A design method of composite deoxidizer for producing high-quality soft magnetic stainless steel

Abstract

The application discloses a design method of a composite deoxidizer for producing high-quality soft magnetic stainless steel, and the design method comprises three parts of data processing, model optimization and thermodynamic calculation, parameters and models used in the thermodynamic calculation process are processed and optimized based on the composition characteristics of the soft magnetic stainless steel, and the purpose of guiding the deoxidizer composition design is achieved. The composite deoxidizer designed by the application can form fine inclusions in the stainless steel refining process such as AOD, VOD and LF, and realizes the deep deoxidation target under the condition of high alloy melt extremely low oxygen potential, reduces the oxygen content of the stainless steel to below 10 ppm, and meanwhile, maintains a relatively high saturation magnetic induction intensity, a relatively small coercive force and a relatively small residual magnetic induction intensity, and realizes the balanced design of the cleanliness and magnetic properties of the high-quality soft magnetic stainless steel.

Description

Technical Field

[0001] This invention belongs to the field of deoxidizer technology, and in particular relates to a design method for a composite deoxidizer for producing high-quality soft magnetic stainless steel. Background Technology

[0002] Soft magnetic materials are characterized by low coercivity, easy magnetization, and demagnetization, and are therefore widely used in fields such as radio, computers, home appliances, electrical engineering, and communications. Currently used soft magnetic materials mainly include: electrical pure iron, silicon steel permalloy, ferrochrome magnetic stainless steel, ferrite soft magnetic materials, and nanocrystalline soft magnetic materials. Ferritic stainless steel possesses sufficient soft magnetic properties, excellent corrosion resistance and mechanical properties, as well as advantages such as simple production processes and low cost, making it widely used as a soft magnetic material.

[0003] However, high levels of elements such as oxygen (O) and sulfur (S) in steel can lead to a decline in steel performance, thus necessitating purification treatment of the molten steel. Researchers have long sought new strategies to guide the design of deoxidizing compositions for soft magnetic stainless steel. However, the development of most deoxidizers still relies on experience or extensive trial-and-error experiments. Calculating deoxidation equilibrium diagrams for different elements provides a clear understanding of the main components of inclusions under different steel composition conditions, significantly improving the efficiency of deoxidizer design and development. However, the calculation results of this method are closely related to the selected thermodynamic parameters and models. Therefore, providing an accurate and reliable method for deoxidizer composition design to guide deoxidizer design remains a pressing issue that needs to be addressed in current technology. Summary of the Invention

[0004] The technical problem this invention aims to solve is: how to achieve high cleanliness while ensuring excellent magnetic properties for high-performance soft magnetic stainless steel, given its compositional characteristics. A reasonable and magnetically sound method is provided for deep deoxidation of high-alloy melts of high-quality soft magnetic stainless steel under extremely low oxygen potential, controlling the oxygen content to below 10 ppm.

[0005] To achieve the above objectives, the present invention provides a method for designing a composite deoxidizer for producing high-quality soft magnetic stainless steel, characterized by the following steps:

[0006] S1, Data processing is performed on the compositional characteristics of the soft magnetic stainless steel system to obtain the processed parameters;

[0007] S2, Based on the processed parameters, the thermodynamic model is processed and optimized to obtain the optimized model;

[0008] S3. Based on the processed parameters and the optimized model, the deoxygenation equilibrium calculation results are output using thermodynamic calculation methods to guide the design of deoxygenator components.

[0009] Preferably, S1 includes the following sub-steps:

[0010] S1-1, First-order interaction parameter processing: The activity interaction coefficients under Henry's standard state and the activity coefficients under 1 wt.% standard state are converted to each other to obtain the first-order interaction parameters:

[0011]

[0012] in, It is the activity interaction coefficient under Henry's standard state. It is the activity coefficient at 1 wt.% under standard conditions, M j M is the molar mass of deoxygenated element j. Fe is the molar mass of element iron;

[0013] S1-2, Second-order interaction parameter processing: The missing data were calculated from the elemental molar mass and first-order parameters to obtain the second-order interaction parameters:

[0014]

[0015] in, These are the second-order activity interaction coefficients under the Henry's standard state. It is the second activity coefficient under standard conditions at 1 wt.%.

[0016] S1-3, Second-order cross-interaction parameter processing: Missing data is calculated using the first-order interaction parameters and the second-order interaction parameters to obtain the second-order cross-interaction parameters:

[0017]

[0018] in, These are the second-order cross-interaction parameters;

[0019] S1-4, Equilibrium Constant Treatment: Using the Van't Hoff isotherm equation, the equilibrium constant expression is calculated based on the Gibbs free energy change under standard conditions.

[0020] ΔG Θ =-RTlnK Θ (1-4)

[0021] Wherein, ΔG Θ The value represents the Gibbs free energy change under standard conditions, where R is the ideal gas constant, T is the temperature, and K is the kJ / L. Θ This is the equilibrium constant.

[0022] Preferably, S2 includes the following sub-steps:

[0023] S2-1, Based on the processed parameters, the binary enthalpy of mixing of different elements is calculated:

[0024]

[0025] Where H is the binary enthalpy of mixture, n Fe n i The amounts of substance of component Fe and deoxygenating element i, H Fe H i Here, T represents the enthalpy of component Fe and deoxygenating element i, and k is the temperature. B is the Boltzmann constant.

[0026] S2-2, Based on the relative magnitudes of the binary enthalpy of mixture, assess the strength of elemental interactions.

[0027] If the elements are strongly interacting, such as Al, Mg, and Ca, they are treated as short-range ordered structures.

[0028] If the elements are weakly interacting, such as Fe, Si, and Cr, then they should be treated as random mixtures.

[0029] S2-3, by calculating the partial excess Gibbs energy, based on whether the Gibbs-Duhem equation is satisfied:

[0030] ∑ i (X i dlnγ i )=0 (1-6)

[0031] The relationship with Maxwell is used to determine whether thermodynamic consistency is achieved.

[0032]

[0033] If this condition is met, then thermodynamic consistency is achieved;

[0034] If these conditions are not met, then thermodynamic consistency is lacking, and the product can only be used at limited concentrations.

[0035] Among them, X i γ is the atomic percentage of component i. i G is the activity coefficient of component i. ex For the system's excess Gibbs energy.

[0036] Preferably, S3 includes the following sub-steps:

[0037] S3-1, Based on the processed parameters and the optimized model, the element consumption is determined using the equilibrium constant method and the mass conservation equation;

[0038] S3-2, Determine the deoxidation limit and type of deoxidizer according to actual production needs.

[0039] S3-3, Based on the element consumption, the deoxygenation limit, and the type of deoxygenating agent, a deoxygenation balance diagram is automatically output by computer.

[0040] This invention also provides an application of the design method for composite deoxidizers in the stainless steel refining process, characterized in that the above-mentioned design method for composite deoxidizers in the steel refining process is applied to Al-Si composite deoxidizers and Mg-Al composite deoxidizers.

[0041] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0042] 1. The present invention provides a composite deoxidizer design method for producing high-quality soft magnetic stainless steel. The method includes three parts: data processing, model optimization, and thermodynamic calculation, and can provide deoxidizer composition design guidance according to production needs.

[0043] 2. The composite deoxidizer guided by this invention can form small inclusions during smelting processes such as AOD, VOD, and LF, which can reduce the oxygen content of stainless steel to below 10ppm, purify the molten steel, reduce impurities in the steel, significantly reduce steelmaking costs, and maintain a high saturation magnetic induction intensity with low coercivity and remanent magnetic induction intensity. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the 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.

[0045] Figure 1 This is a flowchart of a composite deoxidizer design method for producing high-quality soft magnetic stainless steel according to Embodiment 1 of the present invention.

[0046] Figure 2 The graph shows the thermodynamic calculation results of Al, Si, and Mg unary deoxidation in Example 3 of this invention.

[0047] Figure 3 This is a graph showing the thermodynamic calculation results of Al-Si binary composite deoxidation guided by Example 3 of the present invention;

[0048] Figure 4 The figure shows the thermodynamic calculation results of Mg-Al binary composite deoxidation guided by Example 4 of the present invention. Detailed Implementation

[0049] The present invention provides a composite deoxidizer for producing high-quality soft magnetic stainless steel and its design method. It uses the activity interaction coefficient, which is readily available and closely related to other thermodynamic properties, as the basic data. The thermodynamic parameters are processed by transformation relationship, and the thermodynamic calculation model is evaluated in combination with the characteristics of soft magnetic stainless steel composition. Using thermodynamic calculation method, a composite deoxidizer with a high deoxidation limit is successfully designed. Moreover, the composition design method is simple to operate, accurate in positioning, and highly targeted, providing a brand-new idea for the composition design of composite deoxidizers.

[0050] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0051] Example 1:

[0052] This embodiment provides a method for designing composite deoxidizers for producing high-quality soft magnetic stainless steel.

[0053] Figure 1 This is a flowchart of a composite deoxidizer design method for producing high-quality soft magnetic stainless steel according to Embodiment 1 of the present invention.

[0054] like Figure 1 As shown, this embodiment 1 provides a method for designing a composite deoxidizer for producing high-quality soft magnetic stainless steel, including the following steps:

[0055] S1: Data processing was performed on the compositional characteristics of the 430 soft magnetic stainless steel system to obtain the processed parameters.

[0056] Specifically, S1 includes the following four aspects:

[0057] S1-1: First-order interaction parameter processing: The activity interaction coefficient under Henry's standard state and the activity coefficient under 1 wt.% standard state are converted to each other using the following formula:

[0058]

[0059] in, It is the activity interaction coefficient under Henry's standard state. It is the activity coefficient at 1 wt.% under standard conditions, M j M is the molar mass of deoxygenated element j. Fe denoted as , where is the molar mass of iron.

[0060] S1-2, Second-order interaction parameter processing: The missing data were calculated from the elemental molar mass and first-order parameters to obtain the second-order interaction parameters:

[0061]

[0062] in, These are the second-order activity interaction coefficients under the Henry's standard state. It is the second activity coefficient under standard conditions at 1 wt.%.

[0063] S1-3, Second-order cross-interaction parameter processing: Missing data is calculated using the first-order interaction parameters and the second-order interaction parameters to obtain the second-order cross-interaction parameters:

[0064]

[0065] in, These are the second-order cross-interaction parameters;

[0066] S1-4: Equilibrium Constant Treatment: Using the Van't Hoff isotherm equation, the equilibrium constant is calculated based on the Gibbs free energy change under standard conditions.

[0067] ΔG Θ =-RTlnK Θ (1-4)

[0068] Wherein, ΔG Θ The value represents the Gibbs free energy change under standard conditions, where R is the ideal gas constant, T is the temperature, and K is the kJ / L. Θ This is the equilibrium constant.

[0069] S2: Based on the processed parameters, the thermodynamic model is processed and optimized to obtain the optimized model.

[0070] Specifically, S2 includes the following sub-steps:

[0071] S2-1, based on the processed parameters, calculates the binary enthalpy of mixture for different elements:

[0072]

[0073] Where H is the binary enthalpy of mixture, n Fe n i The amounts of substance of component Fe and deoxygenating element i, H Fe H i Here, T represents the enthalpy of component Fe and deoxygenating element i, and k is the temperature. B is the Boltzmann constant.

[0074] S2-2, Based on the relative magnitudes of the binary enthalpy of mixture, assess the strength of elemental interactions.

[0075] If the elements are strongly interacting, such as Al, Mg, and Ca, they are treated as short-range ordered structures.

[0076] If the elements are weakly interacting, such as Fe, Si, and Cr, then they should be treated as random mixtures.

[0077] S2-3, by calculating the partial excess Gibbs energy, based on whether the Gibbs-Duhem equation is satisfied:

[0078] ∑ i (X i dlnγ i )=0 (1-6)

[0079] The relationship with Maxwell is used to determine whether thermodynamic consistency is achieved.

[0080]

[0081] If this condition is met, then thermodynamic consistency is achieved;

[0082] If these conditions are not met, then thermodynamic consistency is lacking, and the product can only be used at limited concentrations.

[0083] Among them, X i γ is the atomic percentage of component i. i G is the activity coefficient of component i. ex For the system's excess Gibbs energy.

[0084] S3: Based on the parameters processed above and the optimized model, the deoxidation equilibrium calculation results are output using thermodynamic calculation methods to guide the design of deoxidizer components.

[0085] Specifically, the thermodynamic calculation process includes the following sub-steps:

[0086] S3-1, Based on the processed parameters and the optimized model, the element consumption is determined using the equilibrium constant method and the mass conservation equation;

[0087] S3-2, Determine the deoxidation limit and type of deoxidizer according to actual production needs.

[0088] S3-3, Based on the element consumption, the deoxygenation limit, and the type of deoxygenating agent, a deoxygenation balance diagram is automatically output by computer.

[0089] Example 2:

[0090] This embodiment 2 provides an application of the above-mentioned design method for composite deoxidizers in stainless steel refining processes, applying the above-mentioned design method for composite deoxidizers in stainless steel refining processes to Al-Si composite deoxidizers and Mg-Al composite deoxidizers.

[0091] The following are specific examples of binary composite deoxidizers designed using the above design method, taking Al-Si composite deoxidizer (Example 3) and Mg-Al composite deoxidizer (Example 4) as examples.

[0092] Figure 2 The graph shows the results of the thermodynamic calculations for the Al, Si, and Mg unary deoxidation in Example 3 of this invention. The thermodynamic equilibrium of Al, Si, and Mg unary deoxidation in the 430 stainless steel system at 1873 K was calculated, and the deoxidation limits from high to low were compared and analyzed to be Mg, Al, and Si.

[0093] Example 3:

[0094] Figure 3 The figure shows the thermodynamic calculation results of Al-Si binary composite deoxidation guided by Example 3 of the present invention.

[0095] like Figure 3 As shown, Example 3 provides a design method and application of a composite deoxidizer for stainless steel refining, applied to the deoxidation and refining process of 430 soft magnetic stainless steel. The steel liquid composition is Cr: 17.5%, Ti: 0.3%, Mn: 0.2%, C: 0.008%, with Fe as the balance. The steps include:

[0096] like Figure 2 As shown, the thermodynamic equilibrium of Al, Si, and Mg unary deoxidation in the 430 stainless steel system at 1873 K was calculated. A comparative analysis showed that the deoxidation limits, from highest to lowest, were Mg, Al, and Si. The selected deoxidizer combination was Al-Si.

[0097] S1. Based on the compositional characteristics of the 430 soft magnetic stainless steel system,

[0098] Optimization of interaction parameters and equilibrium constants among oxygen, silicon, and aluminum at 1873 K in the Fe-Cr-Ti-Al-Mn-Si-Cu-Mo-CPNSO system.

[0099] Specifically, the steps are basically the same as in Example 1, with the difference being:

[0100] In the formula, i corresponds to element O, and j corresponds to elements Al and Si respectively, that is:

[0101] S1-1: Formula (1-1) becomes:

[0102]

[0103]

[0104] The calculation result is:

[0105] The first-order interaction parameter of aluminum-oxygen is -1.170.

[0106] The first-order interaction parameter of silicon and oxygen is -0.066.

[0107] S1-2: Formula (1-2) becomes:

[0108]

[0109]

[0110] The calculation result is:

[0111] The second-order interaction parameter of aluminum-oxygen is -0.001.

[0112] The second-order interaction parameter between oxygen and aluminum is -92.32.

[0113] S1-3: Formula (1-3) becomes:

[0114]

[0115]

[0116] The calculation result is:

[0117] The second-order cross-interaction parameter of aluminum-oxygen is -47.45.

[0118] The second-order cross-interaction parameter of oxygen and aluminum is -0.094.

[0119] In S1-4, the Gibbs free energy changes of Si and Al under standard states are substituted into formula (1-4) to obtain the corresponding equilibrium constants:

[0120] The equilibrium constant expression for aluminum is:

[0121] The equilibrium constant of silicon is expressed as follows:

[0122] If component i corresponds to components A1 and Si, then:

[0123] S2-1: Formula (1-5) becomes:

[0124]

[0125]

[0126] The calculation result is:

[0127] The enthalpy of mixing between iron and aluminum is: H Fe-Al = -21kJ / mol,

[0128] The enthalpy of mixing between iron and silicon is: H Fe-Si = -42kJ / mol.

[0129] S2-2: Evaluate the strength of elemental interactions based on the relative magnitudes of the binary enthalpy of mixture.

[0130] In this embodiment, H Fe-Al >H Fe-Si ,but:

[0131] Iron and aluminum are strongly interacting elements and are treated according to a short-range ordered structure model.

[0132] Iron and silicon are weakly interacting elements and are treated according to a random mixing model.

[0133] S2-3: In formula (1-6), component i is substituted into Fe, Al, and Si respectively for judgment.

[0134] In formula (1-7), the Maxwell relationship of components Fe and Al and the Maxwell relationship of components Fe and Si are determined respectively, and the results show that they are thermodynamically consistent.

[0135] S3: Based on the processed parameters and optimized model, the deoxidation equilibrium calculation results are output using thermodynamic calculation methods to guide the design of deoxidizer components.

[0136] Specifically, S3 includes the following sub-steps:

[0137] S3-1: Based on initial data such as the composition (430) and temperature (1873K) of the molten steel, and the processed and selected model, the equilibrium constants of aluminum and silicon and the mass conservation equations of oxygen are substituted: Determine element consumption;

[0138] S3-2: Based on actual production needs, the deoxidation limit is set at 10 ppm. By simultaneously solving the Al and Si deoxidation equilibrium equations, the expressions for Al and Si binary composite deoxidation are obtained as follows:

[0139]

[0140]

[0141] S3-3: Based on element consumption, deoxygenation limit, and deoxygenation expression, the computer automatically outputs a deoxygenation equilibrium diagram.

[0142] like Figure 3 The diagram shows the dominant region of Al-Si composite deoxidation products. Selecting deoxidizer components in the shaded areas of the dominant region diagram can achieve a deep deoxidation effect of 10 ppm.

[0143] In stainless steel, silicon readily combines with elements such as carbon and nitrogen to form intermetallic compounds, thereby reducing magnetic aging caused by the desolvation of carbon and nitrogen in the ferrite matrix and improving magnetic properties. Furthermore, silicon can coarsen grains and reduce crystal anisotropy, thus decreasing coercivity and magnetoresistance, making magnetization easier.

[0144] Increasing the aluminum content allows for coarser grains, reducing the resistance of grain boundaries to domain wall displacement and facilitating magnetization. Furthermore, aluminum reduces the magnetocrystalline anisotropy of ferritic stainless steel. The saturation magnetostriction coefficient decreases with increasing aluminum content, which is beneficial for improving magnetization and reducing coercivity and remanence.

[0145] Table 1 shows the effect of Al and Si content on the magnetic properties of stainless steel, indicating that stainless steel has excellent magnetic properties.

[0146] Table 1. Effects of Si and Al content on the magnetic properties of stainless steel

[0147]

[0148] Example 4:

[0149] Figure 4 The figure shows the thermodynamic calculation results of Mg-Al binary composite deoxidation guided by Example 4 of the present invention.

[0150] like Figure 4 As shown, Example 4 provides a design method for a composite deoxidizer in the stainless steel refining process and its application. This method is applied to the deoxidation and refining process of 430 soft magnetic stainless steel. The molten steel composition is Cr 17.5%, Ti 0.3%, Mn 0.2%, C 0.008%, with Fe as the balance. The selected deoxidizer combination is Mg-Al. The process includes the following steps:

[0151] S1. Based on the compositional characteristics of the 430 soft magnetic stainless steel system,

[0152] The data optimization of the interaction parameters and equilibrium constants between oxygen, magnesium and aluminum in the Fe-Cr-Ti-Al-Mn-Si-Cu-Mo-CPNSO system at 1873 K yielded the optimized elemental properties.

[0153] Specifically, the steps are basically the same as in Example 1, with the difference being:

[0154] In the formula, i corresponds to element O, and j corresponds to elements Al and Mg respectively, that is:

[0155] S1-1: Formula (1-1) becomes:

[0156]

[0157]

[0158] The calculation result is:

[0159] The first-order interaction parameter of aluminum-oxygen is -1.170.

[0160] The first-order interaction parameter of magnesium and oxygen is -370.

[0161] S1-2: Formula (1-2) becomes:

[0162]

[0163]

[0164] The calculation result is:

[0165] The second-order interaction parameter between magnesium and oxygen is 5900.

[0166] The second-order interaction parameter between oxygen and magnesium is 145000.

[0167] S1-3: Formula (1-3) becomes:

[0168]

[0169]

[0170] The calculation result is:

[0171] The second-order cross-interaction parameter of magnesium and oxygen is -191400.

[0172] The second-order cross-interaction parameter of oxygen and magnesium is 17940.

[0173] In S1-4, the Gibbs free energy changes of Mg and Al under standard states are substituted into formula (1-4) to obtain the corresponding equilibrium constants:

[0174] The equilibrium constant expression for aluminum is:

[0175] The equilibrium constant expression for magnesium is:

[0176] S2-1: If component i corresponds to components A1 and Mg, then formula (1-5) becomes:

[0177]

[0178]

[0179] The calculation result is:

[0180] The enthalpy of mixing between iron and aluminum is: H Fe-Al = -21kJ / mol,

[0181] The enthalpy of mixing between iron and magnesium is positive, resulting in an immiscible phase.

[0182] S2-2: Evaluate the strength of elemental interactions based on the relative magnitudes of the binary enthalpy of mixture.

[0183] In this embodiment, H Fe-Mg >H Fe-Al ,but:

[0184] Iron and magnesium are strongly interacting elements and are treated according to a short-range ordered structure model.

[0185] Iron and aluminum are weakly interacting elements and are treated according to a random mixture model.

[0186] S2-3: In formula (1-6), component i is substituted into Fe, Al, and Mg respectively for judgment.

[0187] In formulas (1-7), the Maxwell relationships of components Fe and Al, and the Maxwell relationships of components Fe and Mg are determined respectively.

[0188] S3: Based on the processed parameters and optimized model, the deoxidation equilibrium calculation results are output using thermodynamic calculation methods to guide the design of deoxidizer components.

[0189] Specifically, S3 includes the following sub-steps:

[0190] S3-1: Based on initial data such as the composition (430°C) and temperature (1873K) of the molten steel, and the processed and selected model, the equilibrium constants of aluminum and magnesium and the mass conservation equation of oxygen are substituted into the model. Determine element consumption;

[0191] S3-2: Based on actual production needs, the deoxidation limit is set at 10 ppm. By simultaneously solving the Al and Mg deoxidation equilibrium equations, the expressions for Al and Mg binary composite deoxidation are obtained as follows:

[0192]

[0193]

[0194] S3-3: Based on element consumption, deoxygenation limit, and deoxygenation expression, the computer automatically outputs a deoxygenation equilibrium diagram.

[0195] like Figure 4 The diagram shows the dominant region of Mg-Al composite deoxidation products. Selecting deoxidizer components in the shaded areas of the dominant region diagram can achieve a deep deoxidation effect of 10 ppm.

[0196] Comparative Example 1

[0197] A composite deoxidizer design method for producing high-quality soft magnetic stainless steel without data processing is presented. Unless otherwise specified, the steps are essentially the same as in Example 3, except that the selected model, as shown in Table 2, does not consider the influence of the soft magnetic composition on the thermodynamic activity calculation when performing Al-Si binary composite deoxidation calculations. S1 is not considered, which is unreasonable because the soft magnetic system has a high chromium content, up to 30%, which has a significant impact on the activity calculations of oxygen and deoxidizing elements. Although second-order and cross-interaction parameters are considered, the calculation results still deviate significantly from actual production. Therefore, a design that does not consider the influence of the compositional characteristics of soft magnetic stainless steel is unreasonable.

[0198] Table 2 compares the design of composite deoxidizers for soft magnetic stainless steel using different models.

[0199]

[0200]

[0201] Comparative Example 2

[0202] A composite deoxidizer design method for producing high-quality soft magnetic stainless steel without model optimization is presented. Unless otherwise specified, the steps are essentially the same as in Example 4, except that the influence of interaction parameters on thermodynamic calculations is not considered when performing Mg-Al binary composite deoxidation calculations. The calculations are performed using Wagnerian first-order interaction parameter expressions, which is unreasonable because the activity coefficients between strong deoxidizing elements and oxygen are typically very small to characterize their interaction. Furthermore, since the Wagner model can only calculate the activity of molten steel components within a specific temperature and composition range, a design that does not consider the applicability of the deoxidation thermodynamic model is inappropriate.

[0203] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for designing a composite deoxidizer for producing high-quality soft magnetic stainless steel, characterized by the following steps: include: S1, Data processing is performed on the compositional characteristics of the soft magnetic stainless steel system to obtain the processed parameters; S2, Based on the processed parameters, the thermodynamic model is processed and optimized to obtain the optimized model; S3. Based on the processed parameters and the optimized model, the deoxidation equilibrium calculation results are output using thermodynamic calculation methods to guide the design of deoxidizer components. S1 includes the following sub-steps: S1-1, First-order interaction parameter processing: The activity interaction coefficients under Henry's standard state and the activity coefficients under 1 wt.% standard state are converted to each other to obtain the first-order interaction parameters: （1-1） in, It is the activity interaction coefficient under Henry's standard state. It is the activity coefficient under standard conditions at 1 wt.%. Let j be the molar mass of the deoxygenating element. is the molar mass of element iron; S1-2, Second-order interaction parameter processing: The missing data were calculated from the elemental molar mass and first-order parameters to obtain the second-order interaction parameters: （1-2） in, The second-order activity interaction coefficient under the Henry's standard state. It is the second activity coefficient under standard conditions at 1 wt.%. S1-3, Second-order cross-interaction parameter processing: Missing data is calculated using the first-order interaction parameters and the second-order interaction parameters to obtain the second-order cross-interaction parameters: （1-3） in, These are the second-order cross-interaction parameters; S1-4, Equilibrium Constant Treatment: Using the Van't Hoff isotherm equation, the equilibrium constant expression is calculated based on the Gibbs free energy change under standard conditions. （1-4） in, The figure represents the Gibbs free energy change under standard conditions, where R is the ideal gas constant and T is the temperature. It is the equilibrium constant; S2 includes the following sub-steps: S2-1, Based on the processed parameters, the binary enthalpy of mixing of different elements is calculated: （1-5） in, It is a binary enthalpy of mixture. The amount of substance of component Fe and deoxygenating element i. Here, T represents the enthalpy values ​​of component Fe and deoxygenating element i, and T is the temperature. Boltzmann's constant; S2-2, Based on the relative magnitudes of the binary enthalpy of mixture, assess the strength of elemental interactions. If the elements are strongly interacting, such as Al, Mg, and Ca, they are treated as short-range ordered structures. If the elements are weakly interacting, such as Mn, Si, and Cr, then they should be treated as random mixtures. S2-3, by calculating the partial excess Gibbs energy, based on whether the Gibbs-Duhem equation is satisfied: （1-6） The relationship with Maxwell is used to determine whether thermodynamic consistency is achieved. （1-7） If this condition is met, then thermodynamic consistency is achieved; If these conditions are not met, then thermodynamic consistency is lacking, and the product can only be used at limited concentrations. in, The atomic percentage of component i. Let be the activity coefficient of component i. For the system's excess Gibbs energy.

2. The composite deoxidizer design method for producing high-quality soft magnetic stainless steel according to claim 1, characterized in that, S3 includes the following sub-steps: S3-1, Based on the processed parameters and the optimized model, the element consumption is determined using the equilibrium constant method and the mass conservation equation; S3-2, Determine the deoxidation limit and type of deoxidizer according to actual production needs; S3-3, Based on the element consumption, the deoxygenation limit, and the type of deoxygenating agent, a deoxygenation balance diagram is automatically output by computer.

3. An application of the design method according to claim 1, characterized in that, The design method of composite deoxidizers for the high-quality soft magnetic stainless steel refining process is applied to Al-Si composite deoxidizers and Mg-Al composite deoxidizers.

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