Intelligent static batching method, system, device and storage medium for electric arc furnace

Abstract

This invention relates to the field of electric arc furnace steelmaking technology, and discloses an intelligent static batching method, system, equipment, and storage medium for electric arc furnaces. The method includes: collecting a dataset of raw materials for electric arc furnace steelmaking and establishing a structured database; receiving a smelting plan; based on the structured database and the smelting plan, establishing an objective equation with the minimum total cost of batching and electricity as the objective function, constructing material balance constraint equations, heat balance constraint equations, total charge constraint equations, and single raw material addition constraint equations, and establishing a multi-constraint optimization model; solving the multi-constraint optimization model to obtain the recommended addition amounts of hydrogen-based direct reduced iron (DRI), scrap steel, and smelting auxiliary materials, and generating and outputting an intelligent static batching sheet. This application, based on the material balance and heat balance constraints of steelmaking, introduces the target component requirements of steel grades, quantitatively analyzes and collaboratively optimizes hydrogen-based DRI and scrap steel, realizing intelligent, standardized, and low-carbon configuration of the charge structure during electric arc furnace smelting.

Description

Technical Field

[0001] This invention relates to the field of electric arc furnace steelmaking technology, and in particular to an intelligent static batching method, system, equipment and storage medium for electric arc furnaces. Background Technology

[0002] Electric arc furnace (EAF) short-process steelmaking is one of the core directions for the steel industry to achieve green and low-carbon transformation, with significant advantages in energy conservation, emission reduction, and resource recycling. Direct reduced iron (DRI), as a core supporting product in zero-carbon and low-carbon metallurgy, is an ideal clean raw material for producing high-quality steel due to its stable composition and extremely low residual element content. In actual industrial production, scrap steel is widely available and relatively inexpensive, effectively reducing smelting costs. DRI can compensate for the quality risks caused by fluctuations in scrap steel composition, ensuring the purity of molten steel. Therefore, to balance raw material costs and molten steel quality, DRI is often mixed with scrap steel in a specific ratio as electric arc furnace charge. However, DRI and scrap steel have fundamentally different physicochemical properties: DRI has a low density and a high gangue content, requiring more heat to melt; scrap steel, on the other hand, is characterized by its diverse types, large compositional fluctuations, inconsistent density, and unstable market prices. The mixed use of the two places special demands on batching technology.

[0003] Existing batching technologies are mostly developed for pure scrap steel smelting or conventional hot metal-scrap steel mixed smelting scenarios. Their optimization objectives are often singular, focusing either on minimizing raw material costs or emphasizing heat balance control and charging rhythm optimization. They fail to fully consider the differences in physicochemical properties of the specific furnace charge structure of "hydrogen-based DRI + scrap steel" and the requirements of mixed smelting, lacking targeted batching technology support. For the "hydrogen-based DRI + scrap steel" mixed smelting process, plants currently still mainly rely on engineers' experience for trial-and-error batching, which is not only inefficient but also highly susceptible to human factors. It is difficult to consistently obtain the optimal batching scheme that balances economic benefits and quality assurance, often resulting in problems such as fluctuating steel quality or high smelting costs. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide an intelligent static batching method, system, device and storage medium for electric arc furnaces.

[0005] This invention provides the following technical solution: In a first aspect, this disclosure provides an intelligent static batching method for an electric arc furnace, the method comprising: Data sets of raw materials for electric arc furnace steelmaking are collected to establish a structured database. The raw materials include hydrogen-based direct reduced iron, scrap steel, and smelting auxiliary materials. The data set includes real-time electricity prices, chemical composition data of the hydrogen-based direct reduced iron and the scrap steel, current market price data, metal yield data, and the range of available addition amounts. Receive a smelting plan, which includes the target composition range of each element in the target molten steel, the planned steel output, and the constraints of the steelmaking process. Based on the structured database and the smelting plan, an objective equation is established with the minimum total cost of raw materials and electricity as the objective function. Based on the constraints of steel composition in material balance, energy constraints in heat balance, total amount of furnace charge, and single raw material addition amount, material balance constraint equation, heat balance constraint equation, total amount of furnace charge constraint equation, and single raw material addition amount constraint equation are constructed. Based on the objective equation, the material balance constraint equation, the heat balance constraint equation, the total amount of furnace charge constraint equation, and the single raw material addition amount constraint equation, a multi-constraint optimization model is established. The multi-constraint optimization model is solved using a preset solution algorithm to obtain the recommended addition amounts of the hydrogen-based direct reduced iron, the scrap steel raw material, and the smelting auxiliary materials, generating an intelligent static batching list and outputting the intelligent static batching list.

[0006] In optional embodiments, the chemical composition data of the hydrogen-based direct reduced iron includes at least the contents of TFe, C, Si, Mn, P, and S, and gangue composition; the chemical composition data of the scrap steel raw material includes at least the contents of C, Si, Mn, P, and S, and residual element content; the smelting auxiliary materials include carbon powder, lime, and dolomite; the preset solution algorithm includes any one or more combinations of linear weighted sum method, linear programming method, simplex method, interior point method, hierarchical sequence method, direct non-dominated solution method, goal programming method, analytic hierarchy process, multi-objective group decision, and fuzzy decision; and the electric arc furnace includes ultra-high power electric arc furnaces of 50-300t, horizontal scrap steel preheating electric arc furnaces, stepped scrap steel preheating electric arc furnaces, vertical electric arc furnaces, and double-shell electric arc furnaces.

[0007] In an optional implementation, the objective equation is: ; ; In the formula, For total cost, The total cost of raw materials, For total energy cost, For the first The unit price of the steelmaking raw materials mentioned above. For the first The amount of the steelmaking raw materials added, This represents the total number of types of steelmaking raw materials. The total amount of electricity required. The real-time electricity price, For power utilization rate, This represents the total electrical energy required.

[0008] In an optional implementation, the material balance constraint equation is: ; ; In the formula, For the first Elements in the steelmaking raw materials mass percentage, For elements From the The metal yield of the steelmaking raw materials entering the target molten steel. The planned steel output is... Elements in the target molten steel The target content, and elements respectively The lower and upper limits of the content in the target molten steel.

[0009] In an optional implementation, the thermal balance constraint equation is: ; ; ; In the formula, For heat income items, For the physical heat of the furnace charge, The heat of element oxidation and the heat of slag formation, For the total electrical energy required, For heat expenditure items, The physical heat of molten steel, For the physical heat of slag, The heat consumed in the endothermic reaction For the physical heat of furnace gas, The physical heat of smoke and dust, To absorb heat for cooling water, For other heat losses, This refers to the heat loss of the transformer and the short-circuit network system.

[0010] In an optional implementation, the total amount of furnace charge constraint equation is: ; In the formula, For the first The amount of the steelmaking raw materials added, For the first The overall metal recovery rate of the steelmaking raw materials described above. The planned steel output is... This represents the total number of types of steelmaking raw materials.

[0011] In an optional implementation, the constraint equation for the amount of a single raw material added is: ; In the formula, For the first The minimum allowable amount of the steelmaking raw materials mentioned above. For the first The maximum allowable amount of the steelmaking raw materials mentioned above.

[0012] Secondly, this disclosure provides an intelligent static batching system for an electric arc furnace, the system comprising: The database establishment module is used to collect datasets of raw materials for electric arc furnace steelmaking and establish a structured database. The raw materials include hydrogen-based direct reduced iron, scrap steel, and smelting auxiliary materials. The dataset includes real-time electricity prices, chemical composition data of the hydrogen-based direct reduced iron and the scrap steel, current market price data, metal recovery rate data, and the range of available addition amounts. The plan receiving module is used to receive the smelting plan, which includes the target composition range of each element of the target steel grade, the planned steel output, and the constraints of the steelmaking process. The model building module is used to establish an objective equation based on the structured database and the smelting plan, with the goal of minimizing the total cost of raw materials and electricity. Based on the constraints of steel composition in material balance, energy constraints in heat balance, total amount of furnace charge, and single raw material addition, it constructs material balance constraint equations, heat balance constraint equations, total amount of furnace charge constraint equations, and single raw material addition constraint equations. Based on the objective equation, the material balance constraint equation, the heat balance constraint equation, the total amount of furnace charge constraint equation, and the single raw material addition constraint equation, a multi-constraint optimization model is established. The batching sheet output module is used to solve the multi-constraint optimization model using a preset solution algorithm to obtain the recommended addition amounts of the hydrogen-based direct reduced iron, the scrap steel raw material and the smelting auxiliary materials, generate an intelligent static batching sheet and output the intelligent static batching sheet.

[0013] Thirdly, this disclosure provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the intelligent static batching method for electric arc furnaces described in the first aspect.

[0014] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the intelligent static batching method for electric arc furnaces described in the first aspect.

[0015] The beneficial effects of this application are: The intelligent static batching method for electric arc furnaces provided in this application addresses the problems of large fluctuations in hydrogen-based DRI composition, complex scrap steel sources, reliance on experience in the batching process, and difficulty in simultaneously controlling composition, energy consumption, and cost. Based on the constraints of steelmaking material balance and heat balance, it introduces the target composition requirements of steel grades. Through quantitative analysis and synergistic optimization of hydrogen-based DRI and scrap steel, and through comprehensive calculation of raw material composition parameters, process constraints, and target steel indicators, it outputs the optimal batching scheme that meets smelting requirements. This provides directly executable batching guidance for electric arc furnace steelmaking production, realizing the intelligent, standardized, and low-carbon configuration of furnace charge structure during electric arc furnace smelting.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the various drawings, similar components are numbered similarly.

[0018] Figure 1 A flowchart of an intelligent static batching method for an electric arc furnace provided in an embodiment of this application is shown; Figure 2 This illustration shows a schematic diagram illustrating the principle of establishing and solving a multi-constraint optimization model according to an embodiment of this application; Figure 3 This paper shows a schematic diagram of the structure of an intelligent static batching system for an electric arc furnace according to an embodiment of this application; Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] Unless otherwise defined, 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 application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1 For "hydrogen-based DRI + scrap steel" smelting, the core challenge in batching lies in minimizing the unit smelting cost of molten steel while precisely meeting the chemical composition requirements of the target steel grade (especially high-end steel grades with strict requirements on residual elements), making full use of inexpensive scrap steel, and rationally combining it with more expensive but pure hydrogen-based DRI. This is a typical multi-constraint optimization problem. Currently, factories mostly rely on engineers' experience for trial calculations, which is inefficient and makes it difficult to guarantee that the most economically efficient batching scheme can be found every time.

[0023] Therefore, this application proposes an intelligent static batching method for electric arc furnaces that comprehensively considers steel quality control and raw material cost optimization, specifically for the smelting of hydrogen-based DRI and scrap steel mixed electric arc furnaces. This method has significant practical implications for promoting hydrogen-based metallurgy technology and reducing the production cost of high-end steel grades. Figure 1 The diagram shown is a flowchart of an intelligent static batching method for an electric arc furnace according to an embodiment of this application, which specifically includes the following steps: Step S110: Collect a dataset of raw materials for electric arc furnace steelmaking and establish a structured database. The raw materials include hydrogen-based direct reduced iron, scrap steel, and smelting auxiliary materials. The dataset includes real-time electricity prices, chemical composition data of the hydrogen-based direct reduced iron and the scrap steel, current market price data, metal yield data, and the range of available addition amounts.

[0024] In this embodiment, a dataset of raw materials for electric arc furnace steelmaking is first collected to establish a structured database. The raw materials include hydrogen-based direct reduced iron (HDI), scrap steel, and smelting auxiliary materials. The dataset includes real-time electricity prices, chemical composition data of the HDI and scrap steel, current market price data, metal yield data, and the range of available addition amounts. This structured database is the core data support for batching calculations; its data integrity and real-time performance directly determine the accuracy and economy of the batching scheme.

[0025] Specifically, regarding chemical composition data, the chemical composition of hydrogen-based DRIs must at least cover the contents of total iron (TFe), carbon (C), silicon (Si), manganese (Mn), phosphorus (P), sulfur (S), and gangue composition. Gangue composition data is used to predict slag production and slag-forming requirements during the smelting process. The contents of total iron and alloying elements are the basis for ensuring that the composition of molten steel meets the standards. The chemical composition of scrap steel raw materials must at least include the contents of carbon (C), silicon (Si), manganese (Mn), phosphorus (P), sulfur (S), and residual elements (such as Cr, Ni, etc.). The residual element content data is key to meeting the strict quality requirements of high-end steel grades and can avoid exceeding the standards for residual elements due to the mixing of scrap steel components. Smelting auxiliary materials specifically include carbon powder, lime, dolomite, etc. Their composition data can be supplemented and entered according to actual slag-forming requirements to accurately control slag basicity and smelting reaction efficiency.

[0026] It should be noted that the metal recovery rate data is a key parameter derived from historical smelting data, reflecting the proportion of metal elements in the raw materials that enter the molten steel. This data can be dynamically updated based on the type of raw material, the batch added, and smelting process parameters (such as tapping temperature and slag formation). For example, the recovery rate of different batches of hydrogen-based DRI will fluctuate due to differences in reduction degree; dynamic updates can significantly improve the accuracy of material balance calculations. Current market price data covers real-time purchase prices of hydrogen-based DRI, various types of scrap steel (such as carbon scrap, heavy scrap, and light scrap), and smelting auxiliary materials, combined with real-time electricity prices, providing a quantitative basis for minimizing total costs. The usable addition range is determined by the current raw material inventory and the single-furnace process limit. For example, due to its higher cost and greater heat absorption during melting, the addition amount of hydrogen-based DRI per furnace may be limited to 10-30 tons. Light scrap, due to its low density and susceptibility to burnout, may have a maximum addition amount limited to 25 tons to avoid exceeding equipment capacity or affecting smelting operations.

[0027] For example, this embodiment uses the production of low-carbon alloy steel (SWRH82B) in a 100t ultra-high power electric arc furnace as an example, with a planned output of 100t. The structured database is called to extract the real-time parameters of the current materials. Among these parameters, the total iron content of hydrogen-based DRI is 92%, the unit price is 3200 yuan / t, and the metal recovery rate is 94%; the unit price of carbon scrap is 2900 yuan / t, and the metal recovery rate is 96%; the unit price of heavy scrap is 2850 yuan / t, and the metal recovery rate is 96%; the unit price of light and thin scrap is 2200 yuan / t, and the metal recovery rate is 91%; the unit price of carbon powder is 3000 yuan / t; and the real-time power supply price is 0.45 yuan / kWh. Meanwhile, current inventory and process limits are set: to ensure the cleanliness of molten steel and the control of residual elements, this embodiment limits the addition of hydrogen-based DRI to no less than 10t and a maximum of 30t, the maximum addition of carbon scrap steel per furnace to 20t, the maximum addition of light and thin scrap to 25t, and ensures sufficient supply of heavy scrap steel, while guaranteeing that power consumption is controlled between 30,000-45,000 kWh. Some core data selected in this embodiment are shown in Table 1 below.

[0028] Table 1 Quality-Cost Structured Database

[0029] The above steps integrate multi-dimensional data such as raw material chemical composition, price, and yield, and support dynamic updates to ensure that the data source for batching calculation is accurate and real-time. This not only solves the parameter adaptation problem caused by fluctuations in hydrogen-based DRI and scrap steel composition, but also enables rapid response to changes in market prices and inventory, providing solid data support for subsequent model optimization and ensuring the timeliness and accuracy of the batching scheme.

[0030] Step S120: Receive the smelting plan, which includes the target composition range of each element of the target steel grade, the planned steel output, and the constraints of the steelmaking process.

[0031] Understandably, the smelting plan is the core objective input for batching optimization, clarifying the quality requirements, yield requirements, and process boundaries of the smelting process. (1) The target composition range of each element of the target steel grade needs to be precisely defined, including the target range of basic alloying elements (C, Si, Mn) and the upper limit requirements of harmful elements (P, S) and residual elements. For high-end steel grades with extremely high purity requirements, the total amount of residual elements needs to be further strictly limited. (2) The planned steel output is the core basis for the total amount of furnace charge constraint, such as 100t, 150t, etc., which directly determines the total input scale of various raw materials. At the same time, the metal loss in the smelting process must be taken into account (calculated by the yield). (3) Constraints in the steelmaking process include, but are not limited to, tapping temperature (e.g., 1650℃), slag quantity per ton of steel (e.g., 40 kg / t steel), and power consumption range (e.g., 30,000~45,000 kWh). The tapping temperature determines the physical heat requirement of molten steel in the heat balance calculation, the slag quantity per ton of steel affects the input of slag-forming auxiliary materials, and the power consumption range provides boundary constraints for energy cost optimization, avoiding a surge in power consumption due to excessive pursuit of the lowest raw material cost.

[0032] For example, in this embodiment, the carbon content is set between 0.79% and 0.85%, the silicon content between 0.15% and 0.3%, the manganese content between 0.6% and 0.8%, the phosphorus content is less than 0.025%, the sulfur content is less than 0.025%, the tapping temperature is 1650℃, and the slag content is 40 kg / t steel. Table 2 shows the technical requirements for the steel grade to be produced.

[0033] Table 2 Technical Requirements for Steel Grades to be Produced

[0034] The above steps accurately define the target steel composition requirements, steel output and process constraints, clarify the core objectives and boundary conditions of batching optimization, avoid the batching deviates from quality standards or production requirements due to ambiguous requirements, provide clear guidance for the construction of multi-constraint models, and ensure that the final batching scheme conforms to actual smelting needs.

[0035] Step S130: Based on the structured database and the smelting plan, an objective equation is established with the minimum total cost of raw materials and electricity as the objective function. Based on the constraints of steel composition in material balance, energy constraints in heat balance, total amount of furnace charge, and single raw material addition amount, material balance constraint equation, heat balance constraint equation, total amount of furnace charge constraint equation, and single raw material addition amount constraint equation are constructed. A multi-constraint optimization model is established based on the objective equation, the material balance constraint equation, the heat balance constraint equation, the total amount of furnace charge constraint equation, and the single raw material addition amount constraint equation.

[0036] Furthermore, the principles of establishing and solving multi-constraint optimization models are as follows: Figure 2 As shown, based on the structured database in step S110 and the smelting plan in step S120, the complex batching problem is transformed into a solvable mathematical model by quantifying objectives and constraints, thereby achieving synergistic optimization of quality, cost, and energy. (1) Construction of the objective equation: The objective equation focuses on minimizing the total cost of ingredients and electricity, automatically balancing the price advantage of scrap steel with the cost of additional electricity consumed during hydrogen-based DRI melting. The expression is: ; ; In the formula, For total cost, The total cost of raw materials, For total energy cost, For the first The unit price of various steelmaking raw materials (including hydrogen-based DRI, various scrap steel, and smelting auxiliary materials). For the first The amount of steelmaking raw materials added. This represents the total number of types of raw materials used in steelmaking. The total amount of electricity required. For real-time electricity prices, For power utilization rate, The total electrical energy required is given. The core logic of this objective equation is to balance the "cost advantage of low-priced scrap steel" with the "quality advantage of hydrogen-based DRI + high energy consumption cost of melting". For example, scrap steel is cheap but its composition fluctuates greatly, so it needs to be combined with hydrogen-based DRI to ensure the purity of molten steel. However, the melting of hydrogen-based DRI absorbs more heat, which will increase the energy consumption. The model needs to find the optimal balance point through quantitative calculations.

[0037] For example, in this embodiment, the power utilization rate is 90%. ; (2) Construction of material balance constraint equations: The material balance constraint equation ensures that the content of each element in the molten steel falls within the target range after being converted by the yield from the raw materials. The expression is as follows: ; ; In the formula, For the first Elements in steelmaking raw materials mass percentage, For elements From the Metal yield of a steelmaking raw material entering the target molten steel. For the planned steel output, Target elements in molten steel The target content, and elements respectively The lower and upper limits of the content in the target molten steel. For example, for phosphorus, this equation is needed to ensure that the phosphorus brought in by all raw materials, after being converted by the recovery rate, does not exceed the upper limit of phosphorus content in the target steel grade, so as to avoid quality defects.

[0038] For example, in this embodiment, the carbon content balance constraint is: ; ; Right now: ; ; The silicon content balance constraint is: ; ; Right now: ; ; The manganese content balance constraint is: ; ; Right now: ; ; The phosphorus content balance constraint is: ; ; Right now: ; ; The sulfur content equilibrium constraint is: ; ; Right now: ; ; (3) Construction of thermal equilibrium constraint equations: The heat balance constraint equation ensures that the total heat brought in by the furnace charge is sufficient to cover the total heat consumption of the smelting process, avoiding smelting interruptions or substandard steel temperatures due to insufficient heat. The expression is as follows: ; ; ; In the formula, For heat income items, The physical heat of the furnace charge (the heat carried by the raw materials themselves). This refers to the heat of element oxidation and the heat of slag formation (the heat released by chemical reactions during the smelting process). This is the total electrical energy required (the electrical energy input to the electric arc furnace). For heat expenditure items, The physical heat of molten steel (the heat required to heat molten steel to the tapping temperature). The physical heat of slag (the heat carried away by the slag). The heat consumed in an endothermic reaction (such as the heat consumed in the reduction of gangue). This refers to the physical heat of the furnace gas (the heat carried away by the furnace gas). The physical heat of the smoke and dust (the heat carried away by the smoke and dust). It absorbs heat from the cooling water (the heat carried away by the cooling water). Other heat losses (such as heat dissipation from the furnace body, which accounts for approximately 6% to 9% of the total heat revenue) are also considered. This is due to heat loss from transformers and short-circuit systems (accounting for approximately 5% to 7% of total heat revenue).

[0039] It should be noted that the heat balance calculation method is a commonly used calculation method for energy conservation in the prior art, and will not be described in detail in the embodiments of the present invention.

[0040] For example, in this embodiment, the calculated heat input item is: ; ; ; Heat expenditure items are: ; ; ; ; ; ; ; ; ; And from: ; (4) Construction of the total furnace charge constraint equation: The total amount of furnace charge constraint equation ensures that the total metal amount of all raw materials, after being converted according to the recovery rate, meets the planned steel output requirements, avoiding insufficient metal or waste. The expression is: ; In the formula, For the first The amount of steelmaking raw materials added. For the first The overall metal recovery rate of various steelmaking raw materials. For the planned steel output, This represents the total number of types of raw materials used in steelmaking. For example, the overall metal recovery rate of hydrogen-based DRI is approximately 94%, and that of heavy scrap steel is approximately 96%. This equation allows for the accurate calculation of the total input scale of various raw materials.

[0041] For example, in this embodiment: ; (5) Construction of constraint equations for the amount of single raw material added: The constraint equation for the addition amount of a single raw material limits the usage range of each raw material to avoid exceeding the inventory or process capacity. The expression is: ; In the formula, For the first The minimum allowable amount of steelmaking raw materials (determined by process requirements or the smallest processing unit, and may be zero; for example, some auxiliary materials may be added depending on slag formation requirements). For the first The maximum allowable addition amount of each steelmaking raw material (determined by the current inventory or the maximum process limit per furnace, such as the maximum addition amount of hydrogen-based DRI, which is limited by both inventory and energy consumption limits), the minimum allowable addition amount, and the maximum allowable addition amount are all determined by the range of available addition amounts.

[0042] For example, in this embodiment: ; ; ; ; In summary, the set of constraints can be obtained as follows: ; Solve , , , , , , ; The above steps, for the first time, target the specific furnace charge structure of "hydrogen-based DRI + scrap steel" and achieve synergistic optimization of quality (component compliance), cost (optimal raw materials + electricity), and energy (thermal balance). This transforms the complex batching problem into a quantitative mathematical model, breaking through the limitations of traditional batching single-objective optimization and laying a logical foundation for subsequent efficient solutions.

[0043] Step S140: Solve the multi-constraint optimization model using a preset solution algorithm to obtain the recommended addition amounts of the hydrogen-based direct reduced iron, the scrap steel raw material, and the smelting auxiliary materials, generate an intelligent static batching list, and output the intelligent static batching list.

[0044] The optimal ingredient ratio is automatically generated through algorithmic solutions, eliminating reliance on manual experience. In this embodiment, the preset solution algorithms include any combination of one or more of the following: linear weighted sum method, linear programming method, simplex method, interior point method, hierarchical sequence method, direct non-dominated solution method, goal programming method, analytic hierarchy process (AHP), multi-objective group decision method, and fuzzy decision method. These algorithms are all applicable to cost optimization problems under multiple constraints and can be flexibly selected based on model complexity and solution efficiency requirements. For example, linear programming is suitable for scenarios where both the objective function and constraints are linear, and it offers fast solution speed; fuzzy decision methods can handle uncertainties in some parameters (such as small fluctuations in raw material composition).

[0045] The intelligent static batching sheet output after the solution clearly indicates the specific addition amounts of hydrogen-based DRI, various types of scrap steel (such as carbon scrap steel, heavy scrap steel, and light and thin materials), and smelting auxiliary materials (such as carbon powder and lime). It can also include auxiliary information such as the cost composition of the batching scheme, heat balance calculation results, and element compliance prediction, which makes it convenient for operators to check and adjust.

[0046] For example, in this embodiment, the solution is: ; By solving the multi-constraint optimization model, under the premise of ensuring that the amount of hydrogen-based DRI added is not less than 10t and considering the participation of electrical energy in the heat balance, the recommended feed ratio of this embodiment is: 16t of hydrogen-based DRI, 64t of heavy scrap steel, 25t of light and thin material, supplemented with a small amount of carbon powder for carbon adjustment, and the total power consumption is about 36800kWh.

[0047] It should be noted that the method of this application is applicable to various types of electric arc furnaces, including ultra-high power electric arc furnaces of 50~300t, horizontal scrap preheating electric arc furnaces, stepped scrap preheating electric arc furnaces, vertical electric arc furnaces, and double-shell electric arc furnaces, and has wide process adaptability. The generated batching sheet can be directly used to guide the material preparation process, realizing the standardization and intelligentization of the batching process, avoiding the subjectivity and inefficiency of manual trial calculations, and ensuring that the optimal solution that balances economic benefits and quality assurance can be found for each batching.

[0048] The above steps are solved automatically through a preset algorithm, eliminating the reliance on manual experience-based calculations. The calculation speed is fast and the results are scientific and reliable. The generated standardized batching sheet clearly specifies the exact amount of each raw material and includes auxiliary information such as cost and heat balance. This not only lowers the operating threshold but also stabilizes the production process, providing a practical guidance tool for the standardized application of hydrogen-based DRI and the low-cost smelting of high-end steel grades.

[0049] The intelligent static batching method for electric arc furnaces provided in this application addresses the problems of large fluctuations in hydrogen-based DRI composition, complex scrap steel sources, reliance on experience in the batching process, and difficulty in simultaneously controlling composition, energy consumption, and cost. Based on the constraints of steelmaking material balance and heat balance, it introduces the target composition requirements of steel grades. Through quantitative analysis and synergistic optimization of hydrogen-based DRI and scrap steel, and through comprehensive calculation of raw material composition parameters, process constraints, and target steel indicators, it outputs the optimal batching scheme that meets smelting requirements. This provides directly executable batching guidance for electric arc furnace steelmaking production, realizing the intelligent, standardized, and low-carbon configuration of furnace charge structure during electric arc furnace smelting.

[0050] Example 2 like Figure 3 The diagram shown is a structural schematic of an intelligent static batching system 200 for an electric arc furnace according to an embodiment of this application. The system includes: The database establishment module 310 is used to collect the dataset of raw materials for electric arc furnace steelmaking and establish a structured database. The raw materials for steelmaking include hydrogen-based direct reduced iron, scrap steel and smelting auxiliary materials. The dataset includes real-time electricity price, chemical composition data of the hydrogen-based direct reduced iron and the scrap steel, current market price data, metal recovery rate data and available addition range. The plan receiving module 320 is used to receive the smelting plan, which includes the target composition range of each element of the target steel grade, the planned steel output, and the constraints of the steelmaking process. The model building module 330 is used to establish an objective equation based on the structured database and the smelting plan, with the minimum total cost of raw materials and electricity as the objective function. Based on the constraints of steel composition in material balance, energy constraints in heat balance, total amount of furnace charge, and single raw material addition amount, it constructs material balance constraint equations, heat balance constraint equations, total amount of furnace charge constraint equations, and single raw material addition amount constraint equations. Based on the objective equation, the material balance constraint equation, the heat balance constraint equation, the total amount of furnace charge constraint equation, and the single raw material addition amount constraint equation, a multi-constraint optimization model is established. The batching sheet output module 340 is used to solve the multi-constraint optimization model using a preset solution algorithm to obtain the recommended addition amounts of the hydrogen-based direct reduced iron, the scrap steel raw material and the smelting auxiliary materials, generate an intelligent static batching sheet and output the intelligent static batching sheet.

[0051] The intelligent static batching system for electric arc furnaces provided in this application embodiment can realize all processes of the intelligent static batching method for electric arc furnaces corresponding to Embodiment 1, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0052] Example 3 This application also provides a computer device. Please refer to the following for details. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0053] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with memory 41, processor 42, and network interface 43 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0054] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0055] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D slot compatibility test memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for slot compatibility testing methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0056] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other intelligent static batching chip for electric arc furnaces. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, such as executing computer-readable instructions for the slot compatibility testing method.

[0057] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0058] The computer device provided in this embodiment can execute the above-described intelligent static batching method for electric arc furnaces. Here, the intelligent static batching method for electric arc furnaces can be any of the intelligent static batching methods described in the various embodiments above.

[0059] Example 4 This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the intelligent static batching method for electric arc furnace in this embodiment.

[0060] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the computer-readable storage medium can be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device. Of course, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer-readable storage medium is typically used to store the operating system and various application software installed on the computer device. In addition, the computer-readable storage medium can also be used to temporarily store various types of data that have been output or will be output.

[0061] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in 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 using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0062] In addition, the functional modules or units in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0063] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other medium capable of storing program code.

[0064] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent static batching of materials for an electric arc furnace, characterized in that, The method includes: Data sets of raw materials for electric arc furnace steelmaking are collected to establish a structured database. The raw materials include hydrogen-based direct reduced iron, scrap steel, and smelting auxiliary materials. The data set includes real-time electricity prices, chemical composition data of the hydrogen-based direct reduced iron and the scrap steel, current market price data, metal yield data, and the range of available addition amounts. Receive a smelting plan, which includes the target composition range of each element in the target molten steel, the planned steel output, and the constraints of the steelmaking process. Based on the structured database and the smelting plan, an objective equation is established with the minimum total cost of raw materials and electricity as the objective function. Based on the constraints of steel composition in material balance, energy constraints in heat balance, total amount of furnace charge, and single raw material addition amount, material balance constraint equation, heat balance constraint equation, total amount of furnace charge constraint equation, and single raw material addition amount constraint equation are constructed. Based on the objective equation, the material balance constraint equation, the heat balance constraint equation, the total amount of furnace charge constraint equation, and the single raw material addition amount constraint equation, a multi-constraint optimization model is established. The multi-constraint optimization model is solved using a preset solution algorithm to obtain the recommended addition amounts of the hydrogen-based direct reduced iron, the scrap steel raw material, and the smelting auxiliary materials, generating an intelligent static batching list and outputting the intelligent static batching list.

2. The intelligent static batching method for electric arc furnace according to claim 1, characterized in that, The chemical composition data of the hydrogen-based direct reduced iron includes at least the contents of TFe, C, Si, Mn, P, and S, as well as gangue composition. The chemical composition data of the scrap steel raw material includes at least the contents of C, Si, Mn, P, and S, as well as the contents of residual elements. The smelting auxiliary materials include carbon powder, lime, and dolomite. The preset solution algorithm includes any one or more combinations of linear weighted sum method, linear programming method, simplex method, interior point method, hierarchical sequence method, direct non-dominated solution method, goal programming method, analytic hierarchy process, multi-objective group decision, and fuzzy decision. The electric arc furnace includes ultra-high power electric arc furnaces of 50~300t, horizontal scrap steel preheating electric arc furnaces, stepped scrap steel preheating electric arc furnaces, vertical electric arc furnaces, and double-shell electric arc furnaces.

3. The intelligent static batching method for electric arc furnace according to claim 1, characterized in that, The objective equation is: In the formula, For total cost, The total cost of raw materials, For total energy cost, For the first The unit price of the steelmaking raw materials mentioned above. For the first The amount of the steelmaking raw materials added, This represents the total number of types of steelmaking raw materials. The total amount of electricity required. The real-time electricity price, For power utilization rate, This represents the total electrical energy required.

4. The intelligent static batching method for electric arc furnace according to claim 1, characterized in that, The material balance constraint equation is as follows: In the formula, For the first Elements in the steelmaking raw materials mass percentage, For elements From the The metal yield of the steelmaking raw materials entering the target molten steel. The planned steel output is... Elements in the target molten steel The target content, and elements respectively The lower and upper limits of the content in the target molten steel.

5. The intelligent static batching method for electric arc furnace according to claim 1, characterized in that, The thermal balance constraint equation is: In the formula, For heat income items, For the physical heat of the furnace charge, The heat of element oxidation and the heat of slag formation, For the total electrical energy required, For heat expenditure items, The physical heat of molten steel, For the physical heat of slag, The heat consumed in the endothermic reaction For the physical heat of furnace gas, The physical heat of smoke and dust, To absorb heat for cooling water, For other heat losses, This refers to the heat loss of the transformer and the short-circuit network system.

6. The intelligent static batching method for electric arc furnace according to claim 1, characterized in that, The equation for the total amount of furnace charge constraint is as follows: In the formula, For the first The amount of the steelmaking raw materials added, For the first The overall metal recovery rate of the steelmaking raw materials described above. The planned steel output is... This represents the total number of types of steelmaking raw materials.

7. The intelligent static batching method for electric arc furnace according to claim 1, characterized in that, The constraint equation for the amount of a single raw material added is: In the formula, For the first The minimum allowable amount of the steelmaking raw materials mentioned above. For the first The maximum allowable amount of the steelmaking raw materials mentioned above.

8. An intelligent static batching system for an electric arc furnace, characterized in that, The system includes: The database establishment module is used to collect datasets of raw materials for electric arc furnace steelmaking and establish a structured database. The raw materials include hydrogen-based direct reduced iron, scrap steel, and smelting auxiliary materials. The dataset includes real-time electricity prices, chemical composition data of the hydrogen-based direct reduced iron and the scrap steel, current market price data, metal recovery rate data, and the range of available addition amounts. The plan receiving module is used to receive the smelting plan, which includes the target composition range of each element of the target steel grade, the planned steel output, and the constraints of the steelmaking process. The model building module is used to establish an objective equation based on the structured database and the smelting plan, with the goal of minimizing the total cost of raw materials and electricity. Based on the constraints of steel composition in material balance, energy constraints in heat balance, total amount of furnace charge, and single raw material addition, it constructs material balance constraint equations, heat balance constraint equations, total amount of furnace charge constraint equations, and single raw material addition constraint equations. Based on the objective equation, the material balance constraint equation, the heat balance constraint equation, the total amount of furnace charge constraint equation, and the single raw material addition constraint equation, a multi-constraint optimization model is established. The batching sheet output module is used to solve the multi-constraint optimization model using a preset solution algorithm to obtain the recommended addition amounts of the hydrogen-based direct reduced iron, the scrap steel raw material and the smelting auxiliary materials, generate an intelligent static batching sheet and output the intelligent static batching sheet.

9. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the intelligent static batching method for electric arc furnaces according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the intelligent static batching method for electric arc furnaces according to any one of claims 1-7.

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