An on-line weighing and metering system and method for molten iron ladles

Abstract

The present application belongs to the technical field of weighing measurement, and specifically discloses an online weighing measurement system and method for molten iron ladles, which comprises a transportation data acquisition module, distributed weighing detection points and ladle load state sensors are arranged, and original weighing signals, ladle wall temperatures and position data are collected in real time throughout the whole process; a weighing signal processing module, which performs temperature gradient compensation and zero point translation on the original signals based on the ladle wall temperature to obtain primary corrected data; a data fusion and prediction module, which generates a weight-position-time curve by aligning the data, thereby identifying abnormal weight events, evaluating the erosion state of the ladle lining and predicting the temperature change trend of the molten iron; and a weighing determination output module, which performs secondary correction on the primary corrected data based on the analysis results and outputs the final weight of the molten iron. The present application realizes dynamic weighing that can be traced throughout the whole transportation process, improves the accuracy of the weighing results, and thus effectively supports the precise control of subsequent production operations.

Description

Technical Field

[0001] This invention belongs to the field of weighing and metering technology, and specifically relates to an online weighing and metering system and method for molten iron ladles. Background Technology

[0002] The molten iron ladle is a transportation device used in steel enterprises to receive, transport, and dispense high-temperature molten iron. It directly connects the blast furnace and the converter, and the weight of the molten iron inside determines the effectiveness of multiple tasks such as production scheduling, steelmaking batching, and safety control, thus highlighting the importance of its real-time weight measurement.

[0003] The prior art, such as the online weighing, measuring, temperature measurement and sampling device, system and method for molten iron ladles disclosed in Chinese invention patent application No. 202210974408.3, integrates the three major functions of online weighing, temperature measurement and sampling into one, and improves the turnover rate of molten iron ladles and effective smelting time by centrally and quickly completing the collection of multiple key data and sample acquisition.

[0004] Prior art 2, such as the Chinese invention patent application with application number 201610071977.1, discloses an automatic weighing device for a molten iron pouring station. It detects the tilt angle of the torpedo ladle through an angle sensor and combines it with a preset delay control algorithm to realize automatic control and online weighing of the pouring process. It achieves automatic weighing during the iron receiving process, replacing manual confirmation and meeting the dual needs of fast-paced production and accurate measurement.

[0005] Existing methods mostly involve weighing at fixed points, such as the ladle station or before steelmaking. However, the actual weight of molten iron during transportation is dynamic, and fixed-point measurements cannot represent the real-time net weight of the vehicle en route or upon arrival. This leads to lags and biases in the calculation of batching for converter steelmaking. Furthermore, it is impossible to pinpoint the losses that mainly occur during transportation, thus failing to detect abnormal changes during transport.

[0006] Secondly, although the existing technology 1 integrates weighing, temperature measurement, and sampling, its data still mainly serves the stage before steelmaking and is not linked to transportation and logistics information. It cannot make predictive judgments on the temperature drop trend of the molten iron ladle and the erosion status of the ladle lining based on continuous data, which leads to the inability to accurately predict the temperature of the molten iron when it arrives at the converter, and also makes it impossible to statistically analyze the cumulative effect of erosion of the ladle refractory material. Summary of the Invention

[0007] In view of this, in order to solve the above problems, an online weighing and metering system and method for molten iron ladles is proposed.

[0008] The objective of this invention can be achieved through the following technical solution: This invention provides an online weighing and metering system for molten iron ladles. The system includes a transportation data acquisition module, which is used to collect raw weighing signals, ladle wall temperature and position data in real time throughout the transportation process based on multiple distributed weighing detection points deployed along the transportation path of the molten iron ladle and ladle-mounted status sensors installed on the molten iron ladle.

[0009] The weighing signal processing module is used to correct the original weighing signal based on the tank wall temperature to obtain the initially corrected weighing data.

[0010] The data fusion and prediction module is used to align the initially corrected weighing data and location data with timestamps to generate a weight-location-time curve for the entire process of the molten iron ladle. Based on this curve, the module analyzes and identifies abnormal weight events, ladle lining erosion status, and molten iron temperature change trends during transportation.

[0011] The weighing output module is used to further correct the initially corrected weighing data based on the analysis and identification results during transportation, and output the final weight of the molten iron.

[0012] The present invention also provides an online weighing and measurement method for molten iron ladles, the method comprising: setting up multiple distributed weighing and detection points along the transportation path of the molten iron ladle, and installing ladle-mounted status sensors on the molten iron ladle to collect raw weighing signals, ladle wall temperature and position data in real time throughout the transportation process.

[0013] The original weighing signal is corrected based on the tank wall temperature to obtain the initially corrected weighing data.

[0014] The initially corrected weighing data and location data are timestamped to generate a weight-location-time curve for the entire process of the molten iron ladle. Based on this curve, abnormal weight events, ladle lining erosion, and molten iron temperature change trends during transportation are analyzed and identified.

[0015] Based on the analysis and identification results during transportation, the weighing data after the initial correction is corrected again to output the final weight of molten iron.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention deploys distributed weighing monitoring points and combines them with the ladle status sensor for real-time data acquisition and fusion calibration, thereby realizing continuous monitoring of weight changes during the transportation of molten iron ladles, overcoming the information lag problem caused by fixed point measurement, and thus providing timely and accurate weight basis for converter batching, effectively eliminating batching deviation caused by information lag, and providing a data basis for sensing abnormal weight events that occur during transportation.

[0017] (2) By generating a full weight-location-time curve, the present invention can immediately identify and determine whether an unexpected weight drop event and tank lining erosion have occurred during transportation, and can accurately locate the specific transportation section where the event occurred, thus realizing accurate location of the loss location during the journey, and also realizing real-time detection and location tracing of abnormal weight events.

[0018] (3) By analyzing the historical sequence of the empty tank's self-weight and extracting its long-term decreasing component, this invention achieves a quantitative assessment and health characterization of the tank lining erosion state, intuitively demonstrating the cumulative effect of erosion of the tank's refractory material, and thus provides a reliable information basis for the subsequent correction of the weighing data and provides a direction for the subsequent maintenance of the tank.

[0019] (4) The present invention corrects the weighing data after the initial correction by analyzing and identifying the results during the transportation process. This makes the final output weight of molten iron not only compensate for the tare weight change caused by the erosion of the ladle lining, but also intelligently distinguish and handle the weight deviation caused by different situations such as wall solidification or leakage according to the type of abnormal event. This greatly improves the accuracy and reliability of the final weight result, and also effectively supports the precise control of subsequent production operations. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0022] Figure 2 This is a schematic diagram of the overall implementation process of the present invention.

[0023] Figure 3 This is a schematic diagram of the abnormal weight event analysis and identification process. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0025] Please see Figure 1As shown, the present invention provides an online weighing and metering system for molten iron ladles. The system includes a transportation data acquisition module, a weighing signal processing module, a data fusion and prediction module, and a weighing determination output module.

[0026] The weighing signal processing module is connected to the transportation data acquisition module and the data fusion and prediction module, respectively, and the data fusion and prediction module is connected to the weighing determination output module.

[0027] Furthermore, all the aforementioned modules are connected via an industrial network. The transportation data acquisition module collects raw weighing signals, tank wall temperature, and location data from the weighing detection points and the entire transportation process on the vehicle in real time and uploads them to the weighing signal processing module. After receiving the data, the weighing signal processing module corrects the raw weight signal and outputs the initially corrected weighing data to the data fusion and prediction module. The data fusion and prediction module aligns the initially corrected weighing data with the location data, generates a complete curve, and analyzes anomalies, erosion, and trends in molten iron temperature changes. The weighing determination output module performs a second correction on the weight based on the analysis results of the data fusion and prediction module, outputs the final molten iron weight, and sends a report to the production system. The modules communicate with each other by using the output of one module as the input of the next, achieving automatic signal transmission and functional collaboration.

[0028] The transportation data acquisition module is used to collect raw weighing signals, tank wall temperature and position data in real time throughout the transportation process, based on multiple distributed weighing detection points set up along the transportation route of the molten iron ladle and tank-mounted status sensors installed on the molten iron ladle.

[0029] Because molten iron is in a dynamic process during transportation, its weight will change in real time due to abnormal situations such as leakage and molten iron adhering to the wall. Fixed-point data cannot reflect these changes, resulting in a lag in the batching basis and an inability to locate the specific section where the abnormality occurred, thus creating a safety blind spot in the entire transportation process.

[0030] Based on the above considerations, in one specific embodiment of the present invention, the original weighing signals, tank wall temperature, and location data are collected in real time throughout the transportation process. The specific implementation process includes: selecting key logistics nodes such as after blast furnace tapping, before marshalling yard, and before steelmaking workshop, as well as intermediate sections of long-distance transportation, as weighing detection points along railway or highway lines transporting molten iron. Dynamic weighing units, such as dynamic track scales, are embedded and installed in the track or road surface foundation at these locations, with each dynamic weighing unit connected to an industrial network.

[0031] Weighing sensors are installed on the load-bearing structure between the frame and the ladle body of each molten iron ladle car to directly measure the total weight of the ladle and its contents. Secondly, considering that temperature directly affects the measurement accuracy of the weighing sensors and is also a major factor in assessing the thermal state of the molten iron, predicting temperature drop trends, and assisting in identifying the causes of weight anomalies, it is essential to simultaneously collect ladle wall temperature data. At least three temperature probes are placed on the outside of the ladle body near the weighing sensor mounting base and on the side wall of the ladle body to monitor the local temperature field of the sensors and the temperature of the ladle wall. A triaxial vibration accelerometer is installed on the rigid structure at the bottom of the ladle body. Additionally, each ladle car is equipped with a positioning module, such as a Beidou or GPS locator.

[0032] All the aforementioned sensors are connected to a unified synchronous clock source. When the molten iron ladle car passes the weighing detection point, the weighing unit and the weighing sensor at that point simultaneously collect weight signals. During transportation, the on-board status sensor continuously collects weight, temperature, and vibration acceleration data, while the positioning module continuously collects position data. All data is timestamped during collection. The on-board status sensor consists of a weighing sensor, a temperature probe, a triaxial vibration accelerometer, and a positioning module.

[0033] The embodiments of the present invention use the weighing sensor to collect real-time raw weighing signals, tank wall temperature and position and other multi-dimensional sensing information throughout the transportation process, thereby providing real-time, continuous and spatiotemporally aligned underlying data support for subsequent dynamic weight correction, transportation status analysis and tank health assessment.

[0034] While weighing detection points offer good stability, they also have monitoring blind spots. Weighing sensors, on the other hand, can provide continuous signals, but their measurements can drift due to factors such as temperature changes, resulting in insufficient long-term reliability.

[0035] Based on this, the present invention needs to fuse the original weighing signal collected at the corresponding weighing detection point with the original weighing signal collected by the weighing sensor, and determine the final output original weighing signal. The specific implementation process includes: when the molten iron ladle car equipped with the weighing sensor travels to the weighing detection point set up on the track, the weighing unit of the detection point is triggered and the first original weighing signal is collected. At the same time, the weighing sensor on the ladle car synchronously collects the second original weighing signal, and the first original weighing signal and the second original weighing signal are timestamped together.

[0036] When the molten iron ladle is at the weighing detection point, the first original weighing signal is used as the final output original weighing signal, and the deviation between it and the second original weighing signal is calculated. Based on this deviation, the zero-point offset and sensitivity coefficient of the weighing sensor are automatically updated to complete an online update.

[0037] When the molten iron ladle car leaves the weighing checkpoint and enters the transportation section between the two weighing checkpoints, the weighing sensor that was successfully updated at the most recent weighing checkpoint is regarded as a calibrated and reliable measuring unit. The second original weighing signal output by the sensor is continuously collected and this signal is output as the final original weighing signal for the transportation section.

[0038] This invention achieves continuous monitoring of weight changes during the transportation of molten iron ladles by deploying distributed weighing monitoring points and combining them with ladle-mounted status sensors for real-time data acquisition and fusion calibration. This overcomes the information lag problem caused by fixed-point measurements, thereby providing timely and accurate weight data for converter batching, effectively eliminating batching deviations caused by information lag, and providing a data foundation for detecting abnormal weight events during transportation.

[0039] It should be noted that, in the specific implementation process of determining the final output original weighing signal described above, the following supplementary explanation is required: The specific implementation method for automatically updating the zero-point offset and sensitivity coefficient of the weighing sensor based on this deviation is as follows: taking the first original weighing signal as the known standard weight value and the synchronously acquired second original weighing signal as the current output value, the new zero-point offset and sensitivity coefficient of the weighing sensor are directly calculated through the linear two-point calibration method, and the parameter update is completed.

[0040] Among them, the linear two-point calibration methods, such as the least squares method and recursive filtering, are all existing technologies. Preferably, the present invention uses the least squares method as the linear two-point calibration method, and the specific calculation process of the least squares method is as follows: when the molten iron ladle car passes through multiple weighing detection points, the first original weighing signal corresponding to each point is taken as the abscissa (known standard value), and the second original weighing signal synchronously collected by the weighing sensor on the ladle car is taken as the ordinate (current output value), forming a set of coordinate points. The least squares method is used to linearly fit the set of coordinate points to obtain the best fitting line. The slope of the line is used as the updated sensitivity coefficient, and the intercept of the line on the ordinate axis is used as the updated zero-point offset.

[0041] The weighing signal processing module is used to correct the original weighing signal based on the tank wall temperature to obtain the initially corrected weighing data.

[0042] During the transportation of molten iron ladles, the weighing sensors and their mounting structures are exposed to extreme thermal environments. Traditional temperature compensation methods typically rely on the average temperature of only one or two measuring points, compensating by consulting a standard temperature drift curve. However, on large, non-uniformly heated metal structures like molten iron ladles, this uneven thermal expansion generates complex additional thermal stresses within the sensor's sensitive load-bearing structure, leading to output errors that cannot be corrected by simply averaging the temperature.

[0043] Based on this, the present invention corrects the original weighing signal based on the tank wall temperature, and the specific correction process is as follows: S1, real-time reading of the spatial coordinates and real-time temperature readings of at least three temperature probes arranged at different positions on the weighing sensor mounting base and adjacent bearing structure, and constructing a two-dimensional or three-dimensional continuous temperature field model of the sensor mounting area through a spatial interpolation algorithm, thereby visually clarifying the spatial temperature distribution of the area. The spatial interpolation algorithm can specifically select either Kriging interpolation or linear triangulation interpolation. When constructing a two-dimensional continuous temperature field model, the present invention preferably uses Kriging interpolation, and the spatial interpolation algorithm is an existing algorithm, and its interpolation process will not be described in detail.

[0044] The temperature probe is preferably a high-temperature resistant patch thermocouple or resistance temperature detector (RTD) and is fixed to the surface of the structure by mechanical clips.

[0045] S2. In the above temperature field model, a force-bearing structural region containing the load cell's pressure-bearing point and force transmission path is selected. Within this region, a mesh is created, with each mesh node representing a virtual pressure-bearing point. Then, for each pressure-bearing point, the rate of temperature change in the two orthogonal dimensions (axial and radial) of the sensor is calculated based on the temperature field model. This rate of change represents the degree of temperature non-uniformity in space, i.e., the temperature gradient.

[0046] S3. Based on the spatial coordinates of each pressure point, the axial temperature change rate and the radial temperature change rate, as well as the material thermal expansion coefficient of the sensor mounting structure, calculate the comprehensive axial thermal strain and comprehensive shear thermal strain.

[0047] The calculation process for the combined axial thermal strain and combined shear thermal strain includes: for any bearing point in the stressed structural region... Its axial thermal strain With shear thermal strain The axial temperature gradient at that point and radial temperature gradient With the coefficient of linear expansion of materials Calculations show that , .

[0048] The axial thermal strain and shear thermal strain of all bearing points are averaged to obtain the average axial thermal strain and the average shear thermal strain, which are labeled as the comprehensive axial thermal strain and the comprehensive shear thermal strain, respectively.

[0049] The specific value of the coefficient of thermal expansion of the material used in the sensor mounting structure can be obtained by consulting the material's datasheet.

[0050] S4. Based on the pre-calibrated output sensitivity coefficients of the weighing sensor for axial thermal strain and shear thermal strain, the calculated comprehensive axial thermal strain and comprehensive shear thermal strain are multiplied by the corresponding sensitivity coefficients to convert them into corresponding weighing reading deviations. The sum of these two deviation values ​​yields the total temperature gradient compensation amount. This compensation amount is a positive or negative value, representing the systematic measurement deviation caused by the current temperature gradient state.

[0051] It should be added that the output sensitivity coefficients for both axial thermal strain and shear thermal strain can be obtained through calibration experiments.

[0052] Taking the process of obtaining the output sensitivity coefficient of axial thermal strain as an example, the specific process is as follows: control the temperature field to generate several axial thermal strains on the weighing sensor mounting structure, simultaneously measure the thermal strain value of the mounting structure and the change in the output reading of the weighing sensor, establish a curve of thermal strain value and output reading change with axial thermal strain based on the measured thermal strain value and the change in reading corresponding to each axial thermal strain, extract the slope of the curve through linear fitting, and use the slope of the curve as the output sensitivity coefficient of axial thermal strain.

[0053] S5. Calculate the average temperature of the region based on the entire temperature field model, query the preset temperature drift characteristic curve of the weighing sensor, and determine the zero-point offset corresponding to the average temperature of the region. First, subtract this zero-point offset from the original weighing signal to complete the basic temperature shift correction.

[0054] S6. Subsequently, the calculated temperature gradient compensation is subtracted from the real-time original weighing signal after zero-point translation correction, and the weighing data after the initial correction is output.

[0055] This invention identifies and quantifies the error introduced by uneven heating of the installation structure, a factor that has long been overlooked, thus ensuring the accuracy and reliability of the final weighing data.

[0056] Meanwhile, this correction provides a reliable weight data source for the analysis and identification of subsequent abnormal weight events, ladle lining erosion, and the trend of molten iron temperature changes, ensuring the effectiveness of subsequent corrections to the weighing data after the initial correction.

[0057] It should be noted that the present invention provides only one example and is not a fixed setting. In actual implementation, the above-mentioned temperature probe placement, spatial interpolation algorithm, material thermal expansion coefficient and weighing sensor sensitivity coefficient should be verified and optimized through experimental calibration or on-site debugging. Alternatively, the settings can be customized by the implementers.

[0058] The data fusion and prediction module timestamps the initially corrected weighing data and location data to generate a weight-location-time curve for the entire process of the molten iron ladle. Based on this curve, it analyzes and identifies abnormal weight events, ladle lining erosion, and molten iron temperature change trends during transportation.

[0059] The weight-position-time curve represents the overall state of the transportation process. It can be understood that this curve integrates and reflects the three-dimensional relationship between the weight of the molten iron, its spatial movement, and the passage of time.

[0060] Because the current weighing is done at fixed points, it cannot cover the continuous state of the transportation process in terms of both time and space. On the one hand, when an abnormal weight is detected, the lack of continuous data during transportation makes it impossible to trace the source of the event, locate the location of the incident, and determine the ongoing status. This leads to the loss of the critical window for safety intervention and precise maintenance during the event, resulting in a delayed response. On the other hand, for slow, cumulative damage such as tank lining erosion, the few discrete data points cannot form an effective time series, making it difficult to provide data support for the quantitative assessment of equipment health status and predictive maintenance decisions.

[0061] Based on this, the present invention sets up a parallel-running abnormal weight event identification channel, a ladle lining erosion status assessment channel, and a molten iron temperature change trend prediction channel. Based on the weight-position-time curve, the analysis and processing are performed, and the abnormal weight event characterization, ladle lining erosion status characterization, and molten iron temperature change trend characterization are output respectively. The abnormal weight event characterization refers to the data record that includes the event type, occurrence time interval, and start and end weight values.

[0062] Preferably, please refer to Figure 3 As shown, the specific processing procedure of the abnormal weight event identification channel is as follows: A1. Perform real-time differential calculation on the weight-position-time curve to obtain the real-time weight change rate and weight change acceleration corresponding to each transportation section. The weight change rate reflects the speed of descent, and the weight change acceleration reflects the sudden change in the trend of change.

[0063] A2. Based on the rate of change of weight and the acceleration of weight change, determine whether the following conditions are triggered: the rate of change of weight is continuously negative and its absolute value exceeds the preset rate threshold. The preset rate threshold can be set by the minimum detectable leakage requirement. For example, assuming the minimum detectable leakage is 100kg and the sampling frequency of the weighing sensor is 60 seconds per acquisition, the preset rate threshold can be set to 1.67kg per second.

[0064] If the acceleration due to weight change exceeds a preset acceleration threshold, this threshold should be set to the maximum acceleration along the axis of the weighing sensor when the molten iron ladle car is subjected to maximum traction or braking within the permissible operating specifications.

[0065] A3. If any of the above conditions are triggered in a certain transportation section, it is determined that an unexpected weight drop event has occurred. The start and end points of the transportation section are immediately marked on the weight-position-time curve, and the weight values ​​corresponding to the start and end points are recorded.

[0066] A4. Package and integrate at least one of the aforementioned unexpected weight loss events and the weight values ​​corresponding to their start and end points, and add them to a continuously updated sequence of abnormal events as the output of this channel.

[0067] Preferably, the specific processing procedure of the ladle lining erosion status assessment channel is as follows: B1. In each transportation cycle, when the molten iron ladle is in an empty state and passes through any of the weighing detection points, its empty ladle weight measurement value is collected, and the corresponding passing time is recorded to construct a time-sequenced sequence of empty ladle self-weight. This sequence filters out the interference of molten iron weight and is used to reflect the change in the ladle's own mass.

[0068] B2. The empty tank self-weight history sequence is smoothed to suppress random fluctuations. Subsequently, the smoothed empty tank self-weight history sequence is decomposed into a long-term trend component and a periodic fluctuation component using a trend decomposition algorithm (such as HP filtering). The long-term trend component represents the slow decrease in self-weight caused by tank lining erosion and is labeled as the long-term decrease component of empty tank self-weight.

[0069] B3. Retrieve the structural parameters of the molten iron ladle from the technical archives, and then extract the effective inner surface area of ​​the ladle lining and the density of the lining material from the structural parameters of the molten iron ladle.

[0070] B4. Divide the long-term decreasing component of the empty tank's self-weight by the product of the density of the corresponding lining material and the effective inner surface area of ​​the molten iron tank, and record the result as the equivalent average corrosion thickness. Use this as the characterization of the tank lining corrosion state, which specifically refers to the equivalent average corrosion thickness value obtained through calculation.

[0071] This invention analyzes the historical sequence of the empty tank's self-weight and extracts its long-term decreasing component, thereby achieving a quantitative assessment and health characterization of the tank lining erosion state. It intuitively demonstrates the cumulative effect of erosion of the tank's refractory materials, thus providing a reliable information basis for the subsequent correction of weighing data and providing direction for the subsequent maintenance of the tank.

[0072] In another preferred embodiment, the specific processing procedure of the molten iron temperature change trend prediction channel is as follows: C1, call up the geometric dimensions of the molten iron ladle and the thermal properties of the ladle body structural material, the thermal properties including thermal conductivity, specific heat capacity, emissivity, etc.

[0073] C2. Based on the aforementioned geometric dimensions and thermal property parameters, establish a heat transfer model describing the heat dissipation of molten iron to the environment through the molten iron ladle. Configure the corresponding state estimator for this model; for example, a Kalman filter can be used as the state estimator.

[0074] The model uses an energy conservation equation with time as the independent variable and molten iron temperature as the state variable. The equation is expressed as follows: .

[0075] This represents the rate of change of the internal energy of molten iron. Indicates the weight of molten iron. This indicates the specific heat capacity of molten iron. This indicates the rate of change of molten iron temperature over time.

[0076] This represents the total heat power lost by molten iron to the environment through the ladle.

[0077] Rate of change of molten iron temperature The total heat power currently lost molten iron weight Specific heat capacity of molten iron A joint decision was made in and When relatively stable, heat loss power The larger the value, the faster the temperature decreases. The faster it is.

[0078] It should be noted that the weight of the molten iron is obtained as follows: the total weight of the molten iron ladle at the current moment is read from the weighing data obtained after the initial correction, and the weight of the molten iron ladle in the empty state is subtracted from the collected value to obtain the weight of the molten iron.

[0079] C3. The tank wall temperature collected in real time during transportation is used as a directly observable output signal and input to the state estimator.

[0080] C4. The state estimator synchronously runs the model to predict the state, and compares the predicted ladle wall temperature with the actual measured ladle wall temperature in real time. If they are consistent, the model continues to run. If there is a difference, the state estimator calculates the correction amount for the current estimated value of molten iron temperature in reverse. The sum of the predicted ladle wall temperature and the correction amount is used as the estimated value of the molten iron temperature at the current moment.

[0081] C5. After each update, the state estimator directly outputs the corrected real-time molten iron temperature estimate. This continuously output sequence of molten iron temperature estimates serves as a representation of the molten iron temperature change trend, which refers to the sequence of real-time molten iron temperature estimates output by the state estimator.

[0082] This invention generates a full-process weight-location-time curve, which can immediately identify and determine whether unexpected weight loss events and tank lining erosion have occurred during transportation, and can accurately locate the specific transportation section where the event occurred, thus achieving accurate location of the loss location during the journey. It also enables real-time detection and location tracing of abnormal weight events.

[0083] The weighing determination output module, based on the analysis and identification results during transportation, further corrects the initially corrected weighing data and outputs the final weight of the molten iron.

[0084] Unexpected weight loss during transportation may stem from two fundamentally different causes: irreversible molten iron leakage and reversible molten iron solidification on the walls. Weight curves alone cannot distinguish between the two, and misjudgment could lead to false alarms or missed alarms.

[0085] Secondly, the ladle lining will continuously erode and thin over long-term use, causing the ladle's weight to decrease slowly. If this change is not dynamically compensated for, the zero point of the weighing sensor will drift, and the accumulated error over time will significantly affect the accuracy of key process calculations such as the amount of iron loaded and unloaded.

[0086] Based on this, and based on the analysis and identification results during transportation, the weighing data after the initial correction is further corrected. The specific correction process is as follows: R1. Synchronously read the abnormal weight event characterization, the molten iron temperature change trend characterization, and the ladle lining erosion status characterization. For each unexpected weight drop event recorded in the abnormal weight event characterization, extract its start time point T1, end time point T2, start weight value, and end weight value. The difference between the end weight value and the start weight value is recorded as the weight loss.

[0087] R2. From the trend of molten iron temperature change, obtain the real-time molten iron temperature estimate sequence within the time interval [T1, T2], perform linear fitting on the temperature sequence, and calculate the average rate of change of molten iron temperature within this time interval.

[0088] R3. If the average rate of change of molten iron temperature exceeds the preset rate threshold, the abnormal weight event is determined to be a wall-mounted solidification event; otherwise, the abnormal weight event is determined to be a leakage event.

[0089] R4. When the event is a solidification event, it is determined that the weight loss is caused by partial solidification adhering to the tank wall. Therefore, the original data of the weighing data after the initial correction within the time period [T1, T2] is retained without modification.

[0090] R5. When it is a leakage event, the weight drop is determined to be the actual loss of molten iron. Based on the weight values ​​at the start and end points, a smooth and continuous reconstructed weight data for that time period is generated through linear interpolation, spline interpolation, or other applicable preset interpolation algorithms. This reconstructed weight data is then used to replace the data for the corresponding time period in the initially corrected weighing data.

[0091] R6. Read the equivalent average erosion thickness in the time period [T1, T2] from the erosion status characterization of the ladle lining. Combine the effective inner surface area and lining material density of the molten iron ladle lining to calculate the reduction in ladle weight due to lining loss. The reduction in ladle weight is the product of the equivalent average erosion thickness, the effective inner surface area, and the lining material density.

[0092] R7. Using the reduction in the weight of the tank as the zero-point compensation value, perform zero-point translation correction on the processed weighing data, and output the final weight of the molten iron. That is, the final weight of the molten iron is the sum of the weight of the molten iron represented in the processed weighing data and the zero-point compensation value.

[0093] This invention effectively distinguishes between two weight loss events—wall adhesion and leakage—that have completely different physical mechanisms but appear similar, by introducing the key criterion of the average rate of change in molten iron temperature. This avoids false alarms triggered by temporary wall adhesion of molten iron, while ensuring accurate identification of genuine leakage events, providing a correct decision-making basis for safe response.

[0094] Secondly, for identified leakage events, smooth reconstructed data is generated through interpolation algorithms to replace the original jump data that may contain interference. This allows the weight-time curve to more clearly reflect the true trend and rate of the leakage process, which is beneficial for quantitative analysis and prediction.

[0095] Meanwhile, by calculating and compensating for changes in tank weight caused by lining erosion in real time, the problem of long-term metering drift caused by slow changes in the tank's own weight is fundamentally solved. This ensures the long-term accuracy and reliability of all derived calculation indicators, such as the amount of iron loaded and the amount of residual iron, throughout the entire life cycle of the tank, effectively supporting the precise control of subsequent production operations.

[0096] Please see Figure 2 As shown, the present invention provides an online weighing and measurement method for molten iron ladles. The method includes: setting up multiple distributed weighing and detection points along the transportation path of the molten iron ladle, and installing ladle-mounted status sensors on the molten iron ladle to collect raw weighing signals, ladle wall temperature and position data in real time throughout the transportation process.

[0097] The original weighing signal is corrected based on the tank wall temperature to obtain the initially corrected weighing data.

[0098] The initially corrected weighing data and location data are timestamped to generate a weight-location-time curve for the entire process of the molten iron ladle. Based on this curve, abnormal weight events, ladle lining erosion, and molten iron temperature change trends during transportation are analyzed and identified.

[0099] Based on the analysis and identification results during transportation, the weighing data after the initial correction is corrected again to output the final weight of molten iron.

[0100] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. An online weighing and metering system for molten iron ladles, characterized in that, include: The transportation data acquisition module is used to collect raw weighing signals, tank wall temperature, and location data in real time throughout the transportation process. The weighing signal processing module is used to correct the original weighing signal based on the tank wall temperature to obtain the weighing data after initial correction. The data fusion and prediction module is used to align the initially corrected weighing data with the location data with timestamps, generate the weight-location-time curve of the molten iron ladle throughout the entire process, and analyze and identify the characteristics of abnormal weight events, ladle lining erosion status and molten iron temperature change trends during transportation based on the curve analysis. The weighing output module is used to further correct the initially corrected weighing data based on the analysis results during transportation, and output the final weight of molten iron. The original weighing signal is acquired through the following steps: The first original weighing signal is obtained by distributed weighing detection points deployed along the transportation route, and the second original weighing signal is obtained by weighing sensors installed on the body of the molten iron ladle car. When the molten iron ladle is at the weighing detection point, the first original weighing signal is used as the final output original weighing signal. Calculate the measurement deviation between the first and second original weighing signals, and update the zero point and sensitivity coefficient of the weighing sensor in real time based on the measurement deviation; When the molten iron ladle is in the travel range between two weighing detection points, the second original weighing signal collected by the most recently updated weighing sensor is used as the final output original weighing signal. Correcting the original weighing signal includes: Acquire the spatial coordinates and real-time temperature readings of temperature probes located at at least three different positions on the load cell mounting base and adjacent load-bearing structure; The temperature field of the weighing sensor installation area is constructed using a spatial interpolation algorithm; In this temperature field, a stress structure region containing the pressure-bearing point of the weighing sensor and the force transmission path is selected, and the temperature change rate of each pressure-bearing point in this region in the axial and radial directions of the sensor is calculated. Based on the temperature change rate, determine the temperature gradient compensation amount; The average temperature of the region is calculated based on the temperature field, and the zero-point offset corresponding to the average temperature of the region is located from the preset temperature drift characteristic curve. Based on the zero-point offset, the original weighing signal is zero-point shifted, and the temperature gradient compensation amount is subtracted from the shifted real-time original weighing signal to output the weighing data after the first correction. The analysis and identification process for characterizing the erosion state of the tank lining includes: In each transportation cycle, when the molten iron ladle is empty and passes through any of the aforementioned weighing detection points, its empty ladle weight measurement value is collected, and the corresponding passing time is recorded to construct a historical sequence of the empty ladle's self-weight arranged in chronological order. The historical sequence of empty can weight is smoothed, and the long-term decreasing component of empty can weight, which characterizes can liner wear, is extracted by a trend decomposition algorithm. The effective inner surface area of ​​the lining and the density of the lining material are extracted from the structural parameters of the molten iron ladle. Based on the long-term decreasing component of the empty tank's self-weight, the effective inner surface area of ​​the tank lining, and the density of the lining material, the equivalent average erosion thickness of the tank lining is calculated and used as a characterization of the erosion state of the tank lining. The process of further revising the initially corrected weighing data includes: Simultaneously read the abnormal weight event characterization and the molten iron temperature change trend characterization; For each unexpected weight loss event recorded in the abnormal weight event representation, obtain the recorded starting point weight value and ending point weight value, and calculate the weight loss amount. Obtain the real-time estimated value of molten iron temperature within the time period corresponding to the event, and calculate the average rate of change of molten iron temperature within the time period through linear fitting. The initial corrected weighing data for this time period is processed based on the average rate of change of the molten iron temperature. Read the equivalent average erosion thickness in the tank lining erosion state characterization, and calculate the reduction in tank weight due to lining loss by combining the effective inner surface area of ​​the tank lining and the density of the lining material. The weight reduction is used as the zero-point compensation value. The processed weighing data is then zero-point shifted and corrected to output the final weight of the molten iron.

2. The online weighing and metering system for molten iron ladles as described in claim 1, characterized in that: The determination of the temperature gradient compensation amount includes: Based on the spatial coordinates of each pressure point, the axial and radial temperature change rates, and the thermal expansion coefficient of the material of the sensor mounting structure, the comprehensive axial thermal strain and comprehensive shear thermal strain are calculated. Based on the preset output sensitivity coefficients of the weighing sensor for axial thermal strain and shear thermal strain, the combined axial thermal strain and combined shear thermal strain are converted into corresponding weighing reading deviations. The temperature gradient compensation amount is output by summing the deviations of the two weighing readings.

3. The online weighing and metering system for molten iron ladles as described in claim 1, characterized in that: The analysis and identification process for characterizing the abnormal weight event includes: Real-time differential calculations are performed on the weight-position-time curves to obtain the real-time weight change rate and weight change acceleration for each transportation section. Based on the rate of change of weight and the acceleration of weight change, determine whether the following conditions are triggered: The rate of weight change remains negative and its absolute value exceeds the preset rate threshold. The acceleration due to weight change exceeds a preset acceleration threshold; If any of the above conditions are triggered in a certain transportation section, it is determined that an unexpected weight drop event has occurred, and the start and end points of the transportation section are marked on the weight-position-time curve, and the weight values ​​corresponding to the start and end points are recorded. The weight values ​​corresponding to at least one of the unexpected weight loss events and their starting and ending points are integrated, and the integrated result is used as the characterization of the abnormal weight event.

4. The online weighing and metering system for molten iron ladles as described in claim 1, characterized in that: The analysis and identification process for characterizing the trend of molten iron temperature change includes: A thermodynamic model based on the geometric dimensions of the molten iron ladle is established, taking the tapping temperature of molten iron in the blast furnace as the initial state. The real-time collected data of the tank wall temperature is used as the observed value and input into the state estimator corresponding to the thermodynamic model; The state estimator updates the current molten iron temperature estimate in real time, outputs the updated real-time molten iron temperature estimate, and uses it as a representation of the molten iron temperature change trend.

5. The online weighing and metering system for molten iron ladles as described in claim 1, characterized in that: The processing procedure for the initially corrected weighing data includes: If the average rate of change of molten iron temperature exceeds the preset rate threshold, the abnormal weight event is determined to be a wall-mounted solidification event; otherwise, the abnormal weight event is determined to be a leakage event. When it is a wall-mounted solidification event, the original data of the initially corrected weighing data within that time period is retained; In the event of a leakage incident, based on the weight values ​​at the start and end points, a reconstructed weight data for that time period is generated using an interpolation algorithm, and this reconstructed weight data replaces the corresponding time period data in the initially corrected weighing data.

6. A method for online weighing and measuring molten iron ladles, characterized in that: The process is completed using the system described in any one of claims 1 to 5, including the following steps: Multiple distributed weighing and detection points are set up along the transportation route of the molten iron ladle, and on-board status sensors are installed on the molten iron ladle to collect raw weighing signals, ladle wall temperature and position data in real time throughout the transportation process. The original weighing signal is corrected based on the tank wall temperature to obtain the first corrected weighing data. The initial corrected weighing data and location data are timestamped to generate a weight-location-time curve for the entire process of the molten iron ladle. Based on this curve, abnormal weight events, ladle lining erosion status, and molten iron temperature change trends during transportation are analyzed and identified. Based on the analysis and identification results during transportation, the weighing data after the initial correction is corrected again to output the final weight of molten iron.

Images