A simulation training system for the production and auxiliary facilities operation of a ferroalloy submerged arc furnace.

Abstract

This invention provides a simulation training system for the operation of ferroalloy submerged arc furnace production and auxiliary facilities, comprising: an electric furnace charging subsystem for simulating the process of charging a target electric furnace; an electric furnace electrical control subsystem for acquiring the operating parameters of the target electric furnace and controlling its operating status; an electrode pressing and releasing subsystem for replenishing the electrodes of the target electric furnace through electrode pressing and releasing; and a virtual data processing subsystem for acquiring the operating data of the electric furnace charging subsystem, electric furnace electrical control subsystem, and electrode pressing and releasing subsystem, processing the operating data based on a deep learning algorithm, and sending the processing results to each system to realize virtual reality of the operating status of each system. This system can be used to train smelting personnel, standardize their smelting operation habits, unify their operational levels, reduce furnace condition fluctuations, improve production efficiency, and reduce training costs for employees.

Description

Technical Field

[0001] This invention relates to the fields of simulation and virtual reality interaction technology, and in particular to a simulation training system for the production and operation of ferroalloy submerged arc furnaces and auxiliary facilities. Background Technology

[0002] Currently, due to the difficulty of achieving full automation in the ferroalloy industry, the industry as a whole is basically operated manually or semi-automatically. This requires production units to invest a lot of human and material resources in personnel training. Moreover, due to the uneven skill levels and different operating habits of the training personnel, the technical level of the trained personnel varies greatly, resulting in significant differences in the operation of the submerged arc furnace. This leads to large fluctuations in furnace conditions, large fluctuations in smelting power consumption, and fluctuating smelting costs. Summary of the Invention

[0003] In order to overcome the shortcomings of the existing technology, the purpose of this invention is to provide a simulation training system for the production and operation of ferroalloy submerged arc furnaces and auxiliary facilities.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A simulation training system for the operation of ferroalloy submerged arc furnace production and auxiliary facilities includes:

[0006] The electric furnace charging subsystem is used to simulate the process of charging the target electric furnace.

[0007] The electric furnace control subsystem is used to acquire the operating parameters of the target electric furnace and control the working status of the target electric furnace.

[0008] An electrode pressing and releasing subsystem is used to replenish the electrodes of the target electric furnace through electrode pressing and releasing;

[0009] The virtual data processing subsystem is used to acquire the operating data of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing subsystem, and to process the operating data based on a deep learning algorithm, and send the processing results to the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing subsystem, so as to realize the virtual reality of the operating status of each of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing subsystem.

[0010] Preferably, the internal reactance of the target electric furnace consists of the reactance of the material layer and the reactance of the molten pool.

[0011] Preferably, the operating parameters include: current, voltage, active power, reactive power, power factor, electrode position, electric furnace transformer tap position, and auxiliary system operation status.

[0012] Preferably, it further includes:

[0013] The visualization subsystem is used to establish a three-dimensional visualization information model based on the electric furnace charging subsystem, the electric furnace electrical control subsystem, the electrode pressing and releasing subsystem, and the virtual data processing subsystem, using Dynamo visualization programming and Revit parametric modeling methods.

[0014] The visualization data determination subsystem is used to perform operational status checks using Navisworks based on the aforementioned 3D visualization information model in order to determine operational data.

[0015] Preferably, the visualization subsystem includes:

[0016] The parameterized family creation module is used to create parameterized families.

[0017] The positioning information extraction module is used to extract the positioning information of the model components;

[0018] The parameter information extraction module is used to extract parameter information of model components;

[0019] The table acquisition module is used to organize and summarize the positioning and parameter information of model components, forming a standardized input data table that can be read by the Dynamo tool.

[0020] The model building module is used to run the Dynamo tool in Revit software for visual programming, call the parametric component family, read the input data table, batch adjust the component family parameters, and build the three-dimensional visual information model.

[0021] Preferably, the virtual data processing subsystem includes:

[0022] The data preprocessing module is used to preprocess the acquired operating data of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem to obtain preprocessed data.

[0023] The dataset construction module is used to construct a set from the preprocessed data to obtain the dataset to be processed;

[0024] The model processing module is used to input the dataset to be processed into the constructed deep learning network model for computation, and obtain the optimized processing result.

[0025] The feedback module is used to send the processing result to the corresponding electric furnace charging subsystem, electric furnace electrical control subsystem and electrode pressing and releasing subsystem to realize the virtual reality process of the operating status.

[0026] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0027] This invention provides a simulation training system for the operation of ferroalloy submerged arc furnace production and auxiliary facilities, comprising: an electric furnace charging subsystem for simulating the process of charging a target electric furnace; an electric furnace electrical control subsystem for acquiring the operating parameters of the target electric furnace and controlling its operating status; an electrode pressing and releasing subsystem for replenishing the electrodes of the target electric furnace through electrode pressing and releasing; and a virtual data processing subsystem for acquiring the operating data of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem, processing the operating data based on a deep learning algorithm, and sending the processing results to the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem to realize virtual reality of the operating status of each of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem. This system can be used to train smelting personnel, standardize their smelting operation habits, unify their operational levels, reduce furnace condition fluctuations, improve production efficiency, and reduce training costs for employees. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the structure of the ferroalloy submerged arc furnace production and auxiliary facility operation simulation training system provided in an embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The purpose of this invention is to provide a simulation training system for the production and auxiliary operation of ferroalloy submerged arc furnaces, which can standardize the smelting operation habits of smelting personnel, unify their operating skills, reduce furnace condition fluctuations, improve production efficiency, and reduce the training costs of employees.

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] Figure 1 This is a schematic diagram of the structure of the ferroalloy submerged arc furnace production and auxiliary facility operation simulation training system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the present invention provides a simulation training system for the production and auxiliary operation of ferroalloy submerged arc furnaces, comprising:

[0034] The electric furnace charging subsystem is used to simulate the process of charging the target electric furnace.

[0035] The electric furnace control subsystem is used to acquire the operating parameters of the target electric furnace and control the working status of the target electric furnace.

[0036] An electrode pressing and releasing subsystem is used to replenish the electrodes of the target electric furnace through electrode pressing and releasing;

[0037] The virtual data processing subsystem is used to acquire the operating data of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing subsystem, and to process the operating data based on a deep learning algorithm, and send the processing results to the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing subsystem, so as to realize the virtual reality of the operating status of each of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing subsystem.

[0038] Preferably, the internal reactance of the target electric furnace consists of the reactance of the material layer and the reactance of the molten pool.

[0039] Furthermore, Figure 1 The data gloves in this application are used in simulation and virtual reality (VR) interaction. A specific application scenario is simulation training for the production and operation of ferroalloy submerged arc furnaces and auxiliary supply facilities.

[0040] Preferably, the operating parameters include: current, voltage, active power, reactive power, power factor, electrode position, electric furnace transformer tap position, and auxiliary system operation status.

[0041] Specifically, in this embodiment, the internal reactance of the electric furnace consists of the material layer reactance and the molten pool reactance: ① Electric furnace charging system: When the material layer height changes, the material layer reactance will change, reflecting the raw material consumption in the virtual reality electric furnace smelting process. As the raw material is continuously consumed by the electric furnace, the reactance inside the furnace chamber will increase. The electric furnace charging system replenishes the raw material consumption to offset the increase in internal reactance, ensuring the stability of the material layer resistance and simultaneously reflecting the raw material consumption in the virtual reality electric furnace smelting process; ② Electric furnace electrical control system: This system mainly displays the electric furnace operating parameters (such as current, voltage, active power, reactive power, power factor, electrode position, electric furnace transformer setting, and auxiliary system operation status) and controls the electric furnace transformer setting and electrode height. The system controls the input of electrical energy to the electric furnace, thereby controlling the power supply from the transformer to the furnace. During the smelting process, the total reactance of the electric furnace changes constantly due to various factors. Therefore, the electrode height is adjusted through the electrical control system to stabilize the internal impedance of the furnace. The electrode pressure release system is also used. During the smelting process, the electrodes are constantly consumed, becoming shorter and increasing the reactance of the material layer, which can even lead to an increase in the reactance of the molten pool. To ensure the stability of the total reactance of the furnace, timely replenishment is achieved through electrode pressure release. The virtual data processing system collects a large amount of operational data from each system, performs deep learning, processes the data, and feeds it back to each system, thereby achieving virtual reality of the operating status of each system and realizing the training effect.

[0042] Preferably, it further includes:

[0043] The visualization subsystem is used to establish a three-dimensional visualization information model based on the electric furnace charging subsystem, the electric furnace electrical control subsystem, the electrode pressing and releasing subsystem, and the virtual data processing subsystem, using Dynamo visualization programming and Revit parametric modeling methods.

[0044] The visualization data determination subsystem is used to perform operational status checks using Navisworks based on the aforementioned 3D visualization information model in order to determine operational data.

[0045] Preferably, the visualization subsystem includes:

[0046] The parameterized family creation module is used to create parameterized families.

[0047] The positioning information extraction module is used to extract the positioning information of the model components;

[0048] The parameter information extraction module is used to extract parameter information of model components;

[0049] The table acquisition module is used to organize and summarize the positioning and parameter information of model components, forming a standardized input data table that can be read by the Dynamo tool.

[0050] The model building module is used to run the Dynamo tool in Revit software for visual programming, call the parametric component family, read the input data table, batch adjust the component family parameters, and build the three-dimensional visual information model.

[0051] Optionally, the steps for creating a parameterized family in this embodiment specifically include:

[0052] Classifying bridge components into family groups: Based on the function and geometric characteristics of bridge components, all bridge components are classified into different types of family components;

[0053] Selecting a family template: Based on the different geometric features and positioning methods of family components, select the corresponding family template for different types of family components to create family files;

[0054] Creating a contour family: Add dimension parameters to control the position of the reference plane. By locking the contour line to the reference plane, the geometric dimensions of the family component contour are parameterized.

[0055] Creating component families: Import the profile family using the loft blending tool / hollow loft blending tool to create a parametric component family.

[0056] Specifically, in this embodiment, the method of running the Dynamo tool in Revit software for visual programming, calling the parametric component family, reading the input data table, batch adjusting the component family parameters, and building the three-dimensional visual information model is as follows:

[0057] Use the Data.ImportExcel method to read the input data from the Excel file and store it in the corresponding array; use the positioning information in the array to place the component family; use the Element.SetParameterByName method to assign the read instance parameter data to the placed component family instance; set the parameter name and parameter value to different levels when assigning.

[0058] Preferably, the virtual data processing subsystem includes:

[0059] The data preprocessing module is used to preprocess the acquired operating data of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem to obtain preprocessed data.

[0060] The dataset construction module is used to construct a set from the preprocessed data to obtain the dataset to be processed;

[0061] The model processing module is used to input the dataset to be processed into the constructed deep learning network model for computation, and obtain the optimized processing result.

[0062] The feedback module is used to send the processing result to the corresponding electric furnace charging subsystem, electric furnace electrical control subsystem and electrode pressing and releasing subsystem to realize the virtual reality process of the operating status.

[0063] Specifically, this embodiment combines ferroalloy smelting technology, collects and organizes various data during the production and operation of submerged arc furnaces and auxiliary facilities, such as data from the electric furnace charging system, electric furnace control system, and electrode pressing and releasing system, and uses deep learning algorithms to study the relationships between various smelting parameters and establish an intelligent model. Utilizing data gloves and virtual technology to simulate real-world application scenarios, a virtual reality-based simulation training system for the production and operation of ferroalloy submerged arc furnaces and auxiliary facilities is developed. This system is used to conduct pre-job training for smelting personnel.

[0064] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0065] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A simulation training system for the operation of ferroalloy submerged arc furnace production and auxiliary facilities, characterized in that, include: The electric furnace charging subsystem is used to simulate the process of charging the target electric furnace. The electric furnace control subsystem is used to acquire the operating parameters of the target electric furnace and control the working status of the target electric furnace. An electrode pressing and releasing subsystem is used to replenish the electrodes of the target electric furnace through electrode pressing and releasing; A virtual data processing subsystem is used to acquire the operating data of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem, and to process the operating data based on a deep learning algorithm, and send the processing results to the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem, so as to realize the operating status of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem in virtual reality; Also includes: The visualization subsystem is used to establish a three-dimensional visualization information model based on the electric furnace charging subsystem, the electric furnace electrical control subsystem, the electrode pressing and releasing subsystem, and the virtual data processing subsystem, using Dynamo visualization programming and Revit parametric modeling methods. The visualization data determination subsystem is used to perform operational status checks using Navisworks based on the three-dimensional visualization information model in order to determine operational data. The visualization subsystem includes: The parameterized family creation module is used to create parameterized families; The positioning information extraction module is used to extract the positioning information of the model components; The parameter information extraction module is used to extract parameter information of model components; The table acquisition module is used to organize and summarize the positioning and parameter information of model components, forming a standardized input data table that can be read by the Dynamo tool. The model building module is used to run the Dynamo tool in Revit software for visual programming, call the parametric family, read the input data table, batch adjust the component family parameters, and build the three-dimensional visual information model.

2. The ferroalloy submerged arc furnace production and auxiliary facility operation simulation training system according to claim 1, characterized in that, The internal reactance of the target electric furnace consists of the reactance of the material layer and the reactance of the molten pool.

3. The ferroalloy submerged arc furnace production and auxiliary facility operation simulation training system according to claim 1, characterized in that, The operating parameters include: current, voltage, active power, reactive power, power factor, electrode position, electric furnace transformer tap position, and auxiliary system operation status.

4. The ferroalloy submerged arc furnace production and auxiliary facility operation simulation training system according to claim 1, characterized in that, The virtual data processing subsystem includes: The data preprocessing module is used to preprocess the acquired operating data of the electric furnace charging subsystem, the electric furnace electrical control subsystem, and the electrode pressing and releasing subsystem to obtain preprocessed data. The dataset construction module is used to construct a set from the preprocessed data to obtain the dataset to be processed; The model processing module is used to input the dataset to be processed into the constructed deep learning network model for computation, and obtain the optimized processing result. The feedback module is used to send the processing result to the corresponding electric furnace charging subsystem, electric furnace electrical control subsystem and electrode pressing subsystem to realize the virtual reality process of the operating status.

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