Converter steelmaking static calculation method and device, electronic equipment and storage medium

Abstract

The invention discloses a converter steelmaking static calculation method and device, electronic equipment and a storage medium. The method comprises the following steps of obtaining historical smelting data of a converter; classifying the historical smelting data according to steel types to form a plurality of data tables distinguished by the steel types; searching at least one heat closest to the current heat parameter in the same steel grade or same static group steel grade data table as a reference heat; and obtaining a smelting data recommendation value of the current heat according to the smelting data of the reference heat to guide the smelting of the current heat. According to the method, the obtained data of the reference heat is consistent with or close to the parameters of the current heat, so that the adoption of an increment model or small increment amplitude and small error is avoided, the heat balance of the static model is more accurate, and the calculation result of the static model is optimized according to the specific reference heat data, so that the static model is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of static calculation in converter steelmaking, and particularly to a static calculation method, device, electronic device and storage medium for converter steelmaking. Background Technique

[0002] The current mainstream converter steelmaking models generally consist of a static model and a dynamic model. The dynamic model is very accurate and has been accepted by most converter steel plants; while the static model has a slightly larger deviation, mainly affected by raw material fluctuations and its algorithm.

[0003] The static model mainly solves the calculation of the addition amount of fluxes (mainly lime, lightly burned lime, and raw dolomite) and the addition amount of coolants (mainly pellets and iron sheets). The flux calculation is based on alkalinity, and the calculation formula is relatively simple and generally recognized; mainly, the heat balance calculation is more difficult. The current static model generally refers to the historical data of 6 furnaces, and the hot metal volume, scrap volume, hot metal composition, and hot metal temperature of these 6 furnaces vary greatly.

[0004] The static model calculates based on information such as hot metal volume, hot metal carbon content, hot metal silicon content, hot metal manganese content, hot metal temperature, scrap volume, flux group, other information in the steel grade dictionary, and static self-learning group, etc., so as to give the calculated amount of flux, static total oxygen amount, coolant weight, and give a suitable TSC measurement timing. Currently, the main deviation is in the coolant weight.

[0005] Table 1 Contents of Static Calculation

[0006]

[0007] When both TSC carbon and temperature are within a reasonable range, such as the carbon content [0.20, 0.55], the dynamic model is relatively accurate and can hit the "bull's-eye (target carbon temperature at blow stopping)" well, that is, hit the blow stopping target, which can improve the blow stopping hit rate, reduce reblowing, and reduce the smelting cost.

[0008] Currently, there are two calculation methods for the static model. One is a pure theoretical calculation method based on mechanism, and the other is a calculation method using reference furnace charges. Relatively speaking, the latter is more accurate.

[0009] For the pure theoretical calculation method, many assumptions are made. For the hot metal parameters (weight, composition, temperature), the carbon analysis of the hot metal composition is inaccurate, and often a hypothetical carbon content is used; for the scrap parameters (weight, type, composition), there is no scrap composition, and a hypothetical composition is used. Such oxygen consumption and calorific value calculations are inaccurate, resulting in a relatively large deviation of the pure theoretical static model.

[0010] The calculation method of the reference heat is actually an incremental model. After the steel grade to be smelted is determined, the heat balance and static oxygen content are calculated according to the corresponding self-learning reference group. For a specific steel grade, there is a static self-learning reference group, and a self-learning reference group has 6 heats of data as reference heats. Moreover, the parameter range is wide and the referenceability is poor. The calculation is improved based on the incremental model, but the accuracy is not good. For example, Figure 4 As shown, the hot metal volume, hot metal composition, scrap steel volume, flux addition volume, oxygen consumption, etc. fluctuate greatly. For example, the silicon content ranges from 0.10 - 0.90%, and all 6 heats are different. The referenceable parameters vary greatly, resulting in a large difference in the calculation of the incremental model and generally a large deviation in static calculation.

[0011] From Figure 4 it can be seen that the key parameters fluctuate greatly. For example: the hot metal volume ranges from 249.9 - 263.8 tons, the scrap steel volume ranges from 36.4 - 53.5 tons, and the silicon content ranges from 0.218 - 0.469%. In this way, the referenceability is not strong.

[0012] The hot metal and scrap steel conditions of each heat vary greatly. The hot metal weight, composition (C, Si, Mn), scrap steel weight, and the materials added during blowing (types, quantities) are all different. Directly referring in this way, the parameter correction amount of the incremental model is relatively large, and each correction is large, resulting in a relatively large deviation. SUMMARY OF THE INVENTION

[0013] The present invention aims to solve at least one of the technical problems existing in the prior art, and provides a static calculation method, device, electronic device and storage medium for converter steelmaking.

[0014] In a first aspect, the present invention provides a static calculation method for converter steelmaking, including the following steps:

[0015] Obtain the historical smelting data of the converter;

[0016] Classify the historical smelting data by steel grade to form multiple data tables differentiated by steel grade;

[0017] Find at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group as the reference heat;

[0018] If the flux types of the current heat and the reference heat are inconsistent, convert the flux of the reference heat into the flux type of the current heat, and obtain the recommended value of the smelting data of the current heat according to the smelting data of the reference heat after flux conversion, so as to guide the smelting of the current heat.

[0019] Further, the static calculation method for converter steelmaking according to the present invention further includes the following steps: If the flux types of the current heat are the same as those of the reference heat, directly obtain the recommended value of the smelting data of the current heat based on the smelting data of the reference heat.

[0020] Further, obtaining the recommended value of the smelting data of the current heat specifically includes: When the reference heat is one heat, the recommended value of the smelting data of the current heat is the smelting data of this reference heat;

[0021] When the reference heat is multiple heats, the recommended value of the smelting data of the current heat is the average value of the smelting data of multiple reference heats.

[0022] Further, converting the flux of the reference heat into the flux type of the current heat specifically includes: According to a preset heat conversion table, convert the flux of the reference heat into the flux type of the current heat.

[0023] In a second aspect, the present invention also provides a static calculation method for converter steelmaking, including the following steps:

[0024] Obtain the historical smelting data of the converter;

[0025] Classify the historical smelting data by steel grade to form multiple data tables differentiated by steel grade;

[0026] Find at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group as the reference heat;

[0027] Substitute the smelting data of the reference heat into the static model to replace the existing reference heat, and obtain the recommended value of the smelting data of the current heat.

[0028] Further, finding at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group specifically includes:

[0029] Screen the heats that meet the first-level screening conditions in the data table of the same steel grade or the steel grades in the same static group. If the number of the heats that meet the first-level screening conditions found meets the set requirements, the screening ends, and the heats that meet the first-level screening conditions found are used as the reference heat. Otherwise, continue to screen the heats that meet the second-level screening conditions in the data table of the same steel grade or the steel grades in the same static group, and so on, until the number of the heats that meet the nth-level screening conditions found meets the set requirements, and use the heats that meet the nth-level screening conditions found as the reference heat;

[0030] When the difference between each parameter of a certain heat in the data table of the same steel grade or the steel grades in the same static group and each parameter of the current heat is within the nth-level range, it is determined that this heat meets the nth-level screening conditions;

[0031] The range of the nth level is greater than the range of the (n - 1)th level.

[0032] In a third aspect, the present invention further provides a static calculation device for converter steelmaking, including:

[0033] A data acquisition module, which is used to acquire the historical smelting data of the converter;

[0034] A data classification module, which is used to classify the historical smelting data by steel type to form multiple data tables differentiated by steel type;

[0035] A search and matching module, which is used to find at least one furnace charge closest to the parameters of the current furnace charge in the data tables of the same steel type or the steel types in the same static group as the reference furnace charge;

[0036] A flux conversion module, which is used to perform flux conversion and obtain the recommended value of the smelting data of the current furnace charge based on the smelting data of the reference furnace charge after flux conversion, so as to guide the smelting of the current furnace charge.

[0037] In a fourth aspect, the present invention further provides a static calculation device for converter steelmaking, including:

[0038] A data acquisition module, which is used to acquire the historical smelting data of the converter;

[0039] A data classification module, which is used to classify the historical smelting data by steel type to form multiple data tables differentiated by steel type;

[0040] A search and matching module, which is used to find at least one furnace charge closest to the parameters of the current furnace charge in the data tables of the same steel type or the steel types in the same static group as the reference furnace charge;

[0041] A static model module, which is used to obtain the recommended value of the smelting data of the current furnace charge based on the smelting data of the reference furnace charge, so as to guide the smelting of the current furnace charge.

[0042] In a fifth aspect, the present invention further provides an electronic device, including:

[0043] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the converter steelmaking static calculation method as described in the first aspect or the second aspect.

[0045] In a sixth aspect, the present invention further provides a storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the static calculation method for converter steelmaking described in the first aspect or the second aspect are implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains the historical smelting data of the converter; classifies the historical smelting data according to steel grades to form multiple data tables differentiated by steel grades; finds at least one furnace charge closest to the parameters of the current furnace charge in the data tables of the same steel grade or the steel grades in the same static group as the reference furnace charge. The data of the reference furnace charge obtained by the above method is consistent with or close to the parameters of the current furnace charge, thus avoiding the use of an incremental model with a very small incremental amplitude and very small error, making the heat balance of the static model more accurate, and optimizing the calculation results of the static model based on the specific data of the reference furnace charge to make the static more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of the static calculation method for converter steelmaking provided by the first embodiment of the present disclosure;

[0048] Figure 2 It is a flowchart of the static calculation method for converter steelmaking provided by the second embodiment of the present disclosure;

[0049] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;

[0050] Figure 4 It is a schematic diagram of the parameters of the reference furnace charge in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] To enable those skilled in the art to better understand the technical solutions of the present disclosure, the present disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Unless otherwise defined, the technical terms or scientific terms used in the present disclosure should have the ordinary meanings understood by those of ordinary skill in the art to which the present disclosure belongs. The "first", "second" and similar terms used in the present disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. Similarly, the terms such as "a", "one" or "the" do not denote a quantity limitation, but mean that there is at least one. The terms such as "including" or "comprising" mean that the elements or items appearing before the term cover the elements or items listed after the term and their equivalents, without excluding other elements or items.

[0053] In the various figures, like reference numerals are used to designate like elements. For clarity, not all parts of the figures are drawn to scale. In addition, some well-known parts may not be shown in the figures.

[0054] Numerous specific details of the present disclosure are described below in order to provide a better understanding of the present disclosure. However, as those skilled in the art will appreciate, the present disclosure may be practiced without these specific details.

[0055] The reference heats of the existing incremental model are generally six heats, and the parameters of these six heats vary greatly. The processing method is the reference heat + incremental model. For example, if the silicon is 0.100% higher, how much oxygen needs to be increased and what is the calorific value.

[0056] In order to calculate the heat balance of the current heat, a relatively common incremental model is adopted, and the algorithm is as follows:

[0057] For example, if the Si in the current heat increases by 0.201%, then in order to calculate the increased heat, a heat parameter of Si is introduced here, (Si in the current heat - Si in the reference heat) × the heat parameter of Si;

[0058] For example, if the Mn increases by 0.091%, then in order to calculate the increased heat, a heat parameter of Mn is introduced here, (Mn in the current heat - Mn in the reference heat) × the heat parameter of Mn;

[0059] For example, if the hot metal temperature increases by 37°C, then in order to calculate the increased heat, a heat parameter of the hot metal temperature is introduced here, (T in the current heat - T in the reference heat) × the heat parameter of hot metal T;

[0060] Through this incremental model, the heat balance of the current heat can be calculated by referring to the reference heat.

[0061] Then, several problems will arise based on this algorithm:

[0062] The heat parameters of parameters such as Si, Mn, and hot metal temperature are not very accurately calculated or set themselves, and it is also difficult to calculate them very precisely;

[0063] If the parameters such as Si, Mn, and hot metal temperature have a large difference between the current heat and the reference heat, the correction amount will be very large, resulting in a greater heat deviation.

[0064] Based on this consideration, the present invention uses a big data model to screen this kind of data in historical heats according to steel grades, forming data table A as the overall reference heat A for this steel grade. Then, for a specific heat, the present invention further screens out heats with the same or similar parameters such as Si, Mn, and hot metal temperature from reference heat A to form specific reference heat B. Through the first screening, the amount of reference data is reduced and the screening speed is accelerated; for the heats screened through the second screening, the data of the reference heats is the same as or close to the parameters of the current heat, thus avoiding the use of an incremental model, or the incremental range is very small and the error is very small, so that the heat balance of the static model is more accurate.

[0065] Currently, the processing method based on this consideration has been well verified in many steel mills, and its accuracy is much higher than the current processing method.

[0066] The present invention provides a static calculation method for converter steelmaking, including the following steps:

[0067] Obtain the historical smelting data of the converter;

[0068] Classify the historical smelting data according to steel grades to form multiple data tables differentiated by steel grades;

[0069] Find at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group as the reference heat;

[0070] If the flux types of the current heat and the reference heat are inconsistent, convert the flux of the reference heat into the flux type of the current heat, and obtain the recommended value of the smelting data of the current heat according to the smelting data of the reference heat after flux conversion to guide the smelting of the current heat.

[0071] In the embodiment of the present invention, through big data screening, heats with "the same" parameters are selected as reference heats, thus abolishing the incremental model (with deviation).

[0072] If the flux types added in the current heat and the reference heat change, a heat conversion table is introduced to calculate the heat balance.

[0073] Furthermore, the static calculation method for converter steelmaking of the present invention further includes the following step: if the flux types of the current heat and the reference heat are the same, directly obtain the recommended value of the smelting data of the current heat according to the smelting data of the reference heat.

[0074] Furthermore, obtaining the recommended value of the smelting data of the current heat specifically includes: when the reference heat is one heat, the recommended value of the smelting data of the current heat is the smelting data of this reference heat;

[0075] When there are multiple reference heats, the recommended value of the smelting data for the current heat is the average of the smelting data of the multiple reference heats.

[0076] Further, convert the fluxes of the reference heats into the flux types of the current heat, specifically including: converting the fluxes of the reference heats into the flux types of the current heat according to a preset heat conversion table.

[0077] In some embodiments, finding at least one heat in the steel grade or the steel grade data table of the same static group that is closest to the parameters of the current heat specifically includes:

[0078] Screening the heats that meet the first-level screening conditions in the steel grade or the steel grade data table of the same static group. If the number of the found heats that meet the first-level screening conditions meets the set requirements (set as needed), the screening ends, and the found heats that meet the first-level screening conditions are used as the reference heats. Otherwise, continue to screen the heats that meet the second-level screening conditions in the steel grade or the steel grade data table of the same static group. If the number of the found heats that meet the second-level screening conditions meets the set requirements (set as needed), the screening ends, and the found heats that meet the second-level screening conditions are used as the reference heats. Otherwise,... and so on, until the number of the found heats that meet the nth-level screening conditions meets the set requirements, and the found heats that meet the nth-level screening conditions are used as the reference heats.

[0079] When the differences between the parameters (smelting data parameters) of a certain historical heat in the steel grade or the steel grade data table of the same static group and the parameters of the current heat are all within the nth-level range, it is determined that the historical heat meets the nth-level screening conditions.

[0080] The nth-level range is larger than the (n - 1)th-level range. For example, the nth-level range is -m to +m, and the (n - 1)th-level range is -s to +s, where m is greater than n. Both m and n are positive numbers (generally decimals, set as needed). n is a positive integer greater than or equal to 2. The values of m, s, etc. corresponding to each parameter are different, and the units are also different, set as needed.

[0081] The present invention can set at least two levels of screening conditions. The number of levels of the screening conditions is set as needed.

[0082] For example, the first-level range is: such as Si ± 0.010%, Mn ± 0.010%, temperature ± 2 degrees..

[0083] The second-level range is: such as Si ± 0.020%, Mn ± 0.020%, temperature ± 4 degrees...

[0084] If the Si of the historical heat is 0.451% and the Si of the current heat is 0.443%, then 0.451% - 0.443% = 0.008%. Since 0.008% is less than the first threshold (such as 0.01%), it is determined that the parameter Si of this historical heat meets the first-level screening condition. If all the parameters of this historical heat meet the first-level screening condition, then this historical heat meets the first-level screening condition.

[0085] In the present invention, a relatively narrow range (such as the first range) is set for each parameter, and the heats that meet the conditions can be found immediately; if not, the second range is enabled, and this range is adjustable to prevent the situation where heats with the same or similar conditions cannot be found due to insufficient data volume.

[0086] Of course, the present invention is not limited to the above embodiments. In some other embodiments, at least one heat closest to the parameters of the current heat is searched for in the steel grade data table of the same steel grade or the same static group steel grade, which specifically includes:

[0087] Similarity calculation:

[0088] Calculating the difference: For each historical heat in the data table, calculate the difference between it and the current heat in each parameter. These differences reflect the degree of deviation between the two in the corresponding parameters.

[0089] Weighted summation: According to the importance of each parameter, different weights are assigned to them. Then, multiply the difference of each parameter by the corresponding weight and sum to obtain the total difference. The smaller the total difference, the more similar the two heats are.

[0090] Sorting and screening: Sort all the historical heats in ascending order of the total difference, and select the top N (N is a preset value representing the number of heats to be searched for) as the heats closest to the current heat.

[0091] For different parameters, it may be necessary to standardize the data table to eliminate the influence of dimension differences on the similarity calculation. For example, the content data can be converted into percentage form, or the temperature data can be converted into the same temperature unit.

[0092] Pre-organize the historical data table by steel grade, which specifically includes:

[0093] Read the database in the "Converter L3 System, or L2 System, or Steelmaking Model System" according to steel grades. The main information read includes: "Melting number, steel grade, blowing time, double slag information, hot metal volume, scrap volume, actual Si content in hot metal, Mn in hot metal, actual temperature of hot metal, oxygen blowing volume in double slag, total lime amount, total light burned amount, total unburned dolomite amount, total pellet amount, total scale amount, actual total oxygen amount, oxygen blowing volume during TSC measurement, calculated dynamic oxygen amount, actual dynamic coolant value, TSC measured carbon, TSC measured temperature, TSO measured carbon, TSO measured temperature, calculated dynamic carbon, calculated dynamic temperature, end-point oxygen activity", etc. Generate multiple new data tables A differentiated by steel grades. Classify the existing data first, so that the required data can be searched from relatively less data.

[0094] Table A Data Table (Data Table for the Same Steel Grade or Steel Grades in the Same Static Group)

[0095]

[0096] Search for the smelting data of multiple furnaces (such as 10 furnaces) closest to the conditions of the current heat in the historical data table A and display them.

[0097] When smelting a certain steel grade in the converter, such as 30MnSi, compare and search in the historical data table of 30MnSi. The main comparison information includes: hot metal volume, scrap volume, actual Si content in hot metal, Mn in hot metal, actual temperature of hot metal... When the search conditions are met, the corresponding heat smelting information is brought out.

[0098] Display the data of the 10 closest heats found, and display the data of these 10 heats in the form of Table A to form Data B, which is the optimal reference heat for the current smelting heat. This table is the self-learning optimal reference table. Table B Self-learning Optimal Reference Table (Data Table under the Same Conditions)

[0099]

[0100] For the above 10 heats, the hot metal conditions and scrap conditions are closest to those of the current heat, but still cannot be directly referenced. During different periods, the types of fluxes on site are changing, and there are differences in the types of fluxes added to each heat, so they cannot be directly referenced. For example, in Table C below, the differences in the fluxes added to each heat are large and cannot be used as a reference. Therefore, flux conversion based on the cooling effect is required.

[0101] Table C Historical Heat Data

[0102] Furnace number Lime Light burned Raw white Upper bin material Pellet Sheet iron Coke breeze Slag forming agent Sludge ball Silicon carbide ball 4178 3190 0 3543 715 0 0 0 176 0 0 4878 2970 0 4220 0 610 0 0 198 0 264 5178 3400 0 4018 2225 0 0 309 194 0 0 7896 3860 1520 215 0 0 0 311 0 0 258 7903 3640 2050 200 2220 0 324 0 200 0 240 7924 3930 1525 230 865 220 610 0 176 0 276 7926 3570 1515 220 4380 860 400 0 150 0 264 … … … … … … … … … … …

[0103] In the steelmaking model, there are flux calculation formulas, and lime and light burning are both calculated based on the flux formula; the heat balance is calculated based on the reference heat. If lime and light burning are summarized, and other types of fluxes are converted into pellets according to the cooling ratio, then the heat balance will have good reference value.

[0104] For such complex flux material changes, based on heat balance, a conversion processing method is introduced to calculate and set the cooling ratio and oxygen consumption ratio of each flux, as shown in Table D below.

[0105] Table D Flux Parameter Table

[0106]

[0107]

[0108] Define the cooling ratio of scrap as 1. For example, in a converter with a tapping volume of 278 tons, 1 ton of scrap can reduce the temperature of molten steel by 7°C, and its cooling ratio is 1; it is measured that 1 ton of lime can reduce the temperature of 278 tons of molten steel by 10°C, then the calculated cooling ratio of lime is: Considering the loss of lime, the general value of the lime cooling ratio is taken as 1.2 - 1.4. Similarly, the cooling ratios of other flux types can be measured.

[0109] According to the types of fluxes that can be added in the current heat (such as lime, lightly burned dolomite, pellets), convert other types of fluxes except lime and lightly burned dolomite in the reference heat into pellets, so that it can be directly referenced.

[0110] If the types of fluxes in the current heat are the same as those in the reference heat, directly take the average value of "lime quantity, lightly burned dolomite quantity, raw dolomite quantity, static pellet quantity, static oxygen quantity..." of 10 heats as the recommended value for the current heat, and the current heat does not need to be calculated again. The calculation methods for lime and lightly burned dolomite are based on basicity. As long as the basicity is not adjusted, the calculation amount will not change. The main thing is that the amount of coolant (pellets) is calculated by heat balance, and the reference heat mainly refers to the heat balance calculation part.

[0111] If the types of fluxes are different, convert the fluxes in the reference heat into the types of fluxes added in the current heat according to the conversion Table D.

[0112] If a certain quantity of "lime quantity, raw dolomite quantity, lightly burned dolomite quantity" in the flux is modified, the present invention automatically adjusts the addition amounts of other fluxes according to the calculation rules to ensure its heat balance. For example, the flux conversion module will convert it into other fluxes according to the cooling effect and oxygen consumption ratio. For example, if the lime is modified to 7000 kg, then (7122 - 7000) × CaO content of lime ÷ CaO content of raw dolomite = 200 kg, then the raw dolomite quantity + 200 kg, and then convert the changed cooling energy into pellet quantity.

[0113]

[0114]

[0115] Based on the same inventive concept, the present invention also provides a static calculation device for converter steelmaking, including:

[0116] A data acquisition module, which is used to acquire the historical smelting data of the converter;

[0117] A data classification module, which is used to classify the historical smelting data by steel type to form multiple data tables differentiated by steel type;

[0118] A search and matching module, which is used to find at least one furnace charge closest to the parameters of the current furnace charge in the data tables of the same steel type or the steel types in the same static group as the reference furnace charge;

[0119] A flux conversion module, which is used to perform flux conversion and obtain the recommended value of the smelting data of the current furnace charge according to the smelting data of the reference furnace charge after flux conversion, so as to guide the smelting of the current furnace charge.

[0120] Embodiment 2

[0121] See Figure 2 , the embodiment of the present invention also provides a static calculation method for converter steelmaking, including the following steps:

[0122] Acquire the historical smelting data of the converter;

[0123] Classify the historical smelting data by steel type to form multiple data tables differentiated by steel type;

[0124] Find at least one furnace charge closest to the parameters of the current furnace charge in the data tables of the same steel type or the steel types in the same static group as the reference furnace charge;

[0125] Substitute the smelting data of the reference furnace charge into the static model to obtain the recommended value of the smelting data of the current furnace charge.

[0126] The flux, static total oxygen amount, and static cooling agent amount calculated by the static model can be optimized and modified with reference to Data Table B to form the static model calculation value, or the optimal reference recommended value.

[0127] The present invention takes multiple identical furnace charges of the same steel type or the steel types in the same static group with the same (or close) charging parameters as the reference furnace charges, which has strong reference, makes the static calculation more accurate, thus creating more favorable conditions for the dynamic, and making the dynamic model more accurate.

[0128] Based on the same inventive concept, the present invention also provides a static calculation device for converter steelmaking, including:

[0129] A data acquisition module, which is used to acquire the historical smelting data of the converter;

[0130] A data classification module, which is used to classify historical smelting data by steel type to form multiple data tables differentiated by steel type;

[0131] A search and matching module, which is used to find at least one heat closest to the current heat parameters in the data tables of the same steel type or the same static group of steel types as the reference heat;

[0132] A static model module, which is used to obtain the recommended value of the smelting data of the current heat based on the smelting data of the reference heat to guide the smelting of the current heat.

[0133] Based on the same inventive concept, the embodiments of the present disclosure also provide an electronic device. Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As Figure 3 shown, an electronic device includes: one or more processors 101, a memory 102, and one or more I / O interfaces 103. One or more programs are stored on the memory 102. When the one or more programs are executed by the one or more processors, the one or more processors implement any of the converter steelmaking static calculation methods in the above embodiments; one or more I / O interfaces 103 are connected between the processor and the memory and are configured to implement information interaction between the processor and the memory.

[0134] Among them, the processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU), etc.; the memory 102 is a device with data storage capabilities, including but not limited to a random access memory (RAM, more specifically such as SDRAM, DDR, etc.), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102 and can implement information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus), etc.

[0135] In some embodiments, the processor 101, the memory 102, and the I / O interface 103 are interconnected through a bus 104 and are further connected to other components of the computing device.

[0136] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0137] Based on the same inventive concept, the embodiments of the present disclosure also provide a storage medium. A computer program is stored on the storage medium, and when the program is executed by a processor, the steps in any of the converter steelmaking static calculation methods in the above embodiments are implemented.

[0138] In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a machine-readable medium, the computer program including program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-described functions defined in the system of the present disclosure are performed.

[0139] It should be noted that the storage medium shown in the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any storage medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted by any appropriate medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, and the foregoing module, segment of a program, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0141] It can be understood that the above embodiments are merely exemplary embodiments adopted to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present disclosure, and these modifications and improvements are also regarded as within the protection scope of the present disclosure. It can be understood that the above embodiments are merely exemplary embodiments adopted to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present disclosure, and these modifications and improvements are also regarded as within the protection scope of the present disclosure.

Claims

1. A static calculation method for converter steelmaking, characterized in that, It includes the following steps: Obtain the historical smelting data of the converter; Classify the historical smelting data by steel grade to form multiple data tables differentiated by steel grade; Search for at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group as the reference heat; If the flux type of the current heat is inconsistent with that of the reference heat, convert the flux of the reference heat into the flux type of the current heat, and obtain the recommended value of the smelting data of the current heat based on the smelting data of the reference heat after flux conversion, so as to guide the smelting of the current heat.

2. The static calculation method for converter steelmaking according to claim 1, wherein: It also includes the following steps: If the flux type of the current heat is consistent with that of the reference heat, directly obtain the recommended value of the smelting data of the current heat based on the smelting data of the reference heat.

3. The static calculation method for converter steelmaking according to claim 1 or 2, characterized in that: Obtain the recommended value of the smelting data of the current heat, specifically including: when the reference heat is one heat, the recommended value of the smelting data of the current heat is the smelting data of this reference heat; When the reference heat is multiple heats, the recommended value of the smelting data of the current heat is the average value of the smelting data of multiple reference heats.

4. The static calculation method for converter steelmaking according to claim 1, characterized in that: Convert the flux of the reference heat into the flux type of the current heat, specifically including: according to the preset heat conversion table, convert the flux of the reference heat into the flux type of the current heat.

5. A static calculation method for converter steelmaking, characterized in that, It includes the following steps: Obtain the historical smelting data of the converter; Classify the historical smelting data by steel grade to form multiple data tables differentiated by steel grade; Search for at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group as the reference heat; Input the smelting data of the reference heat into the static model to obtain the recommended value of the smelting data of the current heat.

6. The static calculation method for converter steelmaking according to claim 1 or 5, characterized in that: Search for at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group, specifically including: Screen the heats that meet the first-level screening conditions in the data table of the same steel grade or the steel grades in the same static group. If the number of heats that meet the first-level screening conditions found meets the set requirements, the screening ends, and the heats that meet the first-level screening conditions found are used as the reference heats. Otherwise, continue to screen the heats that meet the second-level screening conditions in the data table of the same steel grade or the steel grades in the same static group, and so on, until the number of heats that meet the nth-level screening conditions found meets the set requirements, and the heats that meet the nth-level screening conditions found are used as the reference heats; When the difference between each parameter of a certain heat in the data table of the same steel grade or the steel grades in the same static group and each parameter of the current heat is within the nth-level range, it is determined that this heat meets the nth-level screening conditions; The nth-level range is greater than the (n - 1)th-level range.

7. A static calculation device for converter steelmaking, characterized in that, It includes: A data acquisition module for acquiring the historical smelting data of the converter; A data classification module for classifying the historical smelting data by steel grade to form multiple data tables differentiated by steel grade; A search and matching module for searching for at least one heat closest to the parameters of the current heat in the data table of the same steel grade or the steel grades in the same static group as the reference heat; The flux conversion module is used to perform flux conversion and obtain the recommended values of the smelting data for the current heat based on the smelting data of the reference heat after flux conversion, so as to guide the smelting of the current heat.

8. A static calculation device for converter steelmaking, characterized in that, It includes: The data acquisition module is used to acquire the historical smelting data of the converter; The data classification module is used to classify the historical smelting data by steel grade, forming multiple data tables differentiated by steel grade; The search and matching module is used to find at least one heat closest to the parameters of the current heat in the data tables of the same steel grade or the steel grades in the same static group as the reference heat; The static model module is used to obtain the recommended values of the smelting data for the current heat based on the smelting data of the reference heat, so as to guide the smelting of the current heat.

9. An electronic device, characterized in that, It includes: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor, so that the at least one processor can execute the static calculation method for converter steelmaking according to any one of claims 1-6.

10. A storage medium, characterized in that, A computer program is stored on the storage medium, wherein when the program is executed by a processor, the steps in the static calculation method for converter steelmaking according to any one of claims 1-6 are implemented.

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