Converter steelmaking process optimization analysis method and computer readable storage medium

Abstract

The invention provides a converter steelmaking process optimization analysis method and a computer readable storage medium, and the method comprises the steps: S1, building a converter steelmaking process optimization analysis system, and recording converter steelmaking data; s2, selecting a converter steelmaking endpoint control target, and analyzing the influence of each process operation parameter on the endpoint control target parameter in the smelting process; s3, process operation parameters are determined, and converter steelmaking is conducted; and S4, converter steelmaking is evaluated. The converter steelmaking process can be optimized.

Description

Technical Field

[0001] The present application relates to the field of metallurgy, and in particular to a converter steelmaking process optimization analysis method and a computer-readable storage medium. Background Art

[0002] In the current process of digital transformation in all walks of life, the rapid realization of digitalization and intelligence in the steel production and manufacturing process is the most urgent, core and difficult part of the digital transformation tasks facing the steel industry.

[0003] Converter steelmaking is a violent physical and chemical process involving high temperature, multi-phase, large material flow, multi-factor interaction coupling, and non-steady state. For such a complex process, successful digitalization and intelligence depends on the accurate selection of typical description parameter sets and model abstraction based on in-depth process theory and experience, that is, it requires the deep integration of information technology methods with process theory and process know-how. The currently published similar technologies have different problems, which are briefly described as follows:

[0004] 1) The converter steelmaking process is a very "non-standard" physical and chemical reaction process, but many of the published analysis methods and systems are established using very "standard" methods, that is, a large number of "standard" and theoretical assumptions are added to the analysis and calculations. These excessive assumptions lead to a significant deviation between the data analysis results and the actual situation.

[0005] 2) The converter steelmaking process involves many process parameters, and the interactions between the parameters are complex. When creating analysis methods and calculation models, accurately and appropriately selecting process descriptive parameters and target descriptive parameters plays a key role in the successful creation of analysis methods and the development of corresponding systems. However, in the similar technologies currently published, there are shortcomings such as inaccurate parameter selection, lack of representativeness, and limitations in the selected main parameters in describing the process.

[0006] 3) Under given raw material conditions, the main operations of the converter steelmaking process include oxygen supply (oxygen blowing amount, oxygen lance position, etc.), slag making (the total amount of various slag making materials added, the addition time, the addition batch, etc.), and stirring (the flow rate of bottom blowing gas, the gas flow control mode, etc.). However, the results of data analysis or model calculation based on the total amount data are unsatisfactory. For example, in the oxygen supply operation, in addition to the total amount of oxygen supply, the oxygen lance position and different characterization methods of changes will significantly affect the results of process analysis, because the oxygen lance position changes dynamically during the blowing process of a furnace, and this change is often irregular, so the simple average lance position cannot truly reflect the impact of the change of the oxygen lance position on the steelmaking process. In the currently reported technology, there is no more in-depth work in this regard. Slag making operation is similar. The steelmaking reaction process is not only affected by the total amount of slag making added, but also by the addition time, addition batch, and the amount of each slag making material added each time. In addition, there are single-slag operation and double-slag operation in slag making mode. These automatic identification and detection technologies for process operation modes are also not found in the published materials.

[0007] 4) Due to the characteristics of the converter steelmaking process, the collected steelmaking-related process quality data usually show large fluctuations, which correspondingly also brings about large fluctuations in the data analysis results. In fact, in the collected data, the data set should be divided accordingly according to different application purposes, so that whether it is data comparison or data correlation analysis, the data has further defined boundaries, such as data collection by time period, workshop, steel type, furnace seat, shift, etc. Among the similar technologies that have been published, no technology has been seen to solve the above problems.

[0008] 5) The main target variables for the endpoint control of converter steelmaking include molten steel temperature, molten steel carbon content, molten steel sulfur content, and molten steel phosphorus content. The process analysis in existing similar technologies mostly stays at the correlation analysis between the target variable and a single process operation parameter, such as the correlation between the endpoint molten steel temperature and the amount of lime added. It does not take into account the characteristics of multi-factor interactive coupling in the converter steelmaking process to conduct a comprehensive analysis of the impact of multiple factors on the selected target parameters, and quantify and rank the magnitude of the impact of each factor, so as to guide process personnel to optimize and modify the current process control method more specifically and accurately.

[0009] 6) Effective process data analysis relies on a complete, accurate and reliable data foundation. In the existing process analysis information system, data is collected, cleaned, stored in the database, matched and integrated with other process procedures, test data, raw material data and other multiple data sources, and finally applied in the system functions. The data has undergone various processing at multiple levels, involving different processing logics and different data processing services. In such systems involving large amounts of data, if any link has an error, it may not be discovered by the user in a timely manner at the application level, so there is a risk of lack of data representativeness and the risk of errors in application output results. Corresponding data health status inspection rules should be formulated, the health status of the data should be monitored in real time, and the monitoring results should be presented on the user interface to reduce or avoid the above risks.

[0010] 7) For long-process steel production enterprises, the data of a single production link will be related to the data of many previous and subsequent production links. As a process analysis information system, its data integration strategy and plan should take this feature into account, and the design and implementation of the data ETL interface should be highly versatile and extensible. Otherwise, it will not only bring great difficulties and incur great additional costs in the future system maintenance and upgrade process, but also cause confusion in data sorting and system problem diagnosis. These are aspects that are not done well enough in the currently published technologies, and need to be fully valued and considered in the construction of the new system.

[0011] 8) With the development and changes of converter steelmaking technology, as well as the changes in the steel product market and the raw and auxiliary material market, the converter steelmaking process will also change. This puts forward requirements for the functional change, maintenance and expansion characteristics of the process analysis system. However, the currently released technologies do not take these aspects into consideration much, which can easily cause the information system to become rigid and outdated, and finally lead to less use and abandonment. Summary of the invention

[0012] In view of this, it is necessary to provide a converter steelmaking process optimization analysis method and a computer-readable storage medium to optimize the converter steelmaking process.

[0013] The present invention provides a converter steelmaking process optimization analysis method, comprising the steps of:

[0014] S1: Establish a converter steelmaking process optimization analysis system to record converter steelmaking data;

[0015] S2: Select the endpoint control target of converter steelmaking and analyze the influence of various process operation parameters on the endpoint control target parameters during the smelting process;

[0016] S3: Determine process operation parameters and carry out converter steelmaking;

[0017] S4: Evaluate converter steelmaking.

[0018] Furthermore, in step S3, it also includes analyzing and calculating the change trajectory of the oxygen lance position to identify and judge the change of the oxygen lance position, so as to obtain the following two characteristic variable values ​​for analyzing the influence of the oxygen lance position on the smelting process:

[0019] 3) Lance Mode (LM): It characterizes the number of stages of lance control and the order of changes between the stages during the blowing cycle of a furnace of steel.

[0020] The input required for its calculation is expressed as follows:

[0021] LM=f(h,t,H BOF1 ,H BOF2 ,LMDM) (Formula 1)

[0022] Where:

[0023] LM: Oxygen gun position mode, the calculation result is expressed as a string. For example: LM-010, there are three numbers "010" after the separator "-", indicating that the oxygen gun is three-stage control; the numbers "0" and "1" respectively indicate whether the corresponding segment is a descending segment or an ascending segment, and "010" indicates that the oxygen gun position control mode is three-stage, the first segment descends, the second segment ascends, and the third segment descends again;

[0024] h: real-time oxygen lance position, cm;

[0025] t: duration calculated from the start of blowing, sec;

[0026] H BOF1 : Converter mouth height, cm;

[0027] H BOF2 : height of refractory surface of converter bottom, cm;

[0028] LMDM: Lance Mode Detecting Model.

[0029] 4) Time Weighted Average Lance Height (TWALH): represents the average lance height calculated by time weight within a blowing cycle.

[0030]

[0031] Where:

[0032] TWALH: time-weighted average gun position, cm;

[0033] Δt i : The time step of the real-time detection of the gun position recorded at time i, sec;

[0034] h i : The detection value of the gun position at time i, cm;

[0035] i: The sequence number of the gun position measurement and recording during the blowing cycle;

[0036] n: the serial number of the last gun position record in the blowing cycle;

[0037] T: blowing cycle, sec.

[0038] Furthermore, in step S3, it also includes identifying and judging different slag making modes, namely, the single slag method or the double slag method.

[0039] Furthermore, if the oxygen lance is lifted out of the furnace once and the auxiliary lance is lowered and lifted out of the furnace twice during the entire smelting cycle, this mode is identified and determined as a single slag mode; if the oxygen lance is lifted out of the furnace twice, the auxiliary lance is lowered and lifted out of the furnace twice, and before the oxygen lance is lifted out of the furnace twice, slag-making material is added in an amount exceeding the weight threshold and blowing exceeds the time threshold, this mode is identified and determined as a double slag mode, and furnaces that do not meet the above definitions of single slag mode or double slag mode are determined to be abnormal operation furnaces.

[0040] Furthermore, step S4 includes: selecting an evaluation time interval, then selecting a target steel grade to be evaluated, and performing statistical calculations on three-level process quality data, where the three levels refer to workshops, furnaces, and work groups; comparing the three-level process quality data, and comparing the actual data with expectations based on process theory, and then performing optimal comparisons for different endpoint targets, and based on the results of the optimal comparisons, providing an evaluation of the process operation level within the selected time period, pointing out existing problems, and providing directions for process improvement.

[0041] Furthermore, if the process parameter to be analyzed is phosphorus content, the mean of the final phosphorus content of molten steel at the workshop level is first calculated as the baseline of the final phosphorus content control level, and then the final phosphorus content of molten steel of each converter is calculated, and compared with the workshop mean to find the best and worst furnace seats, and from the furnace seat with the best phosphorus content control, the final phosphorus content of molten steel corresponding to different teams is calculated, the best team is found, and the mean of the main process parameters controlled by the corresponding team is calculated;

[0042] From the furnace with the worst phosphorus content control, calculate the final phosphorus content of molten steel corresponding to different teams, find the worst team, and calculate the mean of the main process parameters controlled by the corresponding team. Compare the mean process parameter values ​​of the best team in the best furnace with the worst team in the worst furnace, and find out the differences, deficiencies and suggestions for improvement in process parameter control between the worst team in the worst furnace and the best team in the best furnace.

[0043] From the goal of controlling the phosphorus content in molten steel, according to the theory of metallurgical thermodynamics, the operating parameters such as the amount of lime added, the amount of light-burned dolomite added, the oxygen blowing time, and the average gun position in the smelting process have a significant impact on the phosphorus content at the end of molten steel smelting. The method adopted in this patent is:

[0044] Select the original data time interval R based on which the model is calculated day :

[0045] R day = 30 days (30 days forward from the model calculation time)

[0046] Calculate the phosphorus content [%P] of molten steel produced by each converter in the workshop (for example, 3 converters):

[0047]

[0048] Where:

[0049] BOF1, BOF2, BOF3: represent three converters respectively;

[0050] n1, n2, n3: represent the size of the data set (number of data) in their respective computing scenarios.

[0051] Optimize and find the furnace seat with the lowest phosphorus content among all converters:

[0052] [%P] BOF_MIN = min { [%P] BOF1 ,[%P] BOF2 ,[%P] BOF3} (Formula 6) Calculate the average phosphorus content of molten steel produced by the four shifts of the furnace with the lowest phosphorus content in molten steel:

[0053]

[0054] Where:

[0055] TEAM1, TEAM2, TEAM3, TEAM4: represent the same furnace seat A,

[0056] There are 4 teams: B, C, and D;

[0057] m1, m2, m3, m4: respectively represent the number of furnaces smelted by 4 different teams within the statistical time interval.

[0058] Find the team with the lowest phosphorus content in molten steel among the four teams:

[0059] [%P] TEAM_MIN

[0060] = min{[%P] TEAM1 ,[%P]EEAM2 ,[%P] TEAM3 ,[%P] TEAM4}(Formula 11) calculates the mean value of process parameter control in the smelting process of the team and recommends it as the optimization value:

[0061]

[0062] Where:

[0063] W lime_sug ,W dolomite_sug ,T oxyblow_sug ,TWALH sug : Respectively represent the recommended lime addition amount, light burning addition amount, oxygen blowing time, and average gun position;

[0064] W_lime_i, W_dolomite_i, T_oxyblow_i, TWALH_i: respectively represent the amount of lime added, amount of light burning added, oxygen blowing time, and average gun position used in t statistical calculation for a single furnace.

[0065] Furthermore, in step S2, the endpoint control targets include: molten steel temperature, molten steel carbon content, molten steel sulfur content, molten steel phosphorus content; process operation parameters in the smelting process include molten iron weight, temperature, chemical composition; scrap steel weight and category; oxygen supply, oxygen lance position mode, time-weighted average lance position; lime addition amount, batch, batch weight, addition time; bottom blowing stirring gas flow rate.

[0066] The present invention also provides a computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the converter steelmaking process optimization analysis method.

[0067] Beneficial effects:

[0068] (1) Through in-depth analysis of the process. The system is designed to take into account both the versatility of the system and the in-depth process analysis function, thus achieving a deep integration of advanced information technology and rich and in-depth converter steelmaking process theory and process know-how.

[0069] (2) Select the endpoint control targets of converter steelmaking: molten steel temperature, molten steel carbon content, molten steel sulfur content, and molten steel phosphorus content, and comprehensively analyze the impact of various process operation parameters in the smelting process (molten iron weight, temperature, and chemical composition; scrap steel weight and type; oxygen supply, oxygen lance position mode, and time-weighted average lance position; lime addition amount, batch, batch weight, and addition time; bottom blowing stirring gas flow rate, etc.) on the endpoint control target parameters, including single factor impact analysis and multi-factor impact analysis.

[0070] (3) An identification model for oxygen lance position changes is established. By tracking the trajectory of the oxygen lance in the system, the system realizes automatic identification of the average oxygen lance position based on time weighting, the lance position change pattern, etc., and adds two new derived process parameters in addition to the total oxygen supply in the oxygen supply operation, thus achieving a more accurate process description of the oxygen supply process.

[0071] (4) A recognition model for the slag material addition mode was established. The system automatically identified different slag making modes of "single slag method" and "double slag method" based on the original adding data and gun position data such as the slag material addition time point, addition weight, addition batch, oxygen gun position, auxiliary gun position, etc., and automatically calculated the slag material addition time stages and corresponding staged addition amounts based on the entire blowing cycle. A more accurate and detailed description of the slag making operation process is achieved.

[0072] (5) Establish a core process evaluation model to analyze and evaluate the oxygen supply process, slag making process, bottom blowing and stirring process, smelting process of different steel grades, automatic steelmaking level, etc., and provide specific improvement suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A system architecture diagram of an embodiment of the present invention.

[0074] Figure 2 The system operation flow chart of the embodiment of the present invention.

[0075] Figure 3 This is a diagram of a data structure abstraction method according to an embodiment of the present invention.

[0076] Figure 4 This is an oxygen supply mode recognition diagram of an embodiment of the present invention.

[0077] Figure 5 This is a slag-forming pattern recognition diagram of an embodiment of the present invention.

[0078] Figure 6 A process analysis, evaluation and suggestion diagram for an embodiment of the present invention.

[0079] Figure 7 This is a data health status monitoring diagram of an embodiment of the present invention.

[0080] Figure 8 This is a high-level background analysis diagram of an embodiment of the present invention.

[0081] Fig. 9 This is the use effect of the embodiment of the present invention: a graph showing the decrease in the total consumption of slag-making materials.

[0082] Fig.10 This is a diagram showing the effect of using an embodiment of the present invention: reduction of the total iron content in the slag. DETAILED DESCRIPTION

[0083] The embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0084] It should be noted that when a component is considered to be "connected" to another component, it may be directly connected to the other component or there may be a central component at the same time. When a component is considered to be "located on" another component, it may be directly located on the other component or there may be a central component at the same time. The terms "top", "bottom", "upper", "lower", "left", "right", "front", "back", and similar expressions used herein are for illustrative purposes only.

[0085] Some embodiments of the present application are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0086] See also Figure 1-10 The embodiment of the present invention proposes a method for optimizing and analyzing the converter steelmaking process. The present invention is mainly used for the converter steelmaking plant to monitor, analyze, and predict the converter smelting process status in real time and accurately, and to calculate the process control recommendation value based on the optimization model, so as to help the process personnel to comprehensively, systematically and conveniently grasp the converter smelting status, timely discover existing problems, and make necessary interventions in time, and gradually optimize and improve the converter smelting process according to the process analysis results and the model calculation results.

[0087] The system architecture of the present invention adopts a B / S architecture that is easy to deploy and use, so that end users can access the system through a web browser anytime and anywhere. Figure 1 Compared with the common B / S architecture, this system integrates a highly applicable ETL data interface and an API interface associated with the background deep analysis module in the background.

[0088] The system operation process is as follows Figure 2 shown.

[0089] The data connected to the system involves various data sources such as production plans, process specifications, raw material composition, inspection and testing data, production process parameters, etc. Through three interfaces, namely data subscription and push interface, data ETL interface, and data API interface, the data is transmitted and integrated into the system master database.

[0090] In view of the health status of the data stored in the system's main database, a corresponding data status monitoring and diagnosis service is running, and the corresponding status display is given according to the real-time data status, including normal (green), warning (orange) and serious error alarm (red).

[0091] The main application reads data from the main database, abstracts the data structure and constructs data objects. Then, the main application will use different models to calculate and identify the data, and complete the calculation of derived parameters such as oxygen lance position control mode, time-weighted average oxygen lance position, oxygen lance position segment number, slag making mode, etc. According to the different requests of system users, the system background will provide calculation services and output calculation results for various analysis functions such as production analysis, raw material analysis, oxygen supply analysis, slag making analysis, bottom blowing and stirring analysis, carbon oxygen concentration product analysis, endpoint control analysis, automatic steelmaking analysis, and process evaluation and optimization analysis, so as to meet the requirements of users.

[0092] In the data processing part of the main application, scientific data structure abstraction and data structure object construction are carried out for various complex data categories. The data abstraction method is shown in the attached Figure 3 shown.

[0093] The highest level class definition takes steel grade as the object, CGrade. Steel grade contains the second level class definition with furnace number as the object, CHeat, which contains all the important process parameters of the furnace steel from converter to refining to continuous casting, and these parameters are encapsulated and represented by different class definitions at the third level, for example: the class definition CHotMetal containing molten iron weight, temperature, chemical composition, etc.; the class definition CScrap containing scrap steel weight and category information; the class definition CLanPos containing oxygen gun position and change trajectory information; the class definition CSubLan containing sub-gun measurement time and measurement result information; the class definition CLime containing lime addition amount, addition batch, addition time and other information; the class definition CDmite containing light-burned dolomite addition amount, addition batch, addition time and other information; the class definition COrePellet containing pellet ore addition amount, addition batch, addition time and other information, and so on.

[0094] See the attached diagram for identification of converter oxygen lance oxygen supply mode. Figure 4 .

[0095] By using the constructed recognition model, the changing trajectory of the oxygen lance position is analyzed, calculated, identified and judged, so as to obtain several very important derived parameters related to oxygen supply: time-weighted average lance position, lance position change pattern, number of lance position operation sections, etc., so that the process analysis results using these derived parameters to express the oxygen supply process are more accurate and reliable.

[0096] The slag pattern recognition process of the converter blowing process is shown in the attached Figure 5 shown.

[0097] By using the constructed recognition model, the oxygen lance position change data, auxiliary lance detection time and position data, and the time, batch, weight and other information of adding lime are comprehensively analyzed and identified, so as to determine whether the slag making operation of the furnace is single slag method or double slag method. More specifically, by real-time tracking the height of the oxygen lance and auxiliary lance, it is identified whether the oxygen lance or auxiliary lance is lifted out of the furnace and whether the oxygen lance is lifted to a higher non-blowing position (but still in the furnace). If the oxygen lance is lifted out of the furnace once and the auxiliary lance is lowered and lifted out of the furnace twice during the entire smelting cycle, this mode is identified as a single slag mode; if the oxygen lance is lifted out of the furnace twice and the auxiliary lance is lowered and lifted out of the furnace twice, and before the oxygen lance is lifted out of the furnace twice, more than a certain amount (weight threshold) of lime, light-burned dolomite and other slag-making materials are added and a certain length of time (time threshold) of blowing is performed, this mode is identified as a double slag mode. Heats that do not match the above mode definition are considered to be abnormal operation heats. The accurate identification of this slag-forming mode is very important, because there are great differences in the smelting process operation of the converter under the two slag-forming modes. Only accurate identification can ensure the reliability of all subsequent data analysis results.

[0098] In constructing the process analysis evaluation and recommendation model, both single target parameters and comprehensive target parameters were taken into consideration. Comparisons were made between different teams and different furnaces within the same production unit, and between actual results and expectations based on metallurgical process theory, so as to make a comprehensive evaluation.

[0099] The process and technical principle of process analysis, evaluation and suggestion are as follows: select the evaluation time interval, then select the target steel type to be evaluated, and after submitting the request to the system, the system will perform statistical calculations of three-level process quality data (three levels refer to workshop level, furnace level, and team level), such as calculating the phosphorus content of the final molten steel. Compare the three-level process quality data, compare the actual data with the expectations based on process theory, and then perform optimal comparison for different final targets (temperature hit rate, final carbon content, final phosphorus content, etc.). Based on the results of the optimal comparison, give an evaluation of the process operation level within the selected time period, point out the outstanding problems, and further provide the direction of process improvement.

[0100] For example, for a certain type of steel produced in the selected time period, if the process parameter to be analyzed is the phosphorus content, the mean of the final molten steel phosphorus content at the workshop level is first calculated as the baseline for the final phosphorus content control level, and then the final molten steel phosphorus content of each converter is calculated and compared with the workshop mean to find the best and worst furnaces. From the furnace with the best phosphorus content control, the final molten steel phosphorus content corresponding to different teams is calculated, the best team is found, and the mean of the main process parameters controlled by the corresponding team is calculated; from ... the best team is found, and the mean of the main process parameters controlled by the corresponding team is calculated; from the furnace with the best phosphorus content control, the final molten steel phosphorus content corresponding to the best team is calculated. For the worst furnace seat, calculate the final phosphorus content of molten steel corresponding to different teams, find the worst team, and calculate the mean of the main process parameters controlled by the corresponding team. Compare the mean values ​​of process parameters of the two teams (the best team in the best furnace seat and the worst team in the worst furnace seat), and then combine with the basic theory of steelmaking process, such as the influence of temperature, oxidizability, alkalinity, etc. on dephosphorization, to find out the differences, shortcomings and suggestions for improvement in process parameter control between "the worst team in the worst furnace seat" and "the best team in the best furnace seat".

[0101] Since there are many sources of data in the system interface and the categories are rich, the selection of core data (parameters) and the selection of core data (parameter) relationships are very important in data health status monitoring. The appropriate selection of this core data group and the corresponding data relationship is also an important part of the construction of this data status monitoring model.

[0102] Attached Figure 7 The model operation process of the data health status monitoring function is demonstrated:

[0103] (1) First, determine the size of the data monitoring rolling time window;

[0104] (2) Based on the determined core data group, determine the data tables covered by data monitoring;

[0105] (3) Determine data monitoring rules based on the determined core data relationships;

[0106] (4) Determine and implement the triggering method for data monitoring calculation;

[0107] (5) Data monitoring service operation and result output.

[0108] If the data status is normal, the indicator light will be green; if there is a minor problem with the data status, but the main functions of the system can still be used, the indicator light will be yellow; if there is a major error in the data status, using the system at this time may bring the risk of incorrect calculation results, and the indicator light will be red. Users can also click to obtain the specific reasons behind the data status alarm.

[0109] The advanced background analysis module is intended for advanced users with development capabilities. The corresponding data flow and analysis functions are as follows: Figure 8 As shown. The advanced background analysis function provides two new system functions: the first function is to meet the requirements of urgent temporary or phased data in-depth analysis tasks. After the analysis task is completed, these background analysis functions are generated. There are two ways to process them. If the user application is large and the versatility is strong, they will be further improved and optimized to realize the public release of the function on the system; if the user application is small, the function will be retained in the background and will not be publicly released. The second function is to undertake an exploratory development platform for the continuous evolution of system functions and act as an incubation platform for new advanced system functions.

[0110] The specific implementation methods and steps are as follows:

[0111] (1) Install the server database system software and create the database (data tables, views, stored functions, scheduled tasks, triggers, etc.).

[0112] (2) Configure data subscription and push services.

[0113] (3) Configure the ETL interface service.

[0114] (4) Configure the API interface.

[0115] (5) Complete the installation and configuration of background data query, processing, calculation and analysis software modules.

[0116] (6) Complete the installation and configuration of background advanced analysis applications.

[0117] (7) Complete the installation and configuration of the web server (WebServer).

[0118] (8) Complete user login information entry and permission configuration.

[0119] (9) Enable data subscription and push services.

[0120] (10) Enable ETL services that connect to multiple data systems and multiple databases.

[0121] (11) Start the network server.

[0122] (12) At this point, the system server is in normal service status and can respond to various requests from front-end users at any time.

[0123] (13) Install the front-end: You can install any mainstream web browser (Chrome, Edge, FireFox, etc.).

[0124] (14) System access and user login: Enter the URL of the analysis system's homepage in the browser address bar and press Enter to reach the system's user login page. Enter the login information, and after the system is verified, the user can see the main page that displays the system's functions.

[0125] (15) Data status check: Check the data status indicator at the bottom right of the main page. If it is green, it means that the data is in good health and everything is normal; if it is orange, it means that some data is incomplete, but it will not affect the application of the main functions; if it is red, it means that there is a major anomaly or error in the data source, and the problem with the data source needs to be resolved before the system functions can be used.

[0126] (16) Main interface function group 1: process quality overview.

[0127] This function displays important indicators and information in various aspects such as production, process, equipment, quality, etc. within the time period selected by the user, allowing users to easily obtain the production status of the current and any previous selected time period.

[0128] (17) Main interface function group 2: production analysis.

[0129] This function displays the production volume and production efficiency data information associated with different production factors (steel type, time, furnace seat, team, etc.) within the time period selected by the user, with production indicators as statistical analysis and calculation parameters, so as to understand the production status, discover potential problems and improve production efficiency.

[0130] (18) Main interface function group 3: molten iron analysis.

[0131] This function displays the influence of molten iron temperature, main chemical composition (S i, Mn, S, P, Ti), molten iron usage, and molten iron conditions on the converter operation process, as well as the subsequent refining process and end-point composition within the user-selected time period for the purpose of monitoring and optimization.

[0132] (19) Main screen function group 3: scrap steel analysis.

[0133] This function displays the analysis results of the scrap steel consumption per furnace, scrap steel type, and the impact of scrap steel addition on converter endpoint control within the time period selected by the user. From the analysis results, users can see the improvement direction of optimizing the scrap steel usage strategy.

[0134] (20) Main screen function group 4: oxygen supply analysis.

[0135] This function is to analyze the influence of oxygen supply process data (oxygen supply duration, oxygen pressure, oxygen flow, oxygen lance position and its change pattern, etc.) in the converter smelting process on the smelting cycle, final carbon content, final impurity content (phosphorus content, sulfur content), final total iron content in slag, etc., so as to provide important reference information for optimizing oxygen supply operation for the smelting of different steel grades.

[0136] (21) Main screen function group 5: slag analysis.

[0137] This function displays the impact of slag-making process operation on the smooth progress of smelting process, dephosphorization and desulfurization, smelting cycle and final molten steel temperature within the time period selected by the user, so that system users can find out from the analysis results what the optimized slag-making process should be in the coordinated smelting process of a batch of steel.

[0138] (22) Main screen function group 6: bottom blowing and stirring analysis.

[0139] This function displays the working status of the bottom blowing process components, their respective pressures and flows, their impact on the decarburization reaction rate, their impact on the dephosphorization and desulfurization effects, and their impact on the iron oxide content in the slag within the time period selected by the user.

[0140] (23) Main screen function group 7: Carbon oxygen concentration product analysis.

[0141] This function displays the working status of the gas supply components and the erosion status of the furnace lining from the changes in the carbon-oxygen concentration product within the time period selected by the user. By comparison, the differences in the operating characteristics of different teams and the direction of improvement can be found.

[0142] (24) Main screen function group 8: End point temperature hit rate analysis.

[0143] This function displays a multi-dimensional analysis of the factors affecting the endpoint temperature hit rate within the time period selected by the user.

[0144] (25) Main screen function group 9: End point carbon hit rate analysis.

[0145] This function displays the analysis of endpoint carbon hit rate data within the time period selected by the user, showing its changing trend and identifying potential problems.

[0146] (26) Main screen function group 10: dephosphorization and desulfurization control analysis.

[0147] This function displays the multi-dimensional single factor analysis and multi-factor analysis based on multiple regression analysis of the process factors that affect the dephosphorization and desulfurization effects within the time period selected by the user, so as to find out the important influencing factors and how to optimize the relevant factors to achieve optimal control of dephosphorization and desulfurization.

[0148] (27) Main screen function group 11: Automatic steelmaking analysis.

[0149] This function displays the data analysis of automatic steelmaking for each furnace, each team, and each different type of steel within the time period selected by the user, and discovers the factors affecting fully automatic steelmaking and the direction of improvement.

[0150] (28) Main screen function group 12: process analysis and optimization analysis.

[0151] This function displays the endpoint control target of converter smelting within the time period selected by the user as the target variable, and comprehensively determines the process operation parameters and endpoint results of the selected time period through the model, and makes optimization control suggestions in slag making, oxygen supply, metal material addition strategy, etc.

[0152] (29) Advanced background analysis: Based on the established technical architecture and the system service API that is gradually enriched as the system evolves, advanced system users (with development capabilities) can conduct in-depth analysis of specific and personalized process quality data according to their needs, thereby obtaining corresponding results; in addition, as an exploratory development link for the evolution of system functions, users can use the existing functions of the system to develop new system functions and release them to the outside world.

[0153] A converter steelmaking process optimization analysis method comprises the following steps:

[0154] S1: Establish a converter steelmaking process optimization analysis system to record converter steelmaking data;

[0155] S2: Select the endpoint control target of converter steelmaking and analyze the influence of various process operation parameters on the endpoint control target parameters during the smelting process;

[0156] S3: Determine process operation parameters and carry out converter steelmaking;

[0157] S4: Evaluate converter steelmaking.

[0158] Furthermore, in step S3, it also includes analyzing, calculating, identifying and judging the change of the oxygen lance position on the change trajectory of the oxygen lance position.

[0159] Furthermore, in step S3, it also includes identifying and judging different slag making modes, namely, the single slag method or the double slag method.

[0160] Furthermore, if the oxygen lance is lifted out of the furnace once and the auxiliary lance is lowered and lifted out of the furnace twice during the entire smelting cycle, this mode is identified and determined as a single slag mode; if the oxygen lance is lifted out of the furnace twice, the auxiliary lance is lowered and lifted out of the furnace twice, and before the oxygen lance is lifted out of the furnace twice, slag-making material is added in an amount exceeding the weight threshold and blowing exceeds the time threshold, this mode is identified and determined as a double slag mode, and furnaces that do not meet the above definitions of single slag mode or double slag mode are determined to be abnormal operation furnaces.

[0161] Furthermore, step S4 includes: selecting an evaluation time interval, then selecting a target steel grade to be evaluated, and performing statistical calculations on three-level process quality data, where the three levels refer to workshops, furnaces, and work groups; comparing the three-level process quality data, and comparing the actual data with expectations based on process theory, and then performing optimal comparisons for different endpoint targets, and based on the results of the optimal comparisons, providing an evaluation of the process operation level within the selected time period, pointing out existing problems, and providing directions for process improvement.

[0162] Furthermore, if the process parameter analyzed is the phosphorus content, the mean of the final phosphorus content of the molten steel at the workshop level is first calculated as the baseline for the final phosphorus content control level, and then the final phosphorus content of the molten steel of each converter is calculated and compared with the workshop mean to find the best and worst furnace seats. From the furnace seat with the best phosphorus content control, the final phosphorus content of the molten steel corresponding to different teams is calculated, the best team is found, and the mean of the main process parameters controlled by the corresponding teams is calculated; from the furnace seat with the worst phosphorus content control, the final phosphorus content of the molten steel corresponding to different teams is calculated, the worst team is found, and the mean of the main process parameters controlled by the corresponding teams is calculated. The mean of the process parameters of the best team in the best furnace seat and the worst team in the worst furnace seat are compared to find out the differences, deficiencies and suggestions for improvement in the process parameter control of the worst team in the worst furnace seat and the best team in the best furnace seat.

[0163] Furthermore, in step S2, the endpoint control targets include: molten steel temperature, molten steel carbon content, molten steel sulfur content, molten steel phosphorus content; process operation parameters in the smelting process include molten iron weight, temperature, chemical composition; scrap steel weight and category; oxygen supply, oxygen lance position mode, time-weighted average lance position; lime addition amount, batch, batch weight, addition time; bottom blowing stirring gas flow rate.

[0164] The present invention also provides a computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the converter steelmaking process optimization analysis method.

[0165] Whether at the data level or at the functional level, the system of the present invention specifically realizes comprehensive and detailed all-round coverage of the unit process operation of converter smelting, so that users of the system can conveniently obtain all helpful and guiding information according to their specific needs based on their respective positions. From the display of process status to the execution of production plans, from the charging process of the converter to the oxygen supply process, from the slag making process of converter smelting to the control of impurity element content, from the comparison between different steel grades to the comparison between different furnace seats, from the comparison between different teams to the comparison between different shifts, from the evaluation and analysis of the selected process to the recommended values ​​of process parameters given by the optimization calculation of the model, the application of this system has played a great role in promoting the production and process optimization of the workshop, directly or indirectly bringing about effects such as improved production efficiency, improved product quality, and reduced production costs.

[0166] Attached Fig. 9 It shows that the consumption of slag materials in the converter smelting process decreases month by month. The reduction in slag material consumption greatly reduces the cost of auxiliary materials in the steelmaking process.

[0167] Attached Fig.10 The figure shows the monthly reduction of the iron content (total iron content) in the slag during the converter smelting process. The reduction and the magnitude of the reduction shown in the figure mean that the amount of metal carried away through the slag is significantly reduced, that is, the metal recovery rate is significantly increased.

[0168] Calculated based on the improved indicators of reducing slag-making material consumption and iron loss in slag, and combined with the annual output of the workshop, the process optimization has brought the workshop an annual economic benefit of more than 15 million yuan.

[0169] In addition, those skilled in the art should recognize that the above embodiments are only used to illustrate the present application, and are not intended to limit the present application. As long as they are within the spirit and scope of the present application, appropriate changes and modifications to the above embodiments are within the scope of the present application.

Claims

1. A converter steelmaking process optimization analysis method, characterized in that: Includes steps: S1: Establish a converter steelmaking process optimization analysis system to record converter steelmaking data; S2: Select the endpoint control target of converter steelmaking and analyze the influence of various process operation parameters on the endpoint control target parameters during the smelting process; S3: Determine process operation parameters and carry out converter steelmaking; S4: Evaluate converter steelmaking.

2. The converter steelmaking process optimization analysis method according to claim 1, characterized in that: In step S3, it also includes analyzing and calculating the change trajectory of the oxygen lance position to identify and judge the change of the oxygen lance position, so as to obtain the characteristic variable value of the influence of the oxygen lance position on the smelting process: 1) Gun position control mode LM, the input required for calculation is expressed as follows: LM=f(h,t,H BOF1 ,H BOF2 ,LMDM) Where: LM: oxygen gun position mode, the calculation result is expressed as a string; h: real-time oxygen lance position, cm; t: duration calculated from the start of blowing, sec; H BOF1 : Converter mouth height, cm; H BOF2 : height of refractory surface of converter bottom, cm; LMDM: Gun Mode Detection Model. 2) Time-weighted average gun position TWALH: Where: TWALH: time-weighted average gun position, cm; Δt i : The time step of the real-time detection of the gun position recorded at time i, sec; h i : The detection value of the gun position at time i, cm; i: The sequence number of the gun position measurement and recording during the blowing cycle; n: the serial number of the last gun position record in the blowing cycle; T: blowing cycle, sec.

3. The converter steelmaking process optimization analysis method according to claim 2, characterized in that: In step S3, it also includes identifying and judging different slag making modes, namely, single slag method or double slag method.

4. The converter steelmaking process optimization analysis method according to claim 3, characterized in that: If the oxygen lance is lifted out of the furnace once and the auxiliary lance is lowered and lifted out of the furnace twice during the entire smelting cycle, this mode is identified and determined as the single slag mode; if the oxygen lance is lifted out of the furnace twice, the auxiliary lance is lowered and lifted out of the furnace twice, and before the oxygen lance is lifted out of the furnace twice, slag-making material exceeding the weight threshold is added and blowing exceeding the time threshold is performed, this mode is identified and determined as the double slag mode. Furnaces that do not meet the above definitions of the single slag mode or the double slag mode are determined to be abnormal operation furnaces.

5. The converter steelmaking process optimization analysis method according to claim 1, characterized in that: Step S4 includes: selecting an evaluation time interval, then selecting the target steel type to be evaluated, and performing statistical calculations on three-level process quality data, where the three levels refer to workshops, furnaces, and teams; comparing the three-level process quality data, and comparing the actual data with expectations based on process theory, and then performing optimal comparisons for different endpoint targets. Based on the results of the optimal comparisons, an evaluation of the process operation level within the selected time period is given, existing problems are pointed out, and directions for process improvement are given.

6. The converter steelmaking process optimization analysis method according to claim 5, characterized in that: If the process parameter to be analyzed is phosphorus content, first calculate the average phosphorus content of the final steel melt at the workshop level. The final phosphorus content of each converter is calculated and compared with the workshop average to find the best and worst furnaces. From the furnace with the best phosphorus content control, the final phosphorus content of the molten steel corresponding to different teams is calculated to find the best team, and the average of the main process parameters controlled by the corresponding team is calculated; from the furnace with the worst phosphorus content control, the final phosphorus content of the molten steel corresponding to different teams is calculated to find the worst team. group, and calculate the mean of the main process parameters controlled by the corresponding teams, compare the mean of the process parameters of the best team in the best furnace seat with the worst team in the worst furnace seat, and find out the differences, shortcomings and suggestions for improvement in the process parameter control between the worst team in the worst furnace seat and the best team in the best furnace seat.

7. The converter steelmaking process optimization analysis method according to claim 5, characterized in that: In step S2, the endpoint control targets include: molten steel temperature, molten steel carbon content, molten steel sulfur content, molten steel phosphorus content; the process operation parameters during the smelting process include molten iron weight, temperature, and chemical composition; Scrap steel weight and category; oxygen supply, oxygen lance position pattern, time-weighted average lance position; lime addition amount, batch, batch weight, addition time; bottom blowing stirring gas flow rate.

8. The converter steelmaking process optimization analysis method according to claim 6, characterized in that: The evaluation includes the following steps: (1) Select the original data time interval R for model calculation day : (2) Calculate the phosphorus content [%P] of molten steel produced by each converter in the workshop: Where: BOF stands for Converter; n1 represents the size of the data set in the computing scenario; (3) Optimize and find the furnace seat with the lowest phosphorus content among all converters: (4) Calculate the average phosphorus content of molten steel produced by each team in the furnace with the lowest phosphorus content in molten steel: Where: TEAM represents the team of the same furnace; m 1 represents the number of furnaces smelted by the corresponding team within the statistical time interval; (5) Find the team with the lowest phosphorus content in molten steel among all teams: (6) Calculate the mean control value of the process parameters in the smelting process of the team and recommend it as the optimization value: Where: W lime_sug ,W dolomite_sug ,T oxyblow_sug ,TWALH sug : Respectively represent the recommended lime addition amount, light burning addition amount, oxygen blowing time, and average gun position; W_lime_i, W_dolomite_i, T_oxyblow_i, TWALH_i: respectively represent the amount of lime added, amount of light burning added, oxygen blowing time, and average gun position used in t statistical calculation for a single furnace.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the converter steelmaking process optimization analysis method as described in any one of claims 1 to 8.

Images