A carbon immersion reduction lead-zinc reactor optimization method, system and medium

Abstract

The present invention discloses a method, system, and medium for optimizing a carbon-immersion lead-zinc reduction reactor. The method comprises: presetting basic dimensional parameters of an initial carbon-immersion lead-zinc reduction reactor, constructing a geometric model of the fluid computational domain of the carbon-immersion lead-zinc reduction reactor; determining reaction control equations of the geometric model; initializing operating parameters of the geometric model and meshing the geometric model; performing simulation calculations based on the meshed geometric model to obtain reaction parameters within the carbon-immersion lead-zinc reduction reactor, and obtaining gas-phase and slag two-phase flow field distributions, temperature field distributions, and lead-zinc depletion effects based on the reaction parameters; determining whether an optimization basis satisfies preset conditions: if so, terminating the simulation; if not, adjusting the structural parameters and / or operating parameters of the reactor based on the optimization basis until the preset conditions are met. This achieves efficient reduction and recovery of iron in the depleted slag.

Description

Technical Field

[0001] The present invention relates to the technical field of slag treatment, and in particular to an optimization method, system and medium for a carbon immersion reduction lead-zinc reactor. Background Art

[0002] With the intensified mining of high-grade lead and zinc resources, easy-to-smelt lead-zinc sulfide ores are becoming increasingly scarce. Currently, there are a large number of complex lead-zinc mixed ores. Due to their complex composition and complex lead-zinc metal embedding, it is difficult to separate the valuable metals and they have not been effectively utilized. Therefore, direct smelting of lead-zinc mixed ores without separation is a very effective way to solve the tight supply of lead and zinc industry resources in my country.

[0003] Existing lead-zinc reduction slags are primarily treated using a fuming process, resulting in zinc being produced as secondary zinc oxide, a low-value product that requires further processing to produce metallic zinc. Currently, the iron resources found in non-ferrous smelting residues, such as depleted slag, are difficult to recover and are not recycled, resulting in the limited utilization of high-iron, low-calcium, and silicon tailings at low dosages and low value. Summary of the Invention

[0004] In response to the deficiencies in the background technology, the present invention provides a method, system, and medium for optimizing a carbon-immersion reduction reactor for lead-zinc reduction. The method achieves efficient reduction of lead and zinc components and metallic recovery of lead and zinc through electrically assisted carbon-immersion reduction and zinc vapor condensation in a closed furnace; achieves efficient reduction and recovery of iron in depleted slag, and realizes the cascade recovery of lead, zinc, and iron in smelting slag.

[0005] In a first aspect, the present invention provides a method for optimizing a carbon immersion lead-zinc reduction reactor, comprising:

[0006] S1: Preset the basic size parameters of the initial carbon immersion lead-zinc reduction reactor and construct a geometric model of the fluid calculation domain of the carbon immersion lead-zinc reduction reactor; wherein the geometric model simulates the upper and lower parts of the carbon immersion lead-zinc reduction reactor, the lower part of the model simulates the flow field changes and physical and chemical processes of the slag phase, and the upper part of the model simulates the flow field changes of the gas phase;

[0007] S2: determining a reaction control equation of the geometric model, wherein the reaction control equation is used to simulate a lead-zinc reduction process by carbon immersion in a reactor;

[0008] S3: Initializing the operating parameters of the geometric model and performing meshing on the geometric model; wherein the operating parameters are corresponding boundary conditions and parameter initial values;

[0009] S3: Based on the meshed geometric model, simulation calculations are performed to obtain the reaction parameters inside the carbon immersion lead-zinc reduction reactor. Based on the reaction parameters, the gas and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect are obtained;

[0010] Among them, one or more of the gas phase and slag two-phase flow field distribution, temperature field distribution and lead-zinc depletion effect are selected as optimization basis;

[0011] S4: Determine whether the selected optimization basis meets the preset conditions: if so, output the basic size parameters of the lead-zinc reduction reactor; if not, adjust the structural parameters and / or operating parameters of the reactor based on the optimization basis and return to S3.

[0012] Furthermore, the reaction control equation of S2 is set as follows:

[0013] In order to describe the diffusion and convection process of substances in the reactor, the substance flux vector equation in the carbon immersion lead-zinc reduction reactor is set:

[0014]

[0015] Where i refers to the substance i in the reactor; N i is the material flux vector; D i is the diffusion coefficient of the substance, which indicates the diffusion ability of the substance in the cutoff, c i is the concentration of the substance, is the concentration of a substance c i The sum of the first-order partial differentials in three directions is used to describe the change in concentration; u is the velocity vector of the material fluid;

[0016] The mass transfer equation is used to describe the transfer of substances between different phases or regions, that is, the transport and reaction process of reactants in a reaction, including diffusion and convection processes. The concentrated species transfer model is selected to study chemical mass transfer to obtain the amount of change in the mass fraction of each substance over time. The concentrated species transfer model can calculate the mass fraction of all participating substances. The mass transfer equation is:

[0017]

[0018] Where ρ represents the fluid density (SI unit: kg / m 3 ); u represents the mass average velocity vector of the mixture (SI unit: m / s); ω i represents the quality score; j i Expresses the mass flux relative to the mass average velocity (SI unit: kg / m 2 ·s); R i Indicates the rate at which a substance is generated or consumed (SI unit: kg / m 3 s); is the Laplace operator.

[0019] The chemical reactions contained in the reactor include surface reactions and bulk reactions, and all the reactions involved are irreversible reactions. Among them, the surface reactions include the reactions of carbon on the surface of the carbon rod with lead oxide, zinc oxide and zinc ferrite. The chemical reaction formulas are as follows:

[0020] PbO(l)+C(ads)=Pb(g)+CO(g)

[0021] ZnO(l)+C(ads)=Zn(g)+CO(g)

[0022] ZnFe2O4(l)+2C(ads)=Zn(g)+2FeO(l)+2CO(g)

[0023] The bulk reaction includes the reaction of carbon particles with lead oxide, zinc oxide and zinc ferrite, and the chemical reaction formulas are shown below.

[0024] PbO(l)+C(s)=Pb(g)+CO(g)

[0025] ZnO(l)+C(s)=Zn(g)+CO(g)

[0026] ZnFe2O4(l)+2C(s)=Zn(g)+2FeO(l)+2CO(g)

[0027] The initial mass fractions of the substances in the melt are: PbO 3.23wt%, ZnO 8.07wt%, ZnFe2O4 11.71wt%, FeO 35wt%, CaO 12.33wt%, SiO2 24.66wt%, and other 5wt%. A layer of C particles is spread on the surface of the melt, and its mass percentage relative to the melt is 3.5wt%.

[0028] The lower part of the model uses the two-fluid Euler-Euler model as the control equation to simulate the bubbly flow, and then calculates the volume fraction, flow velocity, and streamline of each phase. The control equation is:

[0029]

[0030] Among them, φ l is the liquid volume fraction (SI unit: m 3 / m 3 );ρ l is the liquid density (SI unit: kg / m 3 ); is the Laplace operator (total differential in all directions of space); u l is the liquid phase velocity vector (SI unit: m / s); p is the pressure (SI unit: Pa); μ l is the liquid phase dynamic viscosity (SI unit: Pa·s); μ Tis the turbulent viscosity (SI unit: Pa·s); g is the gravity vector (SI unit: m / s 2 ); F is the body force (SI unit: N / m 3 ); I represents unit tension; superscript T represents matrix transpose; subscript "l" represents the liquid phase; subscript "g" represents the gas phase;

[0031] The pressure distribution is calculated by the mixed average continuity equation, which is:

[0032]

[0033] Among them, φ g is the gas phase volume fraction (SI unit: m 3 / m 3 );ρ g is the gas phase density (SI unit: kg / m 3 );u g is the gas phase velocity vector (SI unit: m / s);

[0034] The volume fraction of bubbles is tracked by solving the gas phase transport equation for the effective gas density, which is as follows:

[0035]

[0036] Among them, -m gl is the mass transfer rate from gas phase to liquid phase (SI unit: kg / (m 3 ·s));

[0037] The calculation formula for gas velocity is:

[0038] u g =u l +u slip +u drift

[0039] Among them, u g is the gas velocity; u slip is the slip velocity, u drift is the displacement velocity (SI unit: m / s). The displacement velocity is defined as:

[0040]

[0041] Among them, D gc is the gas diffusion coefficient; μ T is the turbulent viscosity; σ Tis the turbulent Schmidt number. The excursion velocity helps analyze the flow characteristics of gas-liquid two-phase flows. Secondly, the gas phase excursion velocity has a significant impact on the mixing and mass transfer of reactants in a reactor. By controlling the excursion velocity, reaction conditions within the reactor can be improved, enhancing reaction efficiency and product quality. Finally, changes in the gas phase excursion velocity can lead to fluid instability, and measuring this excursion velocity allows for observation of its changes.

[0042] The upper part of the model uses the Navier-Stokes formula for compressible flow as the governing equation to calculate its flow field distribution:

[0043]

[0044] Where ρ is the fluid density (SI unit is kg / m 3 ); u is the fluid velocity vector (SI unit is m / s).

[0045] The momentum conservation equation is:

[0046]

[0047] Where p is the fluid pressure (SI unit is Pa); μ is the fluid dynamic viscosity (SI unit is Pa·s); I is the unit tensor; F is the volume force (SI unit is N / m 3 The purpose of the conservation of momentum equation is to describe the change in momentum of a fluid flow. Specifically, the rate of change of an object's momentum over time is equal to the net external force acting on the object. In fluid mechanics, the conservation of momentum equation is used to predict changes in the velocity field of a fluid.

[0048] The governing equations for the gas and slag phases include the continuity equation and the momentum equation, which describe the flow behavior of the fluid, namely the flow characteristics of the gas and slag phases (flow velocity, flow direction, and volume fraction). The continuity equation expresses the conservation of mass, while the momentum equation describes the relationship between the force and acceleration acting on the fluid. By solving these equations, the velocity field and pressure field of the fluid can be obtained, and then the streamlines can be obtained. The flow field distribution characteristics can be obtained by solving the continuity equation, momentum equation, and energy equation simultaneously to obtain the flow field distribution. In specific implementation, you only need to input all the data, and the model can automatically export the calculation results.

[0049] Furthermore, the k-ε model is used in the geometric model to describe the turbulence effect, and the turbulent viscosity μ T It can be expressed as:

[0050]

[0051] Where ρ is the fluid density, k is the turbulent kinetic energy, and ε is the turbulent energy dissipation rate. k and ε satisfy:

[0052]

[0053] Where, the derivative term P k satisfy:

[0054]

[0055] Among them, C μ , σ k , σ ε 、C ε1 、C ε2 , and C ε are all model parameters, S k The fluid turbulence caused by bubbles is represented by the following formula:

[0056]

[0057] Where C k is the model constant; u slip is the slip speed.

[0058] Furthermore, the heat transfer equation describes the heat transfer process in the reactor and is the thermal balance equation of the temperature field. The thermal balance equation of the temperature field distribution is:

[0059]

[0060] Where ρ is the density (SI unit: kg / m 3 );C p is the specific heat capacity at constant pressure (SI unit: J / (kg·K)); T is the absolute temperature (SI unit: K); u is the velocity vector (SI unit: m / s); q is the conduction heat flux (SI unit: W / m 2 ); Q is the heat source (SI unit: W / m 3 );Q P is the pressure work (SI unit: W / m 3 );Q vd The heat generated by fluid viscosity dissipation (SI unit: W / m 3 ), Τ is the temperature gradient, and a is the proportionality coefficient, which is the thermal conductivity (SI unit: W / (m·k)).

[0061] Furthermore, the relationship between the reaction parameters and the two-phase flow field distribution, temperature field distribution and lead-zinc depletion effect of the gas phase and slag phase is as follows: the gas turbulence in the melt in the reactor gradually intensifies, and the temperature and gas distribution gradually become uniform. The enhancement of gas turbulence is conducive to the transfer of heat and mass, thereby improving the uniformity of the flow field and temperature field; the gas turbulence between the electrodes is more intense, the slag stratification effect is weakened, the reduction reaction is more intense, and the lead-zinc depletion effect is enhanced, which shows that the flow and reaction conditions inside the reactor can be optimized by adjusting the electrode parameters; the density and viscosity of the melt in the electric furnace are related to the temperature and its components, and the disturbance caused by the density gradient caused by the temperature difference is one of the main driving forces of the fluid flow; among them, the reaction parameters include the structural parameters of the reactor (such as the gas outlet position, the electrode arrangement) and the operating parameters (such as the temperature and the amount of material).

[0062] Furthermore, when adjusting the reactor using the gas and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect as optimization basis, the optimization strategy is as follows: if the lead-zinc depletion effect is to be improved, the heat-conducting carbon rods are reduced, the materials are reduced, the gas outlet position in the reactor is moved upward, and the temperature is increased; wherein, the reaction parameters include the structural parameters and operating parameters of the reactor.

[0063] Furthermore, the boundary conditions and parameter initial values ​​include: flow field boundary conditions and initial values, reactor concentration field boundary conditions and initial values, and heat transfer field boundary conditions and initial values;

[0064] The initial values ​​corresponding to the flow field include: pressure and gravity conditions of the calculation domain in the carbon immersion lead-zinc reduction reactor;

[0065] The initial values ​​corresponding to the concentration field in the reactor include: the initial overall regional substance concentration setting of the carbon immersion lead-zinc reduction reactor, the diffusion coefficient setting of each substance, and the custom function setting of the chemical reaction rate of zinc oxide and zinc ferrite;

[0066] The initial values ​​corresponding to the heat transfer field include: the shape and size of the heat-conducting object, the physical properties of the heat-conducting object (including density, specific volume, thermal conductivity, etc.), and the temperature distribution within the heat-conducting object at the initial moment;

[0067] The boundary conditions of the flow field in the model are as follows: the surface of the carbon rod in the mixed melt at the bottom of the model is set as the gas inlet surface, the gas generation rate is coupled with the chemical reaction rate, the boundary is non-slip, the two-phase interface is set as the gas outlet surface, and it is also the gas inlet at the top of the model, slipping at the liquid phase boundary. There are two symmetrical gas outlets on the upper wall, which are set as pressure outlets. The other wall surfaces are non-slip surfaces and have no gas flux:

[0068] u l n = 0

[0069]

[0070] u l =0

[0071] Among them, u l is the fluid velocity vector; n is the boundary normal vector; K n is the sum of viscosity and elasticity; ρ l is the fluid density; u τ is the friction speed; y + is the ratio of the fluid velocity vector to the friction velocity; is the gas mass flux (SI unit is kg / (m 2 ·s). When u l = 0, indicating that there is no flux at the interface other than the interface between the gas phase and the slag phase, that is, the fluid is completely stationary at the boundary; K n It is used to describe the dynamic interaction between gas molecules and the wall surface, that is, the upper wall surface has two symmetrical gas outlets, and the other walls are non-slip surfaces with no gas flux.

[0072] The concentration field boundary conditions within the reactor were such that no gas flux boundary conditions were applied on any surface. Only one-third of the furnace height of material was added before the reaction began. The initial mass fractions of the melt in the reactor were 3.23 wt% PbO, 8.07 wt% ZnO, 11.71 wt% ZnFe2O4, 35 wt% FeO, 12.33 wt% CaO, 24.66 wt% SiO2, and 5 wt% other. A layer of C particles was applied to the melt surface, representing 3.5 wt% of the melt.

[0073] The heat transfer boundary conditions are set to an initial melt temperature of 1100°C, a carbon rod temperature of 1250°C, and other walls as thermal insulation surfaces:

[0074] -n·q=0

[0075] Where n is the direction of the boundary normal (the direction in which heat flows perpendicular to the boundary); q is the heat transfer flux.

[0076] Furthermore, the optimization in S4 is based on the following preset conditions:

[0077] Different colors in the simulation diagrams of the gas and slag two-phase flow field distribution and temperature field distribution represent different values. When the values ​​are within the preset fluctuation range, the color distribution is uniform, indicating that the preset conditions are met.

[0078] The lead-zinc depletion effect is achieved by setting the lead-zinc mass fraction to be less than or equal to a preset value, indicating that the preset condition is met. In specific implementation, the preset value of the lead-zinc mass fraction can be adjusted according to specific circumstances, and is set to 3% in this application.

[0079] In a second aspect, the present invention provides an optimization system for an immersion reduction lead-zinc reactor, comprising:

[0080] Geometric model construction model: used to construct a geometric model of the fluid calculation domain of the carbon immersion reduction lead-zinc reactor based on the basic size parameters of the preset initial carbon immersion reduction lead-zinc reactor; wherein the geometric model simulates the upper and lower parts of the carbon immersion reduction lead-zinc reactor, the lower part of the model simulates the flow field changes and physical and chemical processes of the slag phase, and the upper part of the model simulates the flow field changes of the gas phase;

[0081] Reaction control equation determination module: used to determine the reaction control equation of the geometric model, the reaction control equation is used to simulate the carbon immersion reduction process of lead and zinc in the reactor;

[0082] Parameter setting and simulation module: used to initialize the operating parameters of the geometric model and mesh the geometric model; wherein the operating parameters are the corresponding boundary conditions and parameter initial values; using the meshed geometric model to simulate and calculate the reaction parameters inside the carbon immersion lead-zinc reduction reactor, and based on the reaction parameters, the gas phase and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect are obtained;

[0083] Among them, at least the flow field distribution of gas phase and slag phase, temperature field distribution and lead and zinc depletion effect are used as optimization basis;

[0084] Model optimization module: Determine whether the selected optimization basis meets the preset conditions: If so, output the basic size parameters of the lead-zinc reduction reactor; if not, adjust the structural parameters and / or operating parameters of the reactor based on the optimization basis and return to the parameter setting and simulation module.

[0085] The third invention provides a readable storage medium storing a computer program, which is used to execute the steps of the method described above when called by a processor.

[0086] Beneficial effects

[0087] This invention proposes an optimized method, system, and medium for a carbon-immersion lead-zinc reduction reactor. This method achieves efficient reduction of lead and zinc components and metallic recovery of lead and zinc through electrically assisted carbon-immersion reduction and zinc vapor condensation within a sealed furnace. This method effectively overcomes the difficulties encountered in traditional processes in recovering iron from non-ferrous smelting slag, such as depleted slag, which results in the limited utilization of high-iron, low-calcium, silicon tailings at low dosages and low value. Furthermore, it achieves efficient reduction and recovery of iron from depleted slag, enabling a tiered recovery of lead, zinc, and iron from smelting slag and enhancing its efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0089] Figure 1 A geometric diagram of a reactor under a preliminary structural design provided in an embodiment of the present invention;

[0090] Figure 2 A grid diagram of the reactor under the preliminary structural design provided in an embodiment of the present invention;

[0091] Figure 3 Flow field distribution diagrams of the gas phase and slag phase at different times provided by the embodiment of the present invention: (a) is a section at 1 hour; (b) is a section at 2 hours; (c) is a zx section at 1 hour; (d) is a zx section at 2 hours;

[0092] Figure 4 The mass percentage of C particles relative to slag and the mole fraction of CO in the gas phase at different times provided by the embodiment of the present invention are as follows: (a) 1h; (b) 2h;

[0093] Figure 5 The mass percentage of the Pb liquid phase to the slag and the mole fraction distribution of the Pb vapor in the gas phase at different times provided by the embodiment of the present invention: (a) is 1 hour; (b) is 2 hours;

[0094] Figure 6 Distribution diagram of the mass fraction of PbO in the slag phase and the molar fraction of Pb vapor in the gas phase at different times provided by the embodiment of the present invention: (a) is 1 hour; (b) is 2 hours;

[0095] Figure 7 Distribution diagram of the mass fraction of ZnO in the slag phase and the molar fraction of Zn vapor in the gas phase at different times provided by the embodiment of the present invention: (a) 1h; (b) 2h;

[0096] Figure 8 Distribution diagram of the mass fraction of ZnFe2O4 in the slag phase and the molar fraction of Zn vapor in the gas phase at different times provided by the embodiment of the present invention: (a) is 1h; (b) is 2h;

[0097] Figure 9 Distribution diagram of FeO mass fraction in slag phase and CO mole fraction in gas phase at different times provided by the embodiment of the present invention: (a) is 1h; (b) is 2h;

[0098] Figure 10Temperature distribution diagrams of gas phase and slag phase at different times provided by the embodiment of the present invention: (a) for 1 hour; (b) for 2 hours;

[0099] Figure 11 Flow field distribution diagram of two electrodes provided in an embodiment of the present invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0100] Figure 12 The mole fractions of CO and Pb in the gas phase of the two electrodes and the mass percentage distribution of carbon particles and lead liquid relative to slag provided by the embodiment of the present invention are as follows: (a) CO, C; (b) Pb, Pb;

[0101] Figure 13 Distribution diagram of the mole fractions of CO and Pb in the gas phase of two electrodes and the mass fractions of FeO and PbO in the slag phase provided by the embodiment of the present invention: (a) CO, FeO; (b) Pb, PbO;

[0102] Figure 14 The molar fraction of Zn in the gas phase of the two electrodes and the mass fraction distribution of ZnFe2O4 and ZnO in the slag phase provided by the embodiment of the present invention are as follows: (a) ZnFe2O4; (b) ZnO

[0103] Figure 15 Temperature distribution diagram of two electrodes provided by an embodiment of the present invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0104] Figure 16 Flow field distribution diagram after the liquid level is lowered provided by an embodiment of the present invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0105] Figure 17 The mole fractions of CO and Pb in the gas phase after the liquid level is lowered and the mass percentage distribution of carbon particles and lead liquid relative to slag provided in the embodiment of the present invention are as follows: (a) CO, C; (b) Pb, Pb;

[0106] Figure 18 The mole fractions of CO and Pb in the gas phase after the liquid level is lowered and the mass fractions of FeO and PbO in the slag phase are shown in the following figure: (a) CO, FeO; (b) Pb, PbO;

[0107] Figure 19 The molar fraction of Zn in the gas phase after the liquid level is lowered and the mass fraction distribution diagram of ZnO and ZnFe2O4 in the slag phase provided in the embodiment of the present invention: (a) ZnO; (b) ZnFe2O4;

[0108] Figure 20 Temperature distribution diagram after liquid level drop provided by an embodiment of the present invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0109] Figure 21 Flow field distribution diagram after the gas outlet is adjusted upward provided by an embodiment of the present invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0110] Figure 22 The mole fractions of CO and Pb in the gas phase of the upward-regulated gas outlet and the mass percentage distribution of carbon particles and lead liquid relative to slag provided in an embodiment of the present invention are as follows: (a) CO, C; (b) Pb, Pb;

[0111] Figure 23 The mole fractions of CO and Pb in the gas phase and the mass fractions of FeO and PbO in the slag phase after the gas outlet is increased according to the embodiment of the present invention are as follows: (a) CO, FeO; (b) Pb, PbO;

[0112] Figure 24 The molar fraction of Zn in the gas phase and the mass fraction distribution of ZnFe2O4 and ZnO in the slag phase after the gas outlet is increased according to the embodiment of the present invention are as follows: (a) ZnFe2O4; b ZnO;

[0113] Figure 25 Temperature distribution diagram after the gas outlet is adjusted upward according to an embodiment of the present invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0114] Figure 26 Flow field distribution diagram after heating provided by an embodiment of the present invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0115] Figure 27 The mole fractions of CO and Pb in the gas phase after heating and the mass percentage distribution of carbon particles and lead liquid relative to slag provided in the embodiment of the present invention are as follows: (a) CO, C; (b) Pb, Pb;

[0116] Figure 28 Distribution diagram of the mole fractions of CO and Pb in the gas phase after heating and the mass fractions of FeO and PbO in the slag phase provided by the embodiment of the present invention: (a) Pb, PbO; (b) CO, FeO;

[0117] Figure 29 The molar fraction of Zn in the gas phase after heating and the mass fraction distribution of ZnO and ZnFe2O4 in the slag phase provided by the embodiment of the present invention are as follows: (a) ZnO; (b) ZnFe2O4;

[0118] Figure 30 Temperature distribution diagram after heating provided by this embodiment of the invention: (a) cross-sectional view; (b) zx cross-sectional view;

[0119] Figure 31 It is a flow chart of a method for optimizing a carbon immersion reduction lead-zinc reactor provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0120] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0121] like Figure 31 As shown, the present invention provides a method for optimizing a carbon immersion reduction reactor for lead and zinc, and proposes a new idea for electrically enhanced depletion. By electrically assisted carbon immersion reduction of lead and zinc vapor condensation in a closed furnace, efficient reduction of lead and zinc components and metallic recovery of lead and zinc are achieved; plasma-enhanced reduction is used to create a supercritical reduction environment, thereby achieving efficient reduction and recovery of iron in depleted slag, and realizing cascade recovery of lead, zinc, and iron in smelting slag. The method mainly includes the following steps:

[0122] Step 1: Based on the basic dimensional parameters of the carbon-immersion lead-zinc reduction reactor under the preliminary structural design, a geometric model of the fluid calculation domain of the carbon-immersion lead-zinc reduction reactor is established. The main purpose is to determine the main structure of the reactor and the location of the gas outlet. The basic dimensions of the carbon-immersion lead-zinc reduction reactor and the locations of each inlet and outlet are parameterized to construct the geometric model.

[0123] Step 2: Determine the reaction control equations of the geometric model. In order to simulate the lead-zinc reduction process in the reactor, the model needs to couple physical fields such as the gas phase and slag phase two-phase flow field, the chemical reaction mass transfer field, and the heat transfer field.

[0124] Step 3: Define the boundary conditions and initial values ​​of the model under certain operating parameters, and correctly set the model parameters; and mesh the geometric model. For two-dimensional models, triangular meshing is used, and for three-dimensional models, tetrahedral meshing is used. The meshing can be controlled by physical field characteristics.

[0125] Step 4: Perform CFD finite element simulation to solve the distribution characteristics of the model and the concentration distribution structure of the reaction substances, and obtain the dissociation effect, gas-liquid two-phase velocity distribution, gas phase distribution and other results of the carbon immersion reduction lead-zinc reactor under the set operating parameters;

[0126] Step 5: Based on the calculation results, analyze the gas and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect under certain structural operating parameters in the carbon immersion lead-zinc reduction reactor to obtain further optimization basis;

[0127] Step 6. According to step 5, after performing the reactor characteristic analysis, the reactor structure and / or operating parameters are further adjusted according to the actual situation and design ideas, that is, by changing the basic dimensional parameters in step 1 and / or the boundary conditions and initial values ​​in step 3, specifically: the basic shape and size of the carbon immersion lead-zinc reduction reactor, and then performing simulations according to the methods of steps 4 and 5, comparing the calculated data obtained under different conditions, until the results of the carbon immersion lead-zinc reduction reactor structure and / or operating parameter design optimization are obtained to achieve the purpose of efficient reduction and recovery of iron in depleted slag and cascade recovery of lead, zinc and iron in smelting slag.

[0128] Example

[0129] 1) According to the basic size parameters of the carbon immersion lead-zinc reduction reactor under the preliminary structural design, the following Figure 1 The reactor geometry model is shown in . The reactor has an axisymmetric geometry and is scaled to reduce the amount of calculation, as shown in Figure 1 As shown, the circular openings on both sides are gas outlets. The reactor is generally cylindrical, with a reactor height of 4.6m, a reactor radius of 1.95m, a top sphere radius of 3.9m, an electrode height of 3.8333m, an electrode radius of 0.117m, a gas outlet height of 0.65m, a gas outlet radius of 0.22, and a phase interface height of 1.5333m.

[0130] 2) Determine the governing equations for the geometric model. To simulate the lead-zinc reduction process in the reactor, the geometric model requires coupling multiple physical fields, including at least the gas and slag two-phase flow fields, the chemical reaction mass transfer field, and the heat transfer field. Based on the nature of the chemical reaction, a concentrated species transport model is selected to study chemical mass transfer and derive the time-varying mass fraction of each species.

[0131] Material flux vector equation in the reactor

[0132]

[0133] Where i refers to the substance i in the reactor; N i is the material flux vector; D i is the diffusion coefficient of the substance, which indicates the diffusion ability of the substance in the cutoff, c i is the concentration of the substance, is the concentration of a substance c iThe sum of the first-order partial differentials in three directions describes the change in concentration; u is the velocity vector of the substance flow. The concentrated species transfer model can calculate the mass fractions of all participating species. The mass flux vector equation within the reactor describes the transport of substances in the fluid. It mathematically expresses the spatial distribution of the substance and the change in mass fraction over time, describing the rate and direction of the substance's transport in space. Analysis of the mass flux vector equation can optimize reactor design and improve reaction efficiency.

[0134] The mass transfer equation is as follows:

[0135]

[0136] Where ρ represents the fluid density (SI unit: kg / m 3 ); u represents the mass average velocity vector of the mixture (SI unit: m / s); ω i represents the quality score; j i Expresses the mass flux relative to the mass average velocity (SI unit: kg / m 2 ·s); R i Indicates the rate at which a substance is generated or consumed (SI unit: kg / m 3 s). The mass transfer equation is used to calculate the rate of material generation or consumption. The mass fraction of a substance, as determined by the mass flux vector equation, can be used to calculate the rate of material generation or consumption. Together, these equations form a mathematical model that describes material transport in fluids and are crucial for mass transfer in reactors.

[0137] In this model, the chemical reactions involved are divided into surface reactions and bulk reactions, and all the reactions involved are irreversible. Among them, the surface reactions include the reactions between the carbon on the surface of the carbon rod and lead oxide, zinc oxide, and zinc ferrite. The chemical reaction formulas are as follows:

[0138] PbO(l)+C(ads)=Pb(g)+CO(g)

[0139] ZnO(l)+C(ads)=Zn(g)+CO(g)

[0140] ZnFe2O4(l)+2C(ads)=Zn(g)+2FeO(l)+2CO(g)

[0141] The bulk reaction includes the reaction of carbon particles with lead oxide, zinc oxide and zinc ferrite. The chemical reaction formulas are as follows:

[0142] PbO(l)+C(s)=Pb(g)+CO(g)

[0143] ZnO(l)+C(s)=Zn(g)+CO(g)

[0144] ZnFe2O4(l)+2C(s)=Zn(g)+2FeO(l)+2CO(g)

[0145] The initial mass fractions of the substances in the melt are PbO3.23wt%, ZnO8.07wt%, ZnFe2O411.71wt%, FeO35wt%, CaO12.33wt%, SiO224.66wt%, and other 5wt%. A layer of C particles is spread on the surface of the melt, and its mass percentage relative to the melt is 3.5wt%.

[0146] According to the distribution of multi-physical fields in the carbon immersion reduction of lead and zinc process, the electric furnace is divided into two parts, the lower part of the model is used to simulate the flow field changes and physical and chemical processes of the slag phase, and the upper part is used to simulate the flow field changes of the gas phase.

[0147] The lower part of the model uses the two-fluid Euler-Euler model as the control equation to simulate the bubbly flow and calculate its physical quantities such as the volume fraction of each phase, flow velocity, and streamlines. The pressure distribution is calculated by the mixed average continuity equation, and the volume fraction of the bubble is tracked by solving the transport equation of the effective gas density. The lower part of the model uses the two-fluid Euler-Euler model as the control equation to simulate the bubbly flow and then calculate the volume fraction, flow velocity, and streamlines of each phase. The control equation is:

[0148]

[0149] Among them, φ l is the liquid volume fraction (SI unit: m 3 / m 3 );ρ l is the liquid density (SI unit: kg / m 3 ); is the Laplace operator (total differential in all directions of space); u l is the liquid phase velocity vector (SI unit: m / s); p is the pressure (SI unit: Pa); μ l is the liquid phase dynamic viscosity (SI unit: Pa·s); μ T is the turbulent viscosity (SI unit: Pa·s); g is the gravity vector (SI unit: m / s 2 ); F is the body force (SI unit: N / m 3 ); I represents unit tension; the superscript T denotes matrix transpose; the subscript "l" denotes the liquid phase; and the subscript "g" denotes the gas phase. The governing equations for the slag phase describe the flow characteristics of the slag phase and can be used to calculate the volume fraction, flow velocity, and streamlines. Changes in the volume fraction directly affect the density distribution of the fluid, which in turn affects the pressure distribution; the flow velocity distribution can be used to infer the trend of the pressure distribution; and the shape and distribution of streamlines can reflect changes in the pressure field.

[0150] The pressure distribution of the slag phase is calculated by the mixed average continuity equation, which is:

[0151]

[0152] Among them, φ g is the gas phase volume fraction (SI unit: m 3 / m 3 );ρ g is the gas phase density (SI unit: kg / m 3 );u g is the gas phase velocity vector (SI unit: m / s);

[0153] The volume fraction of bubbles is tracked by solving the gas phase transport equation for the effective gas density, which is as follows:

[0154]

[0155] Among them, -m gl is the mass transfer rate from gas phase to liquid phase (SI unit: kg / (m 3 ·s));

[0156] The calculation formula for gas velocity is:

[0157] u g =u l +u slip +u drift

[0158] Among them, u g is the gas velocity; u slip is the slip velocity, u drift is the displacement velocity (SI unit: m / s). The displacement velocity is defined as:

[0159]

[0160] Among them, D gc is the gas diffusion coefficient; μ T is the turbulent viscosity; σ T is the turbulent Schmidt number. Gas velocity describes the velocity characteristics of fluid flow and is closely related to the gas transport equation. Solving these equations simultaneously yields a comprehensive description of fluid flow, including gas velocity and pressure distribution.

[0161] The upper part of the model uses the Navier-Stokes formula for compressible flow as the governing equation to calculate its flow field distribution:

[0162]

[0163] Where ρ is the fluid density (SI unit is kg / m 3 ); u is the fluid velocity vector (SI unit is m / s).

[0164] The momentum conservation equation is:

[0165]

[0166] Where p is the fluid pressure (SI unit is Pa); μ is the fluid dynamic viscosity (SI unit is Pa·s); I is the unit tensor; F is the volume force (SI unit is N / m 3 ).

[0167] The k-ε model is used in the geometric model to describe the turbulence effect, and the turbulent viscosity μ T It can be expressed as:

[0168]

[0169] Where ρ is the fluid density, k is the turbulent kinetic energy, and ε is the turbulent energy dissipation rate. k and ε satisfy:

[0170]

[0171] Where, the derivative term P k satisfy:

[0172]

[0173] Among them, C μ , σ k , σ ε 、C ε1 、C ε2 , and C ε are all model parameters, S k The fluid turbulence caused by bubbles is represented by the following formula:

[0174]

[0175] Where C k is the model constant; u slip is the slip speed.

[0176] Theoretically, in gases, it occurs through molecular collisions; in fluids, each molecule occurs by oscillating around its nearest molecule. Based on the law of conservation of energy, the thermal equilibrium equation for the temperature field distribution is established:

[0177]

[0178] Where ρ is the density (SI unit: kg / m 3 );Cp is the specific heat capacity at constant pressure (SI unit: J / (kg·K)); T is the absolute temperature (SI unit: K); u is the velocity vector (SI unit: m / s); q is the conduction heat flux (SI unit: W / m 2 ); Q is the heat source (SI unit: W / m 3 );Q P is the pressure work (SI unit: W / m 3 );Q vd The heat generated by fluid viscosity dissipation (SI unit: W / m 3 ), q is the heat flux by conduction, T is the temperature gradient, and a is the proportionality coefficient, i.e., the thermal conductivity (SI unit: W / (m·k)).

[0179] In a continuous medium, Fourier's law of heat conduction shows that the heat flux q is proportional to the temperature gradient T. The proportionality coefficient k is the thermal conductivity (SI unit: W / (m·k)). A positive value of k means that heat flows from the high temperature area to the low temperature area. Fourier's law of heat conduction:

[0180]

[0181] 3) Define the boundary conditions and initial values ​​of the model. In this example, the surface of the carbon rod in the lower mixed melt is set as the gas inlet surface. The gas generation rate is coupled with the chemical reaction rate. The boundary is non-slip. The two-phase interface is set as the gas outlet surface, which is also the upper gas inlet. Slip occurs at the liquid phase boundary. The upper wall has two symmetrical gas outlets, which are set as pressure outlets. The other wall surfaces are non-slip surfaces and have no gas flux. The initial mass fractions of the various substances in the melt in the reactor are PbO3.23wt%, ZnO8.07wt%, ZnFe2O411.71wt%, FeO35wt%, CaO12.33wt%, SiO224.66wt%, and other 5wt%. A layer of C particles is spread on the surface of the melt, and its mass percentage relative to the melt is 3.5wt%.

[0182] 4) Model grid division. In this embodiment, the grid division is mainly based on the finite element analysis of each physical field equation. The reactor grid division results are as follows: Figure 2 After completing the mesh independence test, the mesh details are as follows: number of boundary layers: 8, rectangular boundary elements form a structured layer on the surface boundaries, and these finer boundary element layers are integrated with the existing triangular mesh elements in the model, boundary stretch factor: 1.2; number of vertex elements: 19, number of boundary elements: 228, number of elements: 305930; minimum element quality: 0.09275.

[0183] 5) After completing steps 1 to 4, perform CFD finite element simulation calculations, couple the gas phase and slag phase two-phase flow control equations, chemical reaction mass transfer and heat transfer equations in the carbon immersion lead-zinc reduction reactor, and solve the flow field distribution characteristics, reaction substance concentration distribution and temperature distribution results of the model; obtain the gas phase and slag two-phase flow field distribution, temperature field distribution, gas phase volume fraction distribution and lead-zinc depletion effect inside the carbon immersion lead-zinc reduction reactor under the set operating parameters, respectively. Figure 3-Figure 10 shown.

[0184] 6) Data analysis and further optimization of structure and parameters. In this embodiment, the numerical simulation of the carbon immersion reduction lead-zinc reactor based on CFD technology obtains the results described in step (5). When the melt level in the electric furnace containing simulated depleted slag containing lead and zinc is 1 / 3 of the furnace height, the initial melt temperature is 1100°C, the carbon rod temperature is 1250°C, and there are three heat-conducting carbon rods, the reaction characteristics of the reactor can be summarized from the flow field velocity distribution, material distribution and temperature distribution inside the reactor. Under the conditions of preliminary structural design and operating parameters, the temperature and gas distribution are relatively uniform; the reaction around the electrode is intense, and the slag stratification is obvious; a large amount of lead-zinc vapor is generated, and the lead-zinc depletion effect is good; the gas turbulence between the electrodes is intense, and the heat transfer effect is good; the lead-zinc vapor migrates to the outlet, and part of it stays at the top. In general, the temperature and gas distribution under this structure and operating parameters are relatively uniform, the lead-zinc depletion effect is better, the lead-zinc vapor migrates toward the outlet, and part of it stays at the top, indicating that the lead-zinc depletion effect under this structure can be improved, the gas turbulence can be more intense and the distribution can be more uniform, and part of the lead-zinc vapor stays at the top, which requires further optimization of the structure and operating parameters.

[0185] Based on steps 1 to 6, the reactor structure is optimized. Reducing the heat-conducting carbon rods, reducing the materials, moving the gas outlet position in the reactor upward, and increasing the temperature are performed respectively. The above steps are repeated to obtain new simulation calculation results. The optimized lead-zinc depletion effect, gas phase volume fraction distribution, flow field distribution, and temperature distribution are shown in the following table. Figure 11-Figure 30 As shown in the figure, it can be found that in the carbon immersion lead-zinc reduction reactor with an optimized structure, changing the electrode arrangement, such as reducing one heat-conducting carbon rod, weakens the melt heat transfer effect, reduces the gas phase flow rate, and makes the distribution uniform; reducing the material, such as reducing the melt liquid level from 1 / 3 of the furnace height to 1 / 4 of the furnace height, makes the gas turbulence between the electrodes more intense, weakens the slag stratification effect, makes the reduction reaction more intense, and enhances the lead-zinc depletion effect; raising the gas outlet position in the reactor, makes the gas distribution in the slag phase more uniform, increases the melt temperature around the electrode, increases the migration of lead-zinc vapor to the outlet, and reduces the retention amount; raising the temperature, i.e., raising the temperature of the slag and the conductive carbon rod in the carbon immersion reactor by 100°C, makes the heat transfer around the electrode more sufficient, enhances the turbulence effect, strengthens the lead-zinc depletion effect, and increases the lead-zinc mass fraction.

[0186] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0187] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

[0188] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0189] The readable storage medium is a computer-readable storage medium, which may be the internal storage unit of the controller described in any of the aforementioned embodiments, such as the hard disk or memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk equipped on the controller, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. Furthermore, the readable storage medium may also include both the internal storage unit of the controller and an external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0190] Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned readable storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

Claims

1. A method for optimizing a carbon immersion reduction lead-zinc reactor, characterized in that: include: S1: Preset the basic size parameters of the initial carbon immersion lead-zinc reduction reactor and construct a geometric model of the fluid calculation domain of the carbon immersion lead-zinc reduction reactor; wherein the geometric model simulates the upper and lower parts of the carbon immersion lead-zinc reduction reactor, the lower part of the model simulates the flow field changes and physical and chemical processes of the slag phase, and the upper part of the model simulates the flow field changes of the gas phase; S2: determining a reaction control equation of the geometric model, wherein the reaction control equation is used to simulate a lead-zinc reduction process by carbon immersion in a reactor; S3: Initializing the operating parameters of the geometric model and performing meshing on the geometric model; wherein the operating parameters are corresponding boundary conditions and parameter initial values; S3: Based on the meshed geometric model, simulation calculations are performed to obtain the reaction parameters inside the carbon immersion lead-zinc reduction reactor. Based on the reaction parameters, the gas and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect are obtained; Among them, one or more of the gas phase and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect are selected as optimization basis; S4: Determine whether the selected optimization basis meets the preset conditions: if so, output the basic size parameters of the lead-zinc reduction reactor; if not, adjust the structural parameters and / or operating parameters of the reactor based on the optimization basis and return to S3.

2. The optimization method for immersion reduction of lead and zinc reactor according to claim 1, characterized in that: The reaction control equation of S2 is set as follows: The material flux vector equation in the carbon immersion lead-zinc reduction reactor is: Where i refers to the substance i in the reactor; N i is the material flux vector; D i is the diffusion coefficient of the substance; c i is the concentration of the substance; is the concentration of a substance c i The sum of the first-order partial differentials in three directions; u is the velocity vector of the material fluid; The mass transfer equation is: Where ρ represents the fluid density; u represents the mass average velocity vector of the mixture; ω i represents the quality score; j i represents the mass flux relative to the mass average velocity; R i Indicates the rate at which a substance is produced or consumed; is the Laplace operator; The chemical reactions contained in the reactor include surface reactions and bulk reactions, and all the reactions involved are irreversible reactions. Among them, the surface reactions include the reactions of carbon on the surface of the carbon rod with lead oxide, zinc oxide and zinc ferrite. The chemical reaction formulas are as follows: PbO(l)+C(ads)=Pb(g)+CO(g) ZnO(l)+C(ads)=Zn(g)+CO(g) ZnFe2O4(l)+2C(ads)=Zn(g)+2FeO(l)+2CO(g) The bulk reaction includes the reaction of carbon particles with lead oxide, zinc oxide and zinc ferrite. The chemical reaction formulas are as follows: PbO(l)+C(s)=Pb(g)+CO(g) ZnO(l)+C(s)=Zn(g)+CO(g) ZnFe2O4(l)+2C(s)=Zn(g)+2FeO(l)+2CO(g) The governing equations for the lower part of the model are: Among them, φ l is the liquid volume fraction; ρ l is the liquid density; is the Laplace operator; u l is the liquid phase velocity vector; p is the pressure; μ l is the dynamic viscosity of the liquid phase; μ T is the turbulent viscosity; g is the gravity vector; F is the volume force; I represents the unit tension; the subscript "l" represents the liquid phase; the subscript "g" represents the gas phase; The mixing continuity equation is: Among them, φ g is the gas phase volume fraction; ρ g is the gas phase density; u g is the gas phase velocity vector; The gas phase transport equation is as follows: Among them, -m gl is the mass transfer rate from gas phase to liquid phase; The calculation formula for gas velocity is: in g =in l +in slip +in drift Among them, u g is the gas velocity; u slip is the slip velocity, u drift is the offset velocity; The formula for the offset velocity is: Among them, D gc is the gas diffusion coefficient; μ T is the turbulent viscosity; σ T is the turbulent Schmidt number; The governing equations for the upper part of the model are: Where ρ is the fluid density; u is the fluid velocity vector; The momentum conservation equation is: Where p is the fluid pressure; μ is the fluid dynamic viscosity; I is the unit tensor; and F is the volume force.

3. The optimization method for immersion reduction of lead and zinc reactor according to claim 2, characterized in that: The k-ε model is used in the geometric model to describe the turbulence effect, and the turbulent viscosity μ T It can be expressed as: Where ρ is the fluid density; k is the turbulent kinetic energy; ε is the turbulent energy dissipation rate; where k and ε satisfy: Where, the derivative term P k satisfy: Among them, C μ , σ k , σ ε 、C ε1 、C ε2 , and C ε are all model parameters, S k The fluid turbulence caused by bubbles is represented by the following formula: Where C k is the model constant; u slip is the slip speed.

4. The optimization method for immersion reduction of lead and zinc reactor according to claim 1, characterized in that: The heat balance equation of the temperature field distribution is: Where ρ is the fluid density; C p is the constant pressure specific heat capacity; T is the absolute temperature; u is the velocity vector; q is the conduction heat flux; Q is the heat source; Q P is the pressure work; Q vd is the heat generated by the viscous dissipation of the fluid; T is the temperature gradient; a is the proportional coefficient, that is, the thermal conductivity.

5. The optimization method for immersion reduction of lead and zinc reactor according to claim 1, characterized in that: The relationship between the reaction parameters and the gas and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect is as follows: the gas turbulence in the melt in the reactor gradually intensifies, and the temperature and gas distribution gradually become uniform; the gas turbulence between the electrodes becomes more intense, the slag stratification effect is weakened, the reduction reaction becomes more intense, and the lead-zinc depletion effect is enhanced; among them, the reaction parameters include the structural parameters and operating parameters of the reactor.

6. The optimization method of the immersion reduction lead-zinc reactor according to claim 1, characterized in that When adjusting the reactor using the gas and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect as optimization basis, the optimization strategy is as follows: if you want to improve the lead-zinc depletion effect, reduce the heat-conducting carbon rods, reduce the material, move the gas outlet position in the reactor upward, and increase the temperature.

7. The optimization method for immersion reduction of lead and zinc reactor according to claim 1, characterized in that: The boundary conditions and parameter initial values ​​include: flow field boundary conditions and initial values, reactor concentration field boundary conditions and initial values, heat transfer field boundary conditions and initial values; The initial values ​​corresponding to the flow field include: pressure and gravity conditions of the calculation domain in the carbon immersion lead-zinc reduction reactor; The initial values ​​corresponding to the concentration field in the reactor include: the initial overall regional substance concentration setting of the carbon immersion lead-zinc reduction reactor, the diffusion coefficient setting of each substance, and the custom function setting of the chemical reaction rate of zinc oxide and zinc ferrite; The initial values ​​corresponding to the heat transfer field include: the shape and size of the heat-conducting object, the physical properties of the heat-conducting object, and the temperature distribution within the heat-conducting object at the initial moment; The boundary conditions of the flow field in the model are as follows: the surface of the carbon rod in the mixed melt at the bottom of the model is set as the gas inlet surface, the gas generation rate is coupled with the chemical reaction rate, and there is no slip at the boundary; the two-phase interface is set as the gas outlet surface, which is also the gas inlet at the top of the model, and slips at the liquid phase boundary. The upper wall has two symmetrical gas outlets, which are set as pressure outlets. The other wall surfaces are non-slip surfaces and have no gas flux: u l ·n=0 u l ＝0 Among them, u l is the fluid velocity vector; n is the boundary normal vector; K n is the sum of viscosity and elasticity; ρ l is the fluid density; u τ is the friction speed; u + is the ratio of the fluid velocity vector to the friction velocity; is the gas mass flux; The boundary condition of the concentration field in the reactor is that no gas flux boundary condition is applied on any surface, and the material is added to the preset furnace height before the reaction starts; The heat transfer boundary conditions are set to an initial melt temperature of 1100°C, a carbon rod temperature of 1250°C, and other walls as thermal insulation surfaces: -n·q=0 Where n is the boundary normal vector and q is the conductive heat flux.

8. The optimization method for immersion reduction of lead and zinc reactor according to claim 1, characterized in that: The optimization in S4 is based on the following preset conditions: Different colors in the simulation diagrams of the gas and slag two-phase flow field distribution and temperature field distribution represent different values. When the values ​​are within the preset fluctuation range, the color distribution is uniform, indicating that the preset conditions are met. The lead-zinc depletion effect is determined by setting the mass fraction of lead and zinc to be less than or equal to a preset value, indicating that the preset conditions are met.

9. A system based on the method according to any one of claims 1 to 8, characterized in that: include: Geometric model construction model: used to construct a geometric model of the fluid calculation domain of the carbon immersion reduction lead-zinc reactor based on the basic size parameters of the preset initial carbon immersion reduction lead-zinc reactor; wherein the geometric model simulates the upper and lower parts of the carbon immersion reduction lead-zinc reactor, the lower part of the model simulates the flow field changes and physical and chemical processes of the slag phase, and the upper part of the model simulates the flow field changes of the gas phase; Reaction control equation determination module: used to determine the reaction control equation of the geometric model, the reaction control equation is used to simulate the carbon immersion reduction process of lead and zinc in the reactor; Parameter setting and simulation module: used to initialize the operating parameters of the geometric model and mesh the geometric model; wherein the operating parameters are the corresponding boundary conditions and parameter initial values; using the meshed geometric model to simulate and calculate the reaction parameters inside the carbon immersion lead-zinc reduction reactor, and based on the reaction parameters, the gas phase and slag two-phase flow field distribution, temperature field distribution, and lead-zinc depletion effect are obtained; Among them, one or more of the gas phase and slag two-phase flow field distribution, temperature field distribution and lead-zinc depletion effect are selected as optimization basis; Model optimization module: Determine whether the selected optimization basis meets the preset conditions: If so, output the basic size parameters of the lead-zinc reduction reactor; if not, adjust the structural parameters and / or operating parameters of the reactor based on the optimization basis and return to the parameter setting and simulation module.

10. A readable storage medium, characterized in that: A computer program is stored, and when the computer program is called by a processor, it is used to execute: the steps of the method according to any one of claims 1 to 8.

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