A method for optimizing the cost of a converter charge

Abstract

The application discloses a converter charge cost optimization method, and belongs to the technical field of steel smelting.The method comprises the following steps: boundary condition determination;scrap classification and establishment of a raw material database;steel grade smelting key parameter setting;acquisition of molten iron, scrap, slag-making material, ore and heat generating agent composition data;determination of the amount of molten iron and scrap;scrap cost performance calculation;scrap use scheme;scrap structure optimization calculation;scrap structure optimization under the global molten iron ratio;scheme optimization, selection of the global calculation optimal value, i.e., the minimum value of the unit molten iron cost, determination of the scrap and molten iron scheme and smelting.The application realizes scrap structure optimization under the global molten iron ratio by scrap classification and establishment of a raw material database (price, composition, etc.), scrap cost performance calculation on the basis of heat balance, oxygen balance, slag balance and iron balance calculation, combination of the scrap use scheme and related constraint conditions and accurate production guidance.

Description

Technical Field

[0001] This invention belongs to the field of iron and steel smelting, and relates to converter smelting process, particularly to the optimization of scrap steel structure under the whole iron ratio in converter steelmaking, thereby realizing a method for optimizing converter charge cost. Background Technology

[0002] Furnace charge is an important material basis for steelmaking production. The ratio of molten iron to scrap steel and the type and amount of scrap steel are its main components. These two factors account for more than 75% of the cost of steelmaking. They represent the production and operation direction of the steel plant and are the basic guarantee for stabilizing process quality, reducing material consumption, and increasing profitability.

[0003] The volatility of the steel market and the frequent fluctuations in the prices of steelmaking raw materials present a key challenge for steel companies. One crucial issue is how to reduce raw material consumption during production while meeting technological requirements, and how to mitigate market fluctuations to lower production costs and effectively manage operations. Another key challenge is determining the appropriate iron-to-stainless steel ratio to reduce production costs, given that molten iron is a primary raw material for steelmaking. A third key challenge is determining the cost-effectiveness of scrap steel while meeting production requirements, given the wide variety of scrap types, fluctuating composition and iron content, and significant price differences.

[0004] To address the aforementioned key issues, small and medium-sized steel enterprises primarily rely on manual experience to determine the furnace charge structure in converter production, lacking a scientific approach. Large enterprises possess ERP systems, which calculate and guide decision-making based on standard material consumption and the structure of steel products produced within the system. However, current ERP systems do not consider the correlation and synergy between raw material and steel market fluctuations, non-standard consumption, and metallurgical science, thus failing to provide precise guidance in production practice. Therefore, while ensuring product quality, it is essential to scientifically control furnace charge costs through research on molten iron, scrap steel, and scrap steel types.

[0005] A search revealed several patents related to scrap steel batching optimization methods. For example, Chinese patent application No. 202011596778.5 proposed "a scrap steel optimized batching method, system, and its application." This application's technical solution establishes constraint equations for scrap steel loading weight, volume, mass, minimum loading amount, and maximum usable quantity based on constraints such as market and inventory supply, production plan requirements for scrap steel ratio and type, scrap steel bulk density and hopper capacity and loading amount, and minimum scrap steel addition amount in each loading operation. An objective equation is established with the goal of maximizing the total scrap steel increase benefit. A linear programming mathematical model is constructed based on this objective equation, and the addition amount of various types of scrap steel, i.e., the batching list, is calculated using this model. However, this application does not consider the correlation and synergy between real-time molten iron conditions, scrap steel ratio, scrap steel type consumption, and metallurgical science, making it difficult to achieve true cost minimization in production practice. Therefore, there is an urgent need for a converter charge cost optimization method to achieve scrap steel structure optimization under the overall hot metal ratio, thereby accurately guiding production and reducing production costs. Summary of the Invention

[0006] 1. The technical problem that the invention aims to solve

[0007] The purpose of this invention is to solve the problem that existing furnace charge batching methods cannot effectively and timely link market price, heat compensation, molten iron ratio, scrap steel structure, proportion, and economic efficiency while basically meeting product quality requirements. This invention provides a method for optimizing converter furnace charge costs. By classifying scrap steel and establishing a raw material database (price, composition, etc.), and based on calculations of heat balance, oxygen balance, slag balance, and iron balance, the cost-effectiveness of scrap steel is calculated. Combined with scrap steel usage plans and related constraints, the scrap steel structure is optimized under the overall molten iron ratio, providing precise guidance for production.

[0008] 2. Technical Solution

[0009] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0010] The present invention provides a method for optimizing converter charge costs, including optimizing the hot metal ratio, selecting scrap steel type, and calculating structural costs, specifically including the following steps:

[0011] Step 1: Determine the boundary conditions;

[0012] Step 2: Scrap steel classification and establishment of raw material database;

[0013] Step 3: Setting key parameters for steel smelting;

[0014] Step 4: Obtaining data on the composition of molten iron, scrap steel, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium balls, etc.), ore, and exothermic agent;

[0015] Step 5: Determine the amount of molten iron and scrap steel to be charged into the furnace: Pre-calculate the amount of molten iron C → Preset the molten iron ratio Mp → Calculate the amount of molten iron M → Calculate the amount of scrap steel S;

[0016] Step Six: Calculate the cost-effectiveness of scrap steel;

[0017] Step 7: Scrap Steel Utilization Plan;

[0018] Step 8: Calculation of scrap steel structure optimization;

[0019] Step 9: Optimization of scrap steel structure under the overall molten iron ratio;

[0020] Step 10: Optimal Scheme Selection. Select the optimal value (minimum unit cost) for the entire calculation, determine the scrap steel and molten iron schemes, and then proceed with the smelting.

[0021] In a further technical solution, step one involves determining the maximum amount of molten iron and scrap steel to be loaded, as well as the maximum amount of coolant and heating agent to be added, based on the carrying capacity of the molten iron and scrap steel crane, the weight and capacity of the molten iron ladle and scrap steel trough, the nominal capacity of the converter, the maximum feeding capacity of the silo, and the maximum amount of heating agent to be added.

[0022] In a further technical solution, step two involves classifying scrap steel into major categories based on its dimensions and weight, according to the national standard for scrap steel (GB / T4223-2017), including heavy scrap steel, medium scrap steel, small scrap steel, light and thin scrap steel, baled blocks, crushed material, and slag steel. Building upon these major categories and considering steelmaking requirements, the scrap steel can be further subdivided based on attributes such as the content of sulfur and other elements. Each type of scrap steel is identified by a subdivision code. Preferably, for ease of management and maintenance, the subdivision code is a combination of the major category code and the attribute code.

[0023] The raw material database includes data on molten iron, scrap steel, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium spheres, etc.), ore, exothermic agent composition, and thermal effect. Molten iron data includes the content of carbon, silicon, manganese, sulfur, phosphorus, vanadium, and titanium, slag carryover, and temperature. Scrap steel data includes typical carbon, silicon, manganese, sulfur, phosphorus, and iron content, as well as impurity carryover (i.e., the impurity content in the scrap steel, which is required for subsequent slag balance calculations). Slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium spheres, etc.) include the content of calcium oxide, silicon dioxide, magnesium oxide, and water. Ore includes the content of iron oxides and silicon dioxide. Exothermic agents include exothermic elements, impurities, and other main components.

[0024] In a further technical solution, step three involves determining the control range and target values ​​for basicity, MgO%, converter endpoint temperature, and endpoint carbon content, based on the type of steel being smelted.

[0025] In a further technical solution, in step five, the charging amount C = (0.90~1.30) × T, where C is the converter charging amount and T is the nominal capacity of the converter, in t; the molten iron ratio Mp = M / C, M = Mp × C, where Mp is the molten iron ratio (%), M is the amount of molten iron, and C is the converter charging amount, in t; the amount of scrap steel charged into the furnace S = CM, where S is the amount of scrap steel, C is the converter charging amount, and M is the amount of molten iron, all in t.

[0026] In a further technical solution, in step six, under the same iron-to-water ratio Mp as in step five, a single type of scrap steel is selected for heat balance, oxygen balance, slag balance, and iron balance calculations. The input quantities determined after the calculations should include: iron-to-water quantity, scrap steel quantity and type, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium balls, etc.), ore, calorific value, and oxygen quantity; the output quantities are steel-to-water quantity and gas quantity; the unit cost of steel-to-water is calculated based on the input quantities and unit prices of the above materials; after all types of scrap steel have been calculated, they are ranked according to cost-effectiveness as the basis for priority selection.

[0027] In a further technical solution, step seven, the scrap steel utilization plan should include key attributes such as scrap steel sub-code, addition quantity range, and resource availability identifier; each steel grade or similar steel grade should be matched with a scrap steel utilization plan; in the scrap steel utilization plan, the constraints such as the scrap steel addition quantity range have a higher priority than the cost-effectiveness of scrap steel.

[0028] In a further technical solution, in step eight, based on the boundary constraints in step one and the scrap steel usage plan in step seven, and under the preset iron-to-metal ratio Mp in step five, combined with the cost-effectiveness of scrap steel in step six, an optimal combination of scrap steel is selected; and calculations are performed for heat balance, oxygen balance, slag balance, and iron balance; the input should include: iron volume, the amount of each type of scrap steel, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium balls, etc.), ore, calorific value, and oxygen content; the output is the amount of molten steel; based on the input and unit price of the above materials, the unit cost of molten steel is calculated, and key data such as iron-to-metal ratio, iron volume, the amount of each type of scrap steel, and unit cost of molten steel are recorded.

[0029] In a further technical solution, step nine involves adjusting the molten iron ratio. Steps five through eight are repeated. Specifically, the loading amount C is the same as the previous setting, and the preset molten iron ratio Mp is increased by 0.05% to 5.0% based on the previous setting. Steps five through eight are repeated for multiple rounds of calculation until Mp = 99.9%. In particular, if Mp exceeds 99.9% due to mismatch in the steps, it is calculated as 99.9%.

[0030] In a further technical solution, step ten involves selecting the optimal value calculated across the entire domain (lowest steel cost) and determining the corresponding scrap steel and molten iron options to complete the optimization of furnace charge costs.

[0031] Compared with existing known technologies, the technical solution provided by this invention has the following significant advantages:

[0032] 1. It realizes the correlation and synergy between molten iron composition, temperature, molten iron ratio, scrap steel structure and consumption, and metallurgical science and cost.

[0033] 2. An optimization algorithm for molten iron ratio, scrap steel structure and usage is provided. It can quickly calculate molten iron ratio and scrap steel structure. The calculation is fast and accurate, highly operable, easy to promote and popularize, and has strong online guidance.

[0034] 3. A method for calculating the cost-effectiveness of scrap steel is provided.

[0035] This invention links constraints, metallurgical principles, and market prices, solving the problem that existing methods cannot effectively and timely correlate price fluctuations with the effectiveness of scrap steel utilization, thus failing to ensure the economic efficiency and timeliness of scrap steel use. This is beneficial for low-cost steelmaking production. By using the furnace charge cost optimization method of this invention, production costs can be reduced by more than 5 yuan / ton of steel, resulting in good economic benefits. Detailed Implementation

[0036] To further understand the content of the present invention, the present invention will be further described below with reference to embodiments.

[0037] Example 1

[0038] A method for optimizing the cost of 300t converter charge includes optimizing the hot metal ratio, selecting scrap steel type, and calculating structural costs. In this example, the produced steel is SPCC, which undergoes LF desulfurization treatment after converter smelting. The composition range after treatment is: Si ≤ 0.05%, C: 0.025%–0.055%, Mn: 0.08%–0.15%, P: ≤ 0.025%, S: ≤ 0.015%, Als: 0.025%–0.045%. The method specifically includes the following steps:

[0039] Step 1: Determine the boundary conditions;

[0040] Step 2: Scrap steel classification and establishment of raw material database;

[0041] Step 3: Setting key parameters for steel smelting;

[0042] Step 4: Obtaining data on the composition of molten iron, scrap steel, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium balls, etc.), ore, and exothermic agent;

[0043] Step 5: Determine the amount of molten iron and scrap steel to be charged into the furnace: Pre-calculate the amount of molten iron C → Preset the molten iron ratio Mp → Calculate the amount of molten iron M → Calculate the amount of scrap steel S;

[0044] Step Six: Calculate the cost-effectiveness of scrap steel;

[0045] Step 7: Scrap Steel Utilization Plan;

[0046] Step 8: Calculation of scrap steel structure optimization;

[0047] Step 9: Optimization of scrap steel structure under the overall molten iron ratio;

[0048] Step 10: Optimal Scheme Selection. Select the optimal value (minimum unit cost) for the entire calculation, determine the scrap steel and molten iron schemes, and then proceed with the smelting.

[0049] In step one, based on the carrying capacity of the molten iron and scrap steel cranes (480t and 110t respectively) and the capacity of the molten iron ladle and scrap steel trough (53m³ respectively), 3 90m 3 The self-weight (150t and 40t respectively), the maximum loading capacity of molten iron and scrap steel is 330t and 70t respectively; the nominal capacity of the converter is 300t, the maximum loading capacity is 345t, and the maximum input capacity of coolant and heating agent is determined to be 25t and 6t respectively based on the maximum feeding capacity of the silo and the limit of the addition of heating agent.

[0050] In a further technical solution, step two involves classifying scrap steel into eight categories based on its dimensions and weight, according to the national standard for scrap steel (GB / T4223-2017): heavy scrap steel, medium scrap steel, small scrap steel, light and thin scrap steel, baled scrap, crushed scrap, slag steel, and iron blocks. Building upon these categories, and considering steelmaking requirements, some categories are further subdivided based on the content of elements such as sulfur in the scrap steel. Each type of scrap steel is identified by a subdivision code. Preferably, for ease of management and maintenance, the subdivision code is a combination of the category code and the attribute code.

[0051] Table 1. Detailed Classification of Scrap Steel

[0052]

[0053] The raw material database includes molten iron, scrap steel, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium spheres, etc.), ore, exothermic agent composition, and thermal effect data. Molten iron data includes the content of carbon, silicon, manganese, sulfur, phosphorus, vanadium, and titanium, slag content, and temperature. Scrap steel data includes typical carbon, silicon, manganese, sulfur, phosphorus, and iron content, as well as impurity content. Slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium spheres, etc.) include the content of calcium oxide, silicon dioxide, magnesium oxide, and water. Ore includes the content of iron oxides and silicon dioxide. Exothermic agents include exothermic elements, impurities, and other main components.

[0054] In step three, based on the steel grade DC01, the slag basicity is determined to be 3.8, MgO% to be 6.5%, the converter final temperature to be 1650℃, the final carbon content control range to be 0.020% to 0.050%, and the target value to be 0.035%.

[0055] In step five, the charging amount C = (1.00~1.12) × T, where C is the converter charging amount and T is the nominal capacity of the converter, in t; the molten iron ratio Mp = M / C, M = Mp × C, where Mp is the molten iron ratio (%), M is the amount of molten iron, and C is the converter charging amount, in t; the amount of scrap steel charged into the furnace S = CM, where S is the amount of scrap steel, C is the converter charging amount, and M is the amount of molten iron, all in t.

[0056] The nominal capacity of the 300 converter is T = 300t, and the charging amount is C = (1.00~1.12) × T. Here, the coefficient is specifically taken as 1.1, so the charging amount C = 330t. The preset iron-to-metal ratio is Mp = 80%, so the iron-to-metal quantity M = Mp × C = 80% × 330 = 264t, and the amount of scrap steel charged is S =

[0057] CM = 330 - 264 = 66t.

[0058] In step six, under the same iron ratio Mp = 0.80 as in step five, one type of scrap steel is selected for heat balance, oxygen balance, slag balance, and iron balance calculations. The input should include: iron quantity, scrap steel quantity and type, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium balls, etc.), ore, calorific value, and oxygen quantity. The output is steel quantity and gas quantity. Based on the input and unit price of the above materials, the unit cost of steel is calculated. After all 11 types of scrap steel have been calculated, they are ranked according to cost-effectiveness and selected as priority levels, with a cost-effectiveness of 1 being the highest priority level. The cost-effectiveness of scrap steel is shown in Table 2.

[0059] Table 2 Cost-Effectiveness of Scrap Steel

[0060]

[0061] Furthermore, in step seven, the scrap steel scheme should include key attributes such as scrap steel classification code, addition quantity range, and resource availability identifier; each steel grade or similar steel grade should be matched with a scrap steel scheme. The "minimum usage" and "maximum usage" of the scrap steel scheme are process requirements. When both "selection" and "resource availability" are "√", the "minimum usage" takes precedence over cost-effectiveness.

[0062] Table 3 Scrap Steel Scheme

[0063]

[0064]

[0065] Note: √ indicates selection in "Whether to select", × indicates selection; √ indicates selection in "Resources available", × indicates selection.

[0066] In step eight, based on the boundary constraints of step one and the scrap steel scheme of step seven, and under the preset iron ratio Mp = 0.8 (i.e., 264t of molten iron) in step five, combined with the cost-effectiveness of scrap steel in step six, the optimal combination of scrap steel is selected: According to the above rules, the optimized scrap steel sub-codes are "11", "71", and "31", with usage amounts of 19.8t (i.e., 66t × 30%), 3t, and 44.2t, respectively; the main control parameters of the converter are slag basicity 3.8, MgO% 6.5%, and converter final temperature. At 1650℃, with a final carbon content control range of 0.020% to 0.050% and a target value of 0.035%, calculations were performed for heat balance, oxygen balance, slag balance, and iron balance. The input quantities should include: molten iron quantity, various types of scrap steel quantity, slag-forming materials (lime, dolomite, lightly calcined dolomite, magnesium balls, etc.), ore, calorific value, and oxygen quantity. The output quantity is molten steel quantity. Based on the above material input quantities and unit prices, the unit cost of molten steel was calculated, and key data such as the molten iron ratio, molten iron quantity, various types of scrap steel quantity, and unit cost of molten steel were recorded.

[0067] In step nine, the molten iron ratio is adjusted. Steps five to eight are repeated. Specifically, under the condition of a loading amount of 330t, the preset molten iron ratio Mp is increased by 0.1% based on the previous setting (80%). Steps five to eight are repeated for 199 rounds of calculation.

[0068] In step ten, based on 200 sets of key data such as molten iron ratio, molten iron quantity, amount of scrap steel of various types, and unit cost of molten steel, the optimal value (lowest cost of molten steel) is selected for the entire domain calculation, and the corresponding scrap steel and molten iron scheme is determined to complete the optimization of furnace charge cost.

[0069] The present invention has been described in detail above with reference to specific exemplary embodiments. However, it should be understood that various modifications and variations can be made without departing from the scope of the invention as defined by the appended claims. The detailed description should be considered illustrative only and not restrictive, and any such modifications and variations shall fall within the scope of the invention described herein. Furthermore, the background art is intended to illustrate the current state of development and significance of the technology and is not intended to limit the present invention or the scope of application of the present application.

[0070] More specifically, although exemplary embodiments of the invention have been described herein, the invention is not limited to these embodiments, but includes any and all embodiments modified, omitted, such as combinations between various embodiments, adaptive changes, and / or substitutions, as would be apparent to those skilled in the art from the foregoing detailed description. The limitations in the claims are to be interpreted broadly as used in the language of the claims and are not limited to the examples described in the foregoing detailed description or during the implementation of this application, which should be considered non-exclusive. Any step listed in any method or process claim may be performed in any order and is not limited to the order set forth in the claims. Therefore, the scope of the invention should be determined solely by the appended claims and their legal equivalents, and not by the description and examples given above.

[0071] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In case of conflict, the definitions in this specification shall prevail. When mass, concentration, temperature, time, or other values ​​or parameters are expressed as ranges, preferred ranges, or a series of upper and lower preferred values, this should be understood as specifically disclosing all ranges formed by any pair of any upper or preferred value with any lower or preferred value, regardless of whether such range is disclosed individually. For example, the range 1-50 should be understood to include any number, combination of numbers, or subrange selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50, as well as all decimal values ​​between the integers mentioned above, such as 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, and 1.9. Regarding subranges, specifically consider "nested subranges" extending from any endpoint of the range. For example, nested sub-ranges of the exemplary range 1-50 may include 1-10, 1-20, 1-30 and 1-40 in one direction, or 50-40, 50-30, 50-20 and 50-10 in another direction.

Claims

1. A method for optimizing converter furnace charge costs, characterized in that, Includes the following steps: Step 1: Determine the boundary conditions; Step 2: Scrap steel classification and establishment of raw material database; Step 3: Setting key parameters for steel smelting; Step 4: Obtaining data on the composition of molten iron, scrap steel, slag material, ore, and exothermic agent; Step 5: Determine the amount of molten iron and scrap steel to be fed into the furnace. The method for determination is as follows: pre-calculate the amount of molten iron C → pre-set the molten iron ratio Mp → calculate the amount of molten iron M → calculate the amount of scrap steel S. Step Six: Calculate the cost-effectiveness of scrap steel; In step six, under the same iron ratio Mp as in step five, a single type of scrap steel is selected for heat balance, oxygen balance, slag balance, and iron balance calculations. The input quantities after the calculations include: iron quantity, scrap steel quantity and type, slag-forming material, ore, calorimetric agent, and oxygen quantity; the output quantities are steel quantity and gas quantity; the unit cost of steel is calculated based on the input quantity and unit price of all materials; after all types of scrap steel are calculated, they are ranked according to cost-effectiveness as the basis for priority selection. Step 7: Scrap Steel Utilization Plan; In step seven, the scrap steel utilization plan includes key attributes such as scrap steel classification code, addition quantity range, and resource availability identifier; each steel type or similar steel type is matched with a scrap steel plan, and in the scrap steel utilization plan, the addition quantity range has higher priority than cost-effectiveness; Step 8: Optimization calculation of scrap steel structure; In step eight, based on the boundary constraints in step one and the scrap steel usage plan in step seven, and under the preset iron ratio Mp in step five, combined with the cost-effectiveness of scrap steel in step six, the optimal combination of scrap steel is selected. For each scrap steel combination plan, heat balance, oxygen balance, slag balance, and iron balance calculations are performed. The input quantities after the calculation should include: iron quantity, quantity of each type of scrap steel, slag-forming material, ore, calorimetric agent, and oxygen quantity; the output quantity is the quantity of molten steel; based on the input quantity and unit price of all materials, the unit cost of molten steel is calculated, and the iron ratio, iron quantity, quantity of each type of scrap steel, and unit cost of molten steel data are recorded. Step 9: Optimization of scrap steel structure under the overall molten iron ratio; In step nine, the molten iron ratio is adjusted. Steps five to eight are repeated. Specifically, the loading amount C is the same as the previous setting, and the preset molten iron ratio Mp is increased by 0.05% to 5.0% based on the previous setting. Steps five to eight are repeated for multiple rounds of calculation until Mp = 99.9%. In particular, if Mp exceeds 99.9% due to mismatch in the steps, it is calculated as 99.9%. Step 10: Select the optimal solution. Choose the optimal value calculated across the entire domain, i.e., the minimum unit cost of molten steel, and determine the scrap steel and molten iron solutions for smelting.

2. The method for optimizing converter charge cost according to claim 1, characterized in that, In step one, the boundary conditions include the maximum amount of molten iron charged, the maximum amount of scrap steel charged, the maximum amount of coolant added, and the maximum amount of exothermic agent added.

3. The method for optimizing converter charge cost according to claim 1, characterized in that, The method for classifying scrap steel is as follows: First, based on the external dimensions and weight of a single piece, scrap steel is classified into major categories according to the GB / T 4223-2017 standard. The major categories include heavy scrap steel, medium scrap steel, small scrap steel, light and thin scrap steel, baled blocks, crushed material, and slag steel. Then, based on the major categories, and in conjunction with the requirements for steelmaking, the scrap steel is further subdivided according to the content of sulfur and phosphorus elements. Finally, each type of scrap steel is identified using a sub-code, which is a combination of a category code and an attribute code.

4. The method for optimizing converter charge cost according to claim 1, characterized in that, The raw material database includes data on molten iron, scrap steel, slag-forming materials, ore, exothermic agent composition, and thermal effect. The molten iron data includes the content of carbon, silicon, manganese, sulfur, phosphorus, vanadium, and titanium, the amount of slag carried, and the molten iron temperature. The scrap steel data includes the content of carbon, silicon, manganese, sulfur, phosphorus, and iron, as well as the amount of impurities. The slag-forming materials include the content of calcium oxide, silicon dioxide, magnesium oxide, and water. The ore includes the content of iron oxides and silicon dioxide. The exothermic agent includes exothermic elements and impurities.

5. The method for optimizing converter feed cost according to claim 1, characterized in that, In step three, the basicity, MgO% and converter endpoint temperature, endpoint carbon content control range and target value are determined according to the steel grade being smelted.

6. A method for optimizing converter charge costs according to any one of claims 1-5, characterized in that, In step five, the charging amount C = (0.90~1.30)×T, where C is the converter charging amount and T is the nominal capacity of the converter, in t; the molten iron ratio Mp = M / C, M = Mp×C, where Mp is the molten iron ratio, M is the amount of molten iron, and C is the converter charging amount, in t; the amount of scrap steel charged into the furnace S = CM, where S is the amount of scrap steel, C is the converter charging amount, and M is the amount of molten iron, all in t.

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