to interaction (mainly ionization loss) during penetration of the blast furnace lining. For muons with fixed penetration path and basically unchanged direction, the transmission rate is directly related to the average density integral (area density) of the material on the path. Transmission attenuation imaging is good at reconstructing large-scale and continuous density changes (such as overall uniform erosion of refractory materials).

[0058] The muon scattering angle information refers to the angle of deflection of the motion direction of muons due to Coulomb scattering with atomic nuclei. The size of the scattering angle (especially the net effect of multiple scattering) is very sensitive to high atomic number elements in the material, and is related to the position of the scattering point and the local characteristics of the material. Advantages and disadvantages of scattering angle imaging: very sensitive to local high atomic number substance anomalies (such as iron-rich areas formed by molten iron penetration) and small structural defects, and can provide excellent local contrast. Therefore, the embodiments of the present application propose to integrate the muon transmission attenuation information and the muon scattering angle information through a data fusion algorithm, which can clearly identify the overall thickness profile of the lining, capture local metal penetration, nodulation or cracks, realize more comprehensive diagnosis of damage types, and further realize more comprehensive imaging dimensions and higher imaging accuracy.

[0059] As a specific imaging method, the three-dimensional imaging unit 302 is further configured to extract muon transmission attenuation information from the muon detection data, generate a three-dimensional density distribution map through iterative reconstruction; extract muon scattering angle information from the muon detection data, generate a three-dimensional atomic number sensitive distribution map through scattering tomographic inversion; and perform data fusion on the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map to generate a three-dimensional imaging result.

[0060] In the data fusion, different dynamic weights of the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map are set for different regions of the hearth.

[0061] That is, first, dual-channel independent reconstruction is carried out, channel one is used to extract muon transmission attenuation information, and a three-dimensional density distribution map is generated through an iterative reconstruction algorithm. The three-dimensional density distribution map mainly reflects the spatial distribution of the area density of the material, and intuitively displays the overall thickness and erosion profile of the refractory material. Channel two is used to extract muon scattering angle information, and a three-dimensional atomic number sensitive distribution map is generated through a scattering tomographic inversion algorithm. The map is highly sensitive to the change of the effective average atomic number in the material, and can highlight the abnormal enrichment area of high atomic number substances such as iron and zinc. Then, the above two three-dimensional images obtained from different physical principles and reflecting different material properties are fused. In the fusion process, according to different regions of the furnace hearth, different weights are given to the two three-dimensional maps during fusion.

[0062] For example, the side wall region of the furnace is expected to be uniform refractory material, and the damage mainly manifests as uniform erosion. During fusion, the three-dimensional density distribution map can be given a higher weight to accurately quantify the thickness damage, such as the three-dimensional density distribution map weight ≥ 0.7, and the three-dimensional atomic number sensitive distribution map weight ≤ 0.3. The junction region of the furnace bottom and the furnace hearth (the foot area): is a high-incidence area of molten iron penetration and abnormal erosion. During fusion, the weight of the three-dimensional atomic number sensitive distribution map can be balanced or appropriately increased to sensitively capture the possible metal penetration features, such as the three-dimensional atomic number sensitive distribution map weight ≥ 0.6, and the three-dimensional density distribution map weight ≤ 0.4.

[0063] It can be seen that, by generating dual three-dimensional distribution maps and dynamically fusing the hearth according to different regions, the damage detection requirements of different regions are accurately matched, and the feature recognition degree is improved.

[0064] Optionally, the data processing module 30 further comprises a multi-source information evaluation unit 303.

[0065] The multi-source information evaluation unit 303 is configured to acquire three-dimensional imaging results and auxiliary measured data, and perform joint analysis based on a preset comprehensive evaluation rule to output a blast furnace damage diagnosis result.

[0066] The auxiliary measured data includes at least one of the following: thermocouple detection data of the blast furnace, and cooling wall heat flow data.

[0067] The thermocouple detection data can be temperature values and historical trends measured by a thermocouple network arranged on the furnace shell, the back of the cooling wall, or between the refractory material layers. The cooling wall heat flow data is the heat flux density of each region of the cooling wall calculated by measuring the temperature difference and flow rate of the cooling water.

[0068] In the joint analysis, the data can be first spatio-temporally aligned to synchronize the spatial coordinate system of the three-dimensional imaging result with the physical coordinate system of the blast furnace, and the positions of the thermocouples and heat flow monitoring points are mapped and labeled in the three-dimensional image to ensure that all analyses are based on the same spatio-temporal reference. Then, from the three-dimensional imaging result, the density, average thickness, minimum thickness, atomic number anomaly index and other features of the region of interest can be extracted. From the auxiliary data, the current temperature, temperature rise rate, heat flow density and its change gradient of the corresponding position and adjacent region are extracted. Then the two groups of data are analyzed together to determine the damage of the blast furnace.

[0069] Optionally, the preset comprehensive evaluation rule at least includes: if the three-dimensional imaging result shows that the density of the first region decreases, and the temperature of the thermocouple corresponding to the first region continuously rises or the heat flow data of the cooling wall corresponding to the first region continuously rises, it is evaluated that the erosion of the first region of the furnace lining is intensified.

[0070] The above rules include two kinds. The first kind is: if the three-dimensional imaging result shows that the density of the first region decreases, and the temperature of the thermocouple corresponding to the first region continuously rises, it is evaluated that the erosion of the first region of the furnace lining is intensified. The second kind is: if the three-dimensional imaging result shows that the density of the first region decreases, and the heat flow data of the cooling wall corresponding to the first region continuously rises, it is evaluated that the erosion of the first region of the furnace lining is intensified.

[0071] It should be noted that when the furnace lining is eroded and the effective thickness is thinned, the thermal resistance is reduced. According to the Fourier heat conduction law, under the driving of the high-temperature heat source in the furnace, the heat flow density passing through the thinned region will increase. The increased heat flow directly leads to the increase of the cooling wall heat flow reading behind or on the side, and may cause the temperature of the shell or the cooling structure to rise.

[0072] If the three-dimensional imaging result shows that the density of the second region decreases, and the temperature of the thermocouple corresponding to the second region remains unchanged or decreases, or the heat flow data of the cooling wall corresponding to the second region decreases, it is evaluated that the second region forms an air gap.

[0073] The above rules include two kinds. The first kind is: if the three-dimensional imaging result shows that the density of the second region decreases, and the temperature of the thermocouple corresponding to the second region remains unchanged or decreases, it is evaluated that the second region forms an air gap. The second kind is: if the three-dimensional imaging result shows that the density of the second region decreases, and the heat flow data of the cooling wall corresponding to the second region decreases, it is evaluated that the second region forms an air gap.

[0074] It is noted that the decrease in lining density can be due to erosion of the working layer of refractory material or due to the formation of an air gap between the refractory material and the shell / cooling wall. The thermal conductivity of air is much lower than that of refractory material. Therefore, although the formation of an air gap reduces the overall surface density, it introduces a huge additional thermal resistance in the heat flow path. As a result, the total thermal resistance does not decrease but increases, resulting in a decrease in heat flow through this region, and the corresponding external temperature measurement point readings show a stable or even decreasing trend.

[0075] Of course, other evaluation rules can also be included, such as: if the three-dimensional imaging result shows that there is a discontinuous density sudden change interface (non-uniform gradient change) in the radial depth of the three regions, and the corresponding thermocouple temperature shows abnormal fluctuations or specific mode step changes, it is inferred that the third region of the lining can have delamination, peeling or macroscopic cracks.

[0076] In summary, the application can improve the accuracy and reliability of diagnosis through the above multi-source information fusion evaluation method, that is, by cross-validation, the ambiguity of a single data source is eliminated, and the false positive and false negative rates are greatly reduced. And it can realize early warning and root cause analysis of damage, such as early warning when the thermal parameter just shows a slight abnormality, combined with three-dimensional structure imaging, which can provide early warning; at the same time, it can distinguish between thickness damage and material performance degradation and other different defects.

[0077] In addition, the blast furnace refractory material layer analysis provided by the embodiments of the application can be further subdivided into blast furnace state evaluation, erosion thickness analysis, material surface shape detection, and provide early warning and decision support, as well as operation interface and visualization.

[0078] Specifically, the blast furnace state evaluation can include state ratings such as good, attention, warning, and danger. It can be determined by calculating indicators such as the average residual thickness ratio of the key region, the erosion volume, and the atomic number abnormal region ratio.

[0079] The erosion thickness analysis can be used to perform fine and quantitative thickness measurement and evolution analysis on the identified damage region.

[0080] The material surface shape detection can combine the aforementioned auxiliary data, use the material flux distribution change, combine prior knowledge such as the blast furnace charging system, and infer the material surface descent curve and the radial coke / ore ratio distribution through the inversion algorithm.

[0081] For early warning and decision-making, a multi-level early warning mechanism is supported, such as setting yellow (attention), orange (warning), and red (danger) three threshold values. The threshold values can be dynamically adjusted based on absolute thickness values, erosion rates, health indexes, and expert rules. When any index breaks through the threshold value, the system automatically generates a warning event and pushes it to the relevant responsible personnel through sound and light, short message, production management system, and the like. The system has a built-in decision support library based on cases and rules, for example, when diagnosing local molten iron penetration of the hearth, the system can automatically associate and recommend rechecking the corresponding cooling wall water temperature difference, suggest taking a plugging operation or adjusting the smelting intensity, and the like operation plan.

[0082] The system supports three-dimensional panoramic visualization, and can display the three-dimensional imaging results (such as erosion area, air gap, and metal penetration area) of the muon in a semi-transparent color superimposed manner on the model, and can be arbitrarily rotated, zoomed, and cut to view.

[0083] Please refer to Figure 5 Based on the same inventive concept, the embodiments of the present application also provide a blast furnace refractory layer analysis method based on muons, which is applied to a data processing module of a blast furnace refractory layer analysis system based on muons, and the method comprises steps 501-503.

[0084] Step 501: receiving muon detection data of a first muon detector and muon detection data of a second muon detector.

[0085] Step 502: three-dimensional reconstruction of the muon detection data of the first muon detector and the muon detection data of the second muon detector.

[0086] Step 503: blast furnace refractory layer analysis based on the three-dimensional imaging result.

[0087] Optionally, the three-dimensional reconstruction of the muon detection data of the first muon detector and the muon detection data of the second muon detector in the above step 502 can comprise a data layer collection and screening process, and the process steps are as follows: receiving the muon detection data of the first muon detector and the muon detection data of the second muon detector, and eliminating interference signals from the muon detection data of the first muon detector and the muon detection data of the second muon detector by analyzing the signal shape difference, and extracting target muon data.

[0088] Optionally, the above step further comprises: constructing a three-dimensional feature vector of the extracted muon detection data, the three-dimensional feature vector comprising a rise time, a pulse width, and an energy deposition parameter of the muon detection data; the energy deposition parameter comprising a signal peak amplitude or an integrated charge amount; and screening signals with a similarity satisfying a preset threshold value as target muon signals by calculating the similarity of the three-dimensional feature vector and a standard muon signal feature vector.

[0089] Optionally, the step 502 of three-dimensional reconstruction of the muon detection data of the first muon detector and the muon detection data of the second muon detector can further include: extracting muon transmission attenuation information and muon scattering angle information from the muon detection data; and integrating the muon transmission attenuation information and the muon scattering angle information through a data fusion algorithm to generate the three-dimensional imaging result.

[0090] Optionally, the step further includes: extracting muon transmission attenuation information from the muon detection data, and generating a three-dimensional density distribution map through iterative reconstruction; extracting muon scattering angle information from the muon detection data, and generating a three-dimensional atomic number sensitive distribution map through scatter tomographic inversion; and fusing the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map to generate the three-dimensional imaging result; wherein different dynamic weights of the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map are set during data fusion for different regions of the hearth.

[0091] Optionally, the step 503 of analyzing the blast furnace refractory layer based on the three-dimensional imaging result can further include: obtaining the three-dimensional imaging result and auxiliary measured data, and jointly analyzing based on a preset comprehensive evaluation rule to output a blast furnace damage diagnosis result; wherein the auxiliary measured data includes at least one of the following: blast furnace thermocouple detection data and cooling wall heat flow data.

[0092] Optionally, the preset comprehensive evaluation rule includes at least: If the three-dimensional imaging result shows that the density of the first region decreases, and the thermocouple temperature corresponding to the first region continuously increases or the cooling wall heat flow data corresponding to the first region continuously increases, it is evaluated that the first region has intensified hearth lining erosion. If the three-dimensional imaging result shows that the density of the second region decreases, and the thermocouple temperature corresponding to the second region remains unchanged or decreases, or the cooling wall heat flow data corresponding to the second region decreases, it is evaluated that the second region forms an air gap.

[0093] It should be noted that the content in the above method embodiments, because of the same concept as the system embodiments of the present application, the steps performed and the technical effects brought about, can be referred to the system embodiment part, and will not be repeated here.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software functional unit. In addition, the specific names of the functional units and modules are only for easy distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0095] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in the above method embodiments.

[0096] The embodiments of the present application provide a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal is caused to implement the steps in the above method embodiments.

[0097] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above embodiment methods, which can be completed by a computer program instructing related hardware. The computer program can be stored in a computer readable storage medium. The computer program is executed by a processor to implement the steps in the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc.

[0098] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0099] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0100] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0101] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A muon-based blast furnace refractory layer analysis system, characterized by, The application relates to a muon-based blast furnace refractory layer analysis system. 3 groups of first muon detectors are distributed outside a hearth region of a blast furnace and are arranged at intervals of 120 degrees along the circumferential direction of the blast furnace; the detection direction of each group of the first muon detectors is towards the blast furnace, and the included angle with the horizontal plane is alpha; 15 DEG <= alpha <= 45 DEG; 3 groups of second muon detectors are distributed outside a hearth bottom region of the blast furnace and are arranged at intervals of 120 degrees along the circumferential direction of the blast furnace; the detection direction of each group of the second muon detectors is towards the blast furnace, and the included angle with the horizontal plane is beta; 45 DEG <= beta <= 75 DEG; the detection region of each group of the first muon detectors intersects with the detection region of the corresponding group of the second muon detectors at the junction region of the hearth and the hearth bottom; a data processing module is connected with the first muon detectors and the second muon detectors respectively; the data processing module is used for three-dimensional reconstruction based on the muon detection data of the first muon detectors and the muon detection data of the second muon detectors, and blast furnace refractory layer analysis based on the three-dimensional imaging result.

2. The muon-based blast furnace refractory layer analysis system of claim 1, wherein, Each group of the first muon detectors comprises six muon detection units; Each group of the second muon detectors comprises six muon detection units.

3. The muon-based blast furnace refractory layer analysis system of claim 1, wherein, The range of the included angle alpha between the detection direction of each group of the first muon detectors and the horizontal plane is 25 DEG <= alpha <= 35 DEG; the projection of the detection direction of each group of the first muon detectors on the horizontal plane points to the central axis of the blast furnace; The range of the included angle beta between the detection direction of each group of the second muon detectors and the horizontal plane is 55 DEG <= beta <= 65 DEG; the projection of the detection direction of each group of the second muon detectors on the horizontal plane points to the central axis of the blast furnace.

4. The muon-based blast furnace refractory layer analysis system of claim 1, wherein, The data processing module comprises a data acquisition and screening unit; the data acquisition and screening unit is used for receiving the muon detection data of the first muon detectors and the muon detection data of the second muon detectors, eliminating interference signals from the muon detection data of the first muon detectors and the muon detection data of the second muon detectors by analyzing signal shape differences, and extracting target muon data.

5. The muon-based blast furnace refractory layer analysis system according to claim 4, wherein the data acquisition and screening unit is further used for constructing a three-dimensional feature vector by using the rise time, pulse width and energy deposition parameter of the extracted muon detection data; the energy deposition parameter comprises a signal peak amplitude or an integral charge amount; and the data acquisition and screening unit is further used for screening signals with a similarity degree satisfying a preset threshold value as the target muon signals by calculating the similarity degree between the three-dimensional feature vector and a standard muon signal feature vector.

6. The mu-based blast furnace refractory layer analysis system of claim 1, wherein, The data processing module further comprises a three-dimensional imaging unit; the three-dimensional imaging unit is used for extracting muon transmission attenuation information and muon scattering angle information in the muon detection data; and the three-dimensional imaging unit is used for integrating and processing the muon transmission attenuation information and the muon scattering angle information by using a data fusion algorithm to generate the three-dimensional imaging result.

7. The muon-based blast furnace refractory layer analysis system according to claim 6, wherein The three-dimensional imaging unit is also specifically used for extracting muon transmission attenuation information in the muon detection data, generating a three-dimensional density distribution map through iterative reconstruction, extracting muon scattering angle information in the muon detection data, generating a three-dimensional atomic number sensitive distribution map through scattering tomography inversion, and fusing the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map to generate the three-dimensional imaging result. Different dynamic weights of the three-dimensional density distribution map and the three-dimensional atomic number sensitive distribution map are set during data fusion for different regions of the hearth.

8. The mu-based blast furnace refractory layer analysis system of claim 1, wherein, The data processing module further comprises a multi-source information evaluation unit. The multi-source information evaluation unit is used for acquiring the three-dimensional imaging result and auxiliary measured data, and performing joint analysis based on a preset comprehensive evaluation rule to output a blast furnace damage diagnosis result. The auxiliary measured data comprises at least one of the following: thermal electric couple detection data of the blast furnace, and cooling wall heat flow data.

9. The mu-based blast furnace refractory layer analysis system of claim 8, wherein, The preset comprehensive evaluation rule at least comprises: If the three-dimensional imaging result shows that the density of a first region decreases, and the thermal electric couple temperature corresponding to the first region continuously increases or the cooling wall heat flow data corresponding to the first region continuously increases, it is evaluated that the first region has intensified hearth lining erosion. If the three-dimensional imaging result shows that the density of a second region decreases, and the thermal electric couple temperature corresponding to the second region remains unchanged or decreases, or the cooling wall heat flow data corresponding to the second region decreases, it is evaluated that the second region forms an air gap.

10. A method for analyzing a layer of blast furnace refractory material based on muons, characterized in that, The method is applied to the data processing module in the muon-based blast furnace refractory layer analysis system of any one of claims 1-9, and the method comprises: Receiving muon detection data of the first muon detector and muon detection data of the second muon detector; Performing three-dimensional reconstruction on the muon detection data of the first muon detector and the muon detection data of the second muon detector; Performing blast furnace refractory layer analysis based on the three-dimensional imaging result.

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