Blast furnace iron channel steel shell thermal load early warning method based on continuous temperature measurement

Abstract

The present application belongs to the technical field of metallurgical engineering, and provides a blast furnace iron runner steel shell heat load early warning method based on continuous temperature measurement, comprising: obtaining historical temperature data of multiple temperature measurement points of the blast furnace iron runner steel shell and corresponding iron tapping process parameters, forming a historical temperature evolution time sequence library of each region of the iron runner at different iron tapping periods, based on the historical temperature evolution time sequence library, combining the residual thickness data of the refractory of the iron runner during shutdown maintenance, backstepping the historical erosion rate of the refractory at the temperature measurement point, and constructing an erosion rate decay prediction model of the whole domain of the iron runner steel shell. By spatially superimposing the potential weak area obtained by prediction and the dynamic abnormal area identified in the real-time temperature field, the composite risk area with long-term erosion accumulation risk and real-time abnormal temperature rise characteristics is locked as the key monitoring object, the problem of resource dispersion and unclear focus caused by uniform monitoring of the whole domain is optimized, and the pertinence and accuracy of the heat load early warning are improved.

Description

Technical Field

[0001] This invention belongs to the field of metallurgical engineering technology, specifically a method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement. Background Technology

[0002] The blast furnace trough is a key channel that guides high-temperature molten iron from the taphole to subsequent processing equipment. The working layer is made of refractory material inside and wrapped with a steel shell on the outside. During the long-term tapping process, the high-temperature molten iron continuously washes and erodes the refractory material, causing the working layer to gradually thin and the heat load of the steel shell to continuously increase. In order to ensure safe production, multiple temperature measuring points have been set at intervals on the surface of the steel shell of the blast furnace trough. The heat load status of the steel shell is monitored by continuously collecting temperature data from each measuring point, and heat load warnings are issued based on the set temperature threshold or temperature change trend.

[0003] During the tapping process in a blast furnace, the molten iron stream oscillates due to pressure fluctuations within the furnace, causing asymmetrical erosion on the sides of the main channel. This results in significant differences in the refractory erosion rate across different areas of the molten iron channel. Furthermore, areas with faster erosion dynamically migrate between the tapping point, the sides of the channel, and the outer side of bends as tapping progresses. However, existing continuous temperature monitoring and early warning methods only focus on whether the absolute value of the real-time temperature exceeds the limit, making it difficult to predict vulnerable areas in advance. Because the location of the vulnerable areas cannot be predicted, the monitoring strategy can only use a uniform acquisition frequency for all temperature measurement points, failing to differentiate and focus on the actual risk status of each area. This leads to insufficient targeting and accuracy of heat load early warnings, making it easy to issue false alarms in non-critical areas. Meanwhile, vulnerable areas facing the risk of burn-through are missed due to monitoring lag, threatening the safety of tapping operations.

[0004] Therefore, this invention provides a method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement, comprising the following steps:

[0007] Historical temperature data and corresponding tapping process parameters of multiple temperature measurement points on the steel shell of the blast furnace iron trough were obtained to form a historical temperature evolution time series library for each area of ​​the iron trough under different tapping periods. Based on the historical temperature evolution time series library and combined with the data of refractory residual thickness during the shutdown and maintenance of the iron trough, the historical erosion rate of refractory at the temperature point was inversely estimated, and an erosion rate attenuation prediction model for the entire steel shell of the iron trough was constructed.

[0008] Based on the erosion rate decay prediction model, the predicted residual refractory thickness in each area of ​​the iron ditch during the current iron tapping period is calculated, and areas where the residual refractory thickness is at the safe lower limit are identified as potential weak areas.

[0009] Real-time temperature data from multiple temperature measurement points are acquired, and the real-time temperature field distribution of the entire steel shell of the iron trough is reconstructed through interpolation analysis. Dynamic abnormal areas are identified by the temperature rise rate in the temperature field distribution. The dynamic abnormal areas are spatially superimposed with potential weak areas to obtain key monitoring areas.

[0010] For key monitoring areas, the temperature acquisition frequency of the temperature measurement points is dynamically adjusted, and the real-time steel shell temperature value and temperature rise rate of the key monitoring areas are obtained. Combined with the predicted residual thickness of refractory material, the current heat load risk index is calculated, and a heat load early warning judgment is made based on the current heat load risk index.

[0011] As a further aspect of the present invention, the process of constructing a prediction model for the erosion rate attenuation of the entire steel shell of the iron ditch is as follows:

[0012] The entire steel shell of the iron ditch is divided into several grid units. The historical erosion rate time series of the grid units is obtained, and the decay trend curve of the erosion rate with the cumulative iron output is fitted.

[0013] If the real-time temperature data of the grid cell deviates from the normal fluctuation range of the historical temperature data of the grid cell under the same iron tapping period, it is determined that the grid cell is currently in an abnormal erosion state. The correction amount is then added to the basic predicted erosion rate corresponding to the decay trend curve to obtain the corrected predicted erosion rate.

[0014] The residual refractory thickness data re-measured during the maintenance of the iron ditch is used as the basis for calibration. Whenever new residual refractory thickness data is obtained during maintenance, the predicted erosion rate of the grid cell in the previous maintenance cycle is compared with the actual erosion rate. The prediction deviation rate is calculated, and the decay trend curve is corrected or retained based on the prediction deviation rate to establish an erosion rate decay prediction model.

[0015] As a further aspect of the present invention: the process for obtaining the historical erosion rate is as follows:

[0016] For each temperature measuring point, the historical temperature data sequence since the replacement or repair of the iron ditch and the actual residual thickness of refractory material measured during each ditch shutdown and maintenance at the temperature measuring point are obtained to construct a complete evolution time-series trajectory of the steel shell temperature at the temperature measuring point as a function of the residual thickness of refractory material.

[0017] Extract the residual refractory thickness values ​​at each of the two adjacent trench shutdown maintenance times from the evolution timeline corresponding to the grid cell. Subtract the residual refractory thickness measured at the current trench shutdown maintenance from the residual refractory thickness measured at the previous shutdown maintenance to obtain the total refractory thinning of the grid cell during the maintenance interval. Divide the total refractory thinning by the cumulative iron tapping increment recorded during the maintenance interval to obtain the historical erosion rate of the grid cell during the iron tapping period.

[0018] As a further aspect of the present invention: the calculation process for the correction amount is as follows:

[0019] Calculate the temperature difference between the real-time temperature data and the upper limit of the normal fluctuation range. Divide the temperature difference by the upper limit of the normal fluctuation range to obtain the temperature deviation rate. Use the temperature deviation rate as a correction coefficient. Multiply the correction coefficient by the preset erosion rate correction factor to obtain the correction amount.

[0020] As a further aspect of the present invention: the process of correcting or retaining the attenuation trend curve based on the prediction deviation rate is as follows:

[0021] If the prediction deviation rate does not exceed the corresponding preset threshold, it means that the prediction results of the grid cells in the current cycle are basically consistent with the actual erosion situation.

[0022] If the prediction deviation rate exceeds the corresponding preset threshold, the predicted residual refractory thickness value of the grid cell at the current cumulative iron output is directly replaced with the actual residual refractory thickness value measured during this trench shutdown maintenance. At the same time, the predicted erosion rate of the grid cell in the previous maintenance cycle is replaced with the actual erosion rate recalculated based on the measured thickness difference. The erosion rate attenuation coefficient of the grid cell at the current cumulative iron output is re-determined based on the ratio of the actual erosion rate to the predicted erosion rate of the previous cycle. The corrected attenuation coefficient is substituted into the attenuation trend curve, and the predicted segment that has not yet occurred after the current cumulative iron output is scaled and adjusted as a whole. The scaling ratio is the ratio of the actual erosion rate to the predicted erosion rate.

[0023] As a further aspect of the present invention: the process for obtaining potential weak areas is as follows:

[0024] Using the current cumulative iron output of the iron ditch and the current iron output period as input, and substituting them into the erosion rate decay prediction model, the predicted erosion rate value of each grid cell in the entire steel shell of the iron ditch is output under the current iron output period.

[0025] For each grid cell, the measured residual refractory thickness value during the last trench shutdown maintenance is extracted from the evolution time trajectory corresponding to the grid cell as the initial thickness value. The predicted residual refractory thickness is obtained by subtracting the product of the predicted erosion rate value and the accumulated iron output during the current iron output period from the initial thickness value.

[0026] Extract mesh cells whose predicted residual refractory thickness is less than or equal to a preset safety lower limit and mark them as potential weak areas.

[0027] As a further aspect of the present invention: the process of obtaining the dynamic abnormal region is as follows:

[0028] Real-time temperature data of each temperature measurement point is continuously acquired at a fixed sampling period. The temperature data of each discrete point is input into a preset spatial interpolation algorithm to reconstruct the real-time temperature field of the entire steel shell of the iron trough at the current moment.

[0029] For any location within the entire steel shell of the iron ditch, the temperature values ​​at two adjacent sampling periods are extracted from the real-time temperature field sequence, and the temperature rise rate is calculated. Locations with a temperature rise rate exceeding a preset threshold are grouped into a dynamic abnormal region set.

[0030] As a further aspect of the present invention: the process for obtaining the key monitoring area is as follows:

[0031] Spatial overlay of the set of potential weak regions and the set of dynamic abnormal regions;

[0032] Grid cells that belong to both the set of potentially weak areas and the set of dynamically abnormal areas are marked as key monitoring areas.

[0033] As a further aspect of the present invention: the process of dynamically adjusting the temperature acquisition frequency of the temperature measurement point is as follows:

[0034] For each key monitoring area, the difference between the predicted residual refractory thickness value of the grid cell at the current moment and the safety lower limit is calculated, and the absolute value of the difference is calculated as the ratio of the safety lower limit, which is used as the safety margin ratio.

[0035] The encrypted sampling frequency is obtained by dividing the normal sampling frequency by the safety margin ratio of the grid cells.

[0036] As a further aspect of the present invention, the process for determining the heat load early warning is as follows:

[0037] Calculate the ratio of the real-time steel shell temperature value to the corresponding steel shell temperature safety threshold, and use it as the temperature correction coefficient. If the real-time steel shell temperature value does not exceed the steel shell temperature safety threshold, the temperature correction coefficient is equal to one.

[0038] The ratio of the rate of temperature rise to the corresponding warning threshold for the rate of temperature rise is calculated and used as a rate correction coefficient. If the rate of temperature rise does not exceed the warning threshold for the rate of temperature rise, the rate correction coefficient is equal to one.

[0039] Multiply the safety margin ratio by the temperature correction factor and the rate correction factor to obtain the current heat load risk index of the key monitoring area at the current moment;

[0040] If the current heat load risk index reaches the warning trigger threshold, a heat load warning signal containing the location information of the key monitoring area will be issued.

[0041] The beneficial effects of this invention are as follows:

[0042] This invention constructs a global erosion rate attenuation prediction model based on historical temperature data and measured residual refractory thickness data during trench maintenance, and introduces real-time temperature deviation to dynamically correct the erosion rate, thereby achieving accurate prediction of refractory erosion status in various areas of the iron ditch and early identification of potential weak areas.

[0043] This invention spatially superimposes the predicted potential weak areas with the dynamic abnormal areas identified in the real-time temperature field, and locks down the composite risk areas that simultaneously have the risk of long-term erosion accumulation and the characteristics of real-time abnormal temperature rise as the key monitoring targets. This optimizes the problems of resource dispersion and unclear focus caused by uniform monitoring across the entire area, and improves the pertinence and accuracy of heat load early warning.

[0044] This invention achieves comprehensive enhancement of monitoring density and risk assessment for high-risk areas by dynamically increasing the temperature acquisition frequency of temperature measuring elements in key monitoring areas and calculating the heat load risk index based on real-time steel shell temperature, temperature rise rate, and predicted residual refractory thickness. At the same time, it differentiates the issuance of heat load early warning signals to ensure timely warnings at weak points that are truly at risk of burn-through, reducing missed reports caused by monitoring lag and improving the safety and continuity of blast furnace tapping operations. Attached Figure Description

[0045] The invention will now be further described with reference to the accompanying drawings.

[0046] Figure 1 This is a flowchart of the steps in an embodiment of the present invention;

[0047] Figure 2 This is a flowchart of step two in an embodiment of the present invention. Detailed Implementation

[0048] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0049] Example

[0050] Please see Figures 1-2 As shown in the embodiment of the present invention, the method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement includes the following steps:

[0051] Step 1: Obtain historical temperature data and corresponding tapping process parameters from multiple temperature measurement points on the steel shell of the blast furnace trough. This will form a historical temperature evolution time series database for each area of ​​the trough under different tapping periods. Based on this database and combined with data on the residual thickness of refractory material during trough shutdown and maintenance, the historical erosion rate of refractory material at the temperature measurement points will be inferred. This will enable the construction of a prediction model for the erosion rate attenuation of the entire steel shell of the blast furnace trough.

[0052] In step one: Several temperature measuring elements are embedded at intervals on the steel shell surface of the molten iron trough along the longitudinal direction of the molten iron flow and in the transverse direction perpendicular to the molten iron flow.

[0053] Real-time temperature data of the steel shell is continuously acquired at a fixed sampling period, and the temperature data at each moment is associated with the corresponding tapping process parameters. The cumulative tapping amount of molten iron at each sampling moment is recorded synchronously. The cumulative tapping amount is obtained in real time by the weighing system of the blast furnace tapping area or the molten iron flow meter. The temperature data at each moment, the tapping process parameters and the corresponding cumulative tapping amount are associated and stored to form a historical temperature evolution time series library.

[0054] Among them, the tapping process parameters include at least tapping time, tapping flow rate, and blast furnace pressure.

[0055] During each maintenance shutdown of the iron trough in history, after removing the residual slag and iron in the iron trough and exposing the surface of the working layer refractory material, the actual residual thickness of the refractory working layer was measured one by one along multiple measuring sections in the longitudinal and transverse directions of the iron trough.

[0056] For each temperature measuring point, obtain the historical temperature data sequence of the temperature measuring point since the current replacement or repair of the iron ditch, as well as the actual residual thickness data sequence of refractory material during each ditch shutdown and maintenance at the temperature measuring point.

[0057] By arranging the historical temperature data sequence and the actual refractory residual thickness data sequence in chronological order, a complete time-series trajectory of the evolution of the steel shell temperature at the temperature measurement point with the change of refractory residual thickness is constructed.

[0058] The entire steel shell of the iron ditch is divided into several grid units;

[0059] The grid unit division is based on the actual placement of the temperature sensing elements. The location of each buried temperature sensing element is a basic grid node. The space defined by the basic grid node as the center and half the distance between adjacent temperature sensing elements as the radius is a grid unit.

[0060] For each grid cell, extract the residual refractory thickness value at the time of two adjacent trench closures and maintenance from the evolution time trajectory corresponding to the grid cell, and calculate the historical erosion rate of the grid cell between the two maintenances.

[0061] The historical erosion rate is calculated as follows: the residual refractory thickness measured during the last ditch shutdown maintenance is subtracted from the residual refractory thickness measured during the current ditch shutdown maintenance to obtain the total refractory thinning of the grid cell during the maintenance interval. The total refractory thinning is then divided by the cumulative iron tapping increment recorded during the maintenance interval to obtain the historical erosion rate of the grid cell during the iron tapping period.

[0062] The historical erosion rate is defined as the thickness of refractory material eroded per unit of cumulative iron output.

[0063] The historical erosion rate during all adjacent maintenance intervals of each grid cell is calculated sequentially to obtain the historical erosion rate distribution data of each location in the entire iron ditch under different iron tapping periods.

[0064] Based on historical erosion rate distribution data, an erosion rate attenuation prediction model is constructed for each grid cell. The construction process is as follows:

[0065] The current cumulative iron output and the current iron output period are used as inputs, and the predicted erosion rate of each grid cell before the next maintenance cycle is used as the output.

[0066] For any grid cell, a decay trend curve of the erosion rate as a function of the cumulative iron output is fitted based on the existing erosion rate time series of the grid cell.

[0067] The decay trend curve uses the cumulative iron content as the horizontal axis and the erosion rate as the vertical axis. Multiple historical erosion rate values ​​of the grid cells are marked on the coordinate plane according to their corresponding cumulative iron content positions. The marked points are connected by the curve fitting method to form a curve that shows a decay trend as the cumulative iron content increases. The vertical axis value is the basic predicted erosion rate value of the grid cell at the corresponding cumulative iron content position.

[0068] The real-time temperature data of the grid cell is compared with the historical temperature data of the grid cell under the same iron tapping period. If the real-time temperature data deviates from the normal fluctuation range of the historical temperature data, the grid cell is determined to be in an abnormal erosion state, and a correction amount is added on the predicted erosion rate corresponding to the decay trend curve.

[0069] The magnitude of the correction is determined based on the degree of deviation between the real-time temperature data and the upper limit of the normal fluctuation range of the historical temperature data, thereby obtaining the corrected predicted erosion rate of the grid cell under the current iron tapping period;

[0070] The correction amount is calculated as follows: calculate the temperature difference between the real-time temperature data and the upper limit of the normal fluctuation range, divide the temperature difference by the upper limit of the normal fluctuation range to obtain the temperature deviation rate, use the temperature deviation rate as the correction coefficient, and multiply the correction coefficient by the preset erosion rate correction factor to obtain the correction amount.

[0071] The preset erosion rate correction factor is a conversion factor obtained by statistically analyzing the increase in the actual erosion rate during the maintenance interval after each abnormal temperature rise in the historical data of the grid cells, relative to the actual erosion rate during the normal period.

[0072] For example, suppose that for a certain grid cell, the steel shell temperature at the five historical iron tapping times closest to the cumulative iron tapping amount at the current iron tapping period is 380℃, 390℃, 370℃, 385℃, and 375℃, respectively. The calculated average value is 380℃ and the standard deviation is 7.1℃. Then, the upper limit of the normal fluctuation range of the historical temperature data of the grid cell is the average value plus twice the standard deviation, which is approximately 394.2℃. This is the upper limit of the normal fluctuation range.

[0073] If the real-time steel shell temperature of the grid cell is 420℃, which exceeds the reference upper limit temperature value of 394.2℃, then the calculated temperature difference is 25.8℃, and the temperature deviation rate is approximately 6.55%. The temperature deviation rate is used as the correction coefficient.

[0074] If the preset erosion rate correction factor of the grid cell is 0.01 mm per hour, then multiplying the correction coefficient by the erosion rate correction factor will give a predicted erosion rate correction of approximately 0.00655 mm per hour.

[0075] If the basic predicted erosion rate corresponding to the grid cell on the decay trend curve is 0.05 mm per hour, then the corrected predicted erosion rate is 0.05655 mm per hour.

[0076] The residual refractory thickness data re-measured during the maintenance of the iron ditch is used as the basis for each model calibration. Whenever new residual refractory thickness data is obtained during maintenance, the predicted erosion rate of the grid cells in the previous maintenance cycle is compared with the actual erosion rate, and the prediction deviation rate is calculated.

[0077] If the prediction deviation rate does not exceed the corresponding preset threshold, it means that the prediction results for the grid cells in the current cycle are basically consistent with the actual erosion situation.

[0078] If the prediction deviation rate exceeds the corresponding preset threshold, it indicates that there is a deviation in the erosion prediction of the grid cell in the current cycle. At this time, the predicted residual refractory thickness value of the grid cell at the current cumulative iron output is directly replaced with the actual residual refractory thickness value measured during this trench shutdown maintenance. At the same time, the predicted erosion rate of the grid cell in the previous maintenance cycle is replaced with the actual erosion rate recalculated based on the difference in measured thickness. The erosion rate attenuation coefficient of the grid cell at the current cumulative iron output is re-determined based on the ratio of the actual erosion rate to the predicted erosion rate of the previous cycle. The corrected attenuation coefficient is substituted into the attenuation trend curve, and the prediction segment that has not yet occurred after the current cumulative iron output is scaled and adjusted as a whole. The scaling ratio is the ratio of the actual erosion rate to the predicted erosion rate, so that the corrected attenuation trend curve coincides with the measured value at the time of this maintenance and continues to extend according to the corrected attenuation coefficient in subsequent predictions. This achieves the gradual calibration and optimization of the erosion rate attenuation prediction model after each trench shutdown maintenance.

[0079] The constructed erosion rate decay prediction model can estimate the erosion rate change trend of each grid unit in the entire iron ditch before the next maintenance cycle based on the current cumulative iron output and the current iron output period.

[0080] Step 2: Based on the erosion rate decay prediction model, calculate the predicted residual refractory thickness in each area of ​​the iron ditch during the current iron tapping period, and identify areas where the residual refractory thickness is at the safe lower limit as potential weak areas.

[0081] In step two, the current cumulative iron output of the iron ditch and the current iron output period are used as inputs and substituted into the erosion rate decay prediction model to output the predicted erosion rate value of each grid cell in the entire steel shell of the iron ditch under the current iron output period.

[0082] For each grid cell, the measured residual refractory thickness value during the last trench shutdown and maintenance is extracted from the evolution timeline trajectory corresponding to the grid cell. This value is used as the starting thickness value of the grid cell in the current iron tapping period. The predicted residual refractory thickness of the grid cell in the current iron tapping period is obtained by subtracting the predicted erosion rate value of the grid cell from the starting thickness value and the product of the accumulated iron tapping amount in the current iron tapping period.

[0083] Calculate the predicted residual refractory thickness of all grid cells to obtain the predicted residual refractory thickness distribution data of the entire iron ditch under the current iron tapping period;

[0084] For each grid cell, the corresponding predicted residual refractory thickness is compared with a preset safety lower limit.

[0085] Among them, the safety lower limit is the minimum safe refractory material thickness value that can ensure the steel shell does not burn through at the grid unit position of the iron trough, and it is set according to the structural strength and heat load bearing capacity of the steel shell in different areas.

[0086] Extract mesh cells whose predicted refractory residual thickness is less than or equal to the preset safety lower limit value. These are identified as regions where the refractory residual thickness is at the safety lower limit and marked as potential weak regions.

[0087] All potential weak areas are identified, and a set of potential weak areas for the entire steel shell of the iron ditch is constructed under the current iron tapping period.

[0088] Step 3: Acquire real-time temperature data from multiple temperature measurement points, reconstruct the real-time temperature field distribution of the entire steel shell of the iron ditch through interpolation analysis, identify dynamic abnormal areas by the temperature rise rate in the temperature field distribution, and spatially overlay the dynamic abnormal areas with potential weak areas to obtain key monitoring areas.

[0089] In step three, during the normal iron tapping operation in the iron ditch, real-time temperature data of each temperature measuring point in the entire steel shell of the iron ditch is continuously acquired at a fixed sampling period to form a set of real-time temperature values ​​at each temperature measuring point in the entire steel shell of the iron ditch.

[0090] The temperature data of each discrete point in the real-time temperature value set is input into the preset spatial interpolation algorithm. The spatial interpolation algorithm takes the surface of the steel shell of the iron ditch as the interpolation plane, the actual layout coordinates of the temperature measuring elements at each temperature measuring point as the interpolation nodes, and the real-time temperature value at each interpolation node as the interpolation basis. The temperature value at any position on the surface of the steel shell of the iron ditch is obtained through interpolation calculation, thereby reconstructing the real-time temperature field of the entire iron ditch steel shell at the current moment.

[0091] By repeating the real-time temperature field reconstruction process over multiple consecutive sampling periods, the real-time temperature field sequence of the entire steel shell of the iron ditch at multiple consecutive moments is obtained.

[0092] For any location within the entire steel shell of the iron ditch;

[0093] Extract the real-time temperature values ​​of the corresponding positions at two adjacent sampling periods from the real-time temperature field sequence, subtract the temperature value of the previous time from the temperature value of the later time, and then divide by the sampling period duration to obtain the temperature rise rate.

[0094] Compare the calculated temperature rise rate at every location in the entire steel shell of the iron trough with the preset temperature rise rate threshold.

[0095] Among them, the temperature rise rate threshold is the maximum allowable temperature rise rate value set based on the historical temperature fluctuation statistics of the steel shell of the iron trough under normal iron tapping conditions at the corresponding position.

[0096] If the rate of temperature rise exceeds the corresponding rate of temperature rise threshold, the corresponding location will be identified as an abnormal location in terms of rate of temperature rise.

[0097] If the rate of temperature rise does not exceed the corresponding rate of temperature rise threshold, the corresponding position is determined to be a normal position for the rate of temperature rise.

[0098] Multiple adjacent locations that are all determined to have an abnormal rate of temperature rise are grouped into a continuous abnormal region, and the area range of each abnormal region is defined to obtain the dynamic abnormal region set of the entire steel shell of the iron ditch at the current moment.

[0099] The potential weak areas set and the dynamic abnormal areas set are superimposed and compared on the spatial location of the entire steel shell of the iron ditch to determine whether there are areas with overlapping spatial locations.

[0100] If a grid cell belongs to both the set of potential weak areas and the set of dynamic abnormal areas, then the corresponding grid cell will be marked as a key monitoring area.

[0101] If a grid cell belongs to only one of the potential weak areas set or only one of the dynamic abnormal areas set, the corresponding grid cell will be marked as a secondary concern unit and will not be subject to key monitoring, so as to concentrate monitoring resources to prioritize coverage of complex risk areas.

[0102] Step 4: For key monitoring areas, dynamically adjust the temperature acquisition frequency of the temperature measurement points, and obtain the real-time steel shell temperature value and temperature rise rate of the key monitoring areas. Combined with the predicted residual thickness of refractory materials, calculate the current heat load risk index, and make a heat load early warning judgment based on the current heat load risk index.

[0103] In step four, all key monitoring areas are acquired. For the temperature measurement points in the key monitoring areas, the temperature acquisition frequency is dynamically adjusted. The process is as follows:

[0104] For each key monitoring area, the difference between the predicted residual refractory thickness value of the grid cell at the current moment and the safety lower limit is calculated, and the ratio of the absolute value of the difference to the safety lower limit is calculated as the safety margin ratio.

[0105] Among them, the smaller the safety margin ratio, the closer the grid cell is to the critical state of burn-through risk, and the higher the urgency of the risk.

[0106] Using the normal sampling frequency as the reference frequency, the encrypted sampling frequency is obtained by dividing the normal sampling frequency by the safety margin ratio of the grid cells.

[0107] It should be noted that when the safety margin ratio approaches zero, the encrypted sampling frequency approaches infinity. At this point, due to the upper limit of the hardware acquisition capability of the temperature sensing element, the maximum allowable sampling frequency of the temperature sensing element is taken as the encrypted sampling frequency of the key monitoring area.

[0108] Obtain the encrypted sampling frequency for each key monitoring area;

[0109] The real-time steel shell temperature value sequence of the key monitoring area is acquired in real time. At the current moment, the absolute value of the difference between the real-time steel shell temperature values ​​of two adjacent encrypted sampling moments (the current moment and the previous encrypted sampling moment) is divided by the sampling interval time to obtain the temperature rise rate at the current moment.

[0110] Obtain the predicted residual refractory thickness value of the grid cell corresponding to the key monitoring area under the current iron tapping period;

[0111] The heat load risk index is calculated based on real-time steel shell temperature, temperature rise rate, and predicted residual refractory thickness. The process is as follows:

[0112] Calculate the ratio of the real-time steel shell temperature value to the corresponding steel shell temperature safety threshold, and use it as the temperature correction coefficient. If the real-time steel shell temperature value does not exceed the steel shell temperature safety threshold, the temperature correction coefficient is equal to one.

[0113] The ratio of the rate of temperature rise to the corresponding warning threshold for the rate of temperature rise is calculated and used as a rate correction coefficient. If the rate of temperature rise does not exceed the warning threshold for the rate of temperature rise, the rate correction coefficient is equal to one.

[0114] Multiply the safety margin ratio by the temperature correction factor and the rate correction factor to obtain the current heat load risk index of the key monitoring area at the current moment;

[0115] The process of determining heat load early warning based on the heat load risk index is as follows:

[0116] The heat load risk index is compared with the preset early warning trigger threshold;

[0117] The early warning trigger threshold is set based on the following: by statistically analyzing the heat load risk index corresponding to the time when the steel shell of the iron trough had a burn-through accident or needed emergency shutdown for maintenance in multiple furnace service cycles in history, the minimum value of the heat load risk index at each accident time is taken as the base value of the early warning trigger threshold. At the same time, taking into account the measurement error of the temperature measuring element, the model prediction deviation rate, and the safety response time required for on-site operators to complete emergency response from receiving the early warning signal, the base value is multiplied by the safety margin coefficient to obtain the final determined early warning trigger threshold.

[0118] If the current heat load risk index reaches the warning trigger threshold, a heat load warning signal containing the location information of the key monitoring area will be issued, prompting the operators to take emergency measures for the key monitoring area immediately, and the warning information will be pushed to the blast furnace control room and the on-site operation terminal at the same time.

[0119] If the current heat load risk index does not reach the warning trigger threshold, no heat load warning signal will be issued, and key monitoring areas will continue to maintain intensive monitoring.

[0120] This embodiment constructs a global erosion rate attenuation prediction model based on historical temperature data and measured residual refractory thickness data during trench maintenance, and introduces real-time temperature deviation to dynamically correct the erosion rate, thereby achieving accurate prediction of refractory erosion status in various areas of the iron ditch and early identification of potential weak areas.

[0121] This embodiment spatially superimposes the predicted potential weak areas with the dynamic abnormal areas identified in the real-time temperature field, and locks down the composite risk areas that simultaneously have the risk of long-term erosion accumulation and the characteristics of real-time abnormal temperature rise as the key monitoring targets. This optimizes the problems of resource dispersion and unclear focus caused by uniform monitoring across the entire area, and improves the pertinence and accuracy of heat load early warning.

[0122] This embodiment dynamically increases the temperature acquisition frequency of temperature measuring elements in key monitoring areas and calculates the heat load risk index based on real-time steel shell temperature, temperature rise rate, and predicted residual refractory thickness. This achieves comprehensive enhancement of monitoring density and risk assessment for high-risk areas. At the same time, it differentiates the issuance of heat load early warning signals to ensure timely warnings at weak points that are truly at risk of burn-through. This reduces missed reports due to monitoring lag and improves the safety and continuity of blast furnace tapping operations.

[0123] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement, characterized in that: Includes the following steps: Historical temperature data and corresponding tapping process parameters of multiple temperature measurement points on the steel shell of the blast furnace iron trough were obtained to form a historical temperature evolution time series library for each area of ​​the iron trough under different tapping periods. Based on the historical temperature evolution time series library and combined with the data of refractory residual thickness during the shutdown and maintenance of the iron trough, the historical erosion rate of refractory at the temperature point was inversely estimated, and an erosion rate attenuation prediction model for the entire steel shell of the iron trough was constructed. Based on the erosion rate decay prediction model, the predicted residual refractory thickness of each area in the iron ditch during the current iron tapping period is calculated, and areas where the residual refractory thickness is at the safe lower limit are identified as potential weak areas. Real-time temperature data from multiple temperature measurement points are acquired, and the real-time temperature field distribution of the entire steel shell of the iron trough is reconstructed through interpolation analysis. Dynamic abnormal areas are identified by the temperature rise rate in the temperature field distribution. The dynamic abnormal areas are spatially superimposed with potential weak areas to obtain key monitoring areas. For key monitoring areas, the temperature acquisition frequency of the temperature measurement points is dynamically adjusted, and the real-time steel shell temperature value and temperature rise rate of the key monitoring areas are obtained. Combined with the predicted residual thickness of refractory material, the current heat load risk index is calculated, and a heat load early warning judgment is made based on the current heat load risk index.

2. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 1, characterized in that: The process of constructing a prediction model for the erosion rate attenuation across the entire steel shell of the iron ditch is as follows: The entire steel shell of the iron ditch is divided into several grid units. The historical erosion rate time series of the grid units is obtained, and the decay trend curve of the erosion rate with the cumulative iron output is fitted. If the real-time temperature data of the grid cell deviates from the normal fluctuation range of the historical temperature data of the grid cell under the same iron tapping period, it is determined that the grid cell is currently in an abnormal erosion state. The correction amount is then added to the basic predicted erosion rate corresponding to the decay trend curve to obtain the corrected predicted erosion rate. The residual refractory thickness data re-measured during the maintenance of the iron ditch is used as the basis for calibration. Whenever new residual refractory thickness data is obtained during maintenance, the predicted erosion rate of the grid cell in the previous maintenance cycle is compared with the actual erosion rate. The prediction deviation rate is calculated, and the decay trend curve is corrected or retained based on the prediction deviation rate to establish an erosion rate decay prediction model.

3. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 2, characterized in that: The process of obtaining the historical erosion rate is as follows: For each temperature measuring point, the historical temperature data sequence since the replacement or repair of the iron ditch and the actual residual thickness of refractory material measured during each ditch shutdown and maintenance at the temperature measuring point are obtained to construct a complete evolution time-series trajectory of the steel shell temperature at the temperature measuring point as a function of the residual thickness of refractory material. Extract the residual refractory thickness values ​​at each of the two adjacent trench shutdown maintenance times from the evolution timeline corresponding to the grid cell. Subtract the residual refractory thickness measured at the current trench shutdown maintenance from the residual refractory thickness measured at the previous shutdown maintenance to obtain the total refractory thinning of the grid cell during the maintenance interval. Divide the total refractory thinning by the cumulative iron tapping increment recorded during the maintenance interval to obtain the historical erosion rate of the grid cell during the iron tapping period.

4. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 2, characterized in that: The calculation process for the correction amount is as follows: Calculate the temperature difference between the real-time temperature data and the upper limit of the normal fluctuation range. Divide the temperature difference by the upper limit of the normal fluctuation range to obtain the temperature deviation rate. Use the temperature deviation rate as a correction coefficient. Multiply the correction coefficient by the preset erosion rate correction factor to obtain the correction amount.

5. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 2, characterized in that: The process of correcting or retaining the decay trend curve based on the prediction deviation rate is as follows: If the prediction deviation rate does not exceed the corresponding preset threshold, it means that the prediction results of the grid cells in the current cycle are basically consistent with the actual erosion situation. If the prediction deviation rate exceeds the corresponding preset threshold, the predicted residual refractory thickness value of the grid cell at the current cumulative iron output is directly replaced with the actual residual refractory thickness value measured during this trench shutdown maintenance. At the same time, the predicted erosion rate of the grid cell in the previous maintenance cycle is replaced with the actual erosion rate recalculated based on the measured thickness difference. The erosion rate attenuation coefficient of the grid cell at the current cumulative iron output is re-determined based on the ratio of the actual erosion rate to the predicted erosion rate of the previous cycle. The corrected attenuation coefficient is substituted into the attenuation trend curve, and the predicted segment that has not yet occurred after the current cumulative iron output is scaled and adjusted as a whole. The scaling ratio is the ratio of the actual erosion rate to the predicted erosion rate.

6. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 1, characterized in that: The process of identifying potential vulnerable areas is as follows: Using the current cumulative iron output of the iron ditch and the current iron output period as input, and substituting them into the erosion rate decay prediction model, the predicted erosion rate value of each grid cell in the entire steel shell of the iron ditch is output under the current iron output period. For each grid cell, the measured residual refractory thickness value during the last trench shutdown maintenance is extracted from the evolution time trajectory corresponding to the grid cell as the initial thickness value. The predicted residual refractory thickness is obtained by subtracting the product of the predicted erosion rate value and the accumulated iron output during the current iron output period from the initial thickness value. Extract mesh cells whose predicted residual refractory thickness is less than or equal to a preset safety lower limit and mark them as potential weak areas.

7. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 1, characterized in that: The process of obtaining dynamic anomaly regions is as follows: Real-time temperature data of each temperature measurement point is continuously acquired at a fixed sampling period. The temperature data of each discrete point is input into a preset spatial interpolation algorithm to reconstruct the real-time temperature field of the entire steel shell of the iron trough at the current moment. For any location within the entire steel shell of the iron ditch, the temperature values ​​at two adjacent sampling periods are extracted from the real-time temperature field sequence, and the temperature rise rate is calculated. Locations with a temperature rise rate exceeding a preset threshold are grouped into a dynamic abnormal region set.

8. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 1, characterized in that: The process of obtaining key monitoring areas is as follows: Spatial overlay of the set of potential weak regions and the set of dynamic abnormal regions; Grid cells that belong to both the set of potentially weak areas and the set of dynamically abnormal areas are marked as key monitoring areas.

9. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 1, characterized in that: The process of dynamically adjusting the temperature acquisition frequency of the temperature measurement points is as follows: For each key monitoring area, the difference between the predicted residual refractory thickness value of the grid cell at the current moment and the safety lower limit is calculated, and the absolute value of the difference is calculated as the ratio of the safety lower limit, which is used as the safety margin ratio. The encrypted sampling frequency is obtained by dividing the normal sampling frequency by the safety margin ratio of the grid cells.

10. The method for early warning of heat load on the steel shell of a blast furnace molten iron trough based on continuous temperature measurement according to claim 1, characterized in that: The process of determining heat load early warning is as follows: The ratio of the real-time steel shell temperature value to the corresponding steel shell temperature safety threshold is calculated and used as the temperature correction coefficient. If the real-time steel shell temperature value does not exceed the steel shell temperature safety threshold, the temperature correction coefficient is equal to one. The ratio of the rate of temperature rise to the corresponding warning threshold for the rate of temperature rise is calculated and used as a rate correction coefficient. If the rate of temperature rise does not exceed the warning threshold for the rate of temperature rise, the rate correction coefficient is equal to one. Multiply the safety margin ratio by the temperature correction factor and the rate correction factor to obtain the current heat load risk index of the key monitoring area at the current moment; If the current heat load risk index reaches the warning trigger threshold, a heat load warning signal containing the location information of the key monitoring area will be issued.

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