A method and system for predicting blast furnace cooling wall nodule formation and coordinating heat recovery

Abstract

This invention relates to the field of blast furnace smelting technology, and in particular to a method and system for predicting and coordinating heat recovery of blast furnace cooling wall nodule formation. The method includes real-time acquisition of data from key areas of the blast furnace cooling wall and data preprocessing; analysis and processing of the acquired data to establish a collaborative optimization model for nodule formation prediction and heat recovery, predicting the risk of nodule formation on the blast furnace cooling wall and calculating heat recovery; dynamically adjusting heat recovery parameters; evaluating the heat recovery rate; and if the effect does not meet the preset standard, returning to step S1 for continuous model self-optimization. This invention achieves early prediction of nodule formation by real-time monitoring of the cooling wall temperature field and heat flow distribution, and simultaneously uses monitoring data to accurately calculate recoverable waste heat, organically combining the safety monitoring and energy recovery of the blast furnace cooling system, thereby improving the safety and energy utilization efficiency of blast furnace operation.

Description

Technical Field

[0001] This invention relates to the field of blast furnace smelting technology, and in particular to a method and system for predicting blast furnace cooling wall nodule formation and coordinating heat recovery. Background Technology

[0002] Blast furnace ironmaking is a core process in modern steel production, and the cooling wall, as a key component of the blast furnace, directly affects the safe operation and service life of the furnace. Under long-term high-temperature and high-pressure operating conditions, the cooling wall is prone to nodule formation, where furnace charge adheres to the surface of the cooling wall and forms lumps. Nodule formation not only reduces cooling efficiency but can also lead to serious accidents such as cooling wall burn-through in severe cases.

[0003] Currently, the industry mainly relies on the following methods to monitor cooling wall nodules: First, manual periodic inspections, which are lagging and cannot achieve real-time monitoring; second, indirect judgment by monitoring changes in cooling water temperature, but with low sensitivity, often only detecting nodules when they are severe; and third, surface temperature detection using equipment such as infrared thermal imagers, but this is greatly affected by the environment and cannot reflect the internal nodule situation. These methods generally suffer from problems such as untimely warnings and inaccurate location.

[0004] On the other hand, the blast furnace cooling system carries away a large amount of heat energy, which is usually directly discharged, resulting in huge waste. Although there are some waste heat recovery technologies, such as the blast furnace cooling water waste heat recovery system, these systems operate independently and have not been organically combined with the cooling wall condition monitoring, resulting in low waste heat recovery efficiency and the hidden danger of increased risk of nodule formation.

[0005] The main problems with existing technologies include: 1) limited detection methods for nodule formation, failing to provide early warning; 2) independent monitoring and heat recovery systems, lacking synergistic optimization; and 3) difficulty in accurately assessing the impact of nodule formation on heat recovery efficiency using traditional methods. These problems hinder the improvement of blast furnace operation safety and energy utilization efficiency.

[0006] With the increasing demands for intelligent manufacturing and energy conservation and emission reduction, there is an urgent need to develop a new technology that can achieve early prediction of cooling wall nodule formation and synergistic optimization of heat recovery, so as to improve energy utilization efficiency while ensuring the safe operation of blast furnaces. Summary of the Invention

[0007] This invention provides a method and system for predicting and recovering heat from blast furnace cooling wall nodules. By monitoring the temperature field and heat flow distribution of the cooling wall in real time, it enables early prediction of nodules. At the same time, it uses the monitoring data to accurately calculate the recoverable waste heat, thus organically combining the safety monitoring and energy recovery of the blast furnace cooling system, thereby improving the safety of blast furnace operation and energy utilization efficiency.

[0008] To achieve the above objectives, the present invention employs the following technical solution:

[0009] A method for predicting blast furnace cooling wall nodule formation and coordinating heat recovery includes the following steps:

[0010] S1. Real-time acquisition of key area data of blast furnace cooling wall and data preprocessing;

[0011] S2. Analyze and process the acquired data, establish a collaborative optimization model for nodule formation prediction and heat recovery, predict the risk of nodule formation on the blast furnace cooling wall, and perform heat recovery calculations.

[0012] S3. Dynamically adjust heat recovery parameters;

[0013] S4. Evaluate the heat recovery rate. If the effect does not meet the preset standard, return to step S1 to continuously cycle the model for self-optimization.

[0014] Furthermore, the key area data of the blast furnace cooling wall includes cooling wall temperature distribution data, cooling wall heat flux density data, cooling water flow rate, cooling water temperature, and heat recovery system status.

[0015] Furthermore, the cooling wall temperature distribution data and cooling wall heat flux density data are obtained through a network of temperature sensors and heat flux sensors installed at key parts of the blast furnace cooling wall, and the cooling water flow rate and cooling water temperature are collected through flow meters and temperature sensors installed at the cooling water inlet and outlet.

[0016] Furthermore, the temperature sensor and heat flow sensor are arranged in a dense array to monitor the area where the temperature field of the cooling water pipe wall changes abnormally, and the sensor sampling frequency is not less than 1 time / minute.

[0017] Furthermore, the analysis and processing of the acquired data includes establishing a dynamic model of the temperature gradient, identifying abnormal change regions in the temperature field, analyzing the spatiotemporal distribution characteristics of heat flux density, and detecting regions of sudden decrease in heat flux and their evolution trends.

[0018] Furthermore, the temperature gradient dynamic model employs a time series analysis method based on machine learning, combined with historical operating data, to establish a predictive model for temperature field changes. When the rate of change of the temperature gradient exceeds a set threshold, a nodule formation warning is triggered.

[0019] Furthermore, the heat recovery calculation is performed based on the monitored cooling water inlet and outlet temperature difference, flow rate, and wall heat flow data to calculate the recoverable waste heat in real time. The calculation formula is as follows:

[0020] Q_rec=ρ·c_p·q_v·(T_out-T_in)+∫Aq"dA;

[0021] Where ρ is the density of cooling water, in kg / m³; c_p is the specific heat capacity of cooling water, in J / (kg·℃); and q_v is the volumetric flow rate of cooling water, in m³ / s. 3 / s; T_out and T_in are the cooling water outlet and inlet temperatures, respectively, in °C; q" is the heat flux density of the blast furnace cooling wall, in MW / m³. 2 A represents the heat exchange area, in meters (m²). 2 .

[0022] Furthermore, the collaborative optimization model for nodulation prediction and heat recovery automatically adjusts the cooling water flow rate and temperature parameters when nodulation risk is predicted, optimizing waste heat recovery efficiency while ensuring cooling effect. The collaborative optimization model for nodulation prediction and heat recovery adopts a multi-objective optimization algorithm, which considers both nodulation inhibition and waste heat recovery objective functions, and determines the optimal operating parameters through the Pareto optimal solution set.

[0023] A blast furnace cooling wall nodule prediction and heat recovery collaborative system includes a sensor network module, a data acquisition module, a nodule prediction and analysis module, a heat recovery calculation module, and a collaborative control module;

[0024] The sensor network module is used to acquire data of key areas of the blast furnace cooling wall in real time and transmit the data to the data acquisition module.

[0025] The data acquisition module is used to preprocess the received data, namely, to clean, align and extract key features from the data.

[0026] The nodule prediction and analysis module is used to establish a collaborative optimization model for nodule prediction and heat recovery to predict the risk of nodule formation on the blast furnace cooling wall.

[0027] The heat recovery calculation module is used to perform heat recovery calculations;

[0028] The collaborative control module is used to dynamically adjust heat recovery parameters, evaluate the heat recovery rate, and perform model self-optimization.

[0029] Furthermore, it also includes a visual monitoring interface for real-time display of the cooling wall temperature field distribution, heat flux density distribution, nodulation risk level, and waste heat recovery efficiency.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1) It breaks through the limitations of traditional monitoring methods, integrates the two independent systems of condition monitoring and energy recovery into a collaborative intelligent system, realizes early and accurate warning of nodule formation, and transforms the traditional post-event treatment into pre-event prevention; it improves the efficiency of waste heat recovery, and can improve the heat recovery rate through accurate calculation and collaborative optimization.

[0032] 2) This invention significantly improves the reliability and economy of blast furnace operation through the coordinated control of nodule prediction and heat recovery. Compared with traditional independent operation systems, it can accurately warn of nodule risk in advance, giving operators sufficient time to take preventive measures, effectively avoiding unplanned shutdown accidents caused by nodule formation, greatly improving the continuous operation rate of blast furnace, reducing operation and maintenance costs through the system, and reducing the frequency of manual inspections and maintenance workload due to the high degree of system automation.

[0033] 3) In terms of energy utilization, this invention maximizes the utilization of waste heat resources by dynamically adjusting heat recovery parameters. Test data shows that the waste heat power generation can be increased by more than 9.2% after adopting this solution, while the fluctuation range of steam production is reduced by 35%, which significantly improves the stability of energy recovery. This energy efficiency improvement not only reduces production costs but also reduces energy waste, and has significant environmental benefits.

[0034] 4) The collaborative control mechanism of this invention achieves the organic unity of safety protection and energy efficiency management. The system can automatically adjust the operation strategy according to the real-time risk level and continuously optimize the heat recovery efficiency while ensuring the safety of the cooling wall. This intelligent operation mode reduces the burden of manual monitoring, reduces the risk of operational errors, and provides reliable technical support for the digital transformation and upgrading of blast furnaces.

[0035] 5) Low implementation cost, applicable to cooling systems of various large and medium-sized blast furnaces, especially suitable for use under harsh conditions such as high temperature, high pressure, and strong corrosion; through the implementation of this invention, the service life of blast furnace cooling walls can be significantly extended, energy utilization efficiency can be improved, and strong support can be provided for the safe production and energy conservation and emission reduction of steel enterprises. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention.

[0037] Figure 2 This is a schematic diagram of the system described in this invention. Detailed Implementation

[0038] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0039] See Figure 1 This is a flowchart of the method of the present invention. The present invention provides a method for predicting blast furnace cooling wall nodule formation and coordinating heat recovery, comprising the following steps:

[0040] S1. Real-time acquisition of key area data of the blast furnace cooling wall, namely cooling wall temperature distribution data, cooling wall heat flux density data, cooling water flow rate, cooling water temperature, and heat recovery system status. A complete sensor network and data analysis platform is constructed. Temperature sensors and heat flux sensors are deployed in key parts of the blast furnace cooling wall to collect temperature distribution data and heat flux density data in real time. Flow meters and temperature sensors installed at the cooling water inlet and outlet collect cooling water flow rate and temperature data. The temperature sensors and heat flux sensors are arranged in a dense array to monitor areas where the temperature field of the cooling water pipe wall is prone to abnormal changes. The sensor sampling frequency is not less than 1 time / minute, and the temperature distribution data and heat flux density data of the cooling wall are collected in real time, forming a monitoring network with high spatiotemporal resolution.

[0041] The data is preprocessed, including cleaning, alignment, and extraction of key features such as temperature gradient and heat flux density.

[0042] S2. Analyze and process the acquired data, establish a collaborative optimization model for nodule formation prediction and heat recovery, predict the risk of nodule formation on the blast furnace cooling wall, and perform heat recovery calculations:

[0043] Based on machine learning-based intelligent analysis algorithms, the system comprehensively analyzes and intelligently processes the collected real-time temperature field and heat flux density data to establish a dynamic temperature gradient model and identify abnormal temperature field change regions. Simultaneously, it employs a heat flux density spatiotemporal distribution analysis method to analyze the spatiotemporal distribution characteristics of heat flux density and detect regions of sudden heat flux reduction and their evolution trends. The dynamic temperature gradient model, trained using machine learning-based time series analysis and combined with historical operating data, can predict the formation and development of nodules in advance, establishing a predictive model for temperature field changes. When the temperature gradient change rate exceeds a set threshold, a nodule warning is triggered.

[0044] The machine learning model outputs a risk level of high / medium / low based on set requirements. It then performs branching decisions based on the prediction results, establishing a collaborative optimization model for nodulation prediction and heat recovery. When nodulation risk is predicted, the model automatically adjusts cooling water flow and temperature parameters to optimize waste heat recovery efficiency while ensuring cooling effectiveness. This model employs a multi-objective optimization algorithm, simultaneously considering both nodulation inhibition and waste heat recovery objective functions, and determines the optimal operating parameters through the Pareto optimal solution set. When the system predicts nodulation risk, it can automatically adjust cooling water flow and temperature parameters to optimize waste heat recovery efficiency while ensuring cooling effectiveness, achieving the dual goals of safety and energy saving.

[0045] By organically combining nodulation prediction with heat recovery calculation, the system calculates recoverable waste heat in real time based on the monitored cooling water inlet and outlet temperature difference, flow rate, and wall heat flow data. The calculation formula comprehensively considers both sensible and latent heat energy to ensure accurate and reliable results. The calculated waste heat data is directly transmitted to the heat recovery system to adjust the operating parameters of the waste heat boiler or other heat recovery devices, achieving efficient recovery and utilization of thermal energy. The calculation formula is as follows:

[0046] Q_rec=ρ·c_p·q_v·(T_out-T_in)+∫A q"dA;

[0047] Where ρ is the density of cooling water, in kg / m³; c_p is the specific heat capacity of cooling water, in J / (kg·℃); and q_v is the volumetric flow rate of cooling water, in m³ / s. 3 / s; T_out and T_in are the cooling water outlet and inlet temperatures, respectively, in °C; q" is the heat flux density of the blast furnace cooling wall, in MW / m³. 2 A represents the heat exchange area, in meters (m²). 2 .

[0048] S3. Dynamically adjust heat recovery parameters;

[0049] When the risk prediction is high, reduce the intensity of heat recovery, such as by lowering the water temperature setting or reducing the flow rate;

[0050] The risk is predicted to be medium / low risk. Maintain or increase the heat recovery intensity, such as by increasing the water temperature setting or increasing the flow rate.

[0051] The heat recovery parameters are dynamically adjusted to specific control parameters, such as valve opening and pump frequency.

[0052] It also performs heat recovery operations to utilize waste heat, and uses the system to monitor and track the status of the cooling wall, the effect of heat recovery, and safety indicators in real time.

[0053] S4. Evaluate the heat recovery rate to determine whether the safety control and set heat recovery targets have been achieved. If the effect is not good or the risk increases, trigger the online learning update of the model or issue an alarm to the operator to perform closed-loop feedback and return to step S1 to continuously cycle the model self-optimization.

[0054] See Figure 2 A collaborative system for predicting and recovering blast furnace cooling wall nodules, namely a data analysis platform, includes a sensor network module, a data acquisition module, a nodule prediction and analysis module, a heat recovery calculation module, a collaborative control module, and a visual monitoring interface. Each module uses a standardized interface for communication to ensure that the system has good scalability and compatibility. The system supports seamless integration with existing blast furnace control systems, which is convenient for promotion and application.

[0055] The sensor network module is used to acquire data of key areas of the blast furnace cooling wall in real time and transmit the data to the data acquisition module.

[0056] The data acquisition module is used to preprocess the received data, namely, to clean, align and extract key features from the data.

[0057] The nodule prediction and analysis module is used to establish a collaborative optimization model for nodule prediction and heat recovery to predict the risk of nodule formation on the blast furnace cooling wall.

[0058] The heat recovery calculation module is used to perform heat recovery calculations;

[0059] The collaborative control module is used to dynamically adjust heat recovery parameters, evaluate the heat recovery rate, and perform model self-optimization.

[0060] The visualization monitoring interface is used to display key parameters such as the temperature field distribution, heat flux density distribution, nodulation risk level, and waste heat recovery efficiency of the cooling wall in real time. The interface supports historical data query and trend analysis, providing operators with intuitive decision support.

[0061] This method breaks through the limitations of traditional monitoring methods and creatively integrates two independent systems, condition monitoring and energy recovery, into a collaborative intelligent system.

[0062] This invention closely integrates and coordinates two key aspects: nodule formation prediction and early warning, and waste heat recovery. Through the same sensor network and data analysis platform, it not only detects potential nodule formation risks in advance and ensures the safe and smooth operation of the blast furnace, but also efficiently recovers the previously wasted heat energy, significantly improving the energy utilization efficiency and operational economy of the blast furnace system, and reducing fuel consumption and carbon emissions.

[0063] The above embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the above embodiments. Unless otherwise specified, the methods used in the above embodiments are conventional methods.

Claims

1. A blast furnace stave cooler accretion prediction and heat recovery synergy method, characterized by, It comprises the following steps: S1, real-time acquisition of blast furnace cooling wall key area data and data preprocessing; S2, analysis and processing of the acquired data, establishment of a collaborative optimization model of coking prediction and heat recovery, prediction of the coking risk of the blast furnace cooling wall, and heat recovery calculation; The collaborative optimization model adopts a multi-objective optimization algorithm, simultaneously considers two objective functions of coking inhibition and waste heat recovery, and determines the optimal operating parameters through a pareto optimal solution set; the machine learning model outputs a risk level of high / medium / low according to the set requirements, makes a branch decision according to the prediction result, establishes a collaborative optimization model of coking prediction and heat recovery, and automatically adjusts the cooling water flow and temperature parameters when the coking risk is predicted, thereby optimizing the waste heat recovery efficiency under the premise of ensuring the cooling effect; S3, dynamic adjustment of heat recovery parameters; S4, heat energy recovery rate effect evaluation, if the effect does not reach the preset standard, return to step S1 for continuous circulation model self-optimization.

2. A method of predicting and recovering heat from accretion on a blast furnace stave according to claim 1, characterized in that, The blast furnace cooling wall key area data includes cooling wall temperature distribution data, cooling wall heat flux density data, cooling water flow, cooling water temperature, and heat recovery system state.

3. A method of predicting accretion of a cooling stave of a blast furnace and recovering heat in cooperation according to claim 2, characterized by, The cooling wall temperature distribution data and cooling wall heat flux density data are acquired by a temperature sensor and heat flow sensor network arranged at the key parts of the blast furnace cooling wall, and the cooling water flow and cooling water temperature are collected by a flow meter and temperature sensor arranged at the cooling water inlet and outlet.

4. The method of claim 3, wherein the method is characterized by, The temperature sensor and heat flow sensor are arranged in a dense array mode to monitor the area where the cooling water pipe wall surface temperature field changes abnormally, and the sensor sampling frequency is not less than 1 time / minute.

5. The method of claim 3, wherein the method is characterized by, The analysis and processing of the acquired data includes establishing a temperature gradient dynamic model, identifying the abnormal change area of the temperature field, analyzing the temporal and spatial distribution characteristics of the heat flux density, and detecting the heat flow sudden reduction area and its evolution trend.

6. A method of predicting accretion of a cooling stave of a blast furnace and recovering heat in cooperation with each other according to claim 5, characterized by, The temperature gradient dynamic model adopts a time series analysis method based on machine learning, combines historical working condition data, establishes a prediction model of temperature field changes, and triggers a coking warning when the temperature gradient change rate exceeds the set threshold.

7. The method of claim 3, wherein the method is characterized by, The heat recovery calculation is based on the monitored cooling water inlet and outlet temperature difference, flow, and wall surface heat flow data to calculate the recoverable waste heat in real time, and the calculation formula is as follows: Q_rec=ρ·c_p·q_v·(T_out-T_in)+∫A q"dA; wherein p is the density of cooling water, in kg / m3; c p is the specific heat capacity of cooling water, in J / (kg·℃); q v is the volume flow rate of cooling water, in m 3 / s; T out and T in are the outlet and inlet temperatures of cooling water, in ℃; q" is the heat flux density of the cooling wall of blast furnace, in MW / m 2 ; A is the heat exchange area, in m 2 .

8. A blast furnace stave cooler accretion prediction and heat recovery coordination system, characterized in that, The system for implementing the method of any one of claims 1-7 comprises a sensor network module, a data acquisition module, a coking prediction analysis module, a heat recovery calculation module, and a collaborative control module; The sensor network module is used to acquire the blast furnace cooling wall key area data in real time and transmit the data to the data acquisition module; The data acquisition module is used to preprocess the received data, i.e., clean, align, and extract key features of the data; The coking prediction analysis module is used to establish a collaborative optimization model of coking prediction and heat recovery, and predict the coking risk of the blast furnace cooling wall; The heat recovery calculation module is used for heat recovery calculation; The synergic control module is used for dynamically adjusting heat recovery parameters, evaluating heat recovery rate effect, and self-optimizing a model.

9. A system for predicting and recovering heat from accretions on a blast furnace stave, according to claim 8, wherein, The visual monitoring interface is also included for displaying the cooling wall temperature field distribution, heat flux density distribution, nodulation risk level, and residual heat recovery efficiency in real time.

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