Flue gas temperature identification-based sintering end point prediction and control method and system

Abstract

The invention discloses a sintering end point prediction and control method and system based on flue gas temperature identification, and the method comprises the steps: generating a corresponding temperature curve through collecting flue gas temperature data and combustion temperature data of a plurality of monitoring points of a sintering machine in real time, fitting the curve through a combustion temperature rise model, and identifying a waste temperature rise point BRP; and then the frequency and / or the machine speed of the main exhaust fan are / is dynamically adjusted according to the deviation between the predicted value and the actual value. The system comprises a data acquisition module, a processing module and a control module, and can realize the functions of data acquisition, curve generation, end point prediction, parameter adjustment and the like. The problem that an existing method is inaccurate and lagged in prediction is solved, the sintering inflection point and the sintering end point can be accurately judged, the control precision is improved, the adaptability to equipment air leakage and raw material air permeability change is good, the sintering end point can be dynamically stable, and energy consumption and raw fuel loss can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of sintering production process control, and in particular to a sintering endpoint prediction and control method and system based on flue gas temperature recognition. Background Art

[0002] The position and accuracy of the sintering endpoint control are critical to ensuring the successful sintering of the mixed material on the sintering trolley. Poor endpoint control can lead to over- and under-burning, negatively impacting blast furnace operation, resulting in raw material and fuel losses and increasing overall plant energy consumption. Given the multivariable, time-delayed, and tightly coupled nature of the sintering process, the sintering endpoint control system is a typical fuzzy system. Specifically, these characteristics include: 1) the sintering endpoint position cannot be directly detected; 2) the success of sintering endpoint adjustment depends primarily on operator experience, and human judgment is often based on ambiguity; and 3) the inheritance of experience is subject to significant uncertainty.

[0003] Current sintering endpoint control typically uses the waste gas temperature rise point (BRP) to calculate the sintering endpoint position. Fuzzy logic control rules are then established to adjust parameters such as sintering machine speed, bellows opening, sintering machine trolley pressure ratio and thickness, and main exhaust fan load in real time to stabilize the sintering endpoint within the system's set range, thereby improving yield and fully utilizing the sintering area. However, the sintering process is subject to numerous interference factors and exhibits significant time lags. Adjusting the BRP requires adjusting operating parameters, and the interactions between these parameters can affect the stability of the sintering process. Therefore, during production, it is desirable to achieve a stable sintering process by adjusting relevant control parameters as little, as little, and as early as possible.

[0004] A search revealed patents related to sintering endpoint prediction and control technologies. For example, Chinese Patent Publication No. CN102540889A provides a sintering endpoint prediction method and system. This solution sets up data tracking queues corresponding to N preset temperatures, updates the queues based on the real-time exhaust gas temperature curve, and selects the valid queue to calculate the sintering endpoint prediction value in the rising area. While this approach improves prediction accuracy through multi-queue data tracking, it relies on the rationality of the preset temperature threshold and fails to fully consider the interference of dynamic factors such as equipment air leakage and raw material permeability on temperature data during the sintering process, which may lead to prediction errors.

[0005] CN107941010A provides a sintering endpoint position control method and system. This solution establishes fuzzy control rules for the main exhaust fan frequency, wind box damper opening, and machine speed. Using deviation values ​​E1 and E2 as the basis for regulation, endpoint control is achieved by primarily adjusting fan frequency and damper, supplemented by machine speed. This method features coordinated multi-parameter regulation, but the strong coupling between parameters during the sintering process can lead to mutual interference in regulation. Furthermore, the method does not correct for temperature data measurement errors (such as thermocouple failures), potentially affecting control accuracy.

[0006] Existing technologies rely on single temperature signatures (such as BRP) or multi-parameter fuzzy control. However, these technologies lack dynamic correction mechanisms for temperature data (e.g., thermocouple operating status and fitting results) and fail to fully address the problem of control lag during the sintering process. Furthermore, coordinated multi-parameter control can easily cause system oscillations, making precise control difficult under complex operating conditions. Summary of the Invention

[0007] The present invention provides a sintering endpoint prediction and control method and system based on flue gas temperature identification, aiming to solve the problem that the existing sintering endpoint prediction method cannot make accurate predictions and is relatively lagging. By considering multiple factors such as exhaust gas temperature, bellows pressure and mixture permeability, the measured exhaust gas temperature is corrected so that the temperature curve can more truly reflect the sintering process, and then the sintering inflection point and sintering endpoint can be more accurately judged, thereby improving the accuracy of sintering state control during the sintering process to ensure that the actual position of the sintering endpoint meets the set requirements.

[0008] To achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides a sintering endpoint prediction and control method based on flue gas temperature recognition, comprising the following steps: Collect flue gas temperature data from multiple monitoring points of the sintering machine in real time, generate a flue gas temperature curve that changes with time, and preliminarily predict the temperature, time, and position range of the sintering endpoint position (BTP); Based on the historical data of the sintering machine, the regional location and distribution characteristics of the sintering end point are preliminarily determined, the combustion temperature data of the sintering end point is collected in real time, and a combustion temperature curve that changes with time is generated; Through the combustion temperature rise model of the sintering material layer, the flue gas temperature curve and the combustion temperature curve are fitted to obtain the prediction curve and identify the waste temperature rise point BRP; According to the fitted prediction curve and the discarded temperature rise point BRP, the sintering end point position BTP is predicted twice; Compare the actual sintering endpoint position BTP with the deviation of the secondary predicted sintering endpoint position BTP from the preset expected position, and dynamically adjust the main exhaust fan frequency and / or speed of the sintering machine.

[0009] As a further optimization solution of the present invention, the combustion temperature rise model is: Where: m is the mass of solid fuel, c is the specific heat capacity of solid fuel, Qreaction is the heat released by the fuel per unit time, h is the convective heat transfer coefficient per unit area, A is the surface area of ​​the fuel, T is the temperature of the solid fuel, is the ambient temperature, λ is the thermal conductivity, is the time rate of change of temperature, is the thermal gradient; Where: T(t) is the instantaneous temperature of the object, T env is the ambient temperature, k is the cooling constant, and t is the time.

[0010] As a further optimization solution of the present invention, the temperature, time and position interval of the preliminary prediction of the sintering endpoint position BTP include: Identify the peak position in the flue gas temperature curve and accurately locate the maximum flue gas temperature, i.e. the temperature at the sintering endpoint (BTP), by setting an appropriate threshold. First, based on the specific working conditions of the sintering machine and the collected flue gas temperature curve, the time point and position range of the sintering end point position BTP are preliminarily determined. Then, by comparing the flue gas temperature changes at different positions and combining the distribution of airflow during the sintering process, the precise position range of the sintering end point position BTP is inferred.

[0011] As a further optimization scheme of the present invention, it also includes: classifying, cleaning and eliminating invalid data of historical temperature data, establishing the boundary of the sintering endpoint distribution range for abnormal data filtering.

[0012] As a further optimization solution of the present invention, the flue gas temperature data and the combustion temperature data are both measured using thermocouples, and the calculation formula for the waste temperature rise point BRP is: Waste gas temperature rising point BRP = (1-normal working coefficient of thermocouple × fitting effect coefficient) × BRP position of prediction curve + normal working coefficient of thermocouple × fitting effect coefficient × flue gas temperature slope change position.

[0013] As a further optimization solution of the present invention, the method for determining the actual sintering endpoint position BTP includes: Determine the position of the wind box with the highest current flue gas temperature and the flue gas temperatures of the 4-5 wind boxes before and after the wind box with the highest flue gas temperature; The temperature drop curve of the sintering material is fitted by the Newton's cooling law model to determine the actual position of the highest temperature, which is the actual sintering end point position BTP.

[0014] As a further optimization solution of the present invention, the method of comparing the deviation between the actual sintering endpoint position BTP and the secondary predicted sintering endpoint position BTP and the preset expected position, and dynamically adjusting the main exhaust fan frequency and / or speed of the sintering machine includes: Determine the difference between the actual sintering endpoint position BTP and the preset expected position. If it is ahead, reduce the main exhaust fan frequency. If it is behind, increase the main exhaust fan frequency. Determine the difference between the predicted sintering endpoint position BTP and the preset expected position. If they are consistent, the adjustment range of the previous adjustment process can be appropriately reduced or no adjustment can be made. If they are consistent with the adjustment expectation of the previous step, follow the control logic of the previous step.

[0015] As a further optimization solution of the present invention, the adjustment range of the main exhaust fan frequency is 38-48HZ, and the step value is 0.3HZ.

[0016] In a second aspect, the present invention provides a sintering endpoint prediction and control system, comprising: The data acquisition module is equipped with temperature sensors at multiple monitoring points of the sintering machine to collect real-time flue gas and sintering endpoint temperature data; The processing module is connected to the data acquisition module and is used to perform the following operations: (i) Based on the flue gas temperature data, a flue gas temperature curve that changes with time is generated, and the temperature, time, and position range of the sintering endpoint position (BTP) are preliminarily predicted; (ii) preliminarily determining the regional location and distribution characteristics of the sintering endpoint based on historical data from the sintering machine, collecting combustion temperature data at the sintering endpoint in real time, and generating a combustion temperature curve that changes over time; (iii) Using the combustion temperature rise model of the sintering material layer, the flue gas temperature curve and the combustion temperature curve are fitted to obtain a prediction curve and identify the waste temperature rise point (BRP); (iv) Secondary prediction of the sintering endpoint position (BTP) based on the fitted prediction curve and the discarded temperature rise point (BRP); The control module is connected to the processing module and is used to perform the following operations: (v) comparing the deviation between the actual sintering endpoint position BTP and the secondary predicted sintering endpoint position BTP and the preset expected position; (vi) Outputting adjustment instructions for the main exhaust fan frequency and / or sintering machine speed.

[0017] As a further optimization solution of the present invention, the control module includes a limiting and stepping unit for restricting the adjustment range and single adjustment step length of the main exhaust fan frequency.

[0018] The present invention provides a sintering endpoint prediction and control method and system based on flue gas temperature recognition, with the following specific beneficial effects: The present invention utilizes the combustion temperature rise model of the sintering material bed (including the mechanism formulas of heat release, convective heat transfer and thermal gradient influence) to fit the flue gas temperature and combustion temperature curves. By identifying the waste temperature rise point BRP and correcting the prediction curve in combination with the working state of the thermocouple, the secondary predicted sintering endpoint position BTP is made closer to the actual working conditions, reducing the error and hysteresis of single variable prediction.

[0019] This invention employs a dual-deviation coordinated adjustment strategy to simultaneously compare the deviation between the actual BTP and the predicted BTP and their preset positions. If the actual BTP deviates, the main exhaust fan frequency is immediately adjusted (reduced if ahead of schedule, increased if behind schedule). If the predicted BTP is consistent with expectations, the adjustment amplitude is reduced or maintained at the original setting, avoiding excessive intervention. This strategy implements "early, small, and minimal adjustment," minimizing the impact of multiple variable parameter adjustments on the sintering process and ultimately achieving dynamic stability at the sintering endpoint. Furthermore, the adjustment amplitude is adjusted based on the predicted deviation to avoid system oscillation caused by excessive parameter intervention.

[0020] This method dynamically corrects the calculation of the discarded temperature rise point (BRP) using the thermocouple operating coefficient and the fitting effect coefficient. This model incorporates sensor errors to avoid misjudgments caused by thermocouple drift or failure. Historical temperature data is categorized and cleaned, and the sintering endpoint distribution boundary is established. Invalid data generated by equipment anomalies (such as maintenance downtime) is automatically eliminated, improving data quality.

[0021] This invention uses the readily available and highly predictive inflection point temperature (BRP) as a predictor of the sintering endpoint. It employs a fuzzy control strategy to control this inflection point. This dynamic fuzzy control of the sintering endpoint achieves dynamic stabilization of the sintering endpoint. This effectively avoids inadequate sintering (under-sintering) or excessive sintering (over-sintering), reducing the return rate and fuel waste. It also enables online operational guidance, serving as a basis for automatic control, and offers accurate and real-time predictions.

[0022] The present invention has good adaptability to changes in the air leakage condition of the sintering machine equipment and changes in the air permeability of the raw materials. DETAILED DESCRIPTION

[0023] The following describes specific embodiments of the present invention to facilitate understanding by those skilled in the art. Obviously, the present invention is not limited to the scope of the specific embodiments. Those skilled in the art will recognize that as long as various variations are within the spirit and scope of the present invention as defined and established by the appended claims, such variations are non-inventive and all inventions and creations utilizing the inventive concept of this invention are protected.

[0024] Example 1 The embodiment of the present invention provides a sintering endpoint prediction and control method based on flue gas temperature recognition, comprising the following steps: 1. Based on the historical data of the sintering machine, roughly determine the regional location and distribution characteristics of the sintering end point.

[0025] The data distribution characteristics of the flue gas temperature in the sintering flue are searched from the historical database. Then, the sintering endpoints under various operating conditions of the sintering machine are analyzed based on the distribution of the red fire layer at the tail of the sintering machine. The limited range of the endpoint distribution is analyzed to avoid the system from abnormal endpoint judgment outside the normal operating range due to abnormal equipment data. The data analysis part includes: S1. Classify and process the original data. The sintering machine operating parameters include characteristic data of different frequencies, and the data needs to be classified and sorted.

[0026] S2. Clean, process and organize the data matched at different times according to the operating mechanism of the sintering machine.

[0027] S3. Clear invalid data generated during equipment maintenance or other downtime.

[0028] 2. Collect the flue gas temperature data of multiple monitoring points of the sintering machine in real time, generate a flue gas temperature curve that changes with time, and preliminarily predict the temperature, time and position range of the sintering end point position BTP.

[0029] The specific steps include: S1. Install temperature sensors (such as thermocouples) at different locations of the sintering machine to collect flue gas temperature data in real time and obtain curve data of flue gas temperature changes over time.

[0030] S2. Identify the peak position in the flue gas temperature curve and accurately locate the maximum value of the flue gas temperature, i.e. the temperature at the sintering endpoint (BTP), by setting an appropriate threshold. S3. First, combine the specific working conditions of the sintering machine and the collected flue gas temperature curve to preliminarily determine the time point and position range of the sintering end point position BTP. Then, by comparing the flue gas temperature changes at different positions and combining the distribution of airflow during the sintering process, infer the precise position range of the sintering end point position BTP.

[0031] 3. Based on the historical data of the sintering machine, the regional location and distribution characteristics of the sintering endpoint are preliminarily determined. The combustion temperature data at the sintering endpoint is collected in real time to generate a combustion temperature curve that changes over time. The logic is optimized and adjusted in combination with historical data to further improve control accuracy.

[0032] Fourth, through the sintering material layer combustion temperature rise model, the flue gas temperature curve and the combustion temperature curve are fitted to obtain a prediction curve. The location where the flue gas temperature rises is identified as the waste temperature rise point BRP, and then the position of the combustion rise point BRP is corrected based on the experience.

[0033] The combustion temperature rise model is: Where: m is the mass of the solid fuel, c is the specific heat capacity of the solid fuel, Qreaction is the heat released by the fuel per unit time (for example, the heat released from the combustion reaction), h is the convective heat transfer coefficient per unit area, A is the surface area of ​​the fuel, T is the solid fuel temperature, is the ambient temperature, λ is the thermal conductivity, is the time rate of change of temperature, is the thermal gradient (i.e., the rate of temperature change). Where: T(t) is the instantaneous temperature of the object, in °C or K; T env is the ambient temperature, with the same unit as T(t); k is the cooling constant, a positive value related to the properties of the object and the heat transfer characteristics of the surrounding medium, with the unit of S -1 ; t is time, in seconds (s).

[0034] The flue gas temperature data and the combustion temperature data are both measured using thermocouples. The calculation formula for the waste gas temperature rise point (BRP) is: Waste gas temperature rise point (BRP) = (1 - thermocouple normal operation coefficient × fitting effect coefficient) × predicted curve BRP position + thermocouple normal operation coefficient × fitting effect coefficient × flue gas temperature slope change position. This formula primarily incorporates the uncertainty of the thermocouple into the prediction system, eliminating measurement errors.

[0035] Fifth, based on the fitted prediction curve and the discarded temperature rise point (BRP), the sintering endpoint position (BTP) is predicted again. Combined with the actual on-site operating process, the approximate sintering endpoint position is determined. The time from the BRP to the BTP is approximately one hour of running time on the strand sintering machine. This allows the sintering endpoint position to be predicted one hour in advance, allowing for early adjustments to optimize production results.

[0036] 6. Compare the deviation between the actual sintering endpoint position BTP and the secondary predicted sintering endpoint position BTP and the preset expected position, and dynamically adjust the main exhaust fan frequency and / or speed of the sintering machine.

[0037] The methods for determining the actual sintering endpoint position BTP include: S1. Determine the location of the windbox with the highest current flue gas temperature and the flue gas temperatures of the 4-5 windboxes before and after the windbox with the highest flue gas temperature. S2. Fit the sintering material temperature drop curve through the Newton's cooling law model to determine the actual position of the highest temperature, which is the actual sintering endpoint position BTP.

[0038] The sintering endpoint temperature is the highest temperature in the sintering process. The way to judge the highest temperature is that if the latter temperature is lower than the previous temperature, then the previous temperature is the endpoint temperature.

[0039] The actual operation on site is combined with the predicted sintering endpoint and the current actual endpoint to adjust the important operating parameters of the sintering machine. The adjustment logic is as follows: S1. Determine the difference between the actual sintering endpoint position BTP and the preset expected position. If it is earlier, reduce the main exhaust fan frequency. If it is later, increase the main exhaust fan frequency.

[0040] S2. Determine the difference between the predicted sintering endpoint position BTP and the preset expected position. If they are consistent, the adjustment range of the previous adjustment process can be appropriately reduced or no adjustment is made. If they are consistent with the adjustment expectation of the previous step, follow the control logic of the previous step.

[0041] By setting the adjustment range and step value of the main exhaust fan frequency, it is possible to avoid system oscillation or unstable operation caused by excessive adjustment amplitude.

[0042] Example 2 Based on a general inventive concept, an embodiment of the present invention provides a sintering endpoint prediction and control system, including: The data acquisition module is equipped with temperature sensors at multiple monitoring points of the sintering machine to collect real-time flue gas and sintering endpoint temperature data; The processing module is connected to the data acquisition module and is used to perform the following operations: (i) Based on the flue gas temperature data, a flue gas temperature curve that changes with time is generated, and the temperature, time, and position range of the sintering endpoint position (BTP) are preliminarily predicted; (ii) preliminarily determining the regional location and distribution characteristics of the sintering endpoint based on historical data from the sintering machine, collecting combustion temperature data at the sintering endpoint in real time, and generating a combustion temperature curve that changes over time; (iii) Using the combustion temperature rise model of the sintering material layer, the flue gas temperature curve and the combustion temperature curve are fitted to obtain a prediction curve and identify the waste temperature rise point (BRP); (iv) Secondary prediction of the sintering endpoint position (BTP) based on the fitted prediction curve and the discarded temperature rise point (BRP); The control module is connected to the processing module and is used to perform the following operations: (v) comparing the deviation between the actual sintering endpoint position BTP and the secondary predicted sintering endpoint position BTP and the preset expected position; (vi) Outputting adjustment instructions for the main exhaust fan frequency and / or sintering machine speed.

[0043] The control module includes a limiter and stepper unit, which constrains the frequency adjustment range and single adjustment step size of the main exhaust fan. The adjustment range is 38-48 Hz, with a step size of 0.3 Hz. Through the collaborative design of data acquisition, processing, and control modules, the entire process, from real-time temperature data acquisition, curve generation, model fitting, to parameter adjustment, is fully automated. For example, the processing module completes predictions in four steps, and the control module outputs precise adjustment instructions.

[0044] In summary, the present invention achieves the stability and efficiency of the production process while improving the prediction accuracy of the sintering endpoint through the fusion of multiple technologies such as model mechanism, data processing, dynamic control and system integration, and has a significant effect on reducing energy consumption and reducing over-burning / under-burning phenomena.

[0045] The present invention has been described in detail above with reference to the embodiments. However, the present invention is not limited to the embodiments described above. Various modifications may be made within the scope of knowledge possessed by a person skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof may be combined with each other unless there is a conflict.

Claims

1. A sintering endpoint prediction and control method based on flue gas temperature recognition, comprising the following steps: Collect flue gas temperature data from multiple monitoring points of the sintering machine in real time, generate a flue gas temperature curve that changes with time, and preliminarily predict the temperature, time, and position range of the sintering endpoint position (BTP); Based on the historical data of the sintering machine, the regional location and distribution characteristics of the sintering end point are preliminarily determined, the combustion temperature data of the sintering end point is collected in real time, and a combustion temperature curve that changes with time is generated; Through the combustion temperature rise model of the sintering material layer, the flue gas temperature curve and the combustion temperature curve are fitted to obtain the prediction curve and identify the waste temperature rise point BRP; According to the fitted prediction curve and the discarded temperature rise point BRP, the sintering end point position BTP is predicted twice; Compare the actual sintering end point position BTP with the deviation of the secondary predicted sintering end point position BTP from the preset expected position, and dynamically adjust the main exhaust fan frequency and / or speed of the sintering machine.

2. The sintering endpoint prediction and control method based on flue gas temperature recognition according to claim 1 is characterized in that: The combustion temperature rise model is: Where: m is the mass of solid fuel, c is the specific heat capacity of solid fuel, Qreaction is the heat released by the fuel per unit time, h is the convective heat transfer coefficient per unit area, A is the surface area of ​​the fuel, T is the temperature of the solid fuel, is the ambient temperature, λ is the thermal conductivity, is the time rate of change of temperature, is the thermal gradient; Where: T(t) is the instantaneous temperature of the object, T env is the ambient temperature, k is the cooling constant, and t is the time.

3. The sintering endpoint prediction and control method based on flue gas temperature recognition according to claim 1 is characterized in that: The temperature, time and position intervals for the preliminary prediction of the sintering endpoint position BTP include: Identify the peak position in the flue gas temperature curve and accurately locate the maximum flue gas temperature, i.e. the temperature at the sintering endpoint (BTP), by setting an appropriate threshold. First, based on the specific working conditions of the sintering machine and the collected flue gas temperature curve, the time point and position range of the sintering end point position BTP are preliminarily determined. Then, by comparing the flue gas temperature changes at different positions and combining the distribution of airflow during the sintering process, the precise position range of the sintering end point position BTP is inferred.

4. The sintering endpoint prediction and control method based on flue gas temperature recognition according to claim 1 is characterized in that: Also includes: The historical temperature data is classified, cleaned and invalid data is eliminated, and the sintering endpoint distribution range boundary is established for abnormal data filtering.

5. The sintering endpoint prediction and control method based on flue gas temperature recognition according to claim 1 is characterized in that: The flue gas temperature data and combustion temperature data are both measured using thermocouples. The calculation formula for the waste temperature rise point (BRP) is: Waste gas temperature rising point BRP = (1-normal working coefficient of thermocouple × fitting effect coefficient) × BRP position of prediction curve + normal working coefficient of thermocouple × fitting effect coefficient × flue gas temperature slope change position.

6. The sintering endpoint prediction and control method based on flue gas temperature recognition according to claim 1 is characterized in that: The method for determining the actual sintering endpoint position BTP includes: Determine the position of the wind box with the highest current flue gas temperature and the flue gas temperatures of the 4-5 wind boxes before and after the wind box with the highest flue gas temperature; The temperature drop curve of the sintering material is fitted by the Newton's cooling law model to determine the actual position of the highest temperature, which is the actual sintering end point position BTP.

7. The sintering endpoint prediction and control method based on flue gas temperature recognition according to claim 1 is characterized in that: The comparing the deviation between the actual sintering endpoint position BTP and the secondary predicted sintering endpoint position BTP and the preset expected position, and dynamically adjusting the main exhaust fan frequency and / or speed of the sintering machine includes: Determine the difference between the actual sintering endpoint position BTP and the preset expected position. If it is ahead, reduce the main exhaust fan frequency. If it is behind, increase the main exhaust fan frequency. Determine the difference between the predicted sintering endpoint position BTP and the preset expected position. If they are consistent, the adjustment range of the previous adjustment process can be appropriately reduced or no adjustment can be made. If they are consistent with the adjustment expectation of the previous step, follow the control logic of the previous step.

8. The sintering endpoint prediction and control method based on flue gas temperature recognition according to claim 7 is characterized in that: The adjustment range of the main exhaust fan frequency is 38-48HZ, and the step value is 0.3HZ.

9. A sintering endpoint prediction and control system, characterized in that: include: The data acquisition module is equipped with temperature sensors at multiple monitoring points of the sintering machine to collect real-time flue gas and sintering endpoint temperature data; The processing module is connected to the data acquisition module and is used to perform the following operations: (i) Based on the flue gas temperature data, a flue gas temperature curve that changes with time is generated, and the temperature, time, and position range of the sintering endpoint position (BTP) are preliminarily predicted; (ii) preliminarily determining the regional location and distribution characteristics of the sintering endpoint based on historical data from the sintering machine, collecting combustion temperature data at the sintering endpoint in real time, and generating a combustion temperature curve that changes over time; (iii) Using the combustion temperature rise model of the sintering material layer, the flue gas temperature curve and the combustion temperature curve are fitted to obtain a prediction curve and identify the waste temperature rise point (BRP); (iv) Secondary prediction of the sintering endpoint position (BTP) based on the fitted prediction curve and the discarded temperature rise point (BRP); The control module is connected to the processing module and is used to perform the following operations: (v) comparing the deviation between the actual sintering endpoint position BTP and the secondary predicted sintering endpoint position BTP and the preset expected position; (vi) Outputting adjustment instructions for the main exhaust fan frequency and / or sintering machine speed.

10. The sintering endpoint prediction and control system according to claim 9, characterized in that: The control module includes a limiting and stepping unit for restricting the adjustment range and single adjustment step length of the main exhaust fan frequency.

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