A method and device for predicting end point phosphorus content of a converter

Abstract

The application discloses a converter endpoint phosphorus content prediction method and device, and relates to the field of steelmaking, which comprises the following steps: obtaining the operation data of the current furnace and the historical furnace before the current furnace; determining the theoretical maximum phosphorus content of the current furnace according to the operation data of the current furnace; determining the theoretical maximum phosphorus content of the historical furnace according to the operation data of the historical furnace; determining the thermodynamic influence coefficient of the current furnace according to the operation data of the current furnace and the historical furnace; and predicting the phosphorus content of the converter endpoint of the current furnace according to the theoretical maximum phosphorus content of the historical furnace, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace and the phosphorus content of the converter endpoint of the historical furnace. After obtaining the operation data of the current furnace, the phosphorus content is automatically predicted, and the result is more accurate compared with artificial judgment. After the prediction is completed, the next tapping operation can be performed without waiting for the test sample, the converter smelting time is shortened, and the production efficiency of the converter is improved.

Description

Technical Field

[0001] This invention relates to the field of steelmaking, and in particular to a method and apparatus for predicting the phosphorus content at the end point of a converter. Background Technology

[0002] Phosphorus in steel causes cold brittleness, reducing its plasticity and impact toughness. Therefore, it is often considered a harmful element and needs to be removed during converter smelting. As steel quality requirements become increasingly stringent, the control requirements for phosphorus content in molten steel also become more stringent. Converter steelmaking is the process of smelting molten iron into steel that meets requirements. The converter endpoint refers to the operational techniques used during converter steelmaking to control oxygen blowing time and supply to ensure that the temperature and composition of the molten steel meet requirements at the end of the blowing process. The goal of endpoint control is that the carbon, phosphorus, and sulfur content and temperature of the molten steel at the endpoint should reach the internal control range required for the steel grade being produced. When the endpoint carbon and phosphorus content is too high or the endpoint temperature is too low, supplementary blowing operations can be performed to bring the endpoint carbon and phosphorus content and temperature back to the internal control range. However, when the endpoint phosphorus and sulfur content is too high, supplementary blowing operations cannot remedy the situation, requiring a reassessment and the production of another steel grade with a wider internal control range, i.e., a change in the production plan. Statistics on heats requiring additional blowing or reassessment at the converter endpoint in steel mills reveal a significant proportion of heats where the phosphorus content in the molten steel exceeds the internal control limit. Therefore, accurate control of the phosphorus content at the converter endpoint is crucial. Two methods exist for determining the phosphorus content at the converter steelmaking endpoint. The first method relies heavily on operator experience to determine whether the phosphorus content meets the target. This method is highly dependent on the operator's experience and lacks accuracy. The second method involves sampling the converter after blowing is complete, sending the samples to a laboratory for analysis, and then determining whether to tap the steel or require additional blowing based on the test results. This is currently the most common method used in steel mills. While the method is the most reliable, the long waiting time for samples at the converter endpoint can disrupt the converter's production rhythm and reduce its efficiency.

[0003] Therefore, an accurate and rapid method for predicting the final phosphorus content of converters is very important for the steelmaking industry. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for predicting the phosphorus content at the end of a converter heat treatment process. This method automatically predicts the phosphorus content, providing more accurate results compared to manual judgment. Furthermore, it predicts the phosphorus content of the current heat after obtaining the operating data, allowing for immediate tapping without waiting for sample analysis, thus shortening converter smelting time and improving converter production efficiency.

[0005] To solve the above-mentioned technical problems, the present invention provides a method for predicting the phosphorus content at the converter endpoint, comprising:

[0006] Obtain the operating data of the current heat and the historical heats before the current heat. The operating data includes the weight of the raw materials for steelmaking, the weight of the auxiliary materials added during the steelmaking process, and the final temperature of the converter.

[0007] The theoretical maximum phosphorus content of the current furnace is determined based on the operating data of the current furnace.

[0008] The theoretical maximum phosphorus content of the historical furnace is determined based on the operating data of the historical furnace.

[0009] The thermodynamic influence coefficient of the current furnace is determined based on the operating data of the current furnace and historical furnaces.

[0010] The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of the historical furnace, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of the historical furnace.

[0011] On the other hand, after obtaining the operating data of the current furnace and the historical furnaces before the current furnace, it also includes:

[0012] Based on the pre-set boundary conditions, the operating data of historical furnaces whose raw material weight is outside the preset weight range, whose converter end temperature is outside the preset temperature range, or whose phosphorus content at the converter end exceeds the upper limit of phosphorus content are removed.

[0013] The raw materials for steelmaking include molten iron, scrap steel, and pig iron.

[0014] On the other hand, determining the theoretical maximum phosphorus content of the current furnace based on the operating data of the current furnace includes:

[0015] The relationship is determined based on the phosphorus content of the first theory. Determine the theoretical maximum phosphorus content (P) in the molten steel of the converter pool for the current heat. max ;

[0016] The theoretical maximum phosphorus content of the historical furnace is determined based on the operating data of the historical furnace, including:

[0017] The relationship is determined based on the second theory of phosphorus content. Determine the theoretical maximum phosphorus content (P) in the molten steel pool of the converter in historical heats. k,max ;

[0018] The thermodynamic influence coefficient of the current furnace is determined based on the operating data of the current furnace and historical furnaces, including:

[0019] Based on the historical operating data, the current operating data, and the thermodynamic influence coefficient relationship...

[0020] Determine the thermodynamic influence coefficient K for the current furnace batch;

[0021] The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of the historical furnaces, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of the historical furnaces, including:

[0022] According to the predictive relation Determine the phosphorus content [P] at the end of the converter run for the current heat.

[0023] in, The amount of phosphorus charged in the historical furnace batch. The weight of molten steel in the molten pool for the aforementioned historical heat. W represents the phosphorus charge amount for the current furnace charge. bath Where is the weight of molten steel in the pool of the current heat, K is the thermodynamic influence coefficient, [P] k The phosphorus content at the converter endpoint of the aforementioned historical furnace batch.

[0024] On the other hand, the process of determining the phosphorus charge amount for the current furnace charge includes:

[0025] According to the first phosphorus loading formula Determine the amount of phosphorus charged in the current furnace batch;

[0026] The process for determining the phosphorus charge amount for the historical furnace batches includes:

[0027] According to the second phosphorus loading formula Determine the phosphorus charge amount for the aforementioned historical furnace batches;

[0028] Among them, W HM The weight of molten iron in the current heat is given, HMP is the phosphorus content of the molten iron in the current heat, and W is the phosphorus content of the molten iron in the current heat. SC W represents the weight of scrap steel in the current heat. HMK HMP represents the weight of molten iron from the aforementioned historical furnace batch. k The phosphorus content of the molten iron from the aforementioned historical furnace batch.

[0029] On the other hand, the operational data also includes the final residue quality, endpoint composition, and final residue composition;

[0030] Wherein, the endpoint components include the endpoint carbon content, and the final residue components include the final residue calcium oxide content;

[0031] The process of determining the thermodynamic influence coefficient includes:

[0032] Based on the historical operating data, the current operating data, and the thermodynamic influence coefficient relationship... Determine the thermodynamic influence coefficient;

[0033] Among them, W slag The final slag quality of the current furnace batch. T represents the final slag quality of the historical heat, and T represents the converter final temperature of the current heat. k C represents the converter endpoint temperature of the historical heat, and C represents the carbon content at the converter endpoint of the current heat. k The carbon content at the converter endpoint of the historical heat cycle is given, and CaO is the mass of calcium oxide in the final slag of the current heat cycle. k B represents the mass of calcium oxide in the final slag of the historical furnace batch, and B represents the basicity of the final slag of the current furnace batch. k c1 is the final slag basicity of the historical furnace batch; c2 is the influence coefficient of the final temperature on dephosphorization; c3 is the influence coefficient of the final carbon content on dephosphorization; c4 is the influence coefficient of the final slag calcium oxide content on dephosphorization; and c5 is the influence coefficient of the slag quantity on dephosphorization.

[0034] On the other hand, the operating data also includes the weight of molten iron, and the auxiliary materials added during the steelmaking process include one or more combinations of lime, lightly calcined dolomite, limestone, raw dolomite and ore.

[0035] The process for determining the final slag basicity of the current furnace batch includes:

[0036] The relationship W is determined based on the mass of calcium oxide. CaO =W Lime *K LCaO *C Lime +W LimeStone *K LSCaO *C LimeStone +W BDolo *K BCaO *C BDolo +W RDolo *K RCaO *C RDolo +W Ore *K OCaO *C Ore Determine the weight of calcium oxide in the auxiliary materials added during the current steelmaking process;

[0037] The relationship W is determined based on the mass of silicon dioxide. SiO2 =W Lime *K LSiO2 *C Lime +W LimeStone *K LSSiO2 *C LimeStone +W BDolo *K BSiO2 *C BDolo +W Dolo *KRSiO2 *C RDolo +W Ore *K OSiO2 *C Ore +(W HM *HMSi+(W SC -W CP) *0.15+W CP *0.35)*60 / 28*10 determines the weight of silicon dioxide in the auxiliary materials added during the current steelmaking process;

[0038] According to the first final residue alkalinity relationship B = W CaO / W SiO2 Determine the final slag basicity of the current furnace batch;

[0039] Where CaO = W CaO / W slag W CaO W represents the weight of calcium oxide charged in the current furnace batch. Lime K represents the weight of lime charged in the current batch. LCaO C represents the calcium oxide content in the lime. Lime W represents the yield of the lime. LimeStone K represents the weight of limestone charged in the current furnace batch. LSCaO C represents the calcium oxide content in the limestone. LimeStone W represents the yield of the limestone. BDolo The weight of light-burned dolomite charged in the current furnace, K BCaO C represents the calcium oxide content in the lightly calcined dolomite. BDolo W represents the yield of the lightly calcined dolomite. RDolo The weight of the secondary dolomite charged into the current furnace, K RCaO C represents the calcium oxide content in the raw dolomite. RDolo W represents the yield of the raw dolomite. Ore K represents the weight of ore charged in the current furnace. OCaO C represents the calcium oxide content in the ore. Ore Let W be the yield of the ore. SiO2 K represents the weight of silica loaded in the current furnace charge. LSiO2 The content of silicon dioxide in the lime; K LSSiO2 The content of silica in the limestone; K BSiO2 The silica content K in the lightly calcined dolomite RSiO2 The content of silica in the raw dolomite; K OSiO2 W represents the silica content in the ore. HM The weight of molten iron in the current heat; HMSi is the silicon content of the molten iron in the current heat; WSC W represents the weight of scrap steel in the current heat. CP B represents the weight of pig iron in the current furnace batch, and B represents the final slag basicity in the current furnace batch.

[0040] The process for determining the final slag basicity of the historical furnace batches includes:

[0041] According to the formula for the alkalinity of the second final residue Determine the final slag basicity B of the historical furnace batch. k .

[0042] On the other hand, the weight of the raw materials for steelmaking includes the weight of molten iron, scrap steel, and pig iron.

[0043] The process for determining the final slag weight of the current furnace batch includes:

[0044] According to the first final residue weight relationship W slag =(W Flux +W HMS ) / C slag Determine the final slag weight of the current furnace batch;

[0045] The process for determining the final slag weight of the historical furnace batches includes:

[0046] According to the formula for the weight of the second final residue Determine the final slag weight of the aforementioned historical furnace batch;

[0047] Among them, W Flux W represents the effective weight of auxiliary materials added in the current furnace batch. HMS C represents the amount of slag generated from molten iron scrap in the current heat. slag W is the coefficient for calculating slag volume. HMK HMMn represents the weight of molten iron from the aforementioned historical furnace batch. k The manganese content of the molten iron from the aforementioned historical furnace batches; MnO k The content of manganese oxide in the final slag of the aforementioned historical furnace batch;

[0048] The process for determining the weight of molten steel in the current heat pool includes:

[0049] According to the first formula for the weight of molten steel, W bath =(W HM +W SC )*C bath Determine the weight of the molten steel in the pool for the current heat.

[0050] Among them, W HM C represents the weight of the molten iron in the current furnace batch. bath For steel yield;

[0051] The process for determining the weight of molten steel in the molten pool of the aforementioned historical heats includes:

[0052] According to the second formula for the weight of molten steel Determine the weight of molten steel in the molten pool for the aforementioned historical heats;

[0053] Among them, W SCK The weight of scrap steel from the aforementioned historical furnace batch.

[0054] On the other hand, the process for determining the effective weight of auxiliary materials added in the current furnace includes:

[0055] According to the formula W for the weight of auxiliary materials Flux =W Lime *C lime +W LimeStone C LimeStone +W BDolo *C BDolo +W RDolo *C RDolo +W Ore *C Ore Determine the effective weight of the auxiliary materials added in the current furnace batch;

[0056] Among them, W Flux The effective weight of the auxiliary materials added in the current furnace batch;

[0057] The process for determining the amount of slag generated from the scrap steel of the molten iron in the current heat includes:

[0058] According to the formula W for the amount of slag generated HMS =(W HM *HMSi+(W SC -W CP )*0.15+W CP *0.35)*60 / 28*10+(W HM *HMMn*71 / 55*10)+(W HM *HMP*142 / 80*10) determines the amount of slag generated from molten iron scrap in the current heat.

[0059] Wherein, HMSi is the silicon content of the molten iron in the current furnace, HMP is the phosphorus content of the molten iron in the current furnace, and HMMn is the manganese content of the molten iron in the current furnace.

[0060] On the other hand, it acquires operational data for the current furnace run and historical furnace runs prior to the current furnace run, including:

[0061] Obtain the operating data of the current furnace run and multiple historical furnace runs preceding the current furnace run;

[0062] The theoretical maximum phosphorus content of the historical furnace is determined based on the operating data of the historical furnace, including:

[0063] The theoretical maximum phosphorus content for each of the historical furnaces is determined based on the operating data of each historical furnace.

[0064] The thermodynamic influence coefficient of the current furnace is determined based on the operating data of the current furnace and each historical furnace.

[0065] The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of the historical furnaces, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of the historical furnaces, including:

[0066] Based on the theoretical maximum phosphorus content of each historical furnace, the theoretical maximum phosphorus content of the current furnace, and the phosphorus content at the converter endpoint of each historical furnace, the phosphorus content at the converter endpoint of multiple current furnaces is predicted.

[0067] The average value of the phosphorus content at the converter endpoint of the current furnace is obtained from the prediction.

[0068] The average value is used as the phosphorus content at the end of the converter for the current furnace cycle.

[0069] To solve the above-mentioned technical problems, the present invention also provides a device for predicting the phosphorus content at the converter endpoint, characterized in that it comprises:

[0070] Memory, used to store computer programs;

[0071] A processor is used to execute the computer program to implement the steps of the above-described method for predicting the phosphorus content at the converter endpoint.

[0072] This invention discloses a method and apparatus for predicting the phosphorus content at the converter endpoint, relating to the steelmaking field. The method includes: acquiring operational data of the current heat and historical heats prior to the current heat; determining the theoretical maximum phosphorus content of the current heat based on the operational data of the current heat; determining the theoretical maximum phosphorus content of historical heats based on the operational data of historical heats; determining the thermodynamic influence coefficient of the current heat based on the operational data of the current heat and historical heats; and predicting the phosphorus content at the converter endpoint of the current heat based on the theoretical maximum phosphorus content of historical heats, the theoretical maximum phosphorus content of the current heat, the thermodynamic influence coefficient of the current heat, and the phosphorus content at the converter endpoint of historical heats. The method automatically predicts the phosphorus content after obtaining the operational data of the current heat, providing a more accurate result compared to manual judgment. After prediction, there is no need to wait for sample testing before proceeding to the next steel tapping operation, shortening converter smelting time and improving converter production efficiency. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the prior art and embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 A flowchart of a method for predicting phosphorus content at the final stage of a converter, provided by the present invention;

[0075] Figure 2 This is a schematic diagram of the structure of a converter endpoint phosphorus content prediction device provided by the present invention. Detailed Implementation

[0076] The core of this invention is to provide a method and apparatus for predicting the phosphorus content at the end of a converter heat treatment process. This method automatically predicts the phosphorus content, providing more accurate results compared to manual judgment. Furthermore, it predicts the phosphorus content of the current heat after obtaining the operating data, allowing for immediate tapping without waiting for sample analysis, thus shortening converter smelting time and improving converter production efficiency.

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] Figure 1 The flowchart of a method for predicting the phosphorus content at the converter endpoint provided by the present invention includes:

[0079] S11: Obtain the operating data of the current heat and the historical heats before the current heat. The operating data includes the weight of the raw materials for steelmaking, the weight of the auxiliary materials added during the steelmaking process, and the final temperature of the converter.

[0080] Phosphorus in steel causes "cold brittleness," reducing its plasticity and impact toughness. Therefore, it is often considered a harmful element and needs to be removed during converter smelting. As steel quality requirements become increasingly stringent, the control requirements for phosphorus content in molten steel also become more stringent. Statistics on heats requiring additional blowing or reassessment at the converter's final stage show a significantly higher proportion of heats where the phosphorus content in the molten steel exceeds the internal control limit. The accurate control of phosphorus content at the converter's final stage is even more important than the control of the final stage temperature and carbon content.

[0081] Currently, there are two main methods for determining the final phosphorus content in converter steelmaking. The first method involves operators judging whether the final phosphorus content meets the target requirements based on their personal experience. This method heavily relies on the operator's experience and is not very accurate. The second method involves taking samples after the converter blowing process and sending them to a laboratory for analysis. The decision to tap the steel or perform additional blowing is made after the test results are available. This is currently the most mainstream method used by steel mills. This method is the most reliable, but the long waiting time for samples at the converter's final stage can affect the converter's production rhythm and reduce its efficiency.

[0082] This application uses the operating data of historical furnaces prior to the current furnace to predict the phosphorus content at the converter endpoint of the current furnace.

[0083] Specifically, considering the law of conservation of mass, matter cannot be created or destroyed out of thin air. Since phosphorus is detected in the final product, it indicates that phosphorus must have been carried over from the raw materials and auxiliary materials added during steelmaking, leading to its presence at the converter endpoint. Therefore, the operational data obtained must include the weight of the raw materials and the weight of the auxiliary materials added during steelmaking. The temperature at the converter endpoint affects phosphorus formation, so the converter endpoint temperature also needs to be included in the operational data.

[0084] Taking the six historical furnaces preceding the current furnace as an example, we obtain the operating data of the six consecutive furnaces preceding the current furnace, and at the same time, we need to determine the phosphorus content at the converter endpoint of each historical furnace, so as to make subsequent predictions for the current furnace.

[0085] S12: Determine the theoretical maximum phosphorus content of the current furnace based on the operating data of the current furnace.

[0086] S13: Determine the theoretical maximum phosphorus content of historical furnaces based on historical furnace operation data;

[0087] The theoretical maximum phosphorus content should be related to the weight of the raw materials for steelmaking, the weight of the auxiliary materials added during the steelmaking process, and the final temperature of the converter. The higher the phosphorus content in the raw materials for steelmaking and the auxiliary materials added during the steelmaking process, the higher the final phosphorus content at the converter end.

[0088] During the operation of a steelmaking furnace, there will be a deviation between the theoretical maximum phosphorus content and the actual phosphorus content. The cause of this deviation may be temperature or other factors. By comparing the theoretical maximum phosphorus content of historical heats with the actual phosphorus content at the converter endpoint, and combining this with the theoretical maximum phosphorus content of the current heat, the phosphorus content at the converter endpoint of the current heat can be predicted.

[0089] S14: Determine the thermodynamic influence coefficient of the current furnace based on the operating data of the current furnace and historical furnaces;

[0090] S15: Predict the phosphorus content at the converter endpoint of the current furnace based on the theoretical maximum phosphorus content of historical furnaces, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of historical furnaces.

[0091] Related technologies also utilize endpoint phosphorus content prediction models to forecast the final phosphorus content in converters. This method saves significant time and improves converter production efficiency, but it demands high model accuracy. Currently, common endpoint phosphorus content prediction models employ various machine learning algorithms (neural networks, ensemble learning, etc.) to build models based on large amounts of collected data. These models require substantial data and exhibit high accuracy when data is abundant and raw materials are relatively stable; however, in actual production, steel mill production conditions fluctuate greatly, resulting in poor adaptability of such models.

[0092] The significance of obtaining recent historical furnace conditions before the current furnace in this application is that the furnace conditions of the historical furnace and the current furnace are similar. The furnace condition of the converter is a condition that is difficult to quantify. By using recent historical furnace conditions for calculation, this difficulty can be bypassed, and then the collected data can be used for relevant calculations.

[0093] This invention discloses a method for predicting the phosphorus content at the converter endpoint, relating to the steelmaking field. The method includes: acquiring operational data of the current heat and historical heats prior to the current heat; determining the theoretical maximum phosphorus content of the current heat based on the operational data of the current heat; determining the theoretical maximum phosphorus content of historical heats based on the operational data of historical heats; determining the thermodynamic influence coefficient of the current heat based on the operational data of the current heat and historical heats; and predicting the phosphorus content at the converter endpoint of the current heat based on the theoretical maximum phosphorus content of historical heats, the theoretical maximum phosphorus content of the current heat, the thermodynamic influence coefficient of the current heat, and the phosphorus content at the converter endpoint of historical heats. The method automatically predicts the phosphorus content after obtaining the operational data of the current heat, resulting in more accurate results compared to manual judgment. Compared to big data models, it avoids the influence of complex furnace conditions, and the calculation of the phosphorus content at the converter endpoint of the current heat can be completed using only a small amount of historical heat operational data. After the prediction is completed, there is no need to wait for laboratory samples before proceeding to the next steel tapping operation, shortening the converter smelting time and improving the converter's production efficiency.

[0094] Based on the above embodiments:

[0095] In some embodiments, after obtaining the operating data of the current furnace and historical furnaces prior to the current furnace, the method further includes:

[0096] Based on the pre-set boundary conditions, the operating data of historical furnaces whose raw material weight is outside the preset weight range, whose converter end temperature is outside the preset temperature range, or whose phosphorus content at the converter end exceeds the upper limit of phosphorus content are removed.

[0097] The raw materials for steelmaking include molten iron, scrap steel, and pig iron.

[0098] In this step, data with missing values ​​are first removed; if an item in the running data is missing, all running data of the historical furnace corresponding to the missing item are deleted.

[0099] Then, boundary conditions were set for the relevant calculation data to filter historical data. This was to ensure that the data used in subsequent incremental calculations was within the normal range. The boundary conditions set were: molten iron weight range of 105-130t, scrap steel weight range of 15-40t, molten iron silicon content range of 0.1-0.75%, endpoint temperature range of 1540-1680℃, endpoint manganese content range of 0.02-0.2%, and endpoint phosphorus content less than the endpoint phosphorus internal control upper limit.

[0100] When the running data does not meet the boundary conditions or is missing, the running data corresponding to that historical furnace needs to be deleted, and then a new historical furnace needs to be added. Taking six historical furnaces as an example, if the current furnace is the Nth furnace, then furnaces N-1, N-2, N-3, N-4, N-5, and N-6 need to be obtained. If furnace N-2 has missing data or does not meet the boundary conditions, then furnace N-7 is taken as the historical furnace, and this process continues until six furnaces are completed without missing data and all running data are within the boundary conditions.

[0101] In some embodiments, determining the theoretical maximum phosphorus content of the current furnace based on the operating data of the current furnace includes:

[0102] The relationship is determined based on the phosphorus content of the first theory. Determine the theoretical maximum phosphorus content (P) in the molten steel of the converter pool for the current heat. max ;

[0103] The theoretical maximum phosphorus content of historical furnaces is determined based on their operating data, including:

[0104] The relationship is determined based on the second theory of phosphorus content. Determine the theoretical maximum phosphorus content (P) in the molten steel pool of the converter in historical heats. k,max ;

[0105] Based on the historical operating data, the current operating data, and the thermodynamic influence coefficient relationship...

[0106] Determine the thermodynamic influence coefficient K for the current furnace batch;

[0107] The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of the historical furnaces, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of the historical furnaces, including:

[0108] The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of historical furnaces, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of historical furnaces, including:

[0109] According to the predictive relation Determine the phosphorus content [P] at the end of the converter run for the current heat.

[0110] in, This refers to the phosphorus charge amount for historical furnace cycles. This refers to the weight of molten steel in the pool from a historical furnace batch. W represents the phosphorus charge amount for the current furnace charge. bath Let P be the weight of molten steel in the pool for the current heat, K be the thermodynamic influence coefficient, and [P] be the weight of molten steel in the pool for the current heat. k The phosphorus content at the end of the converter run in the historical furnace cycle.

[0111] The theoretical phosphorus content should be the ratio of the total weight of phosphorus-containing elements added to the weight of the molten steel. Therefore, the theoretical maximum phosphorus content for a historical heat should be the ratio of the amount of phosphorus charged in that heat to the weight of the molten steel in the molten pool during that heat. The theoretical maximum phosphorus content for the current heat should be the ratio of the amount of phosphorus charged in the current heat to the weight of the molten steel in the current heat.

[0112] The phosphorus content at the converter endpoint of the current heat is calculated using an incremental calculation method. The ratio of the theoretical maximum phosphorus content of historical heats to the actual phosphorus content at the converter endpoint, combined with the thermodynamic influence coefficient, yields the phosphorus content at the converter endpoint of the current heat.

[0113] In some embodiments, the process of determining the phosphorus charge amount for the current furnace charge includes:

[0114] According to the first phosphorus loading formula Determine the amount of phosphorus charged in the current furnace batch;

[0115] The process for determining the phosphorus charge amount for historical furnace batches includes:

[0116] According to the second phosphorus loading formula Determine the phosphorus charge amount for each historical furnace batch;

[0117] Among them, W HM The weight of molten iron in the current heat is given by W, where HMP is the phosphorus content of the molten iron in the current heat. SC W represents the weight of scrap steel in the current heat. HMKHMP represents the weight of molten iron from historical furnace batches. k The phosphorus content of molten iron from historical furnace batches.

[0118] Both molten iron and scrap steel are raw materials for production, and the phosphorus content in both molten iron and scrap steel affects the amount of phosphorus charged. The product of the amount of molten iron charged and its phosphorus content yields the weight of phosphorus in the molten iron. Similarly, the product of the weight of scrap steel and its phosphorus content yields the weight of phosphorus in the scrap steel. This application uses fixed values ​​for the phosphorus content in the scrap steel of the current heat and the phosphorus content in the scrap steel of historical heats. Therefore, the phosphorus charging amount for the current heat and historical heats can be obtained according to the first and second phosphorus charging amount formulas, respectively. Substituting these values ​​into the first and second theoretical phosphorus content determination formulas yields the theoretical phosphorus content values ​​for the current heat and historical heats, thereby predicting the phosphorus content of the current heat.

[0119] In some embodiments, the operating data also includes the final residue quality, endpoint composition, and final residue composition;

[0120] The endpoint components include the endpoint carbon content, and the final residue components include the final residue calcium oxide content.

[0121] The process of determining the thermodynamic influence coefficient includes:

[0122] Based on historical operating data, current operating data, and the thermodynamic influence coefficient formula Determine the thermodynamic influence coefficient;

[0123] Among them, W slag The final slag quality for the current furnace batch. T represents the final slag quality of historical heats, and T represents the converter final temperature of the current heat. k C represents the converter endpoint temperature of a historical heat, and C represents the carbon content at the converter endpoint of the current heat. k The carbon content at the converter endpoint of a historical heat cycle is represented by CaO, and the mass of calcium oxide in the final slag of the current heat cycle is represented by CaO. k B represents the mass of calcium oxide in the final slag of a historical furnace, and B represents the basicity of the final slag of the current furnace. k c1 represents the final slag basicity of historical furnace batches, c2 represents the influence coefficient of final temperature on dephosphorization, c3 represents the influence coefficient of final carbon content on dephosphorization, c4 represents the influence coefficient of final slag calcium oxide content on dephosphorization, and c5 represents the influence coefficient of final slag basicity on dephosphorization.

[0124] The thermodynamic influence coefficient is determined by the ratio of various data points between the current furnace and historical furnaces, the product of final slag quality and phosphorus charge, the converter endpoint temperature, the carbon content at the converter endpoint, the mass of calcium oxide in the final slag, and the pH of the final slag. The thermodynamic influence coefficient K is obtained by taking the ratio of the data corresponding to the current furnace and historical furnaces, and then multiplying these ratios by the corresponding calculation parameters.

[0125] It should also be noted that the endpoint components include: endpoint carbon content, endpoint manganese content, and endpoint phosphorus content; the final slag components include: final slag calcium oxide content, final slag silica content, final slag magnesium oxide content, final slag iron oxide content, final slag manganese oxide content, and final slag phosphorus pentoxide content.

[0126] In some embodiments, the operating data also includes the weight of molten iron, and the auxiliary materials added during the steelmaking process include one or more combinations of lime, lightly calcined dolomite, limestone, raw dolomite and ore.

[0127] The process of determining the final slag basicity of the current furnace batch includes:

[0128] The relationship W is determined based on the mass of calcium oxide. CaO =W Lime *K LCaO *C Lime +W LimeStone *K LSCaO *C LimeStone +W BDolo *K BCaO *C BDolo +W RDolo *K RCaO *C RDolo +W Ore *K OCaO *C Ore Determine the weight of calcium oxide in the auxiliary materials added during the current steelmaking process;

[0129] The relationship W is determined based on the mass of silicon dioxide. SiO2 =W Lime *K LSiO2 *C Lime +W LimeStone *K LSSiO2 *C LimeStone +W BDolo *K BSiO2 *C BDolo +W Dolo *K RSiO2 *C RDolo +W Ore *K OSiO2 *C Ore +(W HM *HMSi+(W SC -WCP) *0.15+W CP *0.35)*60 / 28*10 determines the weight of silicon dioxide in the auxiliary materials added during the current steelmaking process;

[0130] According to the first final residue alkalinity relationship B = W CaO / W SiO2 Determine the final slag basicity for the current furnace batch;

[0131] Where CaO = W CaO / W slag W CaO W represents the weight of calcium oxide charged in the current furnace. Lime K represents the weight of lime charged in the current batch. LCaO C represents the calcium oxide content in lime. Lime W represents the yield of lime. LimeStone K represents the weight of limestone charged in the current furnace. LSCaO C represents the calcium oxide content in limestone. LimeStone W represents the yield of limestone. BDolo The weight of lightly calcined dolomite charged in the current furnace, K BCaO To determine the calcium oxide content in lightly calcined dolomite, C BDolo To determine the yield of lightly calcined dolomite, W RDolo The weight of secondary dolomite charged into the furnace, K RCaO To determine the calcium oxide content in raw dolomite, C RDolo To increase the yield of dolomite, W Ore K represents the weight of ore charged in the current furnace. OCaO C represents the calcium oxide content in the ore. Ore Let W be the yield of the ore. SiO2 The weight of silica charged in the current furnace, K LSiO2 The content of silicon dioxide in lime; K LSSiO2 The content of silica in limestone; K BSiO2 The silica content (K) in lightly calcined dolomite RSiO2 The silica content in raw dolomite; K OSiO2 The content of silica in the ore; W HM HMSi represents the weight of molten iron in the current heat; W represents the silicon content of the molten iron in the current heat. SC W represents the weight of scrap steel in the current heat. CP B represents the weight of pig iron in the current furnace batch, and B represents the final slag basicity in the current furnace batch.

[0132] The process of determining the final slag basicity of historical furnace batches includes:

[0133] According to the formula for the alkalinity of the second final residue Determine the final slag basicity B of historical furnace batchesk .

[0134] The weights of calcium oxide and silica affect the final slag basicity. These weights are related to the weight of the additives added. The added additives include: lime, lightly calcined dolomite, limestone, raw dolomite, and ore. The weight of calcium oxide added for each additive is the product of its mass, its calcium oxide content, and its yield. Summing the weights of calcium oxide added for each additive yields the total weight of calcium oxide added for all additives. Similarly, the weight of silica added for each additive is the product of its mass, its silica content, and its yield. Summing the weights of silica added for each additive yields the total weight of silica added for all additives. The final slag basicity is obtained by the ratio of the weight of calcium oxide to the weight of silica. The final slag basicity of the current furnace and the final slag basicity of the historical furnace can be obtained by calculating them in the above manner.

[0135] In some embodiments, the weight of the raw materials for steelmaking includes the weight of molten iron, scrap steel, and pig iron.

[0136] The process for determining the final slag weight of the current furnace run includes:

[0137] According to the first final residue weight relationship W slag =(W Flux +W HMS ) / C slag Determine the final slag weight for the current furnace batch;

[0138] The process for determining the final ash weight of historical furnace batches includes:

[0139] According to the formula for the weight of the second final residue Determine the final slag weight for each historical furnace batch;

[0140] Among them, W Flux W represents the effective weight of auxiliary materials added in the current furnace batch. HMS C represents the amount of slag generated from molten iron scrap in the current heat. slag W is the coefficient for calculating slag volume. HMK HMMn represents the weight of molten iron from historical furnace batches. k The manganese content of molten iron from historical furnace batches; MnO k The content of manganese oxide in the final slag of historical furnace batches;

[0141] The process of determining the weight of molten steel in the current heat pool includes:

[0142] According to the first formula for the weight of molten steel, W bath =(W HM +WSC )*C bath Determine the weight of molten steel in the current heat pool;

[0143] Among them, W HM C represents the weight of the molten iron in the current furnace. bath For steel yield;

[0144] The process of determining the weight of molten steel in the pool of historical heats includes:

[0145] According to the second formula for the weight of molten steel Determine the weight of molten steel in the molten pool for each historical heat.

[0146] Among them, W SCK This refers to the weight of scrap steel from historical furnace batches.

[0147] In some embodiments, the process of determining the effective weight of auxiliary materials added in the current furnace includes:

[0148] According to the formula W for the weight of auxiliary materials Flux =W Lime *C lime +W LimeStone C LimeStone +W BDolo *C BDolo +W RDolo *C RDolo +W Ore *C Ore Determine the effective weight of auxiliary materials added in the current furnace batch;

[0149] Among them, W Flux The effective weight of auxiliary materials added in the current batch;

[0150] The process of determining the amount of slag generated from molten iron scrap in the current heat includes:

[0151] According to the formula W for the amount of slag generated HMS =(W HM *HMSi+(W SC -W CP )*0.15+W CP *0.35)*60 / 28*10+(W HM *HMMn*71 / 55*10)+(W HM *HMP*142 / 80*10) determines the amount of slag generated from molten iron scrap in the current heat.

[0152] Wherein, HMSi is the silicon content of the molten iron in the current furnace, HMP is the phosphorus content of the molten iron in the current furnace, and HMMn is the manganese content of the molten iron in the current furnace.

[0153] The final slag weight for the current heat is related to the effective weight of auxiliary materials added and the amount of slag generated from the hot metal scrap. The effective weight of auxiliary materials added includes the weight of lime, lightly calcined dolomite, limestone, raw dolomite, and ore. The effective weight of auxiliary materials added is obtained by multiplying the corresponding weight of the added materials by the yield. The amount of slag generated from the hot metal scrap is related to manganese, phosphorus, and silicon elements; it is calculated by multiplying the weight of the hot metal by the content of the corresponding element.

[0154] The final slag weight of historical furnace batches can be calculated based on the manganese content of the molten iron.

[0155] Finally, the value of K is obtained and substituted into the calculation of [P] to obtain the phosphorus content of the current furnace.

[0156] In some embodiments, obtaining operational data for the current furnace run and historical furnace runs prior to the current furnace run includes:

[0157] Obtain the operating data of the current furnace and multiple historical furnaces preceding the current furnace;

[0158] The theoretical maximum phosphorus content of historical furnaces is determined based on their operating data, including:

[0159] The theoretical maximum phosphorus content for each historical furnace was determined based on the operating data of each furnace.

[0160] The thermodynamic influence coefficient of the current furnace is determined based on the operating data of the current furnace and each historical furnace.

[0161] The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of the historical furnaces, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of the historical furnaces, including:

[0162] The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of historical furnaces, the theoretical maximum phosphorus content of the current furnace, and the phosphorus content at the converter endpoint of historical furnaces, including:

[0163] Based on the theoretical maximum phosphorus content of each historical furnace, the theoretical maximum phosphorus content of the current furnace, and the phosphorus content at the converter endpoint of each historical furnace, the phosphorus content at the converter endpoint of multiple current furnaces will be predicted.

[0164] The average value of the phosphorus content at the converter endpoint of multiple current furnace cycles is obtained;

[0165] The average value is used as the phosphorus content at the end of the converter for the current furnace cycle.

[0166] Considering the availability of operational data from multiple historical furnace runs, this application combines the theoretical maximum phosphorus content of the current furnace run with the theoretical maximum phosphorus content of each historical furnace run and the phosphorus content at the converter endpoint to calculate a phosphorus content at the converter endpoint for the current furnace run. Finally, the average of the predicted phosphorus content at the converter endpoint for each furnace run is taken as the phosphorus content at the converter endpoint for the current furnace run.

[0167] Specifically, if six historical operating data points are taken, six predicted phosphorus contents for the converter endpoint will be obtained. This application will take the average value of the six predicted phosphorus contents for the converter endpoint and finally use the average value as the predicted value of phosphorus contents for the current furnace.

[0168] Figure 2 This is a schematic diagram of a converter endpoint phosphorus content prediction device provided by the present invention. The converter endpoint phosphorus content prediction device includes:

[0169] Memory 21 is used to store computer programs;

[0170] Processor 22 is used to execute computer programs to implement the steps of the above-described method for predicting the phosphorus content at the converter endpoint.

[0171] Please refer to the above embodiments for a description of the converter endpoint phosphorus content prediction device provided in this application, and it will not be repeated here.

[0172] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0173] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0174] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of predicting the end point phosphorus content of a converter, characterized in that, include: The system acquires the operating data of the current heat and previous historical heats. The operating data includes the weight of the raw materials for steelmaking, the weight of the auxiliary materials added during the steelmaking process, the converter endpoint temperature, the final slag mass, the endpoint composition, and the final slag composition. The endpoint composition includes the endpoint carbon content, and the final slag composition includes the final slag calcium oxide content. The theoretical maximum phosphorus content of the current furnace is determined based on the operating data of the current furnace. The theoretical maximum phosphorus content of the historical furnace is determined based on the operating data of the historical furnace. The thermodynamic influence coefficient of the current furnace is determined based on the operating data of the current furnace and the historical furnaces. The converter endpoint phosphorus content of the current furnace is predicted based on the theoretical maximum phosphorus content of the historical furnace, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of the historical furnace. The theoretical maximum phosphorus content of the current furnace is determined based on the operating data of the current furnace, including: The relationship is determined based on the phosphorus content of the first theory. Determine the theoretical maximum phosphorus content in the molten steel pool of the converter for the current heat. ; The theoretical maximum phosphorus content of the historical furnace is determined based on the operating data of the historical furnace, including: The relationship is determined based on the second theory of phosphorus content. Determine the theoretical maximum phosphorus content in the molten steel pool of the converter in historical heats. ; The thermodynamic influence coefficient of the current furnace is determined based on the operating data of the current furnace and the historical furnaces, including: Based on the historical furnace operation data, the current furnace operation data, and the thermodynamic influence coefficient relationship... Determine the thermodynamic influence coefficient of the current furnace batch. ; The phosphorus content at the converter endpoint of the current furnace is predicted based on the theoretical maximum phosphorus content of the historical furnaces, the theoretical maximum phosphorus content of the current furnace, the thermodynamic influence coefficient of the current furnace, and the phosphorus content at the converter endpoint of the historical furnaces, including: According to the predictive relation Determine the phosphorus content at the converter endpoint of the current heat. ; in, The amount of phosphorus charged in the historical furnace batch. The weight of molten steel in the molten pool for the aforementioned historical heat. The amount of phosphorus charged in the current furnace. The weight of molten steel in the pool for the current heat is [weight missing]. Thermodynamic influence coefficient, The phosphorus content at the converter endpoint of the aforementioned historical furnace batch. The final slag quality of the current furnace batch. The final slag quality of the aforementioned historical furnace batches. The converter endpoint temperature for the current furnace cycle. The converter endpoint temperature of the aforementioned historical furnace batch. The carbon content at the end of the converter run for the current heat. The carbon content at the converter endpoint of the aforementioned historical furnace batch. The mass of calcium oxide in the final slag of the current furnace batch. The mass of calcium oxide in the final slag of the aforementioned historical furnace batch. The final slag basicity of the current furnace batch. The final slag basicity of the aforementioned historical furnace batches. The coefficient representing the effect of the final temperature on dephosphorization; The effect coefficient of the final carbon content on dephosphorization; The coefficient representing the effect of calcium oxide content in the final residue on dephosphorization. The coefficient representing the effect of final residue alkalinity on dephosphorization; The coefficient representing the effect of slag quantity on dephosphorization.

2. The method for predicting the phosphorus content at the converter endpoint as described in claim 1, characterized in that, After obtaining the operational data of the current furnace and historical furnaces prior to the current furnace, it also includes: Based on the pre-set boundary conditions, the operating data of historical furnaces whose raw material weight is outside the preset weight range, whose converter end temperature is outside the preset temperature range, or whose phosphorus content at the converter end exceeds the upper limit of phosphorus content are removed. The raw materials for steelmaking include molten iron, scrap steel, and pig iron.

3. The method for predicting the phosphorus content at the converter endpoint as described in claim 1, characterized in that, The process for determining the phosphorus charge amount for the current furnace charge includes: According to the first phosphorus loading formula Determine the amount of phosphorus charged in the current furnace batch; The process for determining the phosphorus charge amount for the historical furnace batches includes: According to the second phosphorus loading formula Determine the phosphorus charge amount for the aforementioned historical furnace batches; in, The weight of molten iron in the current furnace batch. The phosphorus content of the molten iron in the current furnace batch. The weight of scrap steel in the current heat. The weight of scrap steel from the aforementioned historical heats. The weight of molten iron from the aforementioned historical furnace batch. The phosphorus content of the molten iron from the aforementioned historical furnace batch.

4. The method for predicting the phosphorus content at the converter endpoint as described in claim 1, characterized in that, The operating data also includes the weight of molten iron, and the auxiliary materials added during the steelmaking process include one or more combinations of lime, lightly calcined dolomite, limestone, raw dolomite, and ore. The process for determining the final slag basicity of the current furnace batch includes: The relationship is determined based on the mass of calcium oxide. Determine the weight of calcium oxide in the auxiliary materials added during the current steelmaking process; The relationship is determined based on the mass of silicon dioxide. Determine the weight of silicon dioxide in the auxiliary materials added during the current steelmaking process; According to the first final residue alkalinity relationship formula Determine the final slag basicity of the current furnace batch; in, , The weight of calcium oxide charged in the current furnace batch. The weight of lime loaded in the current batch. The content of calcium oxide in the lime is [missing information]. The yield of the lime is given. The weight of limestone loaded in the current furnace batch. The content of calcium oxide in the limestone. The yield of the limestone is given. The weight of lightly calcined dolomite to be loaded into the current furnace is as follows: The calcium oxide content in the lightly calcined dolomite is given. The yield of the lightly calcined dolomite. The weight of the secondary dolomite in the current furnace is specified. The content of calcium oxide in the raw dolomite is given. The yield of the raw dolomite is given. The weight of ore charged in the current furnace. The content of calcium oxide in the ore. The yield of the ore. The weight of silica loaded in the current furnace batch. The content of silicon dioxide in the lime; The content of silica in the limestone; The silica content in the lightly calcined dolomite; The content of silica in the raw dolomite; The content of silicon dioxide in the ore; The weight of molten iron in the current furnace batch; The silicon content of the molten iron in the current furnace batch; The weight of scrap steel in the current heat batch; The weight of the secondary pig iron in the current furnace. The final slag basicity of the current furnace batch; The process for determining the final slag basicity of the historical furnace batches includes: According to the formula for the alkalinity of the second final residue Determine the final slag basicity of the historical furnace batches. .

5. The method for predicting the phosphorus content at the converter endpoint as described in claim 4, characterized in that, The weight of the raw materials for steelmaking includes the weight of molten iron, scrap steel, and pig iron. The process for determining the final slag weight of the current furnace batch includes: According to the formula for the weight of the first final residue Determine the final slag weight for the current furnace batch; The process for determining the final slag weight of the historical furnace batches includes: According to the formula for the weight of the second final residue Determine the final slag weight of the aforementioned historical furnace batch; in, The effective weight of auxiliary materials added for the current furnace batch. The amount of slag generated from the scrap steel of the molten iron in the current furnace batch. The coefficient for calculating slag volume, The weight of molten iron from the aforementioned historical furnace batch. The manganese content of the molten iron from the aforementioned historical furnace batch; The content of manganese oxide in the final slag of the aforementioned historical furnace batch; The process for determining the weight of molten steel in the current heat pool includes: According to the first formula for the weight of molten steel Determine the weight of the molten steel in the pool for the current heat. in, The weight of the molten iron in the current furnace batch. For steel yield; The process for determining the weight of molten steel in the molten pool of the aforementioned historical heats includes: According to the second formula for the weight of molten steel Determine the weight of molten steel in the molten pool for the aforementioned historical heats; in, The weight of scrap steel from the aforementioned historical furnace batch.

6. The method for predicting the phosphorus content at the converter endpoint as described in claim 5, characterized in that, The process for determining the effective weight of auxiliary materials added in the current batch includes: Based on the relationship of auxiliary material weight Determine the effective weight of the auxiliary materials added in the current furnace batch; in, The effective weight of the auxiliary materials added in the current furnace batch; The process for determining the amount of slag generated from the scrap steel of the molten iron in the current heat includes: Based on the formula for the amount of slag generated Determine the amount of slag generated from the molten iron scrap in the current furnace batch; in, The silicon content of the molten iron in the current furnace batch. The phosphorus content of the molten iron in the current furnace batch. The manganese content of the molten iron in the current furnace batch.

7. The method for predicting the phosphorus content at the converter endpoint as described in any one of claims 1 to 6, characterized in that, Retrieve operational data for the current furnace run and historical furnace runs prior to the current furnace run, including: Obtain the operating data of the current furnace run and multiple historical furnace runs preceding the current furnace run; The theoretical maximum phosphorus content of the historical furnace is determined based on the operating data of the historical furnace, including: The theoretical maximum phosphorus content for each of the historical furnaces is determined based on the operating data of each historical furnace. Predicting the phosphorus content at the converter endpoint of the current furnace based on the theoretical maximum phosphorus content of the historical furnaces, the theoretical maximum phosphorus content of the current furnace, and the phosphorus content at the converter endpoint of the historical furnaces includes: Based on the theoretical maximum phosphorus content of each historical furnace, the theoretical maximum phosphorus content of the current furnace, and the phosphorus content at the converter endpoint of each historical furnace, the phosphorus content at the converter endpoint of multiple current furnaces is predicted. The average value of the phosphorus content at the converter endpoint of the current furnace is obtained from the prediction. The average value is used as the phosphorus content at the end of the converter for the current furnace cycle.

8. A device for predicting the final phosphorus content of a converter, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for predicting the phosphorus content at the converter endpoint as described in any one of claims 1 to 7.

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