A dynamic optimization method for proportion of scrap steel in converter smelting charge

Abstract

This invention relates to the field of iron and steel smelting technology, and in particular to a dynamic optimization method for the addition of a large proportion of scrap steel in converter smelting. The method includes calculating the basic available heat and basic required heat for the current heat cycle; dynamically allocating the optimal addition amount and timing for various types of scrap steel; dynamically calculating the amount of heat-replenishing agent and / or the preheating temperature of the scrap steel to generate a heat-replenishing scheme; using an optimization algorithm to solve for and output the optimal dynamic addition scheme for the furnace charge; and dynamically correcting the weighting coefficients of the scrap steel cooling effect coefficient, the comprehensive heat loss coefficient, and / or the comprehensive benefit-cost index. The advantages of this invention are: it integrates dynamic heat balance calculation, multi-dimensional scrap steel evaluation, scrap steel addition timing optimization, and environmentally friendly heat-replenishing agent formulation correction into a single system, constructing a closed-loop intelligent decision-making system capable of responding in real time to fluctuations in molten iron conditions, scrap steel inventory, and market prices; and forming an integrated decision-making system from heat assessment and resource allocation to prescription generation.

Description

Technical Field

[0001] This invention relates to the field of iron and steel smelting technology, and in particular to a dynamic optimization method for the addition of a large proportion of scrap steel to the furnace during converter smelting. Background Technology

[0002] Under the "dual carbon" strategy, continuously increasing the proportion of solid ferrous raw materials, represented by scrap steel, has become an inevitable path for the green and low-carbon transformation of the steel industry, and has significant strategic importance for reducing carbon emissions in the steel industry.

[0003] However, existing scrap steel blending models still face several challenges, particularly when dealing with a large proportion (≥30%) of scrap steel entering the furnace. Many models are based on static principles, making it difficult to respond in real-time to fluctuations in hot metal temperature and composition, as well as changes in production pace. Furthermore, models for hot metal assessment, scrap steel blending, scrap steel preheating, and reheating are relatively isolated, lacking integrated dynamic coordination. Under conditions of a large proportion of scrap steel entering the furnace, the contradiction between heat supply and demand is prominent, and existing methods cannot meet the urgent need for precise control of dynamic heat balance and overall cost optimization. Therefore, a new dynamic optimization method for the blending of large proportions of scrap steel in converter smelting is urgently needed to solve these problems and achieve efficient, low-carbon smelting of large proportions of scrap steel in converters. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic optimization method for the addition of a large proportion of scrap steel in converter smelting, aiming to solve the problems of isolated batching and reheating processes in the existing technology, difficulty in real-time response to fluctuations in molten iron, and prominent contradictions between heat supply and demand. This invention achieves maximum scrap steel ratio, minimum cost per ton of steel, and stable smelting process by dynamic heat balance calculation, multi-dimensional evaluation of scrap steel, and optimization of scrap steel addition timing, and introduces biomass char reheating and / or scrap steel preheating. This supports green and low-carbon smelting.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for dynamically optimizing the proportion of scrap steel added to the furnace charge in converter smelting includes: S1: Based on real-time molten iron data and target steel grade requirements, calculate the basic available heat and basic heat demand for the current heat cycle; S2: Construct a comprehensive benefit-cost index based on at least one of the following attributes: cooling effect, price, scrap steel recovery rate, and residual element content of each type of scrap steel; screen available scrap steel that meets the quality requirements of steel grades based on the comprehensive benefit-cost index; and dynamically allocate the optimal addition amount and timing of each type of scrap steel using the comprehensive benefit-cost index and / or scrap steel melting characteristic parameters as input. S3: Based on the amount of various scrap steels added determined in step S2, calculate the heat required for scrap steel melting and heating, and combine the basic available heat and basic required heat obtained in step S1 to determine the heat deficit value; when the heat deficit value is greater than the preset threshold, dynamically calculate the amount of heat supplementing agent added and / or the preheating temperature of scrap steel according to the heat deficit value, the thermal efficiency parameters of the heat supplementing agent and / or the preheating capacity of scrap steel, and generate a heat supplementing scheme; S4: Using the optimal addition amount of various scrap steels, the addition amount of heat-replenishing agent and / or the preheating temperature of scrap steel as decision variables, with heat balance and meeting the quality requirements of steel grades as constraints, and with the objective function of minimizing the total cost per ton of steel under the condition of a large proportion of scrap steel, the optimization algorithm is used to solve the optimal dynamic addition scheme of the furnace charge. S5: Store the actual production data for each heat, compare the actual measured endpoint temperature and endpoint composition with the target value, and dynamically correct the weighting coefficients of the scrap steel cooling effect coefficient, comprehensive heat loss coefficient and / or comprehensive benefit-cost index when the deviation between the measured value and the target value exceeds the preset range.

[0006] The basic available heat for the current furnace in S1 is expressed as follows: ; In the formula: The physical heat of molten iron is expressed in kJ. The heat of oxidation of elements in molten iron and the heat of slag formation are expressed in kJ. The heat released during the oxidation of flue gas is expressed in kJ. The heat released by carbon oxidation in the furnace lining is expressed in kJ. This represents the basic available heat for the current furnace cycle, expressed in kJ.

[0007] The basic heat requirement in S1 is expressed as follows: ; In the formula; This is the heat required to heat molten steel to the final temperature, expressed in kJ. The physical heat of slag is expressed in kJ. The physical heat of the furnace gas is expressed in kJ. The physical heat of iron beads in slag is expressed in kJ. The heat of the sputtered metal is expressed in kJ. The physical heat of flue gas is expressed in kJ. = The unit is kJ; in: This is due to heat loss in the converter; To optimize the overall heat loss coefficient, the actual measured value is compared with the predicted value and the coefficient is dynamically adjusted in step S5. This is the basic heat requirement, expressed in kJ.

[0008] The optimal addition amount and timing of various types of scrap steel in S2 are dynamically allocated based on a melting characteristic and quality control model that includes parameters such as cooling effect, bulk density, and residual element content.

[0009] The expression for the comprehensive benefit-cost index in S2 is: ; In the formula: For the first Comprehensive benefit-cost index of scrap steel; The metal recovery rate of scrap steel, % The cooling effect coefficient of scrap steel is given in MJ / t. It is the comprehensive equivalent of residual elements in scrap steel; The purchase price of scrap steel is in yuan / ton; , , For weighting coefficients, the weighting coefficients are preset values ​​associated with the quality requirements and cost strategies of the target steel grade, and / or parameters adaptively determined by the optimization algorithm based on the objective function; The properties of various types of scrap steel include chemical composition, physical properties, thermal properties, economic properties, and technological properties.

[0010] In S3, the heat supplement agent is biochar. Based on the size of the heat deficit, the theoretical thermal effect of biochar, and its overall thermal efficiency, the minimum economic addition amount is dynamically calculated, expressed as: ; In the formula: The amount of biochar added is expressed in kg. The remaining heat loss after scrap steel optimization, in kJ; The theoretical heat effect per unit of biochar is given in kJ / kg. For overall thermal efficiency; ; The total heat required for melting and heating various types of scrap steel, in kJ.

[0011] In S3, scrap steel preheating is performed when there is still a heat deficit after supplementing with a heat-replenishing agent. At the same time, the waste heat of the converter flue gas is used to preheat the scrap steel entering the furnace. The scrap steel is preheated to 200-1000℃ according to the actual heat demand.

[0012] In S4, meeting the steel quality requirements includes: when using biomass char as a heat exchanger, ensuring that the sulfur content of the final molten steel does not exceed the upper limit allowed for the steel grade is one of the constraints for optimization.

[0013] When the sulfur increment introduced by biomass char during the multi-objective optimization process causes the sulfur content of the final molten steel to exceed the upper limit allowed by the steel grade, the optimization algorithm adjusts the amount of biomass char added and / or the preheating temperature of scrap steel in the reheating scheme until the sulfur content constraint is met.

[0014] In S4, the optimization algorithm is a multi-objective dynamic optimization algorithm that integrates furnace thermal balance constraints, steel grade quality constraints, and total cost per ton of steel. The multi-objective dynamic optimization algorithm includes at least one of genetic algorithm, particle swarm algorithm, or linear weighted summation method.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. Existing technologies may focus on static batching, or only on the addition of scrap steel in a single process, or study heat replenishment technology separately, resulting in isolated and fragmented decision-making processes for scrap steel preheating, batching, and heat replenishment. This invention integrates dynamic heat balance calculation, multi-dimensional scrap steel evaluation, optimization of scrap steel addition timing, and adjustment of environmentally friendly heat replenishment agent formulation for the first time, constructing a closed-loop decision-making system that can respond in real time to fluctuations in molten iron conditions, scrap steel inventory, and market prices. It comprehensively optimizes the addition strategy of scrap steel in different batches and at multiple nodes in the converter, and links it with the formulation of biomass charcoal heat replenishment agent, forming an integrated decision-making system from heat assessment and resource allocation to prescription generation, significantly improving the system's collaborative efficiency and adaptability. 2. Based on more accurate dynamic heat balance calculation, by collecting data such as molten iron temperature and composition in real time, the basic available heat and basic required heat are dynamically calculated. Under the premise of ensuring the hit rate of the smelting endpoint temperature, the scrap steel ratio in the furnace can be accurately pushed to the critical maximum value under the current production conditions. The results of the example show that the scrap steel ratio can reach 31.4% under the condition of molten iron temperature of 1300℃. At the same time, through global optimization with the objective function of minimizing the total cost per ton of steel, the optimal ratio of scrap steel and heat supplement agent is found, achieving multiple benefits of increasing the scrap steel ratio, reducing the cost of raw materials and stabilizing the smelting process. This effectively solves the core contradiction between economy and process in the smelting of large proportions of scrap steel. 3. Biochar is preferred as a heat supplement agent because it is a renewable resource with carbon emissions far lower than traditional heat supplement agents (such as coke and special heating agents). The minimum economic addition amount of biochar is accurately calculated by using the heat deficit to avoid waste or pollution caused by excessive use. At the same time, sulfur content constraints are incorporated into the optimization scheme to ensure that the addition of the heat supplement agent does not affect the steel quality, thus taking into account both environmental protection and process feasibility. 4. By storing the actual production data for each heat and comparing it with the predicted value, when the deviation exceeds the preset range, the weighting coefficients of the scrap steel cooling effect coefficient, the comprehensive heat loss coefficient, and the comprehensive benefit-cost index are dynamically corrected. This can adapt to changes in production conditions such as furnace age and raw material fluctuations, and continuously improve the prediction accuracy and control effect. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the dynamic optimization method for the addition of a large proportion of scrap steel to the furnace during converter smelting.

[0017] Figure 2 This is a module architecture diagram of a dynamic optimization system for the addition of a large proportion of scrap steel to the furnace during converter smelting. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the accompanying drawings, but it should be noted that the implementation of the present invention is not limited to the following embodiments.

[0019] The following embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments. Unless otherwise specified, the methods used in the following embodiments are conventional methods.

[0020] Example 1:

[0021] See Figure 1 A dynamic optimization method for the addition of high-proportion scrap steel in converter smelting is proposed. This method minimizes the cost of high-proportion scrap steel smelting through comprehensive dynamic heat balance calculations, optimization of the timing of adding multiple types of scrap steel, and optimized reheating. The method includes the following steps: The real-time hot metal data for this furnace is as follows: hot metal quantity 120t, hot metal temperature 1300℃, hot metal composition [C]=4.25%, [Si]=0.50%, [Mn]=0.28%, [P]=0.085%. The endpoint control requirements for the target steel grade HRB400E are: tapping endpoint temperature 1660℃, endpoint [C]=0.05%, endpoint [P]≤0.020%, and tapping quantity approximately 157t.

[0022] Step S1: Based on real-time molten iron data and target steel grade requirements, calculate the basic available heat and basic heat demand for the current heat cycle; S11, Basic Available Heat The calculation is expressed as: ; in; Physical heat of molten iron =1.42×10 8 kJ (calculated based on 120t of molten iron, 1300℃ of temperature and specific heat capacity of molten iron); Exothermic oxidation of elements in molten iron, heat of slag formation =1.0×10 8 kJ (calculated based on the content of elements such as C, Si, Mn, and P and their oxidation exothermic coefficients); Exothermic oxidation of smoke and dust =8.0×10 6 kJ; Carbon oxidation exothermic in furnace lining =4.0×10 5 kJ.

[0023] Calculations show that =1.42×10 8 +1.0×10 8 +8.0×10 6 +4.0×10 5 +=2.50×10 8 kJ.

[0024] S12, Basic Demand Heat The calculation is expressed as: ; in: The heat required to raise the molten steel to the tapping final temperature =2.24×10 8 kJ; Physical heat of slag =3.0×10 7 kJ; Furnace gas physical heat =1.08×10 7kJ; Physical heat of iron beads in slag =1.92×10 6 kJ; Splashed metal heat =1.74×10 6 kJ; Physical heat of smoke and dust =3.14×10 6 kJ; Heat loss during the blowing process =9.97×10 6 kJ.

[0025] Calculations show that = 2.24×10 8 +3.0×10 7 +1.08×10 7 +1.92×10 6 +1.74×10 6 +3.14×10 6 +9.97×10 6 =+2.80×10 8 kJ.

[0026] Step S2: Scrap steel screening and addition amount and timing allocation based on multi-dimensional evaluation; The available scrap steel inventory attributes for this heat are shown in Table 1: Table 1 shows the inventory attributes of scrap steel.

[0027] Considering the relatively lenient requirements for residual elements ([Cu]≤0.25%) in the HRB400E steel grade, the weighting coefficients are set as w1=0.3 (yield weight), w2=0.4 (heat balance weight), and w3=0.3 (residual element weight).

[0028] Substitute the properties of each scrap steel in Table 1 into the comprehensive benefit-cost index formula: , in: Indicates the first Comprehensive benefit-cost index of scrap steel; This indicates the metal recovery rate of scrap steel, expressed as % . The coefficient of cooling effect of scrap steel is expressed in MJ / t. This indicates the total equivalent of residual elements in scrap steel; This indicates the purchase price of scrap steel, expressed in yuan / ton.

[0029] like: ; ; ; ; The calculation results show that the comprehensive benefit-cost index, from highest to lowest, is as follows: heavy scrap steel (0.00418), rebar briquettes (0.00246), stamped briquettes (-0.00853), and crushed material (-0.01583). After verification of the residual element content constraint ([Cu]≤0.25%), the Cu residual equivalents of the above four types of scrap steel are 50ppm, 65ppm, 160ppm, and 210ppm, respectively, all far below the upper limit requirement of 2500ppm (0.25%). Therefore, all four types of scrap steel meet the quality requirements of HRB400E.

[0030] Dynamic allocation of addition amount and timing: Based on melting characteristics and quality control rules (hereinafter referred to as scrap steel melting characteristic methods) that include parameters such as cooling effect, bulk density, and residual element content, the amount and timing of adding various types of scrap steel to the converter are dynamically allocated. The allocation is based on the following: Heavy scrap steel: It has large block size, high bulk density (about 2.5t / m³), large cooling effect (1.18MJ / t) and slow melting rate (about 8-10 minutes). It should be added to the converter in the first batch before blowing to ensure that it is fully melted during the blowing process. Stamped blocks: with moderate compressibility (approximately 1.8 t / m³) and a relatively fast melting rate (approximately 6-8 minutes), they can be added to the converter along with heavy scrap steel before the start of blowing. Steel bar briquettes: moderate cooling effect (1.08 MJ / t), bulk density of about 2.0 t / m³, added to the converter in the second batch during the early stage of blowing to balance the molten pool temperature during the blowing process; Crushed material: low bulk density (approximately 0.8 t / m³), small cooling effect (0.96 MJ / t), fast melting rate (approximately 4-6 minutes), added to the converter in the third batch during the blowing process to precisely regulate the temperature of the molten pool.

[0031] Based on the above melting characteristics, combined with the hot metal conditions of this heat (temperature 1300℃) and the target steel output (157t), the converter scrap addition scheme is preliminarily determined according to the following method: 1. Determine the maximum amount of scrap steel to be added: Based on the preliminary estimate of heat balance, the maximum amount of scrap steel that can be added under the existing molten iron conditions is about 55-60t; 2. Allocation based on index priority: Prioritize the use of scrap steel with high comprehensive benefit-cost index, namely heavy scrap steel and steel bar briquettes; 3. Meet the cooling effect constraint: Ensure that the total cooling effect of all types of scrap steel does not exceed the compensation capacity of available heat; 4. Meet process capacity constraints: The maximum amount of scrap steel added to the converter in each batch is limited by the volume of the scrap steel bucket (maximum 20t per bucket).

[0032] Based on the above calculations, the preliminary scrap steel addition scheme for the converter is determined as follows: 30t of heavy scrap steel and 8t of stamped blocks are added before blowing begins; 10t of steel bar blocks are added in the early stage of blowing; and 7t of crushed material is added in the middle stage of blowing, for a total scrap steel addition of 55t.

[0033] S3. Calculation of heat deficit and generation of heat replenishment plan; Based on the amount of various types of scrap steel added determined in step S2, calculate the heat required for the scrap steel to melt and heat up. Based on the amount of various types of scrap steel added and their endothermic enthalpy values ​​(heavy scrap steel 1200kJ / t, stamped briquettes 1050kJ / t, rebar briquettes 1150kJ / t, crushed material 950kJ / t): =30×1200+8×1050+10×1150+7×950==6.255×10 7 kJ.

[0034] Based on the calculation results of step S1, calculate the heat deficit value. : =6255×10 7 +2.80×10 8 -2.50×10 8 kJ = 9.255 × 10 7 kJ.

[0035] When the heat deficit value (approximately 92.55 MJ) exceeds the preset threshold (set to 0), a heat replenishment scheme is generated.

[0036] Using biochar as a heat replenishing agent, the industrial analysis results of biochar show a fixed carbon content of 85.2% and a unit theoretical heat effect. Take 28.0 MJ / kg (i.e., 28000 kJ / kg); biochar is added during the middle stage of blowing, and the overall thermal efficiency is... Take 0.85. Calculate the minimum economic addition amount of biochar: =9.255×10 7 / (0.85×28000)kg=3889kg.

[0037] Sulfur content verification: Assuming the sulfur content of the biochar used is 0.5% (typical value), then 3889 kg of biochar will introduce sulfur: 3889 × 0.5% = 19.44 kg. For 157t of molten steel (steel output 157t, considering the total mass of other metal materials approximately 167t), the sulfur increment is approximately: 19.44 kg / 167000 kg = 0.0116%. Assuming the initial sulfur content of the molten iron is 0.030%, the final sulfur content is expected to be 0.0416%, which does not exceed the 0.045% upper limit typically required by HRB400E. Therefore, the sulfur content does not constitute a limitation.

[0038] The heat generation scheme is as follows: 3889 kg of biochar is added, and no preheating of scrap steel is required.

[0039] S4, Global Optimization Solution Using the amounts of various types of scrap steel added in step S2, the timing of the addition batches, and the amount of biomass char added in step S3 as decision variables, and with the constraints of heat balance and the scrap steel residual element content meeting the steel grade quality requirements (including the endpoint [Cu] ≤ 0.25%) and the endpoint sulfur content of the molten steel not exceeding the allowable range (calculated according to the HRB400E requirement, endpoint [S] ≤ 0.045%), and with the objective function of minimizing the total cost per ton of steel under a high proportion of scrap steel, a multi-objective dynamic optimization solution is performed using the linear weighted summation method. The specific process is as follows: 1. Setting decision variables: set up , , , .

[0040] 2. Objective function: ,in, The unit price of biochar is 1200 yuan / t.

[0041] 3. Constraints: Thermal equilibrium constraint: ; The overall thermal efficiency of biochar; The theoretical heat effect per unit of biochar is expressed in kJ / kg. Sulfur content constraints: ; The mass fraction of sulfur in biochar is expressed as % (%). The mass of molten iron is expressed in tons (t). The amount of scrap steel of type j added is expressed in tons (t). The maximum allowable final sulfur content for a given steel grade, expressed in % (%). The initial sulfur content of the molten iron entering the furnace, expressed in % (%). Process capacity constraint: The amount added in each batch is ≤20t / batch.

[0042] 4. Optimization solution: The linear weighted summation method is adopted to transform the multi-objective problem into a single objective, and the optimal solution is found through iterative calculation.

[0043] After optimization calculations, the optimal dynamic feed ratio for this converter furnace was obtained as follows: 28t of heavy scrap steel and 10t of stamped briquettes were added before blowing; 9t of rebar briquettes were added during the early blowing stage; and 8t of crushed material was added during the middle blowing stage, for a total of 55t of converter scrap steel and 3889kg of biochar. Compared with the preliminary scheme (30t of heavy scrap steel, 8t of stamped briquettes, 10t of rebar briquettes, and 7t of crushed material), this scheme, while maintaining the total scrap steel amount (55t), further reduces the feed ratio by increasing the amount of crushed material (7t→8t) and stamped briquettes (8t→10t), while reducing the amount of heavy scrap steel (30t→28t) and rebar briquettes (10t→9t). The sulfur contribution of biochar in this scheme was calculated to be 0.0116%, which did not exceed the final sulfur content control limit. The specific feed ratio is shown in the table below.

[0044] S5, Production Execution and Model Self-Correction; The smelting was carried out according to the optimal scheme output in step S4, and the scrap steel ratio reached 31.4% (scrap steel amount 55t / (molten iron amount 120t + scrap steel amount 55t) = 55 / 175 = 31.4%).

[0045] The measured data after the furnace were as follows: final temperature 1658℃, final [C]=0.048%, final [P]=0.019%, final [Cu]=0.18%.

[0046] The process for calculating and verifying the deviation between the measured value and the target value is as follows: The deviations between the measured and calculated values ​​were all within the preset range. The actual production data for this heat was stored in the database for fine-tuning the weighting coefficients of the scrap steel cooling effect coefficient, the comprehensive heat loss coefficient, and / or the comprehensive benefit-cost index. For example, based on the fact that the measured endpoint temperature was 2℃ lower than expected, the comprehensive heat loss coefficient was adjusted accordingly. The value was revised from 1.00 to 1.002 to improve the calculation accuracy for subsequent furnace cycles.

[0047] Example 2

[0048] This embodiment provides a precise heat replenishment optimization method combined with scrap preheating under high scrap ratio conditions, applied to the smelting of 22MnB5 steel in a 120-ton nominal capacity converter, specifically including the following steps.

[0049] S1. Calculation of basic available heat and basic demand heat The real-time hot metal data for this furnace is as follows: hot metal quantity 85t, hot metal temperature 1360℃, hot metal composition [C]=4.40%, [Si]=0.62%, [Mn]=0.32%, [P]=0.072%. The endpoint control requirements for the target steel grade 22MnB5 are: tapping endpoint temperature 1650℃, endpoint [C]=0.06%, strict requirements for residual element Cu (finished product [Cu]≤0.08%), and tapping quantity approximately 118t.

[0050] Step S1: Base available heat and basic demand Calculation; Calculations show that =1.85×10 8 kJ; =2.2×10 8 kJ.

[0051] Step S2: Construction of the comprehensive benefit-cost index of scrap steel and dynamic allocation of the optimal addition amount and timing; The available scrap steel inventory attributes for this heat are shown in Table 2: Table 2 shows the inventory attributes of scrap steel.

[0052] Considering the extreme sensitivity of 22MnB5 steel to residual element Cu (finished product [Cu] ≤ 0.08%), weighting coefficients were set as w1 = 0.2 (yield weight), w2 = 0.3 (heat balance weight), and w3 = 0.5 (residual element weight). Substituting the properties of each scrap steel in Table 2 into the comprehensive benefit-cost index formula: ; in, Indicates the first Comprehensive benefit-cost index of scrap steel; This indicates the metal recovery rate of scrap steel, expressed as % . The coefficient of cooling effect of scrap steel is expressed in MJ / t. This indicates the total equivalent of residual elements in scrap steel; The price of scrap steel is expressed in yuan / t. Calculations show that the comprehensive benefit-cost index of high-quality heavy-duty cut scrap and high-quality rolled scrap are both positive and significantly higher than that of ordinary bundled scrap. Further verification using residual element content constraints for different steel grades reveals that if ordinary bundled scrap is used, its residual Cu equivalent is as high as 240 ppm. Combined with the Cu content introduced by the molten iron, calculations based on the steel mass conservation equation show that the finished product [Cu] will exceed the upper limit of 0.08%. Therefore, ordinary bundled scrap is excluded.

[0053] Based on a melting characteristic and quality control model incorporating parameters such as cooling effect, bulk density, and residual element content, high-quality heavy scrap, due to its large size and high cooling effect, should be added to the converter in the first batch before blowing begins, ensuring its full melting throughout the blowing process. High-quality rolled scrap, due to its moderate cooling effect, low Cu content, and relatively small size, should be added to the converter in the second batch during the early blowing stage. Model calculations preliminarily determine the converter scrap addition scheme as follows: 25 tons of high-quality heavy scrap added before blowing begins, and 20 tons of high-quality rolled scrap added during the early blowing stage, for a total converter scrap addition of 45 tons.

[0054] S3, Calculation of Heat Deficit and Generation of Heating Replenishment Plan Based on the amount of various types of scrap steel added determined in step S2, calculate the heat required for the scrap steel to melt and heat up. : =5.190×10 7 kJ.

[0055] Based on the calculation results of step S1, calculate the heat deficit value: =5.190×10 7 +2.20×10 8 1.85×10 8 kJ = 8.69 × 10 7 kJ If the heat deficit exceeds a preset threshold, a heat replenishment plan will be generated.

[0056] First, according to the method described in claim 6, biochar is used as a heat replenishing agent. The industrial analysis results of the biochar used in this embodiment show a fixed carbon content of 85.2% and a unit theoretical heat effect... Take 28 MJ / kg (i.e., 28000 kJ / kg); biochar is added during the middle stage of blowing, and the overall thermal efficiency is... Take 0.85. Preliminary calculation of biochar addition amount: =8.69×10 7 / (0.85×28000)kg=3651kg In the formula, The amount of biochar added is expressed in kg. This indicates the amount of heat still missing after scrap steel optimization, in kJ; The theoretical heat effect per unit of biochar is expressed in kJ / kg. This represents the overall thermal efficiency and is a process parameter related to the location and method of adding the heat-replenishing agent. Sulfur content verification: Assuming the sulfur content of biomass char is 0.5%, then 3651 kg of biomass char introduces 18.26 kg of sulfur. For 118 tons of molten steel, the sulfur increment is 18.26 kg / 118000 kg = 0.02%. Assuming the initial sulfur content of the molten iron is 0.015%, the final sulfur content has reached 0.035%, far exceeding the 0.025% requirement of 22MnB5. Preliminary verification shows that the total sulfur introduced by this amount of biomass char will cause the final sulfur content of the molten steel to exceed the upper limit of the 22MnB5 requirement ([S] ≤ 0.025%). This preliminary solution does not meet the process constraints.

[0057] Due to the aforementioned limitations, we proceed to the comprehensive solution optimization phase.

[0058] S4, Global Optimization Solution To resolve the contradiction of excessive sulfur content in step S3, the scrap steel blending scheme determined in step S2 and the amount of biochar added in step S3 are both incorporated into the global optimization model as decision variables. The objective function is to minimize the total cost per ton of steel under a high proportion of scrap steel, and a multi-objective dynamic optimization algorithm is used for global optimization. During the optimization process, driven by the sulfur content constraint, the algorithm automatically searches for the optimal combination of scrap steel preheating temperature and biochar addition amount. The calculation process is as follows: Determining the upper limit of biochar based on sulfur content: Permissible sulfur increment: 0.025% - 0.015% = 0.01%.

[0059] The corresponding maximum allowable sulfur carryover is: 118000kg × 0.01% = 11.8kg.

[0060] Maximum allowable biochar addition (based on sulfur): 11.8 kg / 0.5% = 2360 kg.

[0061] The amount of heat provided by this addition is: 2360kg × 0.85 × 28000kJ / kg = 5.62 × 10⁻⁶ 7 kJ.

[0062] Heat deficit and preheating requirements: The remaining heat that needs to be compensated by preheating: –5.62×10 7 kJ = 8.69 × 10 7 –5.62×10 7=3.07×10 7 kJ.

[0063] Required preheating temperature: ΔT = 3.07 × 10 7 kJ / (20000kg×0.699kJ / (kg·℃))=977℃‌.

[0064] Preheating temperature: =25℃ + 977℃ = 1002℃. Considering equipment capacity and control precision, taking 1000℃ is reasonable.

[0065] Final biochar addition amount: take the maximum value of 2360 kg under the constraint of sulfur content.

[0066] After optimization, the optimal dynamic feed ratio for this heat is as follows: 25t of high-quality heavy-duty scrap preheated to 1000℃ is added before blowing begins; 20t of high-quality rolled scrap preheated to 1000℃ is added during the early blowing stage; the total amount of converter scrap added is 45t; and the amount of biomass char added is 2360kg. The endpoint [S] corresponding to this addition amount meets the upper limit of operation required for 22MnB5 steel.

[0067] S5, Production Execution and Model Self-Correction The process was carried out on-site according to the above plan, with a scrap steel ratio of 34.6%. The actual measured data after the furnace were: final temperature 1652℃, final [C] = 0.055%, finished product [Cu] = 0.072%, finished product [S] = 0.008%. The deviation between the measured values ​​and the target values ​​was within the preset range. The actual production data for this furnace was stored in the database for use in determining the cooling effect coefficient and comprehensive heat loss coefficient of high-quality rolled scrap steel. The corrections are made for calculation optimization in subsequent furnace cycles.

[0068] Example 3:

[0069] A dynamic optimization system for the addition of high-proportion scrap steel to the converter smelting furnace to implement the above method is shown in the figure. The system includes: The data acquisition module and the dynamic thermal balance calculation module are used to execute step S1; The multi-dimensional evaluation and selection module for scrap steel is used to execute step S2; The dynamic adjustment module for molten pool heating is used to execute step S3; The multi-objective optimization solution module is used to execute step S4; The self-learning and adaptive correction module is used to execute step S5; In addition, there is a scrap steel knowledge base and a production process database that communicate with the above modules.

[0070] The system is built on the existing automation system, testing instruments and database of steel plants. It does not require a lot of additional hardware investment. Through modular design, it can be flexibly configured according to the equipment conditions, process characteristics and cost structure of different enterprises, and has good portability and promotion value.

[0071] Existing technologies may focus on static batching, single-process scrap steel addition, or separate heat replenishment techniques, leading to isolated and fragmented decision-making processes for scrap steel preheating, batching, and molten pool heat replenishment. This invention, for the first time, integrates dynamic heat balance calculation, multi-dimensional scrap steel evaluation, scrap steel addition timing optimization, and environmentally friendly heat replenishment agent formulation correction, constructing a closed-loop decision-making system capable of real-time response to fluctuations in molten iron conditions, scrap steel inventory, and market prices. It comprehensively optimizes the addition strategy of scrap steel at multiple nodes and in different batches within the converter, and links it with the formulation of biomass charcoal heat replenishment agents and scrap steel preheating, forming an integrated decision-making system from heat assessment and resource allocation to prescription generation, significantly improving the system's collaborative efficiency and adaptability. With more accurate dynamic heat balance calculation as its core, by collecting real-time data such as molten iron temperature and composition, it dynamically calculates the basic available heat and basic required heat, enabling the scrap steel ratio to be precisely pushed to the critical maximum value under current production conditions while ensuring the accuracy of the smelting endpoint temperature. Example results show that at a molten iron temperature of 130°C... At 0℃, the scrap steel ratio can reach 31.4%. Simultaneously, through global optimization with the objective function of minimizing the total cost per ton of steel, the optimal ratio of scrap steel to heat-generating agent is found, achieving multiple benefits simultaneously: increasing the scrap steel ratio, reducing raw material costs, and stabilizing the smelting process. This effectively resolves the core contradiction between economics and processability in high-proportion scrap steel smelting. Biochar is selected as the heat-generating agent because it is a renewable resource with carbon emissions far lower than traditional heat-generating agents (such as coke and special heating agents). The minimum economic addition amount of biochar is accurately calculated using the heat deficit to avoid waste or pollution from excessive use. Furthermore, sulfur content constraints are incorporated into the optimization scheme to ensure that the addition of the heat-generating agent does not affect steel quality, balancing environmental protection and process feasibility. By storing actual production data for each furnace and comparing it with predicted values, the weighting coefficients of the scrap steel cooling effect coefficient, comprehensive heat loss coefficient, and comprehensive benefit-cost index are dynamically corrected when the deviation exceeds the preset range. This adapts to changes in furnace age, raw material fluctuations, and other production conditions, continuously improving prediction accuracy and control effectiveness.

Claims

1. A method for dynamically optimizing the addition of a large proportion of scrap steel to the furnace charge in converter smelting, characterized in that, include: S1: Based on real-time molten iron data and target steel grade requirements, calculate the basic available heat and basic heat demand for the current heat cycle; S2: Construct a comprehensive benefit-cost index based on at least one of the following attributes: cooling effect, price, scrap steel recovery rate, and residual element content of each type of scrap steel; screen available scrap steel that meets the quality requirements of steel grades based on the comprehensive benefit-cost index; and dynamically allocate the optimal addition amount and timing of each type of scrap steel using the comprehensive benefit-cost index and / or scrap steel melting characteristic parameters as input. S3: Based on the amount of various scrap steels added determined in step S2, calculate the heat required for scrap steel melting and heating, and combine the basic available heat and basic required heat obtained in step S1 to determine the heat deficit value; when the heat deficit value is greater than the preset threshold, dynamically calculate the amount of heat supplementing agent added and / or the preheating temperature of scrap steel according to the heat deficit value, the thermal efficiency parameters of the heat supplementing agent and / or the preheating capacity of scrap steel, and generate a heat supplementing scheme; S4: Using the optimal addition amount of various scrap steels, the addition amount of heat-replenishing agent and / or the preheating temperature of scrap steel as decision variables, with heat balance and meeting the quality requirements of steel grades as constraints, and with the objective function of minimizing the total cost per ton of steel under the condition of a large proportion of scrap steel, the optimization algorithm is used to solve the optimal dynamic addition scheme of the furnace charge. S5: Store the actual production data for each heat, compare the actual measured endpoint temperature and endpoint composition with the target value, and dynamically correct the weighting coefficients of the scrap steel cooling effect coefficient, comprehensive heat loss coefficient and / or comprehensive benefit-cost index when the deviation between the measured value and the target value exceeds the preset range.

2. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, The basic available heat for the current furnace batch, as described in S1, is expressed as follows: ； In the formula: The physical heat of molten iron is expressed in kJ. The heat of oxidation of elements in molten iron and the heat of slag formation are expressed in kJ. The heat released during the oxidation of flue gas is expressed in kJ. The heat released by carbon oxidation in the furnace lining is expressed in kJ. This represents the base available heat for the current furnace cycle, expressed in kJ.

3. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, The basic heat requirement mentioned in S1 is expressed as follows: ； In the formula; This is the heat required to heat molten steel to the final temperature, expressed in kJ. The physical heat of slag is expressed in kJ. The physical heat of the furnace gas is expressed in kJ. The physical heat of iron beads in slag is expressed in kJ. The heat of the sputtered metal is expressed in kJ. The physical heat of flue gas is expressed in kJ. = The unit is kJ; in: This is due to heat loss in the converter; The overall heat loss coefficient; This refers to the basic heat requirement, expressed in kJ.

4. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, The dynamic allocation of the optimal addition amount and timing of various types of scrap steel described in S2 is based on a dynamic allocation of melting characteristics and quality control model that includes parameters such as cooling effect, bulk density and residual element content.

5. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, The expression for the comprehensive benefit-cost index described in S2 is as follows: ； In the formula: For the first Comprehensive benefit-cost index of scrap steel; The metal recovery rate of scrap steel, % The cooling effect coefficient of scrap steel is given in MJ / t. It is the comprehensive equivalent of residual elements in scrap steel; The purchase price of scrap steel is in yuan / ton; , , For weighting coefficients, the weighting coefficients are preset values ​​associated with the quality requirements and cost strategies of the target steel grade, and / or parameters adaptively determined by the optimization algorithm based on the objective function; The properties of various types of scrap steel include chemical composition, physical properties, thermal properties, economic properties, and technological properties.

6. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, In S3, the heat replenishing agent is biochar, and the minimum economic addition amount is dynamically calculated based on the size of the heat deficit, the theoretical heat effect of biochar, and its overall thermal efficiency. The expression is as follows: ； In the formula: The amount of biochar added is expressed in kg. The remaining heat loss after scrap steel optimization, in kJ; The theoretical heat effect per unit of biochar is given in kJ / kg. For overall thermal efficiency; ； The total heat required for melting and heating various types of scrap steel, in kJ.

7. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, The scrap steel preheating described in S3 refers to the process of using waste heat from converter flue gas to preheat the scrap steel entering the furnace when there is still a heat deficit after using a heat supplementing agent. The scrap steel is preheated to 200~1000℃ according to the actual heat demand.

8. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, The requirement to meet the steel quality standards mentioned in S4 includes: when using biochar as a heat exchanger, ensuring that the sulfur content of the final molten steel does not exceed the upper limit allowed for the steel grade is one of the constraints for the optimization solution.

9. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 8, characterized in that, When the sulfur increment brought in by biomass char during the multi-objective optimization process causes the sulfur content of the final molten steel to exceed the upper limit allowed by the steel grade, the optimization algorithm adjusts the amount of biomass char added and / or the preheating temperature of scrap steel in the reheating scheme until the sulfur content constraint is met.

10. The method for dynamic optimization of the proportion of scrap steel added to the furnace charge in converter smelting according to claim 1, characterized in that, In S4, the optimization algorithm is a multi-objective dynamic optimization algorithm that integrates furnace thermal balance constraints, steel grade quality constraints, and total cost per ton of steel. The multi-objective dynamic optimization algorithm includes at least one of genetic algorithm, particle swarm algorithm, or linear weighted summation method.

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