Converter lining thickness detection system and method based on diversified binding processing

Abstract

The application discloses a converter lining thickness detection system and method based on diversified combination processing, relates to the technical field of converter operation detection, and aims to solve the technical problems that single sensor detection cannot realize multi-parameter collaborative detection and correlation analysis, increases detection blind area and misjudgment risk in the prior art, and specifically realizes the refinement, precision, visualization and intelligentization of detection through the collaborative work of each step, the refinement is reflected in that the converter is divided into structural components for targeted detection, the precision is reflected in that the accurate judgment of risks and abnormalities is realized through the comparison between key parameters and threshold values, the system integrates temperature field distribution detection and inner lining loss identification into two key dimensions, and through a data correlation analysis unit, multi-parameter correlation analysis is realized in combination with an overall converter model, the overall detection and evaluation of the lining state are realized, and the detection blind area and misjudgment are avoided.

Description

Technical Field

[0001] This invention relates to the field of converter operation detection technology, specifically to a converter lining thickness detection system and method based on diversified combined processing. Background Technology

[0002] In steelmaking, the converter is a core piece of equipment, and the condition of its lining directly affects the safety, stability, and cost of production. Under the influence of high-temperature molten steel, slag, and intense thermal shock and mechanical erosion, the converter lining will gradually be eroded. When the lining thickness is reduced to a certain extent, serious safety hazards such as furnace shell burn-through and steel leakage will occur, which may lead to production interruption and equipment damage. At the same time, premature replacement of the lining will result in waste of refractory materials and increased production costs. Therefore, accurately and in real time, monitoring the converter lining thickness is of great significance for rationally arranging the timing of lining maintenance and replacement, ensuring safe production, and reducing production costs.

[0003] Currently, single-sensor detection cannot achieve multi-parameter collaborative detection and correlation analysis, increasing detection blind spots and the risk of misjudgment. At the same time, traditional detection and early warning are also lagging, reducing the timeliness and accuracy of furnace lining loss early warning. To address this, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above by proposing a converter lining thickness detection system and method based on diversified combined processing.

[0005] The objective of this invention can be achieved through the following technical solution: a converter lining thickness detection system based on diversified combined processing, including a thickness detection center, wherein the thickness detection center is communicatively connected to a temperature field distribution detection unit, an inner lining loss identification unit, a structure mapping unit, and a data correlation analysis unit; The temperature field distribution detection unit detects the temperature field distribution during the converter heating process; the lining loss identification unit identifies the loss of the converter lining; the structural components are classified according to the detection and loss identification by the analysis unit; the structural mapping unit performs state mapping based on each type of structural component and constructs an overall converter model to observe the state of each part of the converter. The data correlation analysis unit performs data correlation analysis in conjunction with the overall converter model.

[0006] Furthermore, the process of the temperature field distribution detection unit is as follows: The converter structure covered by the corresponding sensor of the structural mapping unit is divided into several structural components; temperature field distribution detection is performed on all structural components to obtain the temperature field floating span on the surface of the structural component and the temperature field floating speed deviation value of the surface of adjacent structural components. The temperature field floating span on the surface of the structural component and the temperature field floating speed deviation value of the surface of adjacent structural components are compared with the temperature field floating span threshold and the floating speed deviation threshold, respectively.

[0007] Furthermore, if the temperature field fluctuation span on the surface of a structural component exceeds the temperature field fluctuation span threshold, or the temperature field fluctuation velocity deviation value on the surfaces of adjacent structural components exceeds the fluctuation velocity deviation threshold, an abnormal temperature field distribution signal is generated and sent to the thickness detection center. At the same time, the structural mapping unit records the structural component corresponding to the current abnormal temperature field distribution. If the temperature field fluctuation span on the surface of a structural component does not exceed the temperature field fluctuation span threshold, and the temperature field fluctuation velocity deviation on the surfaces of adjacent structural components does not exceed the fluctuation velocity deviation threshold, a normal temperature field distribution signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center continuously monitors the temperature field, and at the same time, the structural mapping unit records the structural component corresponding to the current normal temperature field distribution.

[0008] Furthermore, the process of the lining loss identification unit is as follows: The wear reduction value of the furnace lining insulation layer thickness at the corresponding position of the structural component is obtained, and the deformation generation rate of the furnace lining working layer at the corresponding position of the structural component is also obtained. The wear reduction value of the furnace lining insulation layer thickness at the corresponding position of the structural component and the deformation generation rate of the furnace lining working layer at the corresponding position of the structural component are compared with the wear reduction threshold and the deformation generation rate threshold, respectively.

[0009] Furthermore, if the wear reduction value of the furnace lining insulation layer at the corresponding position of the structural component exceeds the wear reduction threshold, or if the deformation rate of the furnace lining working layer at the corresponding position of the structural component exceeds the deformation rate threshold, a loss risk signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center adjusts the converter operation. At the same time, the structural mapping unit records the structural component corresponding to the current abnormal lining wear. If the wear reduction value of the furnace lining insulation layer at the corresponding position of the structural component does not exceed the wear reduction threshold, and the deformation rate of the furnace lining working layer at the corresponding position of the structural component does not exceed the deformation rate threshold, a loss safety signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center will continuously monitor the converter. At the same time, the structural mapping unit records the structural component corresponding to the current lining loss identification.

[0010] Furthermore, the process of the data correlation analysis unit is as follows: In the overall converter model, the types of each structural component are determined, and the probability increase span of the corresponding structural component thickness anomaly when the temperature field distribution of the structural component is abnormal is obtained. At the same time, the frequency deviation of the corresponding structural component thickness wear when the number of times the structural component is used increases irregularly and regularly is obtained. The probability increase range of abnormal temperature field distribution in structural components corresponding to abnormal thickness, and the frequency deviation of thickness wear in structural components corresponding to irregular and regular increases in the number of times the number of times the number of uses of the structural components increases, are compared with the probability increase range threshold and the thickness wear frequency deviation threshold, respectively: If the probability of abnormal thickness of a structural component due to abnormal temperature field distribution exceeds the probability increase threshold, or if the frequency deviation of thickness wear of a structural component exceeds the thickness wear frequency deviation threshold when the number of times the structural component is used increases irregularly or regularly, a detection and control signal will be generated and sent to the thickness detection center. After receiving the signal, the thickness detection center will collect the thickness of the converter lining and, while continuously collecting thickness data, will need to synchronously control the influencing factors of the current structural component. If the probability increase of an abnormal temperature field distribution in a structural component does not exceed the probability increase threshold, and the frequency deviation of thickness wear in the corresponding structural component does not exceed the thickness wear frequency deviation threshold when the number of times the structural component is used increases irregularly or regularly, then a detection and identification signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center collects the thickness of the converter lining and directly stores and records the current thickness data.

[0011] This invention also proposes a method for detecting the thickness of converter lining based on a multi-faceted combined treatment approach. The specific thickness detection method is as follows: Step 1: Temperature field distribution detection; Temperature field distribution detection is performed on the converter heating process. Step 2: Identify the lining loss; identify the loss of the converter lining; classify the structural components according to the analysis and loss identification. Step 3: Structural mapping. Based on the state mapping of each type of structural component, construct an overall converter model to observe the state of each part of the converter. Step 4: The data correlation analysis unit performs data correlation analysis in conjunction with the overall converter model.

[0012] Compared with the prior art, the beneficial effects of the present invention are: The structural mapping unit maps the state of various structural components and constructs an overall converter model. The beneficial effect is that it enables the visualization of the state of each part of the converter. The overall converter model can integrate the scattered test results of each component into an intuitive overall state, which makes it convenient for staff to quickly and comprehensively understand the temperature field and lining wear of different parts of the converter, avoid the isolated interpretation of the test results of individual components, and improve the ability to control the overall operating state of the converter.

[0013] The temperature field distribution detection unit monitors the converter heating process and divides the structural components according to the structural mapping. It obtains the temperature field fluctuation span and the velocity deviation values ​​of adjacent components and compares them with the threshold. The beneficial effects of this step are reflected in the refinement of the detection and the accuracy of risk assessment. Dividing the converter into several structural components for detection avoids the problem of local anomalies being masked when detecting the whole. By comparing two key temperature parameters with the threshold, it can quickly and accurately identify structural components with risky temperature field distribution, providing a reliable basis for timely temperature field control measures and effectively preventing the furnace lining from being damaged by abnormal temperatures.

[0014] The lining loss identification unit acquires the wear reduction value of the furnace lining insulation layer thickness and the deformation rate of the working layer and compares them with the threshold. The beneficial effect is that it can directly detect and warn of the core indicators of furnace lining loss. The insulation layer thickness and working layer deformation are key parameters reflecting the health status of the furnace lining. By comparing with the threshold, it can accurately determine whether the lining loss is abnormal, generate loss risk signals in a timely manner, and prompt the thickness detection center to adjust the converter operation to avoid safety accidents or affect the service life of the converter due to excessive furnace lining loss. At the same time, continuous monitoring under normal conditions can also ensure dynamic control of the furnace lining loss status.

[0015] The data correlation analysis unit, combined with the overall converter model, obtains the increase range of thickness anomaly probability and the deviation of the number of uses from the thickness wear frequency, and compares them with the threshold. The beneficial effect of this step is to realize in-depth correlation analysis of multi-dimensional data, break through the limitations of a single detection index, and by considering the probabilistic relationship between temperature field anomalies and thickness anomalies, as well as the influence of the number of uses on the wear frequency, it can more comprehensively judge the degree of influence of usage factors on converter lining thickness. This allows the thickness detection results to not only reflect the current state, but also combine historical usage data and probabilistic patterns, providing a more scientific basis for subsequent thickness acquisition and influencing factor control, and improving the intelligence and foresight of the detection system. Attached Figure Description

[0016] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 This is a system principle block diagram of the present invention; Figure 2This is a flowchart of the method of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0020] Please see Figure 1 As shown, the converter lining thickness detection system based on multi-dimensional combined processing includes a thickness detection center, which is connected to a temperature field distribution detection unit, an inner lining loss identification unit, a structure mapping unit, and a data correlation analysis unit. The thickness detection center generates a structural mapping signal and sends it to the structural mapping unit; After receiving the structure mapping signal, the structure mapping unit generates a temperature field distribution detection signal and an inner lining loss identification signal, and sends them to the temperature field distribution detection unit and the inner lining loss identification unit respectively. After receiving the temperature field distribution signal, the temperature field distribution detection unit performs temperature field distribution detection on the converter heating process; The converter structure is divided into several structural components based on the sensor coverage corresponding to the structural mapping unit. Temperature field distribution is detected for all structural components to obtain the temperature field fluctuation span on the surface of each component and the temperature field fluctuation velocity deviation value between adjacent components. The temperature field fluctuation span and the temperature field fluctuation velocity deviation value between adjacent components are then compared with temperature field fluctuation span thresholds and fluctuation velocity deviation thresholds, respectively. If the temperature field fluctuation span on the surface of a structural component exceeds the temperature field fluctuation span threshold, or the temperature field fluctuation velocity deviation value on the surfaces of adjacent structural components exceeds the fluctuation velocity deviation threshold, it is inferred that there is a risk in the temperature field distribution detection of the structural component, generating a temperature field distribution anomaly signal and sending it to the thickness detection center. After receiving the signal, the thickness detection center performs temperature field control, and at the same time, the structural mapping unit records the structural component corresponding to the current temperature field distribution anomaly. If the temperature field fluctuation span on the surface of a structural component does not exceed the temperature field fluctuation span threshold, and the temperature field fluctuation velocity deviation on the surfaces of adjacent structural components does not exceed the fluctuation velocity deviation threshold, then it is inferred that the temperature field distribution detection of the structural component is normal, a normal temperature field distribution signal is generated and sent to the thickness detection center, the thickness detection center receives the signal and continuously monitors the temperature field, and at the same time the structural mapping unit records the structural component corresponding to the current normal temperature field distribution. After receiving the lining loss identification signal, the lining loss identification unit identifies the loss of the converter lining. The wear reduction value of the furnace lining insulation layer thickness at the corresponding location of the structural component is obtained, and the deformation generation rate of the furnace lining working layer at the corresponding location of the structural component is also obtained. The wear reduction value of the furnace lining insulation layer thickness at the corresponding location of the structural component and the deformation generation rate of the furnace lining working layer at the corresponding location of the structural component are compared with the wear reduction threshold and the deformation generation rate threshold, respectively. If the wear reduction value of the furnace lining insulation layer at the corresponding position of the structural component exceeds the wear reduction threshold, or if the deformation rate of the furnace lining working layer at the corresponding position of the structural component exceeds the deformation rate threshold, it is inferred that the lining loss identification of the corresponding structural component is abnormal, a loss risk signal is generated and sent to the thickness detection center, and the thickness detection center adjusts the converter operation after receiving it; at the same time, the structural mapping unit records the structural component corresponding to the current lining loss identification abnormality. If the wear reduction value of the furnace lining insulation layer at the corresponding position of the structural component does not exceed the wear reduction threshold, and the deformation rate of the furnace lining working layer at the corresponding position of the structural component does not exceed the deformation rate threshold, it is inferred that the lining loss identification of the corresponding structural component is normal, a loss safety signal is generated and sent to the thickness detection center, and the thickness detection center will continuously monitor the converter after receiving it; at the same time, the structural mapping unit records the structural component corresponding to the current normal lining loss identification. The structural mapping unit performs state mapping based on various types of structural components and constructs an overall converter model to observe the state of each part of the converter; Simultaneously, a data correlation analysis signal is generated and sent to the data correlation analysis unit; After receiving the data correlation analysis signal, the data correlation analysis unit performs data correlation analysis in conjunction with the overall converter model; In the overall converter model, the types of each structural component are determined, and the probability increase span of the corresponding structural component thickness anomaly when the temperature field distribution of the structural component is abnormal is obtained. At the same time, the frequency deviation of the corresponding structural component thickness wear when the number of times the structural component is used increases irregularly and regularly is obtained. The probability increase range of abnormal temperature field distribution in structural components corresponding to abnormal thickness, and the frequency deviation of thickness wear in structural components corresponding to irregular and regular increases in the number of times the number of times the number of uses of the structural components increases, are compared with the probability increase range threshold and the thickness wear frequency deviation threshold, respectively: If the probability increase of an abnormal temperature field distribution in a structural component exceeds the probability increase threshold, or if the frequency deviation of thickness wear in a structural component exceeds the thickness wear frequency deviation threshold when the number of times the structural component is used increases irregularly or regularly, it is inferred that the thickness in the current overall converter model is affected by use. A detection and control signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center collects the converter lining thickness and needs to synchronously control the influencing factors of the current structural component while continuously collecting thickness data. If the probability increase of an abnormal temperature field distribution in a structural component does not exceed the probability increase threshold, and the frequency deviation of thickness wear in a structural component does not exceed the thickness wear frequency deviation threshold when the number of times the structural component is used increases irregularly or regularly, then it is inferred that the thickness in the current overall converter model has not been affected by use. A detection and identification signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center collects the converter lining thickness and directly stores and records the current thickness data.

[0021] Please see Figure 2 As shown, the converter lining thickness detection system based on multi-faceted processing has the following specific thickness detection method: Step 1: Temperature field distribution detection; Temperature field distribution detection is performed on the converter heating process. Step 2: Identify the lining loss; identify the loss of the converter lining; classify the structural components according to the analysis and loss identification. Step 3: Structural mapping. Based on the state mapping of each type of structural component, construct an overall converter model to observe the state of each part of the converter. Step 4: The data correlation analysis unit performs data correlation analysis in conjunction with the overall converter model.

[0022] Thresholds, preset values, or preset ranges are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or rational factors.

[0023] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A converter lining thickness detection system based on diversified combined processing, characterized in that, It includes a thickness detection center, which is connected to a temperature field distribution detection unit, an inner lining loss identification unit, a structure mapping unit, and a data correlation analysis unit. The temperature field distribution detection unit detects the temperature field distribution during the converter heating process; the lining loss identification unit identifies the loss of the converter lining; the structural components are classified according to the detection and loss identification by the analysis unit; the structural mapping unit performs state mapping based on each type of structural component and constructs an overall converter model to observe the state of each part of the converter. The data correlation analysis unit performs data correlation analysis in conjunction with the overall converter model.

2. The converter lining thickness detection system based on diversified combined processing according to claim 1, characterized in that, The process of the temperature field distribution detection unit is as follows: The converter structure covered by the corresponding sensor of the structural mapping unit is divided into several structural components; temperature field distribution detection is performed on all structural components to obtain the temperature field floating span on the surface of the structural component and the temperature field floating speed deviation value of the surface of adjacent structural components. The temperature field floating span on the surface of the structural component and the temperature field floating speed deviation value of the surface of adjacent structural components are compared with the temperature field floating span threshold and the floating speed deviation threshold, respectively.

3. The converter lining thickness detection system based on diversified combined processing according to claim 2, characterized in that, If the temperature field fluctuation span on the surface of a structural component exceeds the temperature field fluctuation span threshold, or the temperature field fluctuation velocity deviation value on the surfaces of adjacent structural components exceeds the fluctuation velocity deviation threshold, an abnormal temperature field distribution signal is generated and sent to the thickness detection center. At the same time, the structural mapping unit records the structural component corresponding to the current abnormal temperature field distribution. If the temperature field fluctuation span on the surface of a structural component does not exceed the temperature field fluctuation span threshold, and the temperature field fluctuation velocity deviation on the surfaces of adjacent structural components does not exceed the fluctuation velocity deviation threshold, a normal temperature field distribution signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center continuously monitors the temperature field, and at the same time, the structural mapping unit records the structural component corresponding to the current normal temperature field distribution.

4. The converter lining thickness detection system based on diversified combined processing according to claim 1, characterized in that, The process of the lining loss identification unit is as follows: The wear reduction value of the furnace lining insulation layer thickness at the corresponding position of the structural component is obtained, and the deformation generation rate of the furnace lining working layer at the corresponding position of the structural component is also obtained. The wear reduction value of the furnace lining insulation layer thickness at the corresponding position of the structural component and the deformation generation rate of the furnace lining working layer at the corresponding position of the structural component are compared with the wear reduction threshold and the deformation generation rate threshold, respectively.

5. The converter lining thickness detection system based on diversified combined processing according to claim 4, characterized in that, If the wear reduction value of the furnace lining insulation layer at the corresponding location of the structural component exceeds the wear reduction threshold, or if the deformation rate of the furnace lining working layer at the corresponding location of the structural component exceeds the deformation rate threshold, a loss risk signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center adjusts the converter operation. At the same time, the structural mapping unit records the structural component corresponding to the current abnormal lining wear. If the wear reduction value of the furnace lining insulation layer at the corresponding position of the structural component does not exceed the wear reduction threshold, and the deformation rate of the furnace lining working layer at the corresponding position of the structural component does not exceed the deformation rate threshold, a wear safety signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center will continuously monitor the converter. At the same time, the structural mapping unit records the current lining loss and identifies the corresponding structural components that are normal.

6. The converter lining thickness detection system based on diversified combined processing according to claim 1, characterized in that, The process of the data correlation analysis unit is as follows: In the overall converter model, the types of each structural component are determined, and the probability increase span of the corresponding structural component thickness anomaly when the temperature field distribution of the structural component is abnormal is obtained. At the same time, the frequency deviation of the corresponding structural component thickness wear when the number of times the structural component is used increases irregularly and regularly is obtained. The probability increase range of abnormal temperature field distribution in structural components corresponding to abnormal thickness, and the frequency deviation of thickness wear in structural components corresponding to irregular and regular increases in the number of times the number of times the number of uses of the structural components increases, are compared with the probability increase range threshold and the thickness wear frequency deviation threshold, respectively: If the probability of abnormal thickness of a structural component due to abnormal temperature field distribution exceeds the probability increase threshold, or if the frequency deviation of thickness wear of a structural component exceeds the thickness wear frequency deviation threshold when the number of times the structural component is used increases irregularly or regularly, a detection and control signal will be generated and sent to the thickness detection center. After receiving the signal, the thickness detection center will collect the thickness of the converter lining and, while continuously collecting thickness data, will need to synchronously control the influencing factors of the current structural component. If the probability increase of an abnormal temperature field distribution in a structural component does not exceed the probability increase threshold, and the frequency deviation of thickness wear in the corresponding structural component does not exceed the thickness wear frequency deviation threshold when the number of times the structural component is used increases irregularly or regularly, then a detection and identification signal is generated and sent to the thickness detection center. After receiving the signal, the thickness detection center collects the thickness of the converter lining and directly stores and records the current thickness data.

7. A method for detecting converter lining thickness based on diversified combined processing, characterized in that, The specific thickness detection method for the converter lining thickness detection system based on multi-faceted combined processing as described in any one of claims 1-6 is as follows: Step 1: Temperature field distribution detection; Temperature field distribution detection is performed on the converter heating process. Step 2: Identify the lining loss; identify the loss of the converter lining; classify the structural components according to the analysis and loss identification. Step 3: Structural mapping. Based on the state mapping of each type of structural component, construct an overall converter model to observe the state of each part of the converter. Step 4: The data correlation analysis unit performs data correlation analysis in conjunction with the overall converter model.

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