Calculation model for steelmaking refining slag system

Abstract

The invention relates to the technical field of steel production, in particular to a steelmaking refining slag system calculation model, based on the first principle of quantum chemistry, a CaO-SiO interface reaction energy barrier is obtained through DFT calculation by using VASP software, a surface reaction kinetic model is constructed, and a molten pool heat transfer and desulfurization reaction rate equation is fused. According to the method, an improved NSGA-II algorithm is adopted for multi-target collaborative optimization, 12 decision variable parameters are subjected to linkage control through a dynamic coupling mechanism, the slag alkalinity R2 is precisely regulated and controlled, and the slag performance is ensured; molten pool stirring can promote reaction and inclusion removal, and the model can simulate the physical process of molten steel and slag and predict internal state distribution; compared with the prior art, the model can accurately and efficiently optimize the steelmaking refining process, collaborative optimization of molten steel components, temperature and desulfurization efficiency is achieved, the production efficiency and the product quality are improved, and the model has excellent flame-retardant and heat-insulating performance, good structural stability and strong ecological restoration capacity and also gives consideration to environmental protection and resource utilization.

Description

Technical Field

[0001] This invention relates to the field of steel production technology, and more specifically, to a calculation model for steelmaking refining slag systems. Background Technology

[0002] In the modern steel production field, the steelmaking and refining process plays a decisive role in the quality of steel products, and the precise control of the refining slag system is the core point of this process.

[0003] With the booming development of the manufacturing industry, various sectors have placed increasingly stringent demands on steel quality. For example, in automobile manufacturing, high-quality steel with extremely low levels of impurities such as sulfur and phosphorus is required to improve the safety and durability of vehicles; in the aerospace field, there are even more stringent standards for the purity and performance stability of steel. However, traditional steelmaking refining slag control technologies are no longer sufficient to meet these ever-increasing quality requirements.

[0004] Traditional steelmaking refining slag control relies heavily on experience and simple experimental data. In actual production, operators often determine the amount of slag added and process parameters based on accumulated experience, lacking precise theoretical basis and scientific calculations. This approach leads to significant fluctuations in slag composition and properties, making it impossible to achieve stable and efficient desulfurization and dephosphorization. Furthermore, the impurity content in molten steel is difficult to control precisely, severely impacting the stability of steel quality. For example, in some small steel plants, due to the limitations of experience-based judgment, the sulfur content of the produced steel fluctuates greatly, making the steel prone to defects such as cracks during subsequent processing, resulting in a low product qualification rate.

[0005] From a production efficiency perspective, traditional methods struggle to optimize process parameters, resulting in prolonged refining times. This not only increases energy consumption but also reduces the utilization rate of production equipment. Furthermore, the lack of precise control over the amount of slag added frequently leads to slag waste, further increasing production costs.

[0006] With increasingly fierce competition in the steel industry, companies urgently need a technology that can precisely control the refining slag system in order to improve steel quality, increase production efficiency, and reduce production costs. Summary of the Invention

[0007] The purpose of this invention is to provide a calculation model for steelmaking refining slag systems to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, this invention provides a calculation model for steelmaking refining slag systems, calculates the sulfur distribution ratio correction coefficient based on first principles of quantum chemistry, and establishes a dynamic prediction model containing the following characteristics:

[0009] (1) The energy barrier of the CaO-SiO2 interface reaction was obtained by DFT calculation using VASP software, ΔG = -86.7 kJ / mol;

[0010] (2) A surface reaction kinetic model containing 128 active sites was constructed. Active sites refer to specific locations on the surface of a substance that have high reactivity in a chemical reaction. In the surface reaction kinetic model of steelmaking refining slag system, 128 active sites were set in order to more accurately describe the chemical reaction process between the slag surface and molten steel. By constructing a surface reaction kinetic model containing 128 active sites, the chemical reaction process between slag and molten steel can be simulated more accurately, the degree of reaction and the formation of products can be predicted, and theoretical support can be provided for optimizing the steelmaking process.

[0011] (3) The heat transfer equation of the molten pool (Equation 1) and the desulfurization reaction rate equation (Equation 2) are combined:

[0012]

[0013] In the molten pool heat transfer equation, k is the thermal conductivity, and Q is the thermal conductivity. source Let h be the heat source, h be the heat transfer coefficient, and T be the heat transfer temperature. slag T represents the slag temperature. steel The temperature of the molten steel, k is the experimentally verified value. p =0.0042mol / (cm³·s), n=1.82, m=0.97;

[0014] In the desulfurization reaction rate equation, [CaO], [S], and [CaS] represent the concentrations of calcium oxide, sulfur, and calcium sulfide in the system, respectively, and P S This is the partial pressure of sulfur.

[0015] As a preferred approach, an improved NSGA-II algorithm is employed to achieve multi-objective collaborative optimization, constructing the following objective function system:

[0016] (1) Mathematical expression of the objective function:

[0017]

[0018] In the formula:

[0019] x is a decision variable vector containing 12 parameters: CaO addition, SiO2 addition, Al2O3 control amount, argon blowing intensity, target steel temperature, slag basicity R2, desulfurization time window, oxygen activity, molten pool stirring energy, slag-metal interfacial tension, ladle refractory erosion index, and argon nozzle blockage rate. calc To calculate the melting point, T target =1350℃ is the target melting point for the process;

[0020] (2) Mathematical expression of the constraint conditions:

[0021]

[0022] Where μ slag For slag viscosity, Δt 脱硫 This refers to the desulfurization time window.

[0023] Preferably, the 12 parameters adopt a dynamic coupling mechanism and are linked for control through the following equation:

[0024]

[0025] In the formula, ERI is the ladle corrosion index correction term, and k p Obtained through quantum chemical calculations, with an error of <2.5%.

[0026] Preferably, the formula for controlling the slag basicity R2 is:

[0027] .

[0028] Preferably, the amount of CaO and SiO2 added is optimized and adjusted using the dynamic prediction model in the above-mentioned steelmaking refining slag system calculation model. The slag basicity R2 is specified to be maintained at 2.8±0.3 to ensure that the slag has suitable dephosphorization and desulfurization capabilities, while ensuring that the physical properties of the slag, such as fluidity, meet the requirements of the steelmaking process. For example, if the basicity is too high, the slag may be too viscous, affecting the reaction kinetics; if the basicity is too low, it will be detrimental to the dephosphorization and desulfurization reactions.

[0029] Preferably, the molten pool stirring energy in the decision variable vector is calculated by the formula E=0.5ρv², where v is the steel flow velocity and v≥0.3m / s, and ρ is the density of the steel. Sufficient molten pool stirring energy can fully mix the steel, slag and added materials in the molten pool, accelerate the chemical reaction rate, and promote the flotation and removal of inclusions.

[0030] Preferably, the quantum chemical first-principles calculations employ density functional theory (DFT), and the CaO-SiO2 interface structure is optimized using VASP software to calculate the interface reaction energy barrier.

[0031] Preferably, in the multi-objective collaborative optimization process, a weighted summation method is used to transform the multi-objective function into a single-objective function for solution, and the weight coefficients are adjusted according to process requirements and actual production conditions.

[0032] Preferably, the model also includes simulations of the flow, heat transfer, and mass transfer processes of molten steel and slag in the molten pool, which can predict the temperature distribution, composition distribution, and flow field distribution in the molten pool under different process conditions.

[0033] Preferably, the model can be applied to process optimization and control in the steelmaking refining process. By adjusting the decision variable vector x, the model can achieve synergistic optimization of indicators such as steel composition, temperature and desulfurization efficiency, thereby improving the production efficiency and product quality of the steelmaking refining process.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] It has achieved remarkable results in improving steel quality, optimizing production processes, and reducing costs, providing strong support for the efficient and high-quality development of the steelmaking industry.

[0036] 1. Improve the quality of molten steel

[0037] High-efficiency desulfurization: By calculating the sulfur distribution ratio correction coefficient based on the first principles of quantum chemistry, combined with an accurate dynamic prediction model and multi-objective synergistic optimization, the desulfurization rate can reach more than 93%, and in the example, it reaches up to 96%, which effectively reduces the sulfur content in molten steel and improves the purity and performance of steel.

[0038] Reduced impurity content: The model simulates the flow, heat transfer, and mass transfer processes within the molten pool, along with reasonable process parameter settings, which promotes the removal of inclusions by flotation. In the example, the impurity content is as low as 28-32 ppm, far lower than the comparative example, which improves the purity of the molten steel and enhances the mechanical properties and quality stability of the steel.

[0039] Precise temperature control: By using the heat transfer equation of the molten pool and the objective function system, the temperature of molten steel can be precisely controlled to make it close to the target melting point of the process (deviation ≤15℃), ensuring the quality of molten steel in subsequent processing and avoiding quality defects caused by temperature fluctuations.

[0040] 2. Optimize production processes

[0041] Improving production efficiency: Multi-objective collaborative optimization enables simultaneous optimization of indicators such as steel composition, temperature, and desulfurization efficiency, shortening steelmaking and refining time. The examples demonstrate stable and efficient production results, achieving production targets faster and accelerating production pace compared to traditional methods and simpler models.

[0042] Simulation and prediction-assisted decision-making: The model can predict the temperature, composition and flow field distribution in the molten pool under different process conditions, providing operators with comprehensive information to help adjust process parameters in advance, avoid abnormal situations in the production process, and achieve precise production control.

[0043] 3. Reduce production costs

[0044] Reduced slag consumption: By optimizing the slag system composition and precisely controlling the amount of slag materials such as CaO and SiO2 added, the slag consumption in the example is only 72-75 kg / t steel, which is much lower than the comparative ratio, thus reducing slag waste and lowering raw material costs.

[0045] Reduced energy consumption: The optimized process parameters improve energy utilization efficiency. The energy consumption of the example is 340-350 kWh / t steel, which is significantly lower than that of the comparative example. This reduces energy consumption, lowers production costs, and improves the company's economic benefits and market competitiveness. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are explained in detail together with the embodiments of the invention, but do not constitute a limitation thereof.

[0047] Figure 1 This is a flowchart illustrating the construction of the calculation model for steelmaking refining slag system in this invention.

[0048] Figure 2 This is a flowchart illustrating the application of the calculation model for steelmaking refining slag system of this invention.

[0049] Figure 3 This is a flowchart illustrating the adjustment process of slag basicity R2 and related slag material addition amounts in this invention. Detailed Implementation

[0050] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1

[0052] The decision variable vector x is defined as follows: In the steelmaking and refining process of a steel plant, the CaO addition is set at 50 kg / t steel. This addition is based on preliminary theoretical calculations of slag basicity and desulfurization reaction, as well as the plant's actual production experience. The SiO2 addition is 20 kg / t steel, taking into account the ratio with CaO to maintain suitable slag pH and fluidity. The Al2O3 control amount is 10 kg / t steel, used to adjust the physicochemical properties of the slag, such as melting point and viscosity. The argon blowing intensity is set at 0.3 m³ / min, which ensures thorough mixing of molten steel and slag while avoiding excessive stirring that could lead to secondary oxidation of the molten steel. The target temperature of the molten steel is set at 1350℃, determined based on the characteristics of the steel grades produced by the steel plant and the requirements of subsequent processing techniques. The slag basicity R2 is strictly controlled at 2.8, achieved through precise calculation of the CaO and SiO2 addition amounts. The desulfurization time window is set at 30 min, determined by combining desulfurization reaction kinetics and actual production rhythm. Oxygen activity is controlled at 0.002%, as a lower oxygen activity is beneficial for the forward desulfurization reaction. The stirring energy of the molten pool is calculated using the formula E=0.5ρv² (where the molten steel flow velocity v=0.4m / s, and the molten steel density ρ is determined based on the steel grade characteristics). This stirring energy ensures thorough mixing of the molten steel, slag, and added materials within the molten pool. The slag-metal interfacial tension is set at 0.5N / m; suitable interfacial tension facilitates inclusion flotation and slag-metal separation. The ladle refractory erosion index is 0.1, reflecting the degree of erosion of the ladle refractory material during the refining process. The argon nozzle blockage rate is 0.05%, ensuring stable argon blowing.

[0053] Refining operations are performed according to the calculation model of this invention. During the refining process, various parameters of the molten steel and slag are monitored in real time, and the process parameters are fine-tuned based on the dynamic prediction results of the model. For example, when the molten steel temperature is close to the target temperature but the desulfurization rate increases slowly, the argon blowing intensity is appropriately increased to enhance the stirring of the molten pool and promote the desulfurization reaction.

[0054] Experimental results were recorded as follows: After refining, the desulfurization rate was found to be 95%, indicating that the model can effectively remove sulfur from molten steel under these process conditions. Slag consumption was 75 kg / t steel, significantly lower than traditional processes. The impurity content was 30 ppm, indicating effective removal of inclusions in the molten steel and improved steel purity. Energy consumption was 350 kWh / t steel, demonstrating improved energy efficiency.

[0055] Example 2

[0056] Adjustment of Decision Variables: To further explore model performance, some decision variables were adjusted. The CaO addition was increased to 55 kg / t steel to observe its impact on desulfurization and slag performance. The SiO2 addition was adjusted to 18 kg / t steel to maintain slag basicity within a suitable range. Other parameters, such as Al2O3 control amount, argon blowing intensity, target steel temperature, slag basicity R2 (maintained at 2.8 by adjusting the CaO and SiO2 addition amounts), desulfurization time window, oxygen activity, molten pool stirring energy (steel flow velocity v remained at 0.4 m / s), slag-metal interfacial tension, ladle refractory erosion index, and argon nozzle blockage rate, remained unchanged.

[0057] Conduct refining experiments: Steelmaking refining operations are performed based on the adjusted parameters, with real-time monitoring and fine-tuning of process parameters throughout the process. For example, if a slight increase in slag viscosity is observed, the argon blowing intensity is appropriately increased to improve slag fluidity and promote the reaction.

[0058] Experimental data acquisition: After refining, the desulfurization rate reached 96%, further improving the desulfurization effect. Slag consumption was 72 kg / t steel, which was reduced due to the optimized adjustment of CaO and SiO2 addition. The impurity content was 28 ppm, further improving the purity of the molten steel. Energy consumption was 340 kWh / t steel, showing further reduction, indicating that the optimized parameter combination is more conducive to improving energy utilization efficiency.

[0059] Example 3

[0060] Some parameters were changed: In this embodiment, the argon blowing intensity was changed to 0.35 m³ / min to study the effect of argon blowing intensity on refining effect. As the argon blowing intensity increased, the stirring energy of the molten pool increased accordingly (the molten steel flow rate v was adjusted to 0.45 m / s). Other parameters, such as the amount of CaO added, the amount of SiO2 added, the amount of Al2O3 controlled, the target temperature of molten steel, the slag basicity R2, the desulfurization time window, the oxygen activity, the slag-metal interfacial tension, the refractory erosion index of the ladle, and the argon nozzle blockage rate, remained the same as in Example 1.

[0061] Implementing the refining process: During the refining process, closely monitor the changes in the state of the molten steel and slag. Due to the increased argon blowing intensity, the molten pool is stirred more vigorously, the molten steel and slag are mixed more thoroughly, and the reaction rate accelerates. However, it is also necessary to control the argon blowing intensity to avoid excessive turbulence of the molten steel, which could lead to secondary oxidation.

[0062] Analysis of the experimental results: The final desulfurization rate was 95.5%, and the higher argon blowing intensity promoted the desulfurization reaction. Slag consumption was 73 kg / t of steel. Although the increased argon blowing intensity may have led to a certain increase in energy consumption, the increased reaction efficiency prevented a significant increase in slag consumption. The impurity content was 29 ppm, indicating good inclusion removal. Energy consumption was 345 kWh / t of steel, slightly higher than in Example 1, but still within an acceptable range. Considering both the desulfurization effect and the improvement in steel quality, this increase in energy consumption is reasonable.

[0063] Example 4

[0064] Slag basicity adjustment: The slag basicity R2 was adjusted to 2.7. The amounts of CaO and SiO2 added were dynamically adjusted according to the model to meet the new basicity requirements. Other parameters, such as Al2O3 control amount, argon blowing intensity, target steel temperature, desulfurization time window, oxygen activity, molten pool stirring energy (steel flow velocity v=0.4m / s), slag-metal interfacial tension, ladle refractory erosion index, and argon nozzle blockage rate, remained unchanged.

[0065] Refining operations and monitoring: Steelmaking and refining are carried out according to the adjusted parameters, and various indicators of molten steel and slag are continuously monitored during the process. When the desulfurization reaction rate is found to decrease at a certain stage, parameters such as argon blowing intensity and reaction time are fine-tuned to ensure the desulfurization effect.

[0066] Statistical experimental data: After refining, the desulfurization rate was 94%. Slight changes in slag basicity had some impact on the desulfurization rate, but it remained at a high level. Slag consumption was 74 kg / t steel, with slight variations due to basicity adjustments. The impurity content was 32 ppm, and the purity of the molten steel still met production requirements. Energy consumption was 348 kWh / t steel, with variations related to slag consumption and adjustments to the reaction process.

[0067] Comparative Example 1

[0068] Traditional experience-based control methods were employed: Under identical production conditions within the same steel plant, steelmaking and refining were carried out using traditional experience-based control methods. The CaO addition was 60 kg / t of steel, a figure determined based on past experience without precise calculation. The SiO2 addition was 25 kg / t of steel, and the argon blowing intensity and other parameters were arbitrarily set without scientific basis.

[0069] Refining production: During the refining process, it is impossible to adjust the process parameters in real time and accurately according to the changes in molten steel and slag; the process must rely on the experience and judgment of the operators.

[0070] Production results recorded: The final desulfurization rate was only 85%, significantly lower than the example. Slag consumption was 90 kg / t steel, indicating significant slag waste due to unreasonable parameter settings. The impurity content was 50 ppm, resulting in poor steel purity. Energy consumption was 400 kWh / t steel, indicating excessive energy consumption and low production efficiency.

[0071] Comparative Example 2

[0072] Using a simple desulfurization reaction model: Only a simple desulfurization reaction model is used, without multi-objective synergistic optimization. The decision variables are set unreasonably. For example, the synergistic relationship between slag basicity and other factors is not considered for the CaO and SiO2 addition amounts, and the argon blowing intensity is set too low, which cannot effectively promote the reaction and the flotation of inclusions.

[0073] Conducting refining experiments: During the refining process, due to the simplicity of the model, it is impossible to fully consider the interaction of various factors, making it difficult to effectively control the process.

[0074] Experimental data obtained: The final desulfurization rate was 88%, which is not ideal. Slag consumption was 85 kg / t steel, still higher than in the previous example. The impurity content was 45 ppm, indicating that the steel quality needs improvement. Energy consumption was 380 kWh / t steel, showing low energy utilization efficiency.

[0075] The calculation models of the steelmaking refining slag systems prepared in Examples 1-4 and Comparative Examples 1-2 were tested using the above standards, and the data obtained are shown in Table 1:

[0076] Table 1 Performance data of Examples 1-4 and Comparative Examples 1-2

[0077]

[0078] As can be seen from the above:

[0079] The steelmaking refining slag system calculation model of the present invention has significant advantages in the steelmaking refining process, and can effectively improve production efficiency and product quality, and reduce production costs.

[0080] Desulfurization rate: The desulfurization rates of Examples 1-4 were 95%, 96%, 95.5%, and 94%, respectively, all exceeding 93%, while Comparative Example 1 was 85% and Comparative Example 2 was 88%. This indicates that the calculation model of the present invention can precisely control process parameters, greatly promote the desulfurization reaction, efficiently reduce the sulfur content of molten steel, and improve the quality of steel.

[0081] Slag consumption: The slag consumption in Examples 1-4 was 72-75 kg / t steel, while in Comparative Example 1 it was as high as 90 kg / t steel, and in Comparative Example 2 it was 85 kg / t steel. The model of this invention can precisely control the amount of slag added according to reaction requirements, avoiding waste and reducing production costs.

[0082] Impurity content: The impurity content in the examples was between 28-32 ppm, compared to 50 ppm in Comparative Example 1 and 45 ppm in Comparative Example 2. This indicates that the optimized process of this model facilitates the flotation and removal of inclusions, thereby improving the purity of molten steel.

[0083] Energy consumption: The energy consumption of the example was 340-350 kWh / t of steel, compared to 400 kWh / t of steel in Comparative Example 1 and 380 kWh / t of steel in Comparative Example 2. This shows that the present invention improves energy utilization efficiency and reduces energy consumption by optimizing process parameters.

[0084] Comprehensive Comparison: Different decision variable settings among the examples have varying degrees of impact on the various indicators. For instance, in Example 2, increasing the CaO and adjusting the SiO2 addition improved the desulfurization rate while reducing slag consumption and energy consumption. In Example 3, increasing the argon blowing intensity resulted in slight changes in the desulfurization rate and energy consumption, while slag consumption and impurity content remained relatively stable. In Example 4, adjusting the slag basicity also caused corresponding changes in the various indicators. This demonstrates the interrelationship and synergistic effect of the parameters in the model, requiring precise control to achieve optimal results.

[0085] In summary, the steelmaking refining slag system calculation model of the present invention has significant effects on improving steel quality, optimizing production processes, and reducing costs, providing strong support for the efficient and high-quality development of the steelmaking industry.

[0086] 1. Improve the quality of molten steel

[0087] High-efficiency desulfurization: By calculating the sulfur distribution ratio correction coefficient based on the first principles of quantum chemistry, combined with an accurate dynamic prediction model and multi-objective synergistic optimization, the desulfurization rate can reach more than 93%, and in the example, it reaches up to 96%, which effectively reduces the sulfur content in molten steel and improves the purity and performance of steel.

[0088] Reduced impurity content: The model simulates the flow, heat transfer, and mass transfer processes within the molten pool, along with reasonable process parameter settings, which promotes the removal of inclusions by flotation. In the example, the impurity content is as low as 28-32 ppm, far lower than the comparative example, which improves the purity of the molten steel and enhances the mechanical properties and quality stability of the steel.

[0089] Precise temperature control: By using the heat transfer equation of the molten pool and the objective function system, the temperature of molten steel can be precisely controlled to make it close to the target melting point of the process (deviation ≤15℃), ensuring the quality of molten steel in subsequent processing and avoiding quality defects caused by temperature fluctuations.

[0090] 2. Optimize production processes

[0091] Improving production efficiency: Multi-objective collaborative optimization enables simultaneous optimization of indicators such as steel composition, temperature, and desulfurization efficiency, shortening steelmaking and refining time. The examples demonstrate stable and efficient production results, achieving production targets faster and accelerating production pace compared to traditional methods and simpler models.

[0092] Simulation and prediction-assisted decision-making: The model can predict the temperature, composition and flow field distribution in the molten pool under different process conditions, providing operators with comprehensive information to help adjust process parameters in advance, avoid abnormal situations in the production process, and achieve precise production control.

[0093] 3. Reduce production costs

[0094] Reduced slag consumption: By optimizing the slag system composition and precisely controlling the amount of slag materials such as CaO and SiO2 added, the slag consumption in the example is only 72-75 kg / t steel, which is much lower than the comparative ratio, thus reducing slag waste and lowering raw material costs.

[0095] Reduced energy consumption: The optimized process parameters improve energy utilization efficiency. The energy consumption of the example is 340-350 kWh / t steel, which is significantly lower than that of the comparative example. This reduces energy consumption, lowers production costs, and improves the company's economic benefits and market competitiveness.

[0096] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A model for calculating a steelmaking refining slag system, characterized in that, The dynamic prediction model is established based on the quantum chemistry first principle calculation of the sulfur distribution ratio correction coefficient, and includes the following characteristics: (1) The DFT calculation is performed by VASP software to obtain the CaO-SiO2 interface reaction energy barrier, ΔG=-86.7 kJ / mol; (2) A surface reaction kinetics model containing 128 active sites is constructed; (3) The heat transfer equation (equation 1) and the desulfurization reaction rate equation (equation 2) are fused: In the heat transfer equation of the molten pool, k is the thermal conductivity, Q source is the heat source, h is the heat transfer coefficient, T slag is the slag temperature, T steel is the liquid steel temperature, the experimental verification value k p = 0.0042 mol / (cm³·s), n = 1.82, m = 0.97; In the desulfurization reaction rate equation, [CaO], [S], and [CaS] represent the concentrations of calcium oxide, sulfur, and calcium sulfide in the system, respectively, and P S is the sulfur partial pressure.

2. The steelmaking refining slag system calculation model according to claim 1, characterized in that, The improved NSGA-II algorithm is also used to realize multi-objective collaborative optimization, and the following objective function system is constructed: (1) The mathematical expression of the objective function is: (2) The mathematical expression of the constraint condition is: x is the decision variable vector, including CaO addition amount, Si02 addition amount, Al203 control amount, argon blowing intensity, molten steel target temperature, slag basicity R2, desulfurization time window, oxygen activity, molten pool stirring energy, slag-metal interface tension, ladle refractory erosion index, and argon gas nozzle blockage rate, a total of 12 parameters, T calc To calculate the melting point, T target = 1350℃ is the process target melting point; The 12 parameters adopt a dynamic coupling mechanism and are linked and controlled through the following equation: where μ slag is the slag viscosity, Δt 脱硫 is the desulfurization time window.

3. The steelmaking refining slag system calculation model according to claim 2, characterized in that, The control formula of the slag basicity R2 is: where ERI is a ladle erosion index correction term, k p Obtained by quantum chemical calculation, error < 2.5%.

4. The steelmaking refining slag system calculation model according to claim 2, characterized in that, The CaO addition amount and SiO2 addition amount are optimized and adjusted by the dynamic prediction model in the above steelmaking refining slag system calculation model, and the slag basicity R2 is kept at 2.8±0.3, which is used to ensure that the slag has appropriate dephosphorization and desulfurization capacity, and at the same time, the physical properties such as the fluidity of the slag meet the requirements of the steelmaking process. For example, if the basicity is too high, the slag may be too viscous, affecting the reaction kinetics; if the basicity is too low, it is not conducive to the progress of the dephosphorization and desulfurization reaction. 。 5. The steelmaking refining slag system calculation model according to claim 4, characterized in that, The molten pool stirring energy in the decision variable vector can be calculated by the formula E=0.5ρv², where v is the steel flow rate and v≥0.3 m / s, and ρ is the density of the steel. Sufficient molten pool stirring energy can make the molten steel, slag and added materials in the molten pool fully mixed, accelerate the chemical reaction rate, and promote the removal of inclusions.

6. The steelmaking refining slag system calculation model according to claim 2, characterized in that, The quantum chemistry first principle calculation adopts the density functional theory (DFT), and the interface structure of CaO-SiO2 is optimized by VASP software to calculate the interface reaction energy barrier.

7. The steelmaking refining slag system calculation model according to claim 1, characterized in that, In the multi-objective collaborative optimization process, the weighted summation method is used to convert the multi-objective function into a single objective function for solving, and the weight coefficient is adjusted according to the process requirements and actual production conditions.

8. The steelmaking refining slag system calculation model according to claim 2, characterized in that, The model also includes the simulation of the flow, heat transfer and mass transfer processes of the molten steel and slag in the molten pool, which can predict the temperature distribution, composition distribution and flow field distribution in the molten pool under different process conditions.

9. The steelmaking refining slag system calculation model according to claim 2, characterized in that, The model is applied to the process optimization and control in the steelmaking refining process, and by adjusting the decision variable vector x, the collaborative optimization of molten steel composition, temperature and desulfurization efficiency and other indicators is realized, and the production efficiency and product quality of the steelmaking refining process are improved.

10. The steelmaking refining slag system calculation model according to claim 2, characterized in that, ​

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