An ultra-low emission intelligent control method for electric furnace steelmaking

Abstract

The present invention relates to the field of intelligent control technology, specifically to an ultra-low emission intelligent control method for electric furnace steelmaking, comprising the following steps: based on steelmaking raw material data information, extracting key quality data of steelmaking raw materials, evaluating the quality of current steelmaking raw materials, and comparing the current steelmaking raw material quality with the preset benchmark raw material quality, evaluating the degree of deviation of the current steelmaking raw material quality, and obtaining quality deviation information. The present invention reduces energy consumption and waste output caused by raw material quality fluctuations, reduces carbon emissions, and calculates target temperature and steelmaking time in combination with target processing requirements, thereby ensuring high efficiency and environmental protection of the steelmaking process, reducing energy consumption and carbon emissions, and real-time monitoring and comparison with target parameters, making process control more accurate and reducing adjustment time and material waste in the production process.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an ultra-low emission intelligent control method for electric furnace steelmaking. Background Art

[0002] The field of intelligent control technology involves the use of computer technology, artificial intelligence algorithms and automation systems to improve the efficiency and effectiveness of control systems in various industrial and non-industrial processes. This technology includes real-time data processing, model predictive control, adaptive control and machine learning applied to control systems, enabling equipment to automatically adjust its operating parameters without human intervention. It is often used in complex environments, such as variable conditions or highly uncertain industrial processes, to optimize performance, reduce energy consumption, and improve production quality and safety.

[0003] Among them, the ultra-low emission intelligent control method for electric furnace steelmaking focuses on applying intelligent control technology to the operation of electric furnaces in the steel production process to significantly reduce the emission of harmful gases. The method uses monitoring technology to monitor and adjust variables in the steelmaking process, such as temperature, pressure and time, to ensure that while maintaining production efficiency and product quality, environmental pollution is minimized to achieve a more environmentally friendly steel production process, comply with increasingly stringent environmental regulations, and help steel companies improve their market competitiveness and corporate image.

[0004] Traditional control methods rely on preset control parameters and are difficult to adjust in real time to fluctuations in the quality of reaction raw materials, which results in the inability to achieve optimal energy utilization and minimal environmental impact in actual operations. For example, failure to accurately match raw material quality will lead to inefficient energy utilization, unnecessary energy expenditures and environmental burdens. In addition, the shortcomings of traditional methods in real-time feedback and adjustment mechanisms make process control unable to fully adapt to immediate changes in production variables, affecting production efficiency and product quality stability, and increasing energy consumption and carbon emissions. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an ultra-low emission intelligent control method for electric furnace steelmaking.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: an ultra-low emission intelligent control method for electric furnace steelmaking, comprising the following steps:

[0007] S1: Based on the steelmaking raw material data information, extract the key quality data of the steelmaking raw materials, evaluate the quality of the current steelmaking raw materials, and compare the current steelmaking raw material quality with the preset benchmark raw material quality to evaluate the deviation degree of the current steelmaking raw material quality and obtain quality deviation information;

[0008] S2: Based on the quality deviation information, adjusting the usage of steelmaking additives, the steelmaking additives include carburizers and oxidants, and combining the target steel alloy component processing requirements to adjust the addition amount of the alloy material to obtain material addition information;

[0009] S3: Based on the quality deviation information and the material addition information, combined with the target processing requirements, the target temperature in the electric furnace steelmaking process is calculated, and according to the target temperature, the target steelmaking time is calculated to obtain the target steelmaking parameters;

[0010] S4: Implement the target steelmaking parameters, monitor the actual temperature during the electric furnace steelmaking process, compare it with the target steelmaking temperature, calculate the steelmaking time adjustment value according to the deviation size and duration between the actual temperature and the target temperature, adjust the steelmaking time, and obtain real-time adjustment of the steelmaking parameters.

[0011] The present invention is improved in that the method for evaluating the quality of current steelmaking raw materials is:

[0012] S111: extracting key quality data of the steelmaking raw materials based on the steelmaking raw material data information, where the key quality data includes carbon content, sulfur content and iron content information of the steelmaking raw materials;

[0013] S112: Based on the key quality data, data cleaning is performed to remove abnormal values ​​and data beyond a normal range to obtain cleaned data;

[0014] S113: Based on the cleaned data, the formula:

[0015]

[0016] Calculate the quality score Q of steelmaking raw materials, and evaluate the quality of current steelmaking raw materials according to the size of the Q value;

[0017] Among them, w c 、w s and w Fe are the weight coefficients of carbon content, sulfur content and iron content respectively, C max , S max and Fe max are the maximum values ​​of carbon, sulfur and iron contents respectively, λ, ∈ and φ are adjustment coefficients, C, S and Fe represent the contents of carbon, sulfur and iron respectively, and Q is the quality score of steelmaking raw materials.

[0018] The present invention is improved in that the steps of obtaining the quality deviation information are:

[0019] S121: Obtain a benchmark raw material quality score by calculating the average quality score of high-quality batches based on historical quality score data of steelmaking raw materials;

[0020] S122: Based on the benchmark raw material quality score and the quality score Q of the current steelmaking raw material, the formula:

[0021]

[0022] Calculate the deviation index D;

[0023] Among them, Q is the quality score of the current steelmaking raw materials, D is the deviation index, and Q ideal is the benchmark quality score, γ is the weighted index, and β is the adjustment coefficient;

[0024] S123: Based on the deviation index D, according to the size of the deviation index D, the deviation degree of the current steelmaking raw material quality is evaluated to obtain quality deviation information.

[0025] The present invention is improved in that the method for adjusting the usage of the steelmaking additive is:

[0026] S211: Based on the quality deviation information, using the quality deviation index D, by formula:

[0027]

[0028] Calculate the adjustment ratio B of steelmaking additives;

[0029] Wherein, D is the quality deviation index, T is the predetermined threshold, K is the adjustment constant, η is the sensitivity coefficient, b is the logarithmic base, ξ is the environmental factor adjustment parameter, and B is the adjustment ratio of the steelmaking additive;

[0030] S212: Based on the adjustment ratio B of the steelmaking additive, adjusting the usage of the steelmaking additive, including adjusting the usage of the carburizer and the oxidant.

[0031] The present invention is improved in that the step of obtaining the material addition information is:

[0032] S221: based on the target steel alloy composition processing requirements, obtaining the metal element ratio of the target steel alloy and obtaining the target steel alloy composition information;

[0033] S222: Based on the target steel alloy composition information, obtain the composition information of the current steelmaking raw materials, through the formula:

[0034]

[0035] Calculate the percentage deviation Δx of an element i ;

[0036] Among them, C i,a is the actual measured alloying element content, C i,t is the target alloying element content, R is the adjustment parameter, Δx iis the percentage deviation of element i, where i is the index;

[0037] S223: Based on the percentage deviation Δx of the element i , through the formula:

[0038]

[0039] Calculate the adjusted alloy material addition amount A i , get the material adding information;

[0040] Among them, M represents the adjustment coefficient, V represents the total weight of the current batch, and A i is the adjusted alloy material addition amount, i is the index, Δx i is the percentage deviation of element i.

[0041] The present invention is improved in that the method for calculating the target temperature in the electric furnace steelmaking process is:

[0042] S311: extracting a quality deviation index D based on the quality deviation information and the material addition information, and acquiring melting point data of a standard alloy in combination with a target steel alloy type;

[0043] S312: Based on the melting point data of the standard alloy, by the formula:

[0044]

[0045] Calculate the target temperature T for electric furnace steelmaking target ;

[0046] Among them, T base is the reference temperature, T melt is the standard melting point of the target alloy, T target is the target temperature of electric furnace steelmaking, k is the temperature adjustment coefficient, and γ is the nonlinear influence coefficient.

[0047] The present invention is improved in that the step of obtaining the target steelmaking parameters is:

[0048] S321: Based on the target steel alloy type, obtain the thermophysical properties of the target alloy, including specific heat capacity and thermal conductivity;

[0049] S322: Based on the thermophysical properties of the target alloy and the target temperature T of electric furnace steelmaking target , through the formula:

[0050]

[0051] Calculate the target steelmaking time t target , get the target steelmaking parameters, where λ is the adjustment factor, C p is the specific heat, Tambient is the ambient temperature, m is the mass of the material used for steelmaking, P is the heating power of the electric furnace, η is the thermal efficiency, ν is the efficiency adjustment index, T target It is the target temperature for electric furnace steelmaking.

[0052] The present invention is improved in that the step of obtaining the real-time adjustment of steelmaking parameters is:

[0053] S411: Implement the target steelmaking parameters, monitor the actual temperature during the electric furnace steelmaking process, and obtain real-time steelmaking temperature data;

[0054] S412: Based on the real-time steelmaking temperature data and the target temperature of electric furnace steelmaking, the formula:

[0055]

[0056] Calculate the adjustment value Δt of steelmaking time;

[0057] Among them, T actual is the actual steelmaking temperature, T target is the target temperature of electric furnace steelmaking, k is the time adjustment coefficient, α is the deviation impact index, τ is the deviation maintenance time, and Δt is the adjustment value of steelmaking time;

[0058] S413: Based on the adjustment value Δt of the steelmaking time, the steelmaking duration is adjusted to obtain real-time adjustment steelmaking parameters.

[0059] Compared with the prior art, the advantages and positive effects of the present invention are:

[0060] In the present invention, by collecting key quality data and calculating quality scores, the applicability and potential production efficiency of raw materials can be evaluated, and the amount of additives used and the material ratio can be adjusted according to quality deviations, thereby optimizing the steelmaking process, reducing energy consumption and waste output caused by fluctuations in raw material quality, and reducing carbon emissions. By calculating the target temperature and steelmaking time in combination with target processing requirements, the efficiency and environmental protection of the steelmaking process are ensured, energy consumption and carbon emissions are reduced, and real-time monitoring and comparison with target parameters make process control more precise, improve product consistency and quality, and reduce adjustment time and material waste in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of the method of the present invention;

[0062] Figure 2 A flow chart for evaluating the quality of current steelmaking raw materials for the present invention;

[0063] Figure 3 A flow chart for obtaining quality deviation information of the present invention;

[0064] Figure 4 A flow chart for adjusting the usage of steelmaking additives for the present invention;

[0065] Figure 5 A flow chart for obtaining materials to add information to the invention;

[0066] Figure 6 A flow chart for calculating the target temperature in the electric furnace steelmaking process according to the present invention;

[0067] Figure 7 A flow chart for obtaining target steelmaking parameters for the present invention;

[0068] Figure 8 A flow chart for obtaining real-time adjustment of steelmaking parameters for the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0070] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0071] Example

[0072] See also Figure 1 The present invention provides a technical solution: an ultra-low emission intelligent control method for electric furnace steelmaking, comprising the following steps:

[0073] S1: Based on the steelmaking raw material data information, extract the key quality data of the steelmaking raw materials, including the carbon content, sulfur content and iron content of the steelmaking raw materials, calculate the overall quality score of the steelmaking raw materials, evaluate the quality of the current steelmaking raw materials, and compare the current steelmaking raw material quality with the preset benchmark raw material quality, calculate the deviation index, evaluate the deviation degree of the current steelmaking raw material quality, and obtain quality deviation information;

[0074] S2: Based on the quality deviation information, the usage of steelmaking additives is adjusted. Steelmaking additives include carburizers and oxidizers. The subsequent carbon emissions are optimized and reduced. Combined with the processing requirements of the target steel alloy composition, the addition amount of alloy materials is adjusted to obtain material addition information;

[0075] S3: Based on the quality deviation information and material addition information, combined with the target processing requirements, the target temperature in the electric furnace steelmaking process is calculated, and the target steelmaking time is calculated according to the target temperature to obtain the target steelmaking parameters;

[0076] S4: Implement target steelmaking parameters, monitor the actual temperature during the electric furnace steelmaking process, compare it with the target steelmaking temperature, calculate the steelmaking time adjustment value based on the deviation size and duration between the actual temperature and the target temperature, adjust the steelmaking time, and obtain real-time adjustment of steelmaking parameters.

[0077] Quality deviation information includes carbon content deviation, sulfur content deviation and iron content deviation. Material addition information includes alloy material proportion, carburizer addition amount and oxidizer addition amount. Target steelmaking parameters include furnace temperature setting value and heating time setting value. Real-time adjustment of steelmaking parameters includes real-time furnace temperature adjustment value, heating power adjustment value and heating time adjustment value.

[0078] See also Figure 2 , the method for evaluating the quality of current steelmaking raw materials is:

[0079] S111: extracting key quality data of the steelmaking raw materials based on the steelmaking raw material data information, where the key quality data includes carbon content, sulfur content and iron content information of the steelmaking raw materials;

[0080] S112: Based on the key quality data, data cleaning is performed to remove abnormal values ​​and data beyond the normal range to obtain cleaned data;

[0081] S113: Based on the cleaned data, the formula:

[0082]

[0083] Calculate the quality score Q of steelmaking raw materials, and evaluate the quality of current steelmaking raw materials according to the size of the Q value;

[0084] Among them, w c 、w s and w Fe are the weight coefficients of carbon content, sulfur content and iron content respectively, C max , S max and Fe max are the maximum values ​​of carbon, sulfur and iron contents respectively, λ, ∈ and φ are adjustment coefficients, C, S and Fe represent the contents of carbon, sulfur and iron respectively, and Q is the quality score of steelmaking raw materials.

[0085] formula:

[0086]

[0087] Parameter details and how to obtain them:

[0088] w c 、w s and w Fe : Weight coefficients for carbon content, sulfur content, and iron content, which indicate the importance of the components in the overall quality score. The weight coefficients are usually set through historical data analysis and the quality requirements of steel production. For example, if carbon content plays a decisive role in the performance of steel, then w c Will be relatively high.

[0089] C max , S max and Fe max : is the maximum possible value for carbon, sulfur and iron content and is used to standardize the actual measured values ​​of each component. The values ​​are set based on chemical composition standards or industry specifications for steel materials.

[0090] λ, ∈, and φ: adjustment factors that adjust the strength of each component's impact on the overall quality score. For example, if the environmental conditions of temperature and humidity have a greater impact on sulfur content, ∈ may be adjusted to reflect this environmental sensitivity. The factors are usually adjusted based on experimental or historical data;

[0091] C, S and Fe: the contents of carbon, sulfur and iron. Carbon and sulfur are determined from sampled steelmaking raw materials by standardized chemical analysis methods, and iron is measured by a spectrometer in a controlled experimental environment.

[0092] Calculation example:

[0093] Carbon content C = 1.8%, C max =2.5%

[0094] Sulfur content S = 0.03%, S max =0.05%

[0095] Iron content Fe = 99%, Fe max =100%

[0096] Weight coefficient w c =0.5, w s =0.2, w Fe =0.3

[0097] Adjustment coefficient λ=1.2,∈=1.5,φ=1.1

[0098] Calculation process:

[0099] Calculation of standardized component values:

[0100] Normalized Carbon Content:

[0101]

[0102] Normalized sulfur content:

[0103]

[0104] Standardized for Iron Content:

[0105]

[0106] Calculation of quality score:

[0107]

[0108] The calculated result Q≈0.843 represents the comprehensive quality score of the raw material, and a higher value reflects a better quality of the raw material.

[0109] See also Figure 3 , the steps for obtaining quality deviation information are:

[0110] S121: Obtain a benchmark raw material quality score by calculating the average quality score of high-quality batches based on historical quality score data of steelmaking raw materials;

[0111] S122: Based on the benchmark raw material quality score and the current steelmaking raw material quality score Q, the formula:

[0112]

[0113] Calculate the deviation index D;

[0114] Among them, Q is the quality score of the current steelmaking raw materials, D is the deviation index, and Q ideal is the benchmark quality score, γ is the weighted index, and β is the adjustment coefficient;

[0115] S123: Based on the deviation index D, according to the size of the deviation index D, the deviation degree of the current steelmaking raw material quality is evaluated to obtain quality deviation information.

[0116] formula:

[0117]

[0118] Parameter definition and acquisition method:

[0119] Q: The overall quality score of the current steelmaking raw materials, calculated in the previous steps.

[0120] Q ideal : Baseline quality score, representing the optimal quality score under ideal conditions. Based on historical data analysis, it is obtained by calculating the average quality score of high-quality batches, and the average value may be calculated from multiple high-performance batches.

[0121] γ: Weighted index used to adjust the impact of quality deviation. It is adjusted according to quality management requirements and historical data to enhance the model's sensitivity to deviation.

[0122] β: Adjustment factor used to convert the deviation index into percentage form. This helps to standardize the results, making them easier to compare across different production lines and conditions.

[0123] Calculation example:

[0124] Assume the following parameter values:

[0125] Q = 85 (current quality rating)

[0126] Q ideal =90 (best quality score in history)

[0127] γ = 1.2 (weighted exponent, emphasizing that small deviations can also significantly affect perception)

[0128] β = 100 (to convert the result into a percentage)

[0129] Calculation process

[0130] Deviation calculation:

[0131] QQ ideal =85-90=-5

[0132] Deviation index calculation:

[0133]

[0134] The calculated result D≈-6.17% indicates the deviation of the current quality score from the ideal quality score.

[0135] See also Figure 4 , the method for adjusting the usage of steelmaking additives is:

[0136] S211: Based on the quality deviation information, using the quality deviation index D, by formula:

[0137]

[0138] Calculate the adjustment ratio B of steelmaking additives;

[0139] Wherein, D is the quality deviation index, T is the predetermined threshold, K is the adjustment constant, η is the sensitivity coefficient, b is the logarithmic base, ξ is the environmental factor adjustment parameter, and B is the adjustment ratio of the steelmaking additive;

[0140] S212: Based on the adjustment ratio B of the steelmaking additives, the usage of the steelmaking additives is adjusted, including the usage of the carburizer and the oxidant.

[0141] formula:

[0142]

[0143] Parameter details and how to obtain them:

[0144] D: Quality deviation index, calculated by the previous step.

[0145] T: A predetermined threshold value based on industry standards or previously established optimal performance indicators. Usually set by the quality assurance department based on long-term production data and standards.

[0146] K: Adjustment constant, set based on past production data and the specific needs of the production line to ensure that the adjustment range is adapted to different production conditions.

[0147] η: Sensitivity coefficient, which determines the response intensity of the adjustment ratio to quality deviation. Adjust this parameter according to the reaction sensitivity of the production line and the effect of previous adjustments.

[0148] b: Logarithmic base, which affects the sensitivity and range of the logarithmic calculation and is selected based on the processing characteristics of the production materials and environmental factors.

[0149] ξ: Environmental factor adjustment parameter, used to consider the impact of external environment such as temperature and humidity on material processing. This parameter is adjusted based on the readings of environmental monitoring equipment.

[0150] The calculation example assumes the following parameter values:

[0151] D = 15% (the current deviation from the ideal quality)

[0152] T = 10% (industry standard threshold)

[0153] K=0.5(adjustment constant)

[0154] η=2(sensitivity coefficient)

[0155] b = 10 (logarithmic base)

[0156] ξ=1.05 (parameter adjusted by environmental factors, such as slightly higher temperature causing accelerated material reaction)

[0157] Calculation process

[0158] Logarithmic part calculation:

[0159]

[0160] Calculation of adjustment ratio:

[0161]

[0162] The calculated result B≈0.0213 will be used to adjust the dosage of the recarburizer or oxidizer by 2.13%.

[0163] See also Figure 5 , the steps to obtain material addition information are:

[0164] S221: based on the target steel alloy composition processing requirements, obtaining the metal element ratio of the target steel alloy and obtaining the target steel alloy composition information;

[0165] S222: Based on the target steel alloy composition information, obtain the composition information of the current steelmaking raw materials, through the formula:

[0166]

[0167] Calculate the percentage deviation Δx of an element i ;

[0168] Among them, C i,a is the actual measured alloying element content, C i,t is the target alloying element content, R is the adjustment parameter, Δx i is the percentage deviation of element i, where i is the index;

[0169] S223: Element-based percentage deviation Δx i , through the formula:

[0170]

[0171] Calculate the adjusted alloy material addition amount A i , get the material adding information;

[0172] Among them, M represents the adjustment coefficient, V represents the total weight of the current batch, and A i is the adjusted alloy material addition amount, i is the index, Δx i is the percentage deviation of element i.

[0173] formula:

[0174]

[0175] and

[0176]

[0177] Parameter details and how to obtain them:

[0178] C i,t : Target alloying element content, preset according to product specifications or engineering requirements. Usually determined by the design team or quality control department based on material performance requirements and application scenarios.

[0179] Ci,a :The actual measured alloying element content. Obtained through regular testing during the production process using chemical analysis or spectral analysis techniques.

[0180] R: Percent conversion parameter, usually set to 100, used to convert the deviation into a more intuitive percentage form for easier calculation and understanding.

[0181] M: Adjustment factor, adjusted based on historical data analysis and predictive models, used to fine-tune alloy additions to achieve optimal production efficiency and material properties.

[0182] V: Total weight of the current heat, obtained from the production control system, reflecting the total amount of material processed in a single production batch.

[0183] Calculation example:

[0184] Assume the following specific data:

[0185] Target nickel content C Ni,t =2.5%

[0186] Actual nickel content C Ni,a =2.3%

[0187] Adjust parameter R = 100

[0188] Adjustment coefficient M = 0.05

[0189] Total weight of the furnace V = 1000kg

[0190] Calculation process:

[0191] Calculate the deviation percentage:

[0192]

[0193]

[0194] This means that the actual nickel content is 8% lower than the target and the amount of nickel added needs to be increased to reach the target content.

[0195] Calculate the adjustment:

[0196]

[0197] Calculation result A Ni =4kg means that in order to make the nickel content reach the specified target, 4 kg of nickel needs to be added in the current heat.

[0198] See also Figure 6 , the method for calculating the target temperature in the electric furnace steelmaking process is:

[0199] S311: extracting the quality deviation index D based on the quality deviation information and the material addition information, and obtaining the melting point data of the standard alloy in combination with the target steel alloy type;

[0200] S312: Based on the melting point data of standard alloys, by the formula:

[0201]

[0202] Calculate the target temperature T for electric furnace steelmaking target ;

[0203] Among them, T base is the reference temperature, T melt is the standard melting point of the target alloy, T target is the target temperature of electric furnace steelmaking, k is the temperature adjustment coefficient, and γ is the nonlinear influence coefficient.

[0204] formula:

[0205]

[0206] Parameter details and how to obtain them:

[0207] T base : Base temperature, usually set as the starting heating temperature in the electric furnace steelmaking process. The temperature is set based on past production records and standard practices of steelmaking operations to ensure that the material begins to melt and gradually reaches the required processing temperature.

[0208] T melt : The standard melting point of the target alloy, the data is obtained from the material science database or alloy production specifications. The melting point is determined based on the main chemical composition and expected physical properties of the alloy.

[0209] k: Temperature adjustment coefficient, adjusted according to historical production data and experimental results, used to fine-tune the temperature to adapt to changes in quality deviations.

[0210] γ: Nonlinear influence coefficient, used to strengthen or weaken the impact of quality deviation on target temperature adjustment in order to more accurately control the heating process.

[0211] Calculation example:

[0212] Assumed parameters:

[0213] Quality deviation index D = 0.05 (5% deviation)

[0214] Reference temperature T base =1550℃

[0215] Standard melting point T of target alloy melt =1600℃

[0216] Adjustment coefficient k = 2

[0217] Nonlinear influence coefficient γ=0.5

[0218] Calculation of target temperature:

[0219]

[0220] The calculated target temperature T target ≈1628.06℃, the target temperature accommodates quality deviations while ensuring the correct processing of the alloy, meeting the needs of the production process without excessively increasing the temperature of steelmaking, helping to optimize energy efficiency and maintain product quality, reducing carbon emissions.

[0221] See also Figure 7 , the steps to obtain the target steelmaking parameters are:

[0222] S321: Based on the target steel alloy type, obtain the thermophysical properties of the target alloy, including specific heat capacity and thermal conductivity;

[0223] S322: Based on the thermophysical properties of the target alloy and the target temperature T of electric furnace steelmaking target , through the formula:

[0224]

[0225] Calculate the target steelmaking time t target , get the target steelmaking parameters, where λ is the adjustment factor, C p is the specific heat, T ambient is the ambient temperature, m is the mass of the material used for steelmaking, P is the heating power of the electric furnace, η is the thermal efficiency, ν is the efficiency adjustment index, T target It is the target temperature for electric furnace steelmaking.

[0226] formula:

[0227]

[0228] Parameter details and how to obtain them:

[0229] Specific heat capacity C p Specific heat capacity: The energy required to raise the temperature of a unit mass of a material by 1°C. Specific heat capacity is obtained through standard materials science tests, usually from materials science data sheets or experimental determination results.

[0230] Ambient temperature T ambient : It is the actual temperature of the environment where the factory is located, usually monitored in real time by on-site temperature sensors or obtained from local meteorological data.

[0231] Material mass m: refers to the total material mass used in the electric furnace, which is determined based on production orders and batch planning.

[0232] Electric furnace heating power P: This is determined by the design specifications of the electric furnace, and detailed information can be obtained from the equipment manufacturer or equipment operating manual.

[0233] Thermal efficiency η: describes the efficiency of heating equipment in converting input energy into actual heat energy. It is generally provided by the manufacturer or can be estimated based on historical production data.

[0234] Adjustment factor λ: used to adjust the calculation according to different production environments and material characteristics. It is an empirical value, usually determined by historical data analysis and engineering experience.

[0235] Efficiency adjustment index ν: used to adjust the impact of thermal efficiency on duration calculation to more accurately reflect the impact of actual production conditions. It is an adjustment parameter obtained based on experiments and simulation analysis.

[0236] Calculation example:

[0237] Assume the following specific data for calculation:

[0238] C p =0.5J / g℃

[0239] T target =1600℃(target temperature)

[0240] T ambient =25℃(ambient temperature)

[0241] m = 5000kg (total mass of materials in the furnace)

[0242] P = 1000kW (heating power of electric furnace)

[0243] η=0.80(thermal efficiency)

[0244] λ=1.05(adjustment factor)

[0245] ν=0.95(efficiency adjustment index)

[0246] Calculation of target steelmaking time:

[0247]

[0248] The calculated target steelmaking time is about 5499 seconds, or about 91.65 minutes. This means that under the given conditions, it takes about 91.65 minutes to reach the required target temperature, ensuring that the material can be melted in the electric furnace and reach the appropriate processing temperature, in order to ensure that the heat treatment of the material can meet the predetermined quality and performance standards.

[0249] See also Figure 8 , the steps for obtaining real-time adjustment of steelmaking parameters are:

[0250] S411: Implement target steelmaking parameters, monitor the actual temperature during the electric furnace steelmaking process, and obtain real-time steelmaking temperature data;

[0251] S412: Based on the real-time steelmaking temperature data and the target temperature of electric furnace steelmaking, the formula:

[0252]

[0253] Calculate the adjustment value Δt of steelmaking time;

[0254] Among them, T actual is the actual steelmaking temperature, T target is the target temperature of electric furnace steelmaking, k is the time adjustment coefficient, α is the deviation impact index, τ is the deviation maintenance time, and Δt is the adjustment value of steelmaking time;

[0255] S413: Based on the adjustment value Δt of the steelmaking time, the steelmaking time is adjusted to obtain real-time adjustment steelmaking parameters.

[0256] formula:

[0257]

[0258] Parameter details and how to obtain them:

[0259] T target : The target temperature of electric furnace steelmaking, calculated in the previous steps.

[0260] T actual :The actual steelmaking temperature is monitored in real time by the high-precision temperature sensor installed in the electric furnace to ensure the timeliness and accuracy of the temperature reading.

[0261] k: Time adjustment factor, an empirical value used to adjust time calculations to suit specific production environments and goals. The value of k is usually determined based on past production data and engineering commissioning results.

[0262] α: Deviation impact index, which reflects the sensitivity of temperature difference to time adjustment and is set by analyzing historical production data and temperature control accuracy.

[0263] τ: Deviation duration, which indicates the time interval from the start of temperature deviation to its correction, usually recorded in real time by the production monitoring system.

[0264] Calculation Example

[0265] The following parameters are assumed for the calculation:

[0266] T target =1600℃(target steelmaking temperature)

[0267] T actual =1580℃(actual measured temperature)

[0268] k = 120 (time adjustment coefficient)

[0269] α=0.5(deviation influence index)

[0270] τ=1800 seconds (30 minutes, the time the deviation lasts)

[0271] Calculation of target steelmaking time adjustment value:

[0272]

[0273] The calculated adjustment value Δt≈100.57 seconds indicates that in order to compensate for the impact of the actual temperature being lower than the target temperature, the steelmaking time should be increased by about 100.57 seconds to reduce the production efficiency loss and potential material performance problems caused by temperature deviation.

[0274] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An ultra-low emission intelligent control method for electric furnace steelmaking, characterized in that: The following steps are involved: Based on the steelmaking raw material data information, extract the key quality data of the steelmaking raw materials, evaluate the quality of the current steelmaking raw materials, and compare the current steelmaking raw material quality with the preset benchmark raw material quality to evaluate the deviation degree of the current steelmaking raw material quality and obtain quality deviation information; The method for evaluating the quality of current steelmaking raw materials is: Based on the steelmaking raw material data information, extract the key quality data of the steelmaking raw materials, including the carbon content, sulfur content and iron content information of the steelmaking raw materials; Based on the key quality data, data cleaning is performed to remove abnormal values ​​and data beyond the normal range to obtain cleaned data; Based on the cleaned data, the formula: Calculate the quality score Q of steelmaking raw materials, and evaluate the quality of current steelmaking raw materials according to the size of the Q value; Among them, w c 、w s and w Fe are the weight coefficients of carbon content, sulfur content and iron content respectively, C max , S max and Fe max are the maximum values ​​of carbon, sulfur and iron contents, respectively; λ, ∈ and φ are adjustment coefficients; C, S and Fe represent the contents of carbon, sulfur and iron, respectively; and Q is the quality score of steelmaking raw materials; The steps for obtaining the quality deviation information are as follows: Based on the historical quality score data of steelmaking raw materials, the benchmark raw material quality score is obtained by calculating the average quality score of high-quality batches; Based on the benchmark raw material quality score and the quality score Q of the current steelmaking raw material, the formula: Calculate the deviation index D; Among them, Q is the quality score of the current steelmaking raw materials, D is the deviation index, and Q ideal is the benchmark quality score, γ is the weighted index, and β is the adjustment coefficient; Based on the deviation index D, according to the size of the deviation index D, the deviation degree of the current steelmaking raw material quality is evaluated to obtain quality deviation information; Based on the quality deviation information, adjusting the usage of steelmaking additives, the steelmaking additives include carburizers and oxidizers, and combining the target steel alloy component processing requirements to adjust the addition amount of the alloy material to obtain material addition information; Based on the quality deviation information and the material addition information, combined with the target processing requirements, the target temperature in the electric furnace steelmaking process is calculated, and according to the target temperature, the target steelmaking time is calculated to obtain the target steelmaking parameters; The method for calculating the target temperature in the electric furnace steelmaking process is: Based on the quality deviation information and the material addition information, a quality deviation index D is extracted, and in combination with the target steel alloy type, melting point data of the standard alloy is obtained; Based on the melting point data of the standard alloy, the formula: Calculate the target temperature T for electric furnace steelmaking target ; Among them, T base is the reference temperature, T melt is the standard melting point of the target alloy, T target for The target temperature of electric furnace steelmaking, k is the temperature adjustment coefficient, and γ is the nonlinear influence coefficient; The steps for obtaining the target steelmaking parameters are: Based on the target steel alloy type, obtain the thermophysical properties of the target alloy, including specific heat capacity and thermal conductivity; Based on the thermophysical properties of the target alloy and the target temperature T of electric furnace steelmaking target , through the formula: Calculate the target steelmaking time t target , get the target steelmaking parameters, where λ is the adjustment factor, C p is the specific heat, T ambient is the ambient temperature, m is the mass of the material used for steelmaking, P is the heating power of the electric furnace, η is the thermal efficiency, ν is the efficiency adjustment index, T target is the target temperature for steelmaking in an electric furnace; Implement the target steelmaking parameters, monitor the actual temperature during the electric furnace steelmaking process, compare it with the target steelmaking temperature, calculate the steelmaking time adjustment value according to the deviation size and duration between the actual temperature and the target temperature, adjust the steelmaking time, and obtain real-time adjustment of the steelmaking parameters.

2. The ultra-low emission intelligent control method for electric furnace steelmaking according to claim 1 is characterized in that: The method for adjusting the usage of the steelmaking additive is: Based on the quality deviation information, using the quality deviation index D, through the formula: Calculate the adjustment ratio B of steelmaking additives; Wherein, D is the quality deviation index, T is the predetermined threshold, K is the adjustment constant, η is the sensitivity coefficient, b is the logarithmic base, ξ is the environmental factor adjustment parameter, and B is the adjustment ratio of the steelmaking additive; Based on the adjustment ratio B of the steelmaking additive, the usage of the steelmaking additive is adjusted, including adjusting the usage of the carburizer and the oxidant.

3. The ultra-low emission intelligent control method for electric furnace steelmaking according to claim 2 is characterized in that: The steps for obtaining the material addition information are as follows: Based on the processing requirements of the target steel alloy composition, the metal element ratio of the target steel alloy is obtained to obtain the target steel alloy composition information; Based on the target steel alloy composition information, the composition information of the current steelmaking raw materials is obtained, through the formula: Calculate the percentage deviation Δx of an element i ; Among them, C i,a is the actual measured alloying element content, C i,t is the target alloying element content, R is the adjustment parameter, Δx i is the percentage deviation of element i, where i is the index; Based on the percentage deviation Δx of the element i , through the formula: Calculate the adjusted alloy material addition amount A i , get the material adding information; Among them, M represents the adjustment coefficient, V represents the total weight of the current batch, and A i is the adjusted alloy material addition amount, i is the index, Δx i is the percentage deviation of element i.

4. The ultra-low emission intelligent control method for electric furnace steelmaking according to claim 1 is characterized in that: The acquisition steps of adjusting the steelmaking parameters in real time are as follows: Implement the target steelmaking parameters, monitor the actual temperature during the electric furnace steelmaking process, and obtain real-time steelmaking temperature data; Based on the real-time steelmaking temperature data and the target temperature of electric furnace steelmaking, the formula is: Calculate the adjustment value Δt of steelmaking time; Among them, T actual is the actual steelmaking temperature, T target is the target temperature of electric furnace steelmaking, k is the time adjustment coefficient, α is the deviation impact index, τ is the deviation maintenance time, and Δt is the adjustment value of steelmaking time; Based on the adjustment value Δt of the steelmaking time, the steelmaking duration is adjusted to obtain real-time adjustment of steelmaking parameters.

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