Calculation methods, apparatus, equipment and media for metallurgical performance indicators of blast furnace burdens

Abstract

This invention discloses a method, apparatus, equipment, and medium for calculating the metallurgical performance indicators of blast furnace burdens, belonging to the field of numerical simulation technology for metallurgical blast furnaces. The method includes the following steps: conducting detection and analysis, collecting and classifying data; establishing and training a feedforward neural network model to obtain a burden softening droplet temperature prediction model; using the burden softening droplet temperature prediction model to obtain the softening start temperature and softening end temperature of the burden; establishing a blast furnace geometric model; establishing a blast furnace top charging model, simulating the blast furnace top charging process, and obtaining a blast furnace top charging distribution model; analyzing the profile of the blast furnace top charging distribution model to obtain structural parameters; establishing a blast furnace body model; simulating the blast furnace smelting process to obtain key economic indicators of blast furnace smelting; and analyzing and evaluating the influence of the metallurgical performance of the blast furnace burden on the blast furnace smelting state. This invention analyzes the influence of the metallurgical performance of the burden on the internal smelting state of the blast furnace, providing theoretical support for guiding production batching processes.

Description

Technical Field

[0001] This invention relates to a method, apparatus, equipment, and medium for calculating the metallurgical performance indicators of blast furnace burdens. Specifically, it relates to a method, apparatus, equipment, and medium for calculating the metallurgical performance indicators of blast furnace burdens based on data technology and multi-scale simulation technology, belonging to the field of metallurgical blast furnace numerical simulation technology. Background Technology

[0002] Modern blast furnace ironmaking, developed from the ancient vertical shaft furnace method, is a crucial link in modern steel production. Although many new ironmaking methods have emerged, blast furnace ironmaking remains the primary method of modern ironmaking due to its favorable technical and economic indicators, simple process, high production volume, high labor productivity, and low energy consumption.

[0003] In recent years, with the adjustment of the steel industry's production capacity structure and increasingly fierce market competition, improving smelting efficiency and achieving high-quality development has become the future direction of blast furnace ironmaking. Raw materials are the foundation of blast furnace ironmaking; the blast furnace charge includes sintered ore, pellets, and lump ore. The quality of blast furnace ironmaking indicators is inextricably linked to the quality of the raw materials used. A decrease in the overall grade of the charge may lead to the following effects on the blast furnace: reduced output, increased coke ratio, increased load, and increased slag-to-iron ratio. Changes in the blast furnace charge structure have a certain impact on blast furnace stability and economic and technical indicators. Currently, researchers obtain blast furnace charge metallurgical performance data through physical experiments, mainly relying on personal experience and literature references to analyze the potential impact of charge metallurgical performance on the blast furnace smelting state.

[0004] Therefore, this invention proposes a method for calculating the metallurgical performance indicators of blast furnace burdens based on a combination of data technology and various simulation techniques, providing theoretical support for guiding the production batching process. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a method, apparatus, equipment, and medium for calculating the metallurgical performance indicators of blast furnace burdens. This method can analyze the impact of the metallurgical performance of the burdens on the internal smelting state of the blast furnace, providing theoretical support for guiding the production batching process.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows:

[0007] In a first aspect, the present invention provides a method for calculating the metallurgical performance indicators of blast furnace burdens, comprising the following steps:

[0008] Step 1: Conduct tests and analyses on sintered ore, pellets, lump ore, and mixed ore respectively, collect data, and classify and organize the data. The collected data should include at least: the proportion of minerals contained in the furnace charge, the chemical composition of the furnace charge, the softening start temperature, and the softening end temperature.

[0009] Step 2: Establish a feedforward neural network model based on the neural network algorithm, use the collected data as the observation and response values ​​of the neural network model, set the model parameters and train it to obtain the furnace charge softening droplet temperature prediction model.

[0010] Step 3: Select mixed ore as the research object of blast furnace burden, and analyze the proportion of minerals and chemical composition of the burden through physical experiments. The proportion of minerals and chemical composition of the burden are used as input conditions, and the softening droplet temperature prediction model of the burden is used to obtain the softening start temperature and softening end temperature of the burden.

[0011] Step 4: Establish the blast furnace geometric model, set the dimensional parameters of the blast furnace geometric model, and mesh the blast furnace geometric model;

[0012] Step 5: Based on the discrete element method and coupled with the blast furnace geometric model, establish the blast furnace top charging model, simulate the blast furnace top charging process, and obtain the blast furnace top charging distribution model;

[0013] Step 6: Analyze the cross-section of the blast furnace top charging distribution model based on the regular grid method to obtain the structural parameters of the blast furnace top charging distribution;

[0014] Step 7: Establish a blast furnace body model based on computational fluid dynamics and coupled with the blast furnace geometric model;

[0015] Step 8: The structural parameters of the furnace charge softening start temperature, softening end temperature, and blast furnace top charge distribution are used as input conditions for the blast furnace body model. The furnace charge softening droplet temperature prediction model, the blast furnace top charge distribution model, and the blast furnace body model are coupled and the blast furnace smelting process is simulated to obtain key economic indicators of blast furnace smelting.

[0016] Step 9: Based on the obtained key economic indicators of the blast furnace, analyze and evaluate the impact of the metallurgical properties of the blast furnace charge on the blast furnace smelting state.

[0017] As one possible implementation of this embodiment, in step 1, the collected data includes at least: sinter ratio, pellet ratio, lump ore ratio, TFe content, FeO content, SiO2 content, CaO content, Al2O3 content, MgO content, softening start temperature T10, and softening end temperature T40.

[0018] As one possible implementation of this embodiment, in step 2, the observed values ​​include the proportion of sintered ore, the proportion of pellets, the proportion of lump ore, the content of TFe, the content of FeO, the content of SiO2, the content of CaO, the content of Al2O3, and the content of MgO. The response values ​​include the softening start temperature T10 and the softening end temperature T40 of the furnace charge. The model setting parameters include at least the number of hidden layers, the number of neurons, and the training algorithm.

[0019] As one possible implementation of this embodiment, the parameters of the blast furnace top charging distribution model include at least: particle diameter distribution, particle sliding friction coefficient, particle rolling friction coefficient, particle elastic recovery coefficient, particle shear modulus, and Poisson's ratio.

[0020] As one possible implementation of this embodiment, the blast furnace body model includes at least a chemical reaction model, a heat transfer model, a mass transfer model, and a softening zone model.

[0021] As one possible implementation of this embodiment, the key economic indicators of blast furnace smelting include: solving the temperature field, pressure field, velocity field, output, furnace top gas utilization rate, furnace charge porosity, and softening zone permeability index inside the blast furnace.

[0022] As one possible implementation of this embodiment, the transfer function of the feedforward neural network model adopts the Log-Sigmoid function, and the training data, validation data, and test data of the feedforward neural network model are randomly sampled from 75%, 15%, and 15% of the training dataset, respectively; the feedforward neural network model has two hidden layers, and the number of neurons is set to 15 and 10, respectively; the optimization algorithm adopts the Bayesian regularization algorithm.

[0023] Secondly, an embodiment of the present invention provides a device for calculating the metallurgical performance indicators of blast furnace burdens, comprising:

[0024] The data acquisition module is used to detect and analyze sintered ore, pellet ore, lump ore and mixed ore respectively, collect data and classify and organize the data. The collected data includes at least: the proportion of minerals contained in the furnace charge, the chemical composition of the furnace charge, the softening start temperature and the softening end temperature.

[0025] The temperature forecast model building module is used to build a feedforward neural network model based on the neural network algorithm. It uses the collected data as the observation and response values ​​of the neural network model, sets the model parameters and trains the model to obtain the forecast model of the furnace charge softening droplet temperature.

[0026] The droplet temperature prediction module is used to select mixed ore as the research object of blast furnace burden. It analyzes the proportion of minerals and chemical composition of the burden through physical experiments. The proportion of minerals and chemical composition of the burden are used as input conditions, and the softening droplet temperature prediction model of the burden is used to obtain the softening start temperature and softening end temperature of the burden.

[0027] The geometric model creation module is used to create the blast furnace geometric model, set the dimensional parameters of the blast furnace geometric model, and perform mesh generation on the blast furnace geometric model;

[0028] The material distribution model acquisition module is used to establish a blast furnace top material distribution model based on the discrete element method and coupled with the blast furnace geometric model, to simulate the blast furnace top material distribution process and obtain the blast furnace top material distribution model.

[0029] The structural parameter acquisition module is used to analyze the cross-section of the blast furnace top charging distribution model based on the regular grid method to obtain the structural parameters of the blast furnace top charging distribution.

[0030] The ontology model building module is used to build a blast furnace ontology model based on computational fluid dynamics and coupled with a blast furnace geometric model.

[0031] The index calculation module is used to take the structural parameters of the furnace charge softening start temperature, softening end temperature, and blast furnace top charge distribution as input conditions for the blast furnace body model, couple the furnace charge softening droplet temperature prediction model, the blast furnace top charge distribution model and the blast furnace body model, and simulate the blast furnace smelting process to obtain key economic indicators of blast furnace smelting.

[0032] The index analysis module is used to analyze and evaluate the impact of the metallurgical properties of the blast furnace burden on the blast furnace smelting state based on the key economic indicators of the blast furnace obtained from the solution.

[0033] Thirdly, an electronic device provided by an embodiment of the present invention includes a processor, a memory, and a program stored in the memory and executable on the processor. When the electronic device is running, the processor executes the program to implement the steps of the calculation method for any blast furnace charge metallurgical performance index as described above.

[0034] Fourthly, an embodiment of the present invention provides a storage medium storing a program, which, when run by a processor, executes the steps of the calculation method for any of the metallurgical performance indicators of blast furnace charge as described above.

[0035] The technical solutions of the embodiments of the present invention can have the following beneficial effects:

[0036] The present invention provides a method for calculating the metallurgical performance indicators of blast furnace burdens. This method integrates a burden softening droplet temperature prediction model, a blast furnace top burden distribution model, and a blast furnace body model to form an integrated technology. Based on data technology and simulation technology, it analyzes the impact of the metallurgical performance of the burdens on the internal smelting state of the blast furnace. The simulation results evaluate the metallurgical performance of the blast furnace burdens, providing theoretical support for guiding the production batching process. This method solves the problem that the analysis of the metallurgical performance of existing blast furnace burdens relies on physical experiments and experience.

[0037] The calculation device for the metallurgical performance index of blast furnace charge in the technical solution of this invention has the same beneficial effects as the calculation method for the metallurgical performance index of blast furnace charge in the technical solution of this invention. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a method for calculating the metallurgical performance indicators of blast furnace burden according to an exemplary embodiment;

[0039] Figure 2 This is a block diagram illustrating a device for calculating the metallurgical performance indicators of blast furnace charge according to an exemplary embodiment. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0041] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0042] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for calculating the metallurgical performance indicators of blast furnace burdens, including the following steps:

[0043] Step 1: Conduct tests and analyses on sintered ore, pellets, lump ore, and mixed ore respectively, collect data, and classify and organize the data. The collected data should include at least: the proportion of minerals contained in the furnace charge, the chemical composition of the furnace charge, the softening start temperature, and the softening end temperature.

[0044] Step 2: Establish a feedforward neural network model based on the neural network algorithm, use the collected data as the observation and response values ​​of the neural network model, set the model parameters and train it to obtain the furnace charge softening droplet temperature prediction model.

[0045] Step 3: Select mixed ore as the research object of blast furnace burden, and analyze the proportion of minerals and chemical composition of the burden through physical experiments. The proportion of minerals and chemical composition of the burden are used as input conditions, and the softening droplet temperature prediction model of the burden is used to obtain the softening start temperature and softening end temperature of the burden.

[0046] Step 4: Establish the blast furnace geometric model, set the dimensional parameters of the blast furnace geometric model, and mesh the blast furnace geometric model;

[0047] Step 5: Based on the discrete element method and coupled with the blast furnace geometric model, establish the blast furnace top charging model, simulate the blast furnace top charging process, and obtain the blast furnace top charging distribution model;

[0048] Step 6: Analyze the cross-section of the blast furnace top charging distribution model based on the regular grid method to obtain the structural parameters of the blast furnace top charging distribution;

[0049] Step 7: Establish a blast furnace body model based on computational fluid dynamics and coupled with the blast furnace geometric model;

[0050] Step 8: The structural parameters of the furnace charge softening start temperature, softening end temperature, and blast furnace top charge distribution are used as input conditions for the blast furnace body model. The furnace charge softening droplet temperature prediction model, the blast furnace top charge distribution model, and the blast furnace body model are coupled and the blast furnace smelting process is simulated to obtain key economic indicators of blast furnace smelting.

[0051] Step 9: Based on the obtained key economic indicators of the blast furnace, analyze and evaluate the impact of the metallurgical properties of the blast furnace charge on the blast furnace smelting state.

[0052] As one possible implementation of this embodiment, in step 1, the collected data includes at least: sinter ratio, pellet ratio, lump ore ratio, TFe content, FeO content, SiO2 content, CaO content, Al2O3 content, MgO content, softening start temperature T10, and softening end temperature T40.

[0053] As one possible implementation of this embodiment, in step 2, the observed values ​​include the proportion of sintered ore, the proportion of pellets, the proportion of lump ore, the content of TFe, the content of FeO, the content of SiO2, the content of CaO, the content of Al2O3, and the content of MgO. The response values ​​include the softening start temperature T10 and the softening end temperature T40 of the furnace charge. The model setting parameters include at least the number of hidden layers, the number of neurons, and the training algorithm.

[0054] As one possible implementation of this embodiment, the parameters of the blast furnace top charging distribution model include at least: particle diameter distribution, particle sliding friction coefficient, particle rolling friction coefficient, particle elastic recovery coefficient, particle shear modulus, and Poisson's ratio.

[0055] As one possible implementation of this embodiment, the blast furnace body model includes at least a chemical reaction model, a heat transfer model, a mass transfer model, and a softening zone model.

[0056] As one possible implementation of this embodiment, the key economic indicators of blast furnace smelting include: solving the temperature field, pressure field, velocity field, output, furnace top gas utilization rate, furnace charge porosity, and softening zone permeability index inside the blast furnace.

[0057] As one possible implementation of this embodiment, the transfer function of the feedforward neural network model adopts the Log-Sigmoid function. The training, validation, and test data of the feedforward neural network model were randomly sampled from 75%, 15%, and 15% of the training dataset, respectively. The feedforward neural network model has two hidden layers, with the number of neurons set to 15 and 10, respectively. The optimization algorithm adopted is the Bayesian regularization algorithm.

[0058] Secondly, an embodiment of the present invention provides a device for calculating the metallurgical performance indicators of blast furnace burdens, comprising:

[0059] The data acquisition module is used to detect and analyze sintered ore, pellet ore, lump ore and mixed ore respectively, collect data and classify and organize the data. The collected data includes at least: the proportion of minerals contained in the furnace charge, the chemical composition of the furnace charge, the softening start temperature and the softening end temperature.

[0060] The temperature forecast model building module is used to build a feedforward neural network model based on the neural network algorithm. It uses the collected data as the observation and response values ​​of the neural network model, sets the model parameters and trains the model to obtain the forecast model of the furnace charge softening droplet temperature.

[0061] The droplet temperature prediction module is used to select mixed ore as the research object of blast furnace burden. It analyzes the proportion of minerals and chemical composition of the burden through physical experiments. The proportion of minerals and chemical composition of the burden are used as input conditions, and the softening droplet temperature prediction model of the burden is used to obtain the softening start temperature and softening end temperature of the burden.

[0062] The geometric model creation module is used to create the blast furnace geometric model, set the dimensional parameters of the blast furnace geometric model, and perform mesh generation on the blast furnace geometric model;

[0063] The material distribution model acquisition module is used to establish a blast furnace top material distribution model based on the discrete element method and coupled with the blast furnace geometric model, to simulate the blast furnace top material distribution process and obtain the blast furnace top material distribution model.

[0064] The structural parameter acquisition module is used to analyze the cross-section of the blast furnace top charging distribution model based on the regular grid method to obtain the structural parameters of the blast furnace top charging distribution.

[0065] The ontology model building module is used to build a blast furnace ontology model based on computational fluid dynamics and coupled with a blast furnace geometric model.

[0066] The index calculation module is used to take the structural parameters of the furnace charge softening start temperature, softening end temperature, and blast furnace top charge distribution as input conditions for the blast furnace body model, couple the furnace charge softening droplet temperature prediction model, the blast furnace top charge distribution model and the blast furnace body model, and simulate the blast furnace smelting process to obtain key economic indicators of blast furnace smelting.

[0067] The index analysis module is used to analyze and evaluate the impact of the metallurgical properties of the blast furnace burden on the blast furnace smelting state based on the key economic indicators of the blast furnace obtained from the solution.

[0068] The calculation process of the metallurgical performance index of blast furnace charge based on data technology and multi-scale simulation technology is as follows.

[0069] Step 1: Using an iron ore softening and dripping performance tester, 50 types of sintered ore, 50 types of pellets, 50 types of lump ore, and 50 types of mixed ore were tested and analyzed. The test data included: ore type ratio, TFe content, FeO content, SiO2 content, CaO content, Al2O3 content, MgO content, softening start temperature T10, and softening end temperature T40.

[0070] Step 2: Establish a multilayer feedforward neural network model using Matlab R2022a software based on a neural network algorithm. Convert the physical experimental data into double-precision floating-point data. Use the TFe, FeO, SiO2, CaO, Al2O3, and MgO contents of each ore as the observed values ​​for training the neural network model. Use the softening start temperature T10 and softening end temperature T40 of each ore as the response values ​​for training the neural network model. Set the transfer function of the multilayer feedforward neural network model to the Log-Sigmoid function, with the function expression as follows: The parameters of the multilayer feedforward neural network model are set as follows: the training data, validation data, and test data are randomly sampled from 75%, 15%, and 15% of the training dataset, respectively; the hidden layers are set to 2 layers, and the number of neurons is set to 15 and 10, respectively; the optimization algorithm is set to Bayesian regularization algorithm.

[0071] Step 3: Train the feedforward neural network model to obtain a model for predicting the softening droplet temperature of the furnace charge;

[0072] Step 4: Select a mixed ore A as the research object. Through physical experiments, obtain the ore type ratio, TFe content, FeO content, SiO2 content, CaO content, Al2O3 content, and MgO content of the mixed ore. Use this data as input conditions and employ a furnace charge softening droplet temperature prediction model to obtain the softening start temperature T10 of mixed ore A. 混 Softening final temperature T40 混 ;

[0073] Step 5: With an effective volume of 3800m³ 3 The blast furnace is taken as the research object. A geometric model of the blast furnace is established and a mesh is generated.

[0074] Step Six: Based on the discrete element method, simulate the top charging of a blast furnace without a bell, and establish a blast furnace top charging distribution model for mixed ore A. The charge consists of coke and ore, with the ore composed of a large amount of sinter and a small amount of pellets and lump ore. To improve computational efficiency and simplify the material composition and particle diameter distribution, the coke particle size is 40 mm, and the ore particle size is 20 mm. The sliding friction coefficient of coke is determined to be 0.65, and the rolling friction coefficient is 0.15. The sliding friction coefficient of the ore is determined to be 0.6, and the rolling friction coefficient is 0.2.

[0075] Step 7: Simulate the charging process. The furnace charge is discharged from the hopper, forming a particle flow that passes through a rotating chute. After passing through the chute, the particles fall freely under gravity to the furnace throat. The chute rotation speeds are 5, 6, and 7 RPM for blast furnace charging process simulation.

[0076] Step 8: Based on the regular grid method, analyze the structural profile of the blast furnace top charging distribution model of mixed ore A to obtain the radial distribution of coke particles and the porosity of the charge.

[0077] Step 9: Establish a blast furnace body model based on the computational fluid dynamics coupled blast furnace geometric model, including a chemical reaction model, heat transfer model, mass transfer model, softening zone model, pulverized coal injection model, and gas-solid two-phase model.

[0078] Step 10: Softening start temperature T10 of mixed ore A 混 Softening final temperature T40 混 The radial distribution of coke particles and the porosity of the furnace charge are used as input conditions for the blast furnace body model. The furnace charge softening droplet temperature prediction model, the blast furnace top charge distribution model, and the blast furnace body model are coupled to solve the blast furnace smelting process and obtain the furnace temperature field, pressure field, velocity field, output, furnace top gas utilization rate, and softening zone permeability index of the blast furnace smelting process.

[0079] Step 11: Evaluate the impact of the metallurgical properties of mixed ore A on the smelting state inside the blast furnace by analyzing the temperature field, pressure field, velocity field, output, furnace top gas utilization rate, and softening zone permeability index inside the blast furnace.

[0080] This invention addresses the current situation where the analysis of the metallurgical properties of blast furnace burdens mainly relies on physical experiments and human experience. By simulating the blast furnace smelting process, it obtains data such as the temperature field, pressure field, velocity field, output, furnace top gas utilization rate, and softening zone permeability index within the blast furnace, analyzing the impact of burdens with different metallurgical properties on the blast furnace production process. This method offers high calculation accuracy, strong applicability, and reduces trial-and-error costs. It obtains metallurgical performance data of the burdens and their state changes during the blast furnace smelting process through physical and chemical property data. This provides a theoretical basis for guiding the implementation of burden batching processes in production, improving the current efficiency of increasing production capacity and reducing carbon emissions.

[0081] This invention provides an electronic device, including a processor, a memory, and a program stored in the memory and executable on the processor. When the electronic device is running, the processor executes the program to implement the steps of the calculation method for any blast furnace charge metallurgical performance index as described above.

[0082] Specifically, the aforementioned memory and processor can be general-purpose memory and processor, without any specific limitations. When the processor runs the computer program stored in the memory, it can execute the above-mentioned calculation method for the metallurgical performance indicators of blast furnace burden.

[0083] Those skilled in the art will understand that the structure of the electronic device does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine some components, or split some components, or have different component arrangements.

[0084] In some embodiments, the electronic device may further include a touchscreen for displaying a graphical user interface (e.g., an application launch screen) and receiving user actions on the graphical user interface (e.g., launching an application). Specifically, the touchscreen may include a display panel and a touch panel. The display panel may be configured as an LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or similar type. The touch panel can collect user touch or non-touch operations on or near it and generate pre-set operation commands, such as user actions using fingers, styluses, or any suitable object or accessory on or near the touch panel. Additionally, the touch panel may include a touch detection device and a touch controller. The touch detection device detects the user's touch position and posture, and detects the signals generated by the touch operation, transmitting the signals to the touch controller. The touch controller receives touch information from the touch detection device, converts it into information that the processor can process, sends it to the processor, and can also receive and execute commands from the processor. Furthermore, touch panels can be implemented using various types of sensors, including resistive, capacitive, infrared, and surface acoustic wave sensors, as well as any future technologies. Moreover, the touch panel can cover the display panel. Users can operate on or near the touch panel, which is covered by the graphical user interface displayed on the display panel. After detecting the operation on or near the touch panel, the touch panel transmits it to the processor to determine the user input. The processor then responds to the user input by providing corresponding visual output on the display panel. Additionally, the touch panel and display panel can be implemented as two separate components or integrated together.

[0085] Corresponding to the above application startup method, this embodiment of the invention also provides a storage medium storing a program, which, when run by a processor, executes the steps of the calculation method for any blast furnace charge metallurgical performance index as described above.

[0086] The application launch device provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The device provided in this application embodiment has the same implementation principle and technical effects as the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface, and the indirect coupling or communication connection of the apparatus or modules may be electrical, mechanical, or other forms.

[0089] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0090] In addition, the functional modules in the embodiments provided in this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the metallurgical performance indicators of blast furnace burden, characterized in that, Includes the following steps: Step 1: Conduct tests and analyses on sintered ore, pellets, lump ore, and mixed ore respectively, collect data, and classify and organize the data. The collected data should include at least: the proportion of minerals contained in the furnace charge, the chemical composition of the furnace charge, the softening start temperature, and the softening end temperature. Step 2: Establish a feedforward neural network model based on the neural network algorithm, use the collected data as the observation and response values ​​of the neural network model, set the model parameters and train it to obtain the furnace charge softening droplet temperature prediction model. Step 3: Select mixed ore as the research object of blast furnace burden, and analyze the proportion of minerals and chemical composition of the burden through physical experiments. The proportion of minerals and chemical composition of the burden are used as input conditions, and the softening droplet temperature prediction model of the burden is used to obtain the softening start temperature and softening end temperature of the burden. Step 4: Establish the blast furnace geometric model, set the dimensional parameters of the blast furnace geometric model, and mesh the blast furnace geometric model; Step 5: Based on the discrete element method and coupled with the blast furnace geometric model, establish the blast furnace top charging model, simulate the blast furnace top charging process, and obtain the blast furnace top charging distribution model; Step 6: Analyze the cross-section of the blast furnace top charging distribution model based on the regular grid method to obtain the structural parameters of the blast furnace top charging distribution; Step 7: Establish a blast furnace body model based on computational fluid dynamics and coupled with the blast furnace geometric model; Step 8: The structural parameters of the furnace charge softening start temperature, softening end temperature, and blast furnace top charge distribution are used as input conditions for the blast furnace body model. The furnace charge softening droplet temperature prediction model, the blast furnace top charge distribution model, and the blast furnace body model are coupled and the blast furnace smelting process is simulated to obtain key economic indicators of blast furnace smelting. Step 9: Based on the obtained key economic indicators of the blast furnace, analyze and evaluate the impact of the metallurgical properties of the blast furnace charge on the blast furnace smelting state.

2. The method for calculating the metallurgical performance indicators of blast furnace burden according to claim 1, characterized in that, In step 1, the collected data includes at least: sinter ratio, pellet ratio, lump ore ratio, TFe content, FeO content, SiO2 content, CaO content, Al2O3 content, MgO content, softening start temperature T10, and softening end temperature T40.

3. The method for calculating the metallurgical performance indicators of blast furnace burden according to claim 2, characterized in that, In step 2, the observed values ​​include the proportion of sinter, the proportion of pellets, the proportion of lump ore, the content of TFe, the content of FeO, the content of SiO2, the content of CaO, the content of Al2O3, and the content of MgO. The response values ​​include the softening start temperature T10 and the softening end temperature T40 of the furnace charge. The model setting parameters include at least the number of hidden layers, the number of neurons, and the training algorithm.

4. The method for calculating the metallurgical performance indicators of blast furnace burden according to claim 1, characterized in that, The parameters of the blast furnace top charging distribution model include at least: particle diameter distribution, particle sliding friction coefficient, particle rolling friction coefficient, particle elastic recovery coefficient, particle shear modulus, and Poisson's ratio.

5. The method for calculating the metallurgical performance indicators of blast furnace burden according to claim 1, characterized in that, The blast furnace body model includes at least a chemical reaction model, a heat transfer model, a mass transfer model, and a softening zone model.

6. The method for calculating the metallurgical performance indicators of blast furnace burden according to any one of claims 1-5, characterized in that, The key economic indicators for blast furnace smelting include: solving the temperature field, pressure field, velocity field, output, furnace top gas utilization rate, furnace charge porosity, and softening zone permeability index inside the blast furnace.

7. The method for calculating the metallurgical performance indicators of blast furnace burden according to any one of claims 1-5, characterized in that, The transfer function of the feedforward neural network model is the Log-Sigmoid function. The training data, validation data, and test data of the feedforward neural network model are randomly sampled from 75%, 15%, and 15% of the training dataset, respectively. The feedforward neural network model has two hidden layers, and the number of neurons is set to 15 and 10, respectively. The optimization algorithm employs Bayesian regularization.

8. A device for calculating the metallurgical performance indicators of blast furnace burden, characterized in that, include: The data acquisition module is used to detect and analyze sintered ore, pellet ore, lump ore and mixed ore respectively, collect data and classify and organize the data. The collected data includes at least: the proportion of minerals contained in the furnace charge, the chemical composition of the furnace charge, the softening start temperature and the softening end temperature. The temperature forecast model building module is used to build a feedforward neural network model based on the neural network algorithm. It uses the collected data as the observation and response values ​​of the neural network model, sets the model parameters and trains the model to obtain the forecast model of the furnace charge softening droplet temperature. The droplet temperature prediction module is used to select mixed ore as the research object of blast furnace burden. It analyzes the proportion of minerals and chemical composition of the burden through physical experiments. The proportion of minerals and chemical composition of the burden are used as input conditions, and the softening droplet temperature prediction model of the burden is used to obtain the softening start temperature and softening end temperature of the burden. The geometric model creation module is used to create the blast furnace geometric model, set the dimensional parameters of the blast furnace geometric model, and perform mesh generation on the blast furnace geometric model; The material distribution model acquisition module is used to establish a blast furnace top material distribution model based on the discrete element method and coupled with the blast furnace geometric model, to simulate the blast furnace top material distribution process and obtain the blast furnace top material distribution model. The structural parameter acquisition module is used to analyze the cross-section of the blast furnace top charging distribution model based on the regular grid method to obtain the structural parameters of the blast furnace top charging distribution. The ontology model building module is used to build a blast furnace ontology model based on computational fluid dynamics and coupled with a blast furnace geometric model. The index calculation module is used to take the structural parameters of the furnace charge softening start temperature, softening end temperature, and blast furnace top charge distribution as input conditions for the blast furnace body model, couple the furnace charge softening droplet temperature prediction model, the blast furnace top charge distribution model and the blast furnace body model, and simulate the blast furnace smelting process to obtain key economic indicators of blast furnace smelting. The index analysis module is used to analyze and evaluate the impact of the metallurgical properties of the blast furnace burden on the blast furnace smelting state based on the key economic indicators of the blast furnace obtained from the solution.

9. An electronic device, characterized in that, The device includes a processor, a memory, and a program stored in the memory and executable on the processor. When the electronic device is running, the processor executes the program to implement the steps of the method for calculating the metallurgical performance indicators of blast furnace charge as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores a program that, when executed by a processor, performs the steps of the method for calculating the metallurgical performance indicators of blast furnace charge as described in any one of claims 1-7.

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