Method for predicting viscosity of metallurgical slag by using molecular dynamics simulation

Abstract

The invention discloses a method for realizing metallurgical slag viscosity prediction by using molecular dynamics simulation, and belongs to the technical field of metallurgical slag viscosity prediction. The method comprises the following steps: carrying out molecular dynamics simulation by adopting Lammps software according to specific components of a slag system, and obtaining the oxygen bond type and the oxygen bond molar content of the slag by using Matlab software; creatively adopting a radial basis function interpolation method to establish an oxygen bond molar content database of continuous component change in the slag system range; and inputting the oxygen bond molar content of the to-be-predicted slag obtained from the database into the deduced and modified slag viscosity prediction equation to obtain a predicted value of the viscosity of the to-be-measured slag. According to the method, interference of external environmental factors is avoided through a molecular dynamics simulation method, and meanwhile, the flow mechanism of a slag network structure is considered, so that the data precision and efficiency are improved, and the cost is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of metallurgical slag viscosity prediction, and in particular to a method for realizing metallurgical slag viscosity prediction by using molecular dynamics simulation. Background Art

[0002] In the modern steel production process, slag plays an important metallurgical function. From blast furnace and converter refining to continuous casting, slag and molten metal are always present, which has an important impact on metallurgical quality, smelting efficiency and smooth metallurgical operation. Viscosity, as an important physical property of slag, directly affects the performance of its metallurgical function. In the blast furnace smelting process, slag with too high viscosity may lead to poor fluidity, making it difficult to fully separate impurities, thereby affecting the purity and smelting efficiency of molten iron; conversely, slag with too low viscosity may not be able to effectively wrap impurities, resulting in impurities remaining in the molten iron. In the refining process, higher viscosity leads to a decrease in the foaming ability of slag, thereby limiting the contact area between slag and molten steel, reducing the reaction rate, and affecting the efficiency of desulfurization and dephosphorization; while lower viscosity is prone to splashing, while seriously corroding the furnace lining and reducing the life of the furnace lining. During the continuous casting process, too high viscosity will lead to poor slag fluidity, making it difficult for the liquid slag to flow into the gap, resulting in poor lubrication of the crystallizer and adhesion of the billet shell to the wall; while too low viscosity may cause the slag film to become thick and uneven, resulting in uneven heat conduction, stress concentration in the thinner parts of the billet shell, and surface cracks. Therefore, understanding and controlling slag viscosity is of great significance for optimizing steel production processes and improving product quality.

[0003] At present, experimental research and model calculation are mainly used to obtain viscosity data. The viscosity of slag in the experimental research process is mostly tested by high-temperature viscometer. At present, the viscosity prediction models for multi-component slag systems are mainly divided into two categories. One is to determine the activation energy E and parameter A based on the viscosity-temperature characteristic equation, such as: Seetharaman model, KTH model and Weymann model; the other is to directly associate viscosity and components based on empirical formulas and semi-empirical formulas, such as: silicon content model S2 model, Ubrain model, Ribound model and Iida model. These calculation models are optimized based on limited experimental data, and their scope of application is greatly limited. At the same time, the model parameters are only fitted by a large amount of experimental data, lacking practical physical meaning.

[0004] At the microscopic level, viscosity is a measure of the forces between structural groups within the melt, that is, slag viscosity is essentially determined by the microstructure of the slag. Metallurgical slag is a silicate melt, and SiO2 in the slag provides Si 4+ Cations form a network structure through bridging oxygen (BO), CaO, MgO and Na2O not only provide Ca 2+ Mg 2+ and Na+ The cations serve as compensation charges for the network structure and also introduce free O 2- Cut off the bridging oxygen bonds to form non-bridging oxygen (NBO) or free oxygen (FO), while Al2O3 in the melt plays the role of forming a network structure and cutting off the bridging oxygen. From the flow mechanism of silicate melts, it can be seen that the type of oxygen bonds in the melt determines the overall flow properties of the slag. The fluidity of NBO and FO in the melt is significantly stronger than that of BO. The increase in the number of NBO and FO will significantly reduce the viscosity of the slag. In recent years, molecular dynamics simulation calculations, as an effective calculation tool for thermodynamics, structure and transport properties, have developed rapidly in the analysis of melt structure. It can directly analyze the types and quantities of oxygen bonds in high-temperature metallurgical slag from a microscopic perspective without being affected by the external environment, which makes molecular dynamics simulation very attractive in metallurgical melt research.

[0005] Therefore, establishing a viscosity prediction model with high prediction accuracy based on molecular dynamics simulation and the nature of slag viscosity can effectively guide the production process and has important application value for the stable operation of industrial production. Summary of the invention

[0006] (I) Purpose of the invention

[0007] In view of the problems existing in the above-mentioned technology, the purpose of the present invention is to provide a method for predicting the viscosity of metallurgical slag using molecular dynamics simulation, which can quickly and accurately obtain the viscosity of metallurgical slag under different compositions and different temperature conditions, and significantly improve the accuracy and stability of metallurgical slag viscosity prediction.

[0008] (II) Technical solution

[0009] In order to achieve the above object, the present invention provides a method for predicting the viscosity of metallurgical slag using molecular dynamics simulation, comprising the following steps:

[0010] S1. First, according to the specific composition of the slag system, Lammps software is used to perform molecular dynamics simulation, and then Matlab software is used to obtain the oxygen bond type and oxygen bond molar content of the slag;

[0011] S2. Using interpolation method to establish a database of oxygen bond molar content with continuous composition changes within the scope of the slag system;

[0012] S3. Inputting the molar content of oxygen bonds of the slag to be predicted obtained from the database into the pre-trained slag viscosity prediction model to obtain a predicted value of the viscosity of the slag to be measured.

[0013] Furthermore, in step S1, the components of the metallurgical slag to be tested include SiO2, Al2O3, CaO and MgO.

[0014] Furthermore, in the step S1, the in file of the Lammps software running command executed also includes boundary conditions, equilibrium ensemble, temperature control method, pressure control method, cooling rate, summation method and relaxation time.

[0015] Furthermore, the boundary conditions adopt periodic boundary conditions, the ensemble is an NVT canonical ensemble, the temperature control method is a Nose-Hoover heat bath method, the pressure control method is a Parrinello-Rahman method, and the summation method is an Ewald summation method.

[0016] Furthermore, in step S1, the equilibrium structure data is read using statistical analysis software Matlab, the atomic information corresponding to each time step is recorded, and each type of atom is distinguished, and the distance between each oxygen atom, silicon atom, aluminum atom, calcium atom and magnesium atom in the slag is calculated.

[0017] Furthermore, when the distances between an oxygen atom and a silicon atom, an oxygen atom and an aluminum atom, an oxygen atom and a calcium atom, and an oxygen atom and a magnesium atom are less than the bond lengths of the silicon-oxygen bond, the aluminum-oxygen bond, the calcium-oxygen bond, and the magnesium-oxygen bond, it means that the two atoms are connected to each other, thereby obtaining molar contents of different oxygen bonds including Si-O-Si, Si-O-Ca, Si-O-Al, Si-O-Mg, Al-O-Ca, Al-O-Mg, and Al-O-Al.

[0018] Furthermore, in step S2, the radial basis function (RBF) interpolation method, which usually works well when processing multidimensional data and can provide a smooth interpolation result, is used to interpolate the molar contents of different oxygen bonds, thereby obtaining a database of molar contents of oxygen bonds with continuous composition changes within the slag system.

[0019] Furthermore, in step S3, the pre-trained slag viscosity prediction model includes:

[0020] η=A·exp(E η / RT) (0.1)

[0021] lnA=a+bE η (0.2)

[0022]

[0023] Among them, A is the pre-exponential factor, E η is the activation energy of metallurgical slag (J / mol), E v is the activation energy of pure SiO2 and E v=5.21×105 J / mol, R is the gas constant and R=8.314 J / (mol·K), T is the temperature K, a and b are model parameters, α is the parameter of bond strength and α i The value is between 1.8 and 4, N Si-O-Me and N Al-O-Me are the molar contents of different oxygen bonds such as Si-O-Ca, Si-O-Al, Si-O-Mg, Al-O-Ca, Al-O-Mg and Al-O-Al, respectively. O 2- is the molar content of free oxygen in the slag, which is assumed to be 0 in this model.

[0024] Furthermore, the model parameter a is based on the binary slag system composed of oxides and SiO2 in the slag. i Obtained,

[0025]

[0026] The model parameter b is based on the binary slag system b composed of oxides and SiO2 in the slag. i Obtained,

[0027]

[0028] Among them, x i Indicates the molar content of oxide i other than SiO2.

[0029] (III) Beneficial effects

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention performs molecular dynamics simulation on the melt structure of slag with different contents, determines the type and molar content of oxygen bonds, and then uses RBF function to interpolate the molar content of oxygen bonds to obtain an oxygen bond database with continuous composition changes within the simulation range. Based on the Arrhenius equation, according to the flow mechanism of the silicate network structure, combined with the type of oxygen bonds in the melt structure, a viscosity prediction equation dependent on the change of oxygen bonds is established, and finally the nonlinear equation solving function of MATLAB software is used to call the oxygen bond database to solve the viscosity prediction equation. Therefore, the present invention proposes for the first time a method for predicting the viscosity of metallurgical slag using molecular dynamics simulation, and realizes the prediction of slag viscosity under broader test conditions through the microstructure of the slag under finite molecular dynamics simulation, which can provide a reference for designing slag with reasonable smelting performance.

[0032] The present invention proposes a viscosity prediction method for metallurgical slag. According to the Arrhenius equation, based on the flow mechanism of the silicate network structure and combined with the type of oxygen bond, a viscosity prediction equation that depends on the change of oxygen bond is established. The obtained viscosity prediction equation can avoid the complex testing process of slag and directly calculate the specific value under specific conditions. It has been verified that the viscosity prediction method of the present invention can more accurately realize the viscosity prediction under different test conditions and has great applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention is described with the aid of the following drawings:

[0034] Figure 1 This is a flow chart of a method for predicting the viscosity of metallurgical slag using molecular dynamics simulation according to Example 1 of the present invention;

[0035] Figure 2 The figure is a relationship curve diagram between the predicted viscosity value and the measured viscosity value according to Example 1 of the present invention. DETAILED DESCRIPTION

[0036] In order to better understand the technical solution of the present invention, the content of the present invention includes but is not limited to the specific implementation methods described below, and similar technologies and methods should be considered to be within the scope of protection of the present invention. In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0037] It should be clear that the embodiments described in the present invention are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "an" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0039] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0040] Example 1

[0041] The present invention selected 16 groups of metallurgical slags published in foreign high-level SCI journals, and the specific components are shown in Table 1:

[0042] Table 1 Composition of experimental slag of CaO-SiO2-Al2O3-MgO system (mass fraction, %)

[0043]

[0044] The molar ratio is obtained based on the mass ratio of each component of the slag (CaO, SiO2, Al2O3 and MgO), and then the number of different atoms (Si, Al, Ca, Mg and O) is determined based on the total number of atoms of 8000. Then the density of the slag is obtained based on experimental tests and the side length of the box is calculated, as shown in Table 2.

[0045] Table 2 Atomic number, density and box side length of experimental slag of CaO-SiO2-Al2O3-MgO system

[0046]

[0047]

[0048] (1) Based on the above atomic types, numbers and slag density, a randomly distributed amorphous structure model is constructed. The initial structure data file and potential function of the slag to be tested are imported into the in file that executes the Lammps software running command. The initial structure of the slag to be tested is energy minimized in the Lammps software, and periodic boundary conditions are used to form a periodic image. The shape of the simulated cubic box remains unchanged, and the NVT ensemble is selected to maintain its stability. The Parrinello-Rahman and Nose-Hoover heat bath methods are used for pressure and temperature control. In terms of the algorithm, the cutoff radius of the short-range force is set to The cutoff diameter is smaller than the side length of any system box. The Ewald algorithm is used for long-range Coulomb force. The running time step is set to 1fs, and the data is saved every 10 steps through the frog leaping algorithm, for a total of 900,000 steps. In terms of temperature setting, the initial temperature is first set to 5000K, and 200,000 steps are run to fully mix the system to eliminate the initial distribution state of the atoms. Then, the temperature is reduced to 1773K through 200,000 steps, and finally, the temperature is continued to relax at 1773K for 400,000 steps, of which the first 20,000 steps are equilibrium relaxation. All data collection is completed in the last 200,000 steps, and finally the coordinate position file of 8,000 atoms in the system is output.

[0049] (2) Matlab software was used to read the equilibrium structure data, record the atomic information corresponding to each time step, distinguish each type of atom, and calculate the distance between each oxygen atom, silicon atom, aluminum atom, calcium atom, and magnesium atom, thereby obtaining the molar content of different oxygen bonds including Si-O-Si, Si-O-Ca, Si-O-Al, Si-O-Mg, Al-O-Ca, Al-O-Mg, and Al-O-Al, as shown in Table 3.

[0050] Table 3 Oxygen bond content of experimental slag of CaO-SiO2-Al2O3-MgO system

[0051]

[0052] (3) The radial basis function (RBF) method, which usually works well when processing multidimensional data and can provide a smooth interpolation result, is used to interpolate the molar contents of 16 groups of different oxygen bonds, thereby obtaining a database of the molar contents of each oxygen bond with continuous composition changes within the slag system.

[0053] (4) The oxygen bond type and oxygen bond mole fraction of the slag to be tested are obtained from the established oxygen bond mole database and input into the pre-trained slag viscosity prediction model shown in equations (11) to (15), where the model parameter a is a of the three binary slag systems of CaO-SiO2, MgO-SiO2 and Al2O3-SiO2. i Obtained, including:

[0054]

[0055] Among them, x i Indicates the molar content of oxide i other than SiO2.

[0056] η=A·exp(E η / RT) (0.8)

[0057] lnA=a+bE η (0.9)

[0058]

[0059] Among them, A is the pre-exponential factor, E η is the activation energy of metallurgical slag (J / mol), E v is the activation energy of pure SiO2 and E v =5.21×105 J / mol, R is the gas constant and R=8.314 J / (mol·K), T is the temperature K, a and b are model parameters, α is the parameter of bond strength and the α value is within 1.8~4, N Si-O-Me and N Al-O-Me are the molar contents of different oxygen bonds such as Si-O-Ca, Si-O-Al, Si-O-Mg, Al-O-Ca, Al-O-Mg and Al-O-Al, respectively. O 2- is the molar content of free oxygen in the slag, which is assumed to be 0 in this model.

[0060] (5) The viscosity values ​​of the above 16 groups of samples at three different temperatures of 1733K, 1773K and 1823K, totaling 47 groups, were calculated using the model of the present invention. Figure 2 The comparison between the viscosity values ​​calculated by the model and the experimental values ​​is shown, and the errors are within 15%. The Urbain model and the Nakamoto model widely used in the metallurgical industry are also used for comparison. The results show that the prediction of the model of the present invention is 3.5% higher than that of the Urbain model, and 2.5% higher than that of the Nakamoto model, which verifies the accuracy of the model of the present invention.

[0061] Based on the above analysis, the viscosity prediction method of the present invention is applicable to all metallurgical slags with SiO2, Al2O3, CaO and MgO as main components. The present invention fully considers the influence mechanism of the network structure of silicates on fluidity, combines the type of oxygen bonds in the melt structure, and establishes a viscosity prediction model that depends on the change of oxygen bonds.

[0062] The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the application concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not depart from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for predicting the viscosity of metallurgical slag using molecular dynamics simulation, characterized in that: The steps include: S1. First, according to the specific composition of the slag system, Lammps software is used to perform molecular dynamics simulation, and then Matlab software is used to obtain the oxygen bond type and oxygen bond molar content of the slag; S2. Using interpolation method to establish a database of oxygen bond molar content with continuous composition changes within the scope of the slag system; S3. Inputting the molar content of oxygen bonds of the slag to be predicted obtained from the database into the pre-trained slag viscosity prediction model to obtain a predicted value of the viscosity of the slag to be measured.

2. The method according to claim 1, characterized in that In the step S1, the components of the metallurgical slag to be tested include SiO2, Al2O3, CaO and MgO.

3. The method according to claim 1, characterized in that In the step S1, the in file of the Lammps software running command also includes boundary conditions, equilibrium ensemble, temperature control method, pressure control method, cooling rate, summation method and relaxation time.

4. The method according to claim 3, characterized in that The boundary conditions adopt periodic boundary conditions, the ensemble is the NVT canonical ensemble, the temperature control method is the Nose-Hoover heat bath method, the pressure control method is the Parrinello-Rahman method, and the summation method is the Ewald summation method.

5. The method according to claim 1, characterized in that In step S1, the equilibrium structure data is read using statistical analysis software Matlab, the atomic information corresponding to each time step is recorded, and each type of atom is distinguished, and the distance between each oxygen atom, silicon atom, aluminum atom, calcium atom and magnesium atom in the slag is calculated.

6. The method according to claim 5, characterized in that When the distances between an oxygen atom and a silicon atom, an oxygen atom and an aluminum atom, an oxygen atom and a calcium atom, and an oxygen atom and a magnesium atom are less than the bond lengths of the silicon-oxygen bond, the aluminum-oxygen bond, the calcium-oxygen bond, and the magnesium-oxygen bond, it means that the two atoms are connected to each other, thereby obtaining different molar contents of oxygen bonds including Si-O-Si, Si-O-Ca, Si-O-Al, Si-O-Mg, Al-O-Ca, Al-O-Mg, and Al-O-Al.

7. The method according to claim 1, characterized in that In step S2, The radial basis function (RBF) method, which usually works better when processing multidimensional data and can provide a smooth interpolation result, is used to interpolate the molar contents of different oxygen bonds, thereby obtaining a database of molar contents of oxygen bonds with continuous composition changes within the slag system.

8. The method according to claim 1, characterized in that In step S3, the pre-trained slag viscosity prediction model includes: η=A·exp(E η / RT) (0.1) lnA=a+bE η (0.2) Among them, A is the pre-exponential factor, E η is the activation energy of metallurgical slag (J / mol), E v is the activation energy of pure SiO2 and E v =5.21×105 J / mol, R is the gas constant and R=8.314 J / (mol·K), T is the temperature K, a and b are model parameters, α is the parameter of bond strength and α i The value is between 1.8 and 4, N Si-O-Me and N Al-O-Me are the molar contents of different oxygen bonds such as Si-O-Ca, Si-O-Al, Si-O-Mg, Al-O-Ca, Al-O-Mg and Al-O-Al, respectively. O 2- is the molar content of free oxygen in the slag, which is assumed to be 0 in this model.

9. The method according to claim 8, characterized in that The model parameter a is based on the binary slag system composed of oxides and SiO2 in the slag. i Obtained, The model parameter b is based on the binary slag system b composed of oxides and SiO2 in the slag. i Obtained, Among them, x i Indicates the molar content of oxide i other than SiO2.

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