Blast furnace temperature real-time monitoring and control system and method and blast furnace temperature prediction method

Abstract

The invention provides a blast furnace temperature real-time monitoring and control system, which comprises a sensing transmission unit used for acquiring a temperature signal through an optical fiber sensing unit and transmitting the temperature signal to a data acquisition and preprocessing module through an optical fiber; the data acquisition and preprocessing module is used for receiving the temperature signal, preprocessing the temperature signal to obtain temperature data and sending the temperature data to the furnace temperature monitoring and visualization module; the furnace temperature monitoring and visualization module is used for receiving the temperature data and displaying the temperature data in a visual form; and the furnace temperature control module is used for receiving the temperature data and controlling the blast furnace equipment through a fuzzy PID control algorithm according to the temperature data. The invention further provides a corresponding device and a blast furnace temperature prediction method. According to the invention, high-precision real-time measurement of the furnace temperature of the blast furnace can be realized, and precise control of blast furnace equipment can be realized.

Description

Technical Field

[0001] This application relates to the technical field of monitoring and control of blast furnace ironmaking process, and in particular to a real-time monitoring and control system, method and method for blast furnace temperature and a method for predicting blast furnace temperature. Background Technology

[0002] The blast furnace is the core equipment in steel production, and the stability of its temperature directly affects the quality of molten iron, energy consumption, and production safety. As blast furnace ironmaking technology develops towards larger scale, higher efficiency, and greater intelligence, higher demands are placed on the precise sensing and real-time control of the production process.

[0003] Currently, traditional blast furnace temperature monitoring mostly uses thermocouple temperature measurement. Some studies have also attempted to incorporate infrared temperature measurement and acoustic temperature measurement technologies. However, due to the complex environment inside the blast furnace, including high temperatures, dust, and corrosive gases, existing technologies generally suffer from the following drawbacks: On the one hand, existing temperature measurement technologies struggle to balance response speed and measurement accuracy, making it impossible to accurately capture rapid dynamic changes in temperature within the blast furnace. Contact-based temperature measurement methods, such as thermocouples, have a measurement lag of 3-5 minutes, failing to meet the timeliness requirements of real-time control.

[0004] On the other hand, existing temperature measurement technologies have significant shortcomings in terms of measurement accuracy and anti-interference capability under complex working conditions. For example, thermocouple measurement is easily affected by electromagnetic interference, which can lead to data deviation. Its measurement accuracy is significantly affected by factors such as material oxidation and thermoelectric characteristic drift, and its service life is short, requiring frequent replacement.

[0005] On the other hand, control strategies based on existing temperature measurement technologies often rely on lagging furnace temperature measurements, making it difficult to achieve precise control of the blast furnace's thermal state.

[0006] Therefore, the traditional blast furnace temperature monitoring methods suffer from problems such as slow response, low measurement accuracy, and weak anti-interference ability, making it difficult to meet the real-time control requirements of blast furnace production. How to provide a real-time monitoring and control system for blast furnace temperature that can perform high-precision real-time measurement of blast furnace temperature and achieve precise control of blast furnace equipment is a technical problem that needs to be solved. Summary of the Invention

[0007] In view of the above-mentioned problems of the prior art, this application provides a real-time monitoring and control system for blast furnace temperature, which can perform high-precision real-time measurement of blast furnace temperature and realize precise control of blast furnace equipment.

[0008] To achieve the above objectives, the first aspect of this application provides a real-time monitoring and control system for blast furnace temperature, comprising: The sensing transmission unit is used to acquire temperature signals through the fiber optic sensing unit and transmit the temperature signals to the data acquisition and preprocessing module through the fiber optic cable. The data acquisition and preprocessing module is used to receive the temperature signal, preprocess it to obtain temperature data, and send it to the furnace temperature monitoring and visualization module. The furnace temperature monitoring and visualization module is used to receive the temperature data and display the temperature data in a visual form; The furnace temperature control module is used to receive the temperature data and control the blast furnace equipment according to the temperature data through a fuzzy PID control algorithm.

[0009] As described above, by acquiring temperature signals through high-temperature resistant, interference-resistant, and corrosion-resistant fiber optic sensing units, the accuracy deficiencies of existing temperature measurement technologies are overcome. This technology demonstrates excellent performance, particularly in high-temperature and harsh environment monitoring, enabling high-precision real-time measurement of blast furnace temperature. Given the complex spatiotemporal distribution characteristics of blast furnace temperature, fuzzy PID control is used to achieve precise control of the blast furnace equipment, preventing amplified temperature fluctuations due to untimely adjustments.

[0010] As one possible implementation of the first aspect, it also includes: a furnace temperature prediction module, which is used to call the temperature data from the data acquisition and preprocessing module, and based on the temperature data, use an autoregressive integral moving average model to perform fitting calculations to obtain the predicted furnace temperature value at future times; The predicted furnace temperature is used to assess the operating status of the blast furnace or, together with the temperature data, serves as input to the fuzzy PID control algorithm.

[0011] As shown above, due to the significant temperature differences along the height of the blast furnace, the hearth region has the highest and most stable temperature (1450-1550℃), while the blocky zone has a lower temperature (800-1000℃). The temperature near the furnace wall is low, while the temperature at the furnace center is high; therefore, the blast furnace temperature exhibits a gradient distribution within the furnace. Furthermore, the furnace temperature is also affected by factors such as raw materials, blast air supply, and charging, resulting in dynamic fluctuations with a fluctuation period of 30-60 minutes and an amplitude of 20-40℃. Therefore, using the aforementioned autoregressive integral moving average model to fit and calculate the blast furnace temperature curve can yield more accurate predicted furnace temperature values. By knowing the temperature change trend in advance, the current blast furnace operating status can be analyzed and evaluated, providing users with early warning or optimization information. Simultaneously, the predicted furnace temperature can also be used to incorporate fuzzy PID control algorithms, enabling control actions to begin before substantial temperature deviations occur, effectively reducing control lag.

[0012] As one possible implementation of the first aspect, the predicted furnace temperature is fitted and calculated according to the following formula: ; in, Indicates the future Predicted furnace temperature at any given time; They represent the current time. , the previous moment ,...,forward The actual furnace temperature value at any given time, i.e., the temperature data; The autoregressive coefficients are obtained by fitting the above formula; They represent the current time. , the previous moment ,...,forward The deviation between the predicted furnace temperature and the actual furnace temperature at any given time; The moving average coefficient is obtained by fitting the above formula; The constant term is obtained by fitting the above formula.

[0013] As described above, by employing the autoregressive integral moving average model to perform set calculations on the predicted furnace temperature values, the linear trend and periodic fluctuations in the blast furnace temperature data are captured, ensuring the accuracy and reliability of the prediction results.

[0014] As one possible implementation of the first aspect, the optical fiber sensing unit is arranged in a multi-point layer according to the internal region of the blast furnace, which includes: hearth region, dripping zone, softening zone, and blocky zone.

[0015] As shown above, by deploying the technology in layers and multiple points in different process areas, the spatiotemporal distribution characteristics of temperature inside the blast furnace can be fully captured, overcoming the limitations of traditional single-point temperature measurement and providing a rich and comprehensive data source for the entire system.

[0016] As one possible implementation of the first aspect, the optical fiber sensing unit adopts a Raman scattering optical fiber temperature sensor; the optical fiber is a high-temperature resistant quartz optical fiber, and the outer layer is wrapped with a corrosion-resistant stainless steel protective sleeve.

[0017] As described above, distributed temperature measurement using Raman scattering fiber optic temperature sensors, compared to other fiber optic sensing technologies, offers advantages such as a wide temperature range (-200-1700℃), resistance to electromagnetic interference, dust, and corrosive gases, high accuracy (±0.5℃), and fast response (less than 0.5 seconds), meeting the requirements for blast furnace temperature monitoring. The use of high-temperature resistant quartz optical fibers and stainless steel protective sleeves ensures stable signal transmission, guaranteeing the entire monitoring system's ability to adapt to the harsh operating conditions of the blast furnace and achieve long-term, maintenance-free stable operation.

[0018] As one possible implementation of the first aspect, the fuzzy PID control algorithm uses the following formula for control: ; in, The adjustment amount of the hardware controlling the blast furnace equipment at time t; for The deviation between the actual furnace temperature and the preset furnace temperature at any given time; The proportionality coefficient is adjusted using fuzzy rules. The integral time constant is adjusted using fuzzy rules; The differential time constant is adjusted using fuzzy rules.

[0019] Therefore, by introducing fuzzy control rules on the basis of traditional PID control, the dynamic adjustment of PID parameters by fuzzy inference can improve control accuracy and response speed. When faced with different magnitudes of deviations and rates of change, it can automatically achieve the best balance between response speed and stability.

[0020] The second aspect of this application provides a method for real-time monitoring and control of blast furnace temperature, implemented through the system described in the first aspect, including the following steps: Temperature signals are acquired through an optical fiber sensing unit and transmitted to a data acquisition and preprocessing module via optical fiber. Temperature data is obtained after preprocessing the temperature signal; The temperature data is displayed in a visual format; The blast furnace equipment is controlled using a fuzzy PID control algorithm based on the temperature data.

[0021] As a possible implementation of the second aspect, it also includes: Based on the temperature data, the furnace temperature prediction value at future times is obtained by fitting and calculating using an autoregressive integral moving average model. The predicted furnace temperature is used to assess the operating status of the blast furnace or, together with the temperature data, serves as input to the fuzzy PID control algorithm.

[0022] The third aspect of this application provides a method for predicting blast furnace temperature, which involves collecting and preprocessing blast furnace internal temperature data using the system described in the first aspect. Based on the temperature data, the furnace temperature prediction value at future times is obtained by fitting and calculating using an autoregressive integral moving average model. The predicted furnace temperature is used to assess the blast furnace's operating status or to provide a basis for decision-making regarding blast furnace temperature control.

[0023] The fourth aspect of this application provides a computing device, including: a processor and a memory storing program instructions thereon, wherein when executed by the processor, the program instructions cause the processor to perform the real-time monitoring and control method for blast furnace temperature according to any one of the second aspects, or the prediction method for blast furnace temperature according to any one of the third aspects.

[0024] The fifth aspect of this application provides a computer-readable storage medium having program instructions stored thereon, which, when executed by a computer, cause the computer to perform the real-time monitoring and control method for blast furnace temperature according to any one of the second aspects, or the blast furnace temperature prediction method according to any one of the third aspects.

[0025] The sixth aspect of this application provides a computer program product, which includes program instructions that, when executed by a computer, cause the computer to perform the real-time monitoring and control method for blast furnace temperature as described in any of the second aspects, or the blast furnace temperature prediction method as described in any of the third aspects. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the real-time monitoring and control system for blast furnace temperature provided in the first embodiment of this application; Figure 2a This is a schematic diagram of the real-time monitoring and control system for blast furnace temperature provided in the second embodiment of this application; Figure 2b This is a schematic diagram of the deployment of the fiber optic sensing unit provided in the second embodiment of this application; Figure 3 This is a flowchart of the real-time monitoring and control method for blast furnace temperature provided in the third embodiment of this application; Figure 4 This is a flowchart of the blast furnace temperature prediction method provided in the fourth embodiment of this application; Figure 5 This is a schematic structural diagram of a computing device provided in an embodiment of this application.

[0027] It should be understood that the dimensions and shapes of the blocks in the above structural diagrams are for reference only and should not constitute an exclusive interpretation of the embodiments of the present invention. The relative positions and inclusion relationships between the blocks presented in the structural diagrams are only schematic representations of the structural relationships between the blocks, and are not intended to limit the physical connection methods of the embodiments of the present invention. Detailed Implementation

[0028] The technical solutions provided in this application will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the system architecture and business scenarios provided in the embodiments of this application are mainly for illustrating possible implementations of the technical solutions of this application and should not be construed as the sole limitation on the technical solutions of this application. Those skilled in the art will recognize that the technical solutions provided in this application are equally applicable to similar technical problems as system architectures evolve and new business scenarios emerge.

[0029] It should be understood that the real-time monitoring and control scheme for blast furnace temperature provided in this application includes a real-time monitoring and control system, method, computing device, computer-readable storage medium, computer program product, and a method, computing device, computer-readable storage medium, and computer program product for predicting blast furnace temperature. Since these technical solutions solve problems based on the same or similar principles, some repetitive details may not be repeated in the following descriptions of specific embodiments. However, it should be considered that these specific embodiments have mutual references and can be combined with each other.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. In case of any inconsistency, the meaning set forth in this specification or derived from the content described herein shall prevail. Furthermore, the terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application. To accurately describe the technical content of this application and to accurately understand the invention, the following explanations or definitions of the terms used in this specification are provided before describing specific embodiments: 1) Autoregressive Integrated Moving Average Model (ARIMA): This is a classic time series forecasting model, especially suitable for processing non-stationary time series data. This model transforms non-stationary series into stationary series through differencing operations, and then combines autoregressive (AR) and moving average (MA) components to capture the inherent patterns in the data.

[0031] 2) Fuzzy PID Algorithm (Fuzzy Proportional-Integral-Derivative Control Algorithm): This is an advanced control strategy that combines fuzzy logic theory with conventional PID control. The algorithm converts precise quantities such as system deviation and its rate of change into fuzzy quantities, and then performs inference based on preset fuzzy rules, thereby dynamically and nonlinearly adjusting the proportional, integral, and derivative parameters of the PID controller.

[0032] 3) B / S Architecture (Browser / Server): In this architecture, users do not need to install special client software. They only need to access the application interface and interact with the server through a web browser, while the main business logic and data processing are completed on the server side.

[0033] The blast furnace temperature real-time monitoring and control scheme provided in this application can acquire temperature signals through an optical fiber sensing unit, obtain temperature data after preprocessing, and control the blast furnace equipment based on the temperature data using a fuzzy PID control algorithm. This method provides a blast furnace temperature real-time monitoring and control system that can perform high-precision real-time measurement of blast furnace temperature and achieve precise control of blast furnace equipment. This application embodiment can be applied to blast furnace ironmaking process control and optimization scenarios in various fields of iron and steel smelting, industrial process automation, and intelligent manufacturing. The embodiments of this application are described in detail below with reference to the accompanying drawings.

[0034] The first embodiment of this application provides a real-time monitoring and control system for blast furnace temperature, which will be described below in conjunction with... Figure 1 The implementation of each component of the system will be described in detail.

[0035] The sensing and transmission unit is used to acquire temperature signals through the fiber optic sensing unit and transmit the temperature signals to the data acquisition and preprocessing module through the fiber optic cable.

[0036] In some embodiments, the fiber optic sensing unit may employ fiber optic temperature sensors such as fiber optic grating sensors or Brillouin scattering fiber optic sensors.

[0037] Preferably, the fiber optic sensing unit is a Raman scattering fiber optic temperature sensor; the fiber is a high-temperature resistant quartz fiber, and the outer layer is wrapped with a corrosion-resistant stainless steel protective sleeve.

[0038] In some embodiments, the fiber optic sensing unit and the optical fiber employ a transmission scheme combining an industrial-grade fiber optic transceiver and shielded twisted-pair cable. The temperature signal collected by the fiber optic sensing unit is transmitted via high-temperature resistant silica optical fiber to the fiber optic transceiver outside the furnace, and then transmitted to the data storage hardware via the shielded twisted-pair cable. The data storage hardware is used to store historical temperature signal data for subsequent data analysis and tracing.

[0039] In some embodiments, the fiber optic sensing units are arranged in multiple layers according to the internal region of the blast furnace, which includes: the hearth region, the dripping zone, the softening zone, and the blocky zone.

[0040] In some embodiments, the fiber optic sensing unit is installed by means of pre-embedding, sleeve implantation, anchor welding, etc. This application does not limit the installation method of the fiber optic sensing unit.

[0041] In some embodiments, the data acquisition and preprocessing module reads data from the sensing and transmission unit at fixed acquisition intervals. Further, the sampling interval can be configured differently according to the dynamic characteristics of different process zones in the blast furnace. For example, in the hearth zone and reaction zone where temperature fluctuations are more severe, the sampling interval can be set to 1-2 seconds to capture rapid temperature changes; in the upper part of the relatively stable blocky zone, the sampling interval can be set to 5-10 seconds to balance the amount of data and the system processing load.

[0042] In some embodiments, the system further includes: a furnace temperature prediction module, configured to retrieve the temperature data from the data acquisition and preprocessing module, and based on the temperature data, perform fitting calculations using an autoregressive integral moving average model to obtain a predicted furnace temperature value for future times; the predicted furnace temperature value is used to evaluate the operating status of the blast furnace or, together with the temperature data, serves as the input to the fuzzy PID control algorithm.

[0043] In some embodiments, the predicted furnace temperature is fitted and calculated according to the following formula: ; in, Indicates the future Predicted furnace temperature at any given time; They represent the current time. , the previous moment ,...,forward The actual furnace temperature value at any given time, i.e., the temperature data; The autoregressive coefficients are obtained by fitting the above formula; They represent the current time. , the previous moment ,...,forward The deviation between the predicted furnace temperature and the actual furnace temperature at any given time; The moving average coefficient is obtained by fitting the above formula; The constant term is obtained by fitting the above formula.

[0044] Preferably, the That is, the temperature interval is 5 minutes, but understandably, the stated Other time lengths, such as 3 minutes, 10 minutes, or 15 minutes, can be flexibly set by those skilled in the art based on the actual operating conditions and control requirements of the blast furnace.

[0045] In some embodiments, the autoregressive integral moving average model can incorporate key process parameters of blast furnace operation, such as cold blast flow rate, hot blast temperature, and pulverized coal injection rate, to capture the driving influence of external operations on furnace temperature, thereby improving prediction accuracy.

[0046] In some embodiments, the order of the autoregressive integral moving average model can be adaptively adjusted periodically. Based on historical data from the most recent period, the optimal model parameters are automatically re-evaluated and determined, enabling the prediction model to adapt to the dynamic characteristics changes of different production stages such as blast furnace overhaul and stabilization periods.

[0047] The data acquisition and preprocessing module is used to receive the temperature signal, perform preprocessing to obtain temperature data, and send it to the furnace temperature monitoring and visualization module.

[0048] In some embodiments, the data acquisition and preprocessing module performs at least one of the following preprocessing steps on the temperature data: data cleaning, noise reduction filtering, data alignment, data compensation, and data normalization.

[0049] In some embodiments, the temperature data obtained after preprocessing is also stored in data storage hardware for subsequent data analysis and traceability.

[0050] The furnace temperature monitoring and visualization module is used to receive the temperature data and display the temperature data in a visual form.

[0051] In some embodiments, blast furnace operators can access the furnace temperature monitoring and visualization module through a client; preferably, a B / S architecture is adopted, and blast furnace operators can access it through a browser, which is easy to operate.

[0052] In some embodiments, the furnace temperature monitoring and visualization module displays the temperature data, the temperature data and operating status of the fiber optic sensing unit, the two-dimensional / three-dimensional model of the blast furnace and the (color) distribution of the temperature data on it, etc.

[0053] In some embodiments, the predicted furnace temperature is sent by the sensing and transmission unit to the furnace temperature monitoring and visualization module for visualization and display to the user. The predicted furnace temperature can be compared with a preset threshold or range. When the furnace temperature exceeds the threshold or range, a warning message will be displayed to the user to indicate that there is a furnace temperature risk at the time of the predicted furnace temperature.

[0054] In some embodiments, the furnace temperature monitoring and visualization module further includes a control component for displaying the control strategy of the furnace temperature control module, and for interacting with the blast furnace equipment by human control and sending control commands.

[0055] The furnace temperature control module is used to receive the temperature data and control the blast furnace equipment according to the temperature data through a fuzzy PID control algorithm.

[0056] In some embodiments, the fuzzy PID control algorithm uses the following formula for control: ; in, The adjustment amount of the hardware controlling the blast furnace equipment at time t; for The deviation between the actual furnace temperature and the preset furnace temperature at any given time; The proportionality coefficient is adjusted using fuzzy rules. The integral time constant is adjusted using fuzzy rules; The differential time constant is adjusted using fuzzy rules.

[0057] In some embodiments, the fuzzy rules are based on ,Right now The deviation between the actual furnace temperature value and the preset furnace temperature value at any given time is adjusted; the fuzzy rule can also be adjusted based on at least one of the rate of change of the deviation, the predicted furnace temperature value, or the blast furnace operating process parameters.

[0058] In some embodiments, the fuzzy PID control algorithm also incorporates a predicted furnace temperature value for control, as shown in the following formula: ; in, Represents the future at time t. The deviation between the predicted furnace temperature and the preset furnace temperature at any given time; The prediction coefficients can be a preset constant or based on... Fuzzy rule adjustment, for example when hour, Set to 1.0 to quickly suppress anticipated large temperature fluctuations; when hour, Taking 0.3, mainly through... Adjust the control items accordingly.

[0059] In some embodiments, the control commands generated by the furnace temperature control module are sent to the control execution hardware for regulating the blast furnace equipment, which includes at least one of the following: a hot blast regulating valve, an oxygen flow controller, and a pulverized coal injection controller. The hot blast regulating valve regulates the temperature and flow rate of the hot blast used to heat the blast furnace; the oxygen flow controller adjusts the oxygen content to optimize combustion efficiency, thereby regulating the furnace temperature; and the pulverized coal injection controller adjusts the pulverized coal injection rate to change the heat input within the furnace. All control execution hardware is connected to the software system via an industrial Ethernet network, supporting real-time reception and execution of control commands.

[0060] The second embodiment of this application provides a real-time monitoring and control system for blast furnace temperature. The following will refer to... Figure 2a The diagram shown is used for illustration.

[0061] Blast furnaces are the core equipment in steel production. Currently, traditional blast furnace temperature monitoring mostly uses thermocouple temperature measurement. This method is affected by the complex environment inside the blast furnace, such as high temperature, dust, and corrosive gases. It has problems such as a measurement lag time of up to 3-5 minutes, susceptibility to electromagnetic interference leading to data deviation, and short service life requiring frequent replacement.

[0062] Traditional blast furnace temperature monitoring methods suffer from problems such as slow response, low measurement accuracy, and weak anti-interference capabilities, making it difficult to meet the real-time control requirements of blast furnace production. This application employs fiber optic sensing technology to achieve precise monitoring and control of blast furnace temperature based on its spatiotemporal distribution characteristics.

[0063] The blast furnace temperature monitoring and control system described in this application consists of a hardware system and a software system. The hardware system includes an optical fiber sensing unit, a (transmission) optical fiber, data transmission hardware, and control execution hardware. The software system includes a data acquisition and preprocessing module, a furnace temperature monitoring and visualization module, a furnace temperature prediction module, and a furnace temperature control module. The optical fiber sensing unit and the (transmission) optical fiber together constitute the sensing and transmission unit.

[0064] The fiber optic sensing unit uses a Raman scattering fiber optic temperature sensor. Based on Raman scattering fiber optic sensing technology, it is suitable for a wide temperature range (-200-1700℃), is resistant to electromagnetic interference, dust and corrosive gases, has high accuracy (±0.5℃), and fast response speed (less than 0.5 seconds), meeting the requirements for blast furnace temperature monitoring.

[0065] Due to the complex spatiotemporal distribution characteristics of blast furnace temperature, the temperature varies significantly along the height direction, with the highest and most stable temperature in the hearth region (1450-1550℃), and lower temperatures in the blocky zone (800-1000℃). Temperatures are lower near the furnace wall and higher at the furnace center, forming a temperature gradient. Therefore, sensors are deployed in a multi-point, layered manner. Eight sensing points are evenly distributed along the circumference of hearth region 1, each penetrating 30cm into the hearth wall to monitor the temperature of the core area. In the dripping zone 2 and softening zone 3, a layer of four sensing points is arranged every 1.5m along the blast furnace height, for a total of six layers, to monitor temperature changes in the upper and middle parts of the furnace. Three sensing points are arranged radially in the blocky zone, located near the furnace wall to monitor the radial temperature gradient. Figure 2b As shown. (wherein) The (transmission) optical fiber uses high-temperature resistant quartz optical fiber, and is wrapped with a corrosion-resistant stainless steel protective sleeve to prevent the optical fiber from being damaged by high-temperature dust and corrosive gases, ensuring the long-term stable operation of the sensing unit.

[0066] The data transmission hardware employs an industrial-grade fiber optic transceiver combined with shielded twisted-pair cables. Temperature signals collected by the fiber optic sensing unit are transmitted via quartz fiber to the fiber optic transceiver outside the furnace, and then the shielded twisted-pair cables transmit the signals to the data storage hardware. The data storage hardware uses an industrial-grade server, configured with a 2TB SSD and a 16TB HDD. The SSD stores the real-time furnace temperature data to meet real-time retrieval requirements, while the HDD stores historical data for subsequent data analysis and traceability.

[0067] The control execution hardware is responsible for receiving control commands generated by the software system. This part mainly includes the hot air regulating valve, oxygen flow controller, and pulverized coal injection controller. The hot air regulating valve is an electric regulating valve, which controls the furnace temperature by adjusting the hot air temperature and flow rate; the oxygen flow controller is a mass flow controller, which optimizes combustion efficiency by adjusting the oxygen content, thereby regulating the furnace temperature; the pulverized coal injection controller is an S7-1200 programmable logic controller, which changes the heat input in the furnace by adjusting the pulverized coal injection rate. All control execution hardware is connected to the software system via industrial Ethernet, supporting real-time reception and execution of control commands.

[0068] The software's data and preprocessing module acquires furnace temperature data from the fiber optic sensing unit in real time from the hardware system and processes it to address packet loss and noise issues, providing high-quality data. The preprocessed data is then stored in the hardware and transmitted to the furnace temperature monitoring and visualization module and the prediction module in the software system.

[0069] The furnace temperature monitoring and visualization module adopts a B / S architecture, allowing staff to access it via a browser for easy operation. The module interface includes a real-time temperature value area displaying the sensor number, location, temperature, temperature deviation, and operating status in a table. A high-key alarm is triggered when the temperature deviation exceeds ±5℃ or the predicted temperature exceeds the threshold range. The temperature curve area plots temperature change curves for different sensor areas, supporting comparative analysis. The blast furnace 3D model area constructs a 1:1 scale 3D model, using colors to indicate different temperature ranges. Staff can rotate and zoom the model to view the temperature at each location, enabling comprehensive furnace temperature monitoring.

[0070] The furnace temperature prediction module uses an ARIMA (Autoregressive Integral Moving Average) model based on time series to construct the furnace temperature prediction model. This model is suitable for processing time series data with linear trends and periodic fluctuations. For blast furnace temperatures that are affected by factors such as raw materials, air supply, and material distribution, exhibiting dynamic fluctuations with a fluctuation period of 30-60 minutes and an amplitude of 20-40℃, the ARIMA model can effectively capture its dynamic change patterns.

[0071] The furnace temperature prediction formula is as follows: ; in, Indicates the future The predicted furnace temperature at any given time is used to determine whether the future furnace temperature will exceed the set range, providing a basis for subsequent control module adjustments; in this embodiment, the predicted furnace temperature is used... The timeframe is 5 minutes, meaning the furnace temperature is predicted 5 minutes in advance. They represent the current time. , the previous moment ,...,forward The actual furnace temperature value at any given time, which comes from the historical furnace temperature data preprocessed by the software system, is the basic input temperature data for model prediction. The autoregressive coefficients are obtained by fitting the above formula; They represent the current time. , the previous moment ,...,forward The deviation between the predicted furnace temperature and the actual furnace temperature at any given time is q = 2 in this embodiment. The moving average coefficient is obtained by fitting the above formula; The constant term is obtained by fitting the above formula.

[0072] In practical applications, the furnace temperature prediction module retrieves the preprocessed furnace temperature data (a total of 12 data points) from the data storage hardware every 5 minutes, substitutes it into the above formula to calculate the predicted furnace temperature value for the next 5 minutes, and compares the predicted value with the set furnace temperature threshold (upper limit 1550℃, lower limit 1450℃). If the predicted value exceeds the threshold range, the prediction result is immediately sent to the furnace temperature monitoring and visualization module.

[0073] The furnace temperature control module adopts a fuzzy PID control algorithm. Combining the nonlinear and time-varying characteristics of blast furnace temperature, it introduces fuzzy control rules on the basis of traditional PID control, and dynamically adjusts PID parameters through fuzzy inference to improve control accuracy and response speed.

[0074] The core formula for furnace temperature control is the output formula of the PID controller, that is, the formula for calculating the adjustment amount of the control execution hardware: ; in, The adjustment amount of the hardware controlling the blast furnace equipment at time t; for The deviation between the actual furnace temperature and the preset furnace temperature at any given time, if This indicates that the furnace temperature is too low, and it is necessary to increase the heat input (increase the hot air temperature, increase the oxygen flow rate, or increase the pulverized coal injection rate). This indicates that the furnace temperature is too high and heat input needs to be reduced. This is the proportionality coefficient, which determines the system's response speed to deviations. The larger the value, the faster the system response; however, excessively large values ​​can easily lead to system oscillations. This can be addressed by using fuzzy rules based on... The size is dynamically adjusted when hour, We set it to 1.2 to speed up the response; when hour, Use 0.8 to ensure system stability; The integral time constant is used to eliminate the static error of the system. The smaller the value, the stronger the integral effect; similarly, by adjusting through fuzzy rules, when... hour, To reduce the integral effect and avoid overshoot, use 100s. hour, Set the time to 50s to enhance the integration effect and eliminate errors; The differential time constant is used to predict the trend of deviation changes, adjust the control quantity in advance, and avoid system overshoot. The larger the value, the stronger the differential action; according to the rate of change of deviation Adjustment ,when hour, Taking 20s enhances the differential action to suppress rapid changes in bias. hour, Set the time to 10s to reduce the differential effect and ensure system stability.

[0075] The third embodiment of this application provides a method for real-time monitoring and control of blast furnace temperature, implemented using the system described in the first embodiment, such as... Figure 3 As shown, the process includes the following steps S300-S330: S300: Acquires temperature signals through an optical fiber sensing unit and transmits the temperature signals to the data acquisition and preprocessing module through an optical fiber.

[0076] S310: Temperature data is obtained after preprocessing the temperature signal.

[0077] In some embodiments, based on the temperature data, an autoregressive integral moving average model is used for fitting calculation to obtain the predicted furnace temperature at future times; the predicted furnace temperature is used to evaluate the operating status of the blast furnace or, together with the temperature data, as input to the fuzzy PID control algorithm.

[0078] S320: Display the temperature data in a visual format.

[0079] S330: The blast furnace equipment is controlled using a fuzzy PID control algorithm based on the temperature data.

[0080] The fourth embodiment of this application provides a method for predicting blast furnace temperature, implemented using the system described in the first embodiment, such as... Figure 4 As shown, the process includes the following steps S400-S420: S400: The system described in the first embodiment is used to collect and preprocess the furnace temperature data inside the blast furnace.

[0081] S410: Based on the temperature data, the furnace temperature prediction value at future times is obtained by fitting and calculating using an autoregressive integral moving average model.

[0082] S420: The predicted furnace temperature is used for evaluating the operating status of the blast furnace or to provide a basis for decision-making regarding the furnace temperature control.

[0083] Figure 5 This is a schematic structural diagram of a computing device 900 provided in an embodiment of this application. This computing device can execute various optional embodiments of the methods described above. The computing device can be a terminal, or a chip or chip system within the terminal. Figure 5 As shown, the computing device 900 includes: a processor 910, a memory 920, and a communication interface 930.

[0084] It should be understood that Figure 5 The communication interface 930 in the computing device 900 shown can be used to communicate with other devices, and may specifically include one or more transceiver circuits or interface circuits.

[0085] The processor 910 can be connected to the memory 920. The memory 920 can be used to store the program code and data. Therefore, the memory 920 can be a storage unit inside the processor 910, an external storage unit independent of the processor 910, or a component that includes both the storage unit inside the processor 910 and the external storage unit independent of the processor 910.

[0086] Optionally, the computing device 900 may also include a bus. The memory 920 and communication interface 930 can be connected to the processor 910 via the bus. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The symbol is represented by a line without an arrow, but this does not mean that there is only one bus or one type of bus.

[0087] It should be understood that in the embodiments of this application, the processor 910 may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Alternatively, the processor 910 may employ one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0088] The memory 920 may include read-only memory and random access memory, and provides instructions and data to the processor 910. A portion of the processor 910 may also include non-volatile random access memory. For example, the processor 910 may also store device type information.

[0089] When the computing device 900 is running, the processor 910 executes computer execution instructions stored in the memory 920 to perform any of the operational steps of the above method and any of the optional embodiments thereof.

[0090] It should be understood that the computing device 900 according to the embodiments of this application can correspond to the corresponding subject in executing the methods according to the various embodiments of this application, and the above and other operations and / or functions of each module in the computing device 900 are respectively for implementing the corresponding processes of the methods of this embodiment. For the sake of brevity, they will not be described in detail here.

[0091] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0092] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0096] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the above-described method, which includes at least one of the schemes described in the above embodiments.

[0098] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0099] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0100] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0101] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0102] Furthermore, the terms "first, second, third, etc." or similar terms such as module A, module B, and module C used in the specification and claims are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that, where permissible, a specific order or sequence may be interchanged so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0103] In the above description, the labels of the steps involved, such as S110, S120, etc., do not mean that the steps will necessarily be executed. The order of the steps can be interchanged or executed simultaneously if permitted.

[0104] The term "comprising" as used in the specification and claims should not be construed as limiting itself to what follows; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the mentioned feature, integral, step, or component, but does not exclude the presence or addition of one or more other features, integrals, steps, or components, or groups thereof. Thus, the statement "device comprising means A and B" should not be limited to a device consisting solely of components A and B.

[0105] The terms "an embodiment" or "an embodiment" as used in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in at least one embodiment of this application. Therefore, the terms "in one embodiment" or "in an embodiment" appearing throughout this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. Furthermore, in one or more embodiments, the particular features, structures, or characteristics can be combined in any suitable manner, as will be apparent to those skilled in the art from this disclosure.

[0106] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, all of which fall within the scope of protection of this application.

Claims

1. A real-time monitoring and control system for blast furnace temperature, characterized in that, include: The sensing transmission unit is used to acquire temperature signals through the fiber optic sensing unit and transmit the temperature signals to the data acquisition and preprocessing module through the fiber optic cable. The data acquisition and preprocessing module is used to receive the temperature signal, preprocess it to obtain temperature data, and send it to the furnace temperature monitoring and visualization module. The furnace temperature monitoring and visualization module is used to receive the temperature data and display the temperature data in a visual form; The furnace temperature control module is used to receive the temperature data and control the blast furnace equipment according to the temperature data through a fuzzy PID control algorithm.

2. The system according to claim 1, characterized in that, Also includes: The furnace temperature prediction module is used to retrieve the temperature data from the data acquisition and preprocessing module, and based on the temperature data, use an autoregressive integral moving average model to perform fitting calculations to obtain the predicted furnace temperature value at future times. The predicted furnace temperature is used to assess the operating status of the blast furnace or, together with the temperature data, serves as input to the fuzzy PID control algorithm.

3. The system according to claim 2, characterized in that, The predicted furnace temperature value is fitted and calculated according to the following formula: ； in, Indicates the future Predicted furnace temperature at any given time; They represent the current time. , the previous moment ,...,forward The actual furnace temperature value at any given time, i.e., the temperature data; The autoregressive coefficients are obtained by fitting the above formula; They represent the current time. , the previous moment ,...,forward The deviation between the predicted furnace temperature and the actual furnace temperature at any given time; The moving average coefficient is obtained by fitting the above formula; The constant term is obtained by fitting the above formula.

4. The system according to claim 1, characterized in that, The fiber optic sensing units are arranged in multiple layers according to the internal regions of the blast furnace, which include: the hearth region, the dripping zone, the softening zone, and the blocky zone.

5. The system according to claim 1, characterized in that, The fiber optic sensing unit uses a Raman scattering fiber optic temperature sensor; the fiber optic cable is made of high-temperature resistant quartz fiber and is wrapped with a corrosion-resistant stainless steel protective sleeve.

6. The system according to claim 1, characterized in that, The fuzzy PID control algorithm uses the following formula for control: ； in, The adjustment amount of the hardware controlling the blast furnace equipment at time t; for The deviation between the actual furnace temperature and the preset furnace temperature at any given time; The proportionality coefficient is adjusted using fuzzy rules. The integral time constant is adjusted using fuzzy rules; The differential time constant is adjusted using fuzzy rules.

7. A method for real-time monitoring and control of blast furnace temperature, characterized in that, Implemented by the system according to any one of claims 1-5, the system includes the following steps: Temperature signals are acquired through an optical fiber sensing unit and transmitted to a data acquisition and preprocessing module via optical fiber. Temperature data is obtained after preprocessing the temperature signal; The temperature data is displayed in a visual format; The blast furnace equipment is controlled using a fuzzy PID control algorithm based on the temperature data.

8. The method according to claim 7, characterized in that, Also includes: Based on the temperature data, the furnace temperature prediction value at future times is obtained by fitting and calculating using an autoregressive integral moving average model. The predicted furnace temperature is used to assess the operating status of the blast furnace or, together with the temperature data, serves as input to the fuzzy PID control algorithm.

9. A method for predicting blast furnace temperature, characterized in that, The system described in any one of claims 1-5 is used to collect and preprocess the internal furnace temperature data of the blast furnace. Based on the temperature data, the furnace temperature prediction value at future times is obtained by fitting and calculating using an autoregressive integral moving average model. The predicted furnace temperature is used to assess the blast furnace's operating status or to provide a basis for decision-making regarding blast furnace temperature control.

10. A computing device, characterized in that, include: processor, and A memory storing program instructions, which, when executed by the processor, cause the processor to perform the real-time monitoring and control method for blast furnace temperature according to any one of claims 7 to 8, or the method for predicting blast furnace temperature according to claim 9.

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