A numerical simulation method and system for enhancing oxygen leaching of lithium iron phosphate

Abstract

The application provides a numerical simulation method and system for strengthening oxygen leaching of lithium iron phosphate, and relates to the technical field of numerical simulation of hydrometallurgy. According to the technical scheme, a geometric model containing a flow guide cylinder and a microporous gas distributor is established for an internal loop gas-lift reactor, an Euler-Euler multiphase flow model is used to define the physical property parameters of oxygen, dilute sulfuric acid aqueous solution and lithium iron phosphate particles, general control equations containing volume fraction, momentum and component transport are constructed, and leaching and oxidation reaction kinetics equations representing the material conversion law are coupled and solved as source terms, so that the flow field distribution, gas-liquid mass transfer efficiency and chemical reaction process in the reactor can be accurately simulated, and the problem of poor adaptability of the existing simulation method is effectively solved, thereby providing a reliable theoretical basis for optimization of the oxygen leaching process.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology in hydrometallurgy, and in particular to a numerical simulation method and system for enhancing oxygen leaching of lithium iron phosphate. Background Technology

[0002] Lithium iron phosphate (LFP) is the mainstream cathode material for lithium batteries, and wet leaching is a core step in its resource recovery. In existing processes, to reduce production costs, oxygen is typically used to replace part of the hydrogen peroxide as an oxidant to achieve the oxidative leaching of lithium and iron elements from LFP. However, in conventional leaching equipment, oxygen is often dispersed in the form of large bubbles, resulting in low gas-liquid mass transfer efficiency and oxygen utilization of less than 30%, thus leading to low leaching efficiency. Although airlift reactors, with their advantages of no mechanical stirring, good internal circulation, and sufficient gas-liquid-solid three-phase contact, can effectively enhance oxygen mass transfer and improve oxygen utilization, and are suitable for the high solids content of LFP leaching slurry, current numerical simulation methods for this specific operating condition suffer from poor model adaptability, ambiguous parameter settings, and a lack of integration with the actual leaching reaction mechanism. These issues prevent accurate prediction of the flow field, mass transfer efficiency, and leaching effect within the reactor, making it difficult to guide the design of industrial equipment and process optimization. Summary of the Invention

[0003] The purpose of this invention is to provide a numerical simulation method and system for enhancing oxygen leaching of lithium iron phosphate, in order to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions that can accurately simulate the gas-liquid-solid three-phase flow, mass transfer and chemical reaction process in an airlift reactor, thereby providing a reliable basis for reactor structure design and process parameter optimization.

[0004] On the one hand, this application provides a numerical simulation method for enhanced oxygen leaching of lithium iron phosphate, the method being applied to an airlift reactor, the method comprising: Step S100: Establish a geometric model of the airlift reactor and perform mesh generation on the geometric model to obtain a computational mesh for numerical calculation. Step S200: Based on the computational grid, construct an Euler-Euler multiphase flow model and define the physical property parameters of the gas phase, liquid phase and solid phase therein; the gas phase is oxygen, the liquid phase is dilute sulfuric acid aqueous solution and the solid phase is lithium iron phosphate particles. Step S300: Construct general governing equations in the Euler-Euler multiphase flow model to describe the flow and heat and mass transfer laws between the phases; the general governing equations include volume fraction equations, momentum equations and component transport equations; Step S400: The core reaction kinetic equation for the oxygen leaching of lithium iron phosphate is coupled as a source term to the general governing equation to characterize the material transformation law in the reaction process; the core reaction kinetic equation includes the leaching reaction equation and the oxidation reaction equation; Step S500: Based on the general control equation coupled with the core reaction kinetic equation, set boundary conditions and solution parameters, perform numerical solution and iteration on the computational grid, and output the simulation results of the flow field, mass transfer and reaction process in the reactor.

[0005] Further, in step S100, the geometric model is meshed, specifically including: Structured hexahedral meshes are used for partitioning; Boundary layer densification is applied to the gas distributor area, the guide tube wall, and the gas-liquid interface area. The number of boundary layer layers is set to a preset number of boundary layer layers, the growth rate is between a preset minimum boundary layer growth rate and a preset maximum boundary layer growth rate, and the mesh size is refined to between a preset minimum boundary layer mesh size and a preset maximum boundary layer mesh size. For regions where the solid concentration is higher than a preset solid concentration threshold, local mesh refinement is performed, with the mesh size ranging from the preset minimum local mesh size to the preset maximum local mesh size. Among them, the orthogonal quality of the mesh is not lower than the preset orthogonal quality threshold, and the distortion is not higher than the preset distortion threshold.

[0006] Further, in step S300, constructing the general governing equations includes: In the momentum equation, an interphase force source term is introduced, and the Schiller-Naumann drag force model is used to calculate the interphase force source term; the drag force coefficient of the Schiller-Naumann drag force model is limited to a piecewise function calculation: When the particle Reynolds number is less than or equal to the preset first Reynolds number threshold, the preset first drag coefficient calculation formula is used; When the particle Reynolds number is between a preset first Reynolds number threshold and a preset second Reynolds number threshold, the preset second drag coefficient calculation formula is used.

[0007] Furthermore, in step S300, constructing the general governing equations further includes: In the component transport equation, a gas-liquid mass transfer source term is introduced, and the Licht-Stephan gas-liquid mass transfer model is used to calculate the gas-liquid mass transfer source term; the mass transfer equation of the Licht-Stephan gas-liquid mass transfer model includes the gas-liquid volumetric mass transfer coefficient. The range of the gas-liquid volumetric mass transfer coefficient is from the preset minimum gas-liquid volumetric mass transfer coefficient threshold to the preset maximum gas-liquid volumetric mass transfer coefficient threshold.

[0008] Furthermore, in step S300, constructing the general governing equations further includes: In the component transport equation, a diffusion term for the solid-liquid mass transfer process is constructed based on Fick's law. In the diffusion term of the solid-liquid mass transfer process, the value range of the solid-liquid mass transfer coefficient is from a preset minimum solid-liquid mass transfer coefficient threshold to a preset maximum solid-liquid mass transfer coefficient threshold.

[0009] Further, in step S400, the leaching reaction equation is specifically as follows: The reaction rate of lithium iron phosphate reacting with dilute sulfuric acid to generate ferrous ions is positively correlated with the first reaction order power of the dilute sulfuric acid concentration and the solid phase volume fraction. The calculation of the reaction rate of lithium iron phosphate reacting with dilute sulfuric acid to generate ferrous ions is based on the acidolysis reaction rate constant, the concentration of dilute sulfuric acid, the first reaction order, and the solid phase volume fraction. The range of the acidolysis reaction rate constant at the preset reaction temperature is from the preset minimum acidolysis reaction rate constant threshold to the preset maximum acidolysis reaction rate constant threshold.

[0010] Further, in step S400, the oxidation reaction equation is specifically as follows: The reaction rate of ferrous ions being oxidized by oxygen to ferric ions is positively correlated with the product of the concentrations of oxygen, ferrous ions, and hydrogen ions in the liquid phase. The calculation of the reaction rate of ferrous ions being oxidized by oxygen to ferric ions is based on the oxidation reaction rate constant, the oxygen concentration in the liquid phase, the ferrous ion concentration, and the hydrogen ion concentration. The oxidation reaction rate constant is calculated using the Arrhenius equation, and its value ranges from the preset minimum oxidation reaction rate constant threshold to the preset maximum oxidation reaction rate constant threshold at the preset reaction temperature.

[0011] Further, in step S500, setting boundary conditions includes: Set the gas inlet boundary conditions: the inlet velocity is the preset minimum gas inlet velocity to the preset maximum gas inlet velocity, the temperature is the preset reaction temperature, and the oxygen mass fraction is the preset oxygen mass fraction threshold. Set the liquid inlet boundary conditions: the inlet velocity is from the preset minimum liquid inlet velocity to the preset maximum liquid inlet velocity, the sulfuric acid concentration is from the preset minimum sulfuric acid concentration to the preset maximum sulfuric acid concentration, and the solid phase volume fraction is from the preset minimum solid phase volume fraction threshold to the preset maximum solid phase volume fraction threshold. Set wall boundary conditions: Use a no-slip wall and standard wall function, and keep the wall temperature constant at the preset reaction temperature.

[0012] Further, in step S500, numerical solution and iteration are performed on the computational grid, including: The SIMPLE algorithm is used for pressure-velocity coupling. The pressure term is discretized using the PRESTO! scheme, and the momentum, energy, and volume fraction terms are discretized using the second-order upwind scheme. The pressure relaxation factor is set to a preset minimum pressure relaxation factor to a preset maximum pressure relaxation factor, and the momentum relaxation factor is set to a preset minimum momentum relaxation factor to a preset maximum momentum relaxation factor. Set the time step from the preset minimum time step to the preset maximum time step, perform transient solution, until the residual converges to below the preset residual convergence threshold.

[0013] On the other hand, this application provides a numerical simulation system for enhanced oxygen leaching of lithium iron phosphate, used to perform the aforementioned numerical simulation method for enhanced oxygen leaching of lithium iron phosphate, including: The geometric modeling unit is used to build the geometric model of the airlift reactor and perform mesh generation operations, including boundary layer refinement and local mesh refinement. The model configuration unit is used to configure physical models, including the Euler-Euler multiphase flow model, component transport model, Schiller-Naumann drag model, and Licht-Stephan gas-liquid mass transfer model. A kinetic loading unit is used to import the leaching reaction equation and oxidation reaction equation of lithium iron phosphate; The solution control unit is used to couple the geometric model with the physical model, and sets up the SIMPLE algorithm and the second-order upwind scheme to iteratively solve the numerical model obtained after coupling. The result output unit is used to output the oxygen distribution cloud map, the flow field velocity vector map, and the curve of lithium leaching rate changing with time in the airlift reactor.

[0014] The beneficial effects of this invention are as follows: This application provides a numerical simulation method for enhancing oxygen leaching of lithium iron phosphate. This technical solution establishes a geometric model including a guide tube and a microporous gas distributor for an internally circulating airlift reactor. It uses an Euler-Euler multiphase flow model to define the physical properties of oxygen, dilute sulfuric acid aqueous solution, and lithium iron phosphate particles, constructing a general governing equation including volume fraction, momentum, and component transport. The leaching and oxidation reaction kinetic equations characterizing the material transformation are coupled and solved as source terms. This allows for accurate simulation of the flow field distribution, gas-liquid mass transfer efficiency, and chemical reaction processes inside the reactor, effectively solving the problem of poor adaptability in existing simulation methods and providing a reliable theoretical basis for optimizing the enhanced oxygen leaching process. This application also provides a system corresponding to the above method; the beneficial effects of the system are similar to those of the method and will not be elaborated here.

[0015] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a flowchart illustrating the numerical simulation method for enhanced oxygen leaching of lithium iron phosphate provided in this application. Figure 2 This is a schematic diagram of the airlift reactor provided in this application; Figure 3 This is a schematic diagram of the structure of the numerical simulation system for enhanced oxygen leaching of lithium iron phosphate provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0020] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Lithium iron phosphate (LFP) is the mainstream cathode material for lithium batteries, and wet leaching is a core step in its resource recovery. In hydrometallurgical recovery processes, to effectively reduce production costs, the industry commonly uses oxygen to replace some of the expensive hydrogen peroxide as an oxidant, achieving efficient separation of valuable elements such as lithium and iron from LFP through oxidative leaching. However, in this process, the performance of the reaction equipment directly determines the leaching efficiency and resource utilization rate.

[0023] Existing conventional leaching equipment faces significant technical bottlenecks in handling this process. Oxygen is typically dispersed in the liquid as large bubbles, resulting in a small gas-liquid contact area and extremely low mass transfer efficiency. Actual data shows that oxygen utilization in conventional equipment is less than 30%, directly leading to low leaching efficiency and failing to meet the demands of high-efficiency industrial production. Airlift reactors, with their advantages of no mechanical stirring, good internal circulation, and sufficient gas-liquid-solid three-phase contact, can effectively enhance oxygen mass transfer and are well-suited to the high solids content of lithium iron phosphate leaching slurry (28% solids content, 1.35 g / cm³ density, 60℃), making them an ideal choice for improving efficiency.

[0024] However, in practical applications, existing numerical simulation techniques for this specific operating condition still face significant challenges, exhibiting major shortcomings such as poor model adaptability, ambiguous parameter settings, and a lack of integration with actual reaction mechanisms. Existing simulation methods fail to fully incorporate the specific characteristics of the lithium iron phosphate oxygen leaching system, lack detailed parameter settings for the aforementioned actual operating conditions, and often neglect the core oxidation and acidolysis kinetics, failing to accurately reflect the material transformation patterns within the reactor. These deficiencies result in simulation results lacking in guiding significance, making it difficult to accurately predict the flow field, mass transfer efficiency, and leaching effect within the reactor. This creates a significant gap between simulation and actual production, failing to provide reliable theoretical support for the structural design and process parameter optimization of industrial equipment.

[0025] Therefore, developing a targeted, detailed, well-developed, and directly applicable numerical simulation method for enhancing oxygen leaching of lithium iron phosphate using an airlift reactor, clarifying the model selection, parameter settings, equation definition, and solution process in the simulation, and solving the problem of existing simulations being disconnected from actual processes, is of significant theoretical and industrial application value.

[0026] To address the aforementioned issues, this application proposes a numerical simulation method specifically for enhancing the oxygen leaching of lithium iron phosphate in an airlift reactor. The core of this method lies in constructing a precise simulation system that closely reflects actual process conditions. This technique employs an Euler-Euler multiphase flow model and a component transport model, combined with the Schiller-Naumann drag model and the Licht-Stephan gas-liquid mass transfer model, to accurately describe the flow and mass transfer characteristics of the gas, liquid, and solid phases. Specifically, this method couples the kinetic equations of the acidolysis reaction of lithium iron phosphate and the oxidation reaction of ferrous ions as source terms into the general governing equations, achieving dynamic simulation of the chemical reaction process. Through mesh refinement optimization targeting the gas distributor and high-solids-content regions, and through solution settings based on the SIMPLE algorithm, it can accurately predict the flow field distribution, oxygen utilization rate, and lithium leaching rate within the reactor, providing a reliable theoretical basis for reactor structure design and process parameter optimization.

[0027] First, the numerical simulation method for enhanced oxygen leaching of lithium iron phosphate provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0028] Reference Figure 1 The implementation process of the numerical simulation method for enhanced oxygen leaching of lithium iron phosphate provided in this application includes, but is not limited to, the following steps.

[0029] Step S100: Establish the geometric model of the airlift reactor and mesh the geometric model to obtain the computational grid for numerical calculation.

[0030] In step S100, the physical airlift reactor is transformed into a computer-recognizable digital space. By establishing a precise geometric model including the guide tube and microporous gas distributor, the internal circulation structure characteristics of the reactor are established. Subsequent mesh generation, especially the boundary layer refinement and local refinement of the gas distributor region, the guide tube wall, and the high solid concentration region, ensures that the computational mesh can accurately capture the geometric details of the regions where key physical phenomena such as bubble generation, interphase friction, and particle aggregation occur. This provides a high-precision spatial discretization basis for subsequent flow field calculations and effectively avoids numerical discretization errors and simulation distortions caused by coarse meshes.

[0031] Step S200: Based on the computational grid, construct an Eulerian-Eulerian multiphase flow model and define the physical property parameters of the gas phase, liquid phase and solid phase in it.

[0032] The gas phase is oxygen, the liquid phase is dilute sulfuric acid aqueous solution, and the solid phase is lithium iron phosphate particles.

[0033] In step S200, the physical model framework and material properties of the simulation are established to recreate the real leaching system environment. The Euler-Euler multiphase flow model treats the gas, liquid, and solid phases as a continuous, interpenetrating medium, making it particularly suitable for describing dense fluid systems with a solid content as high as 28% in this process. By accurately inputting the physical properties of oxygen, dilute sulfuric acid aqueous solution, and lithium iron phosphate particles, such as density, viscosity, and particle size, the model is given a realistic physical identity. This allows the simulation process to reflect the distribution characteristics and interaction patterns of different phases within the reactor, ensuring a high degree of consistency between the simulation environment and actual hydrometallurgical conditions.

[0034] Step S300: Construct a general governing equation in the Euler-Euler multiphase flow model to describe the flow and heat and mass transfer laws between the phases.

[0035] The general governing equations include the volume fraction equation, the momentum equation, and the component transport equation.

[0036] In step S300, the dynamic behavior of the system is quantitatively described by constructing a set of general governing equations. The volume fraction equation is used to track the spatial distribution evolution of each phase, while the momentum equation incorporates interphase force source terms to calculate the velocity field of the fluid. In particular, the Schiller-Naumann drag model, applicable to the Reynolds number range of this particle, is introduced to accurately calculate the resistance between gas and solid, and between liquid and solid.

[0037] In addition, the component transport equations also integrate gas-liquid mass transfer and solid-liquid mass transfer models to describe the diffusion of oxygen from bubbles to the liquid phase and the migration of ions at the solid-liquid interface, thus fully characterizing the complex fluid dynamics and mass transfer processes within the reactor.

[0038] In step S400, the core reaction kinetic equation for the oxygen leaching of lithium iron phosphate is coupled as a source term into the general governing equation to characterize the material transformation law in the reaction process.

[0039] The core reaction kinetic equations include the leaching reaction equation and the oxidation reaction equation.

[0040] In step S400, the microscopic mechanism of the chemical reaction is coupled with the macroscopic flow field. By adding the kinetic equations of the acidolysis reaction of lithium iron phosphate with acid and the oxidation of ferrous ions by oxygen as source terms to the governing equations, the simulation system can not only calculate the fluid flow but also calculate the consumption and generation of substances due to the chemical reaction in real time. This allows the model to accurately predict the leaching rate of lithium ions, the amount of oxygen consumed, and the generation of reaction heat, realistically reproducing the chemical reaction process of oxygen as an oxidant to enhance leaching, providing a core basis for evaluating leaching efficiency.

[0041] Step S500: Based on the general control equation coupled with the core reaction kinetic equation, set the boundary conditions and solution parameters, perform numerical solution and iteration on the computational grid, and output the simulation results of the flow field, mass transfer and reaction process in the reactor.

[0042] In step S500, visualized engineering data is obtained through numerical calculation. By setting boundary conditions such as gas inlet velocity, liquid inlet concentration, and wall temperature that conform to the actual process, and using the SIMPLE algorithm in conjunction with a second-order upwind scheme for pressure-velocity coupling solution, the stability and convergence accuracy of the calculation are ensured. After repeated iterative calculations, this step finally outputs the flow field vector diagram inside the reactor, the oxygen distribution cloud map, and the curve of lithium leaching rate changing over time.

[0043] These results intuitively reveal the mixing effect, mass transfer bottleneck, and reaction process within the reactor, providing scientific and quantitative theoretical support for optimizing the structural design of airlift reactors and controlling process parameters such as temperature and gas velocity.

[0044] In some embodiments of this application, step S100 involves meshing the geometric model, specifically including the following steps.

[0045] Step S110: Divide the data using a structured hexahedral mesh.

[0046] In step S110, the inherent geometric regularity and orthogonality of the structured hexahedral mesh are utilized to significantly improve the convergence speed of the calculation process and the accuracy of the simulation results. Compared with unstructured meshes, hexahedral meshes can effectively reduce discretization errors in numerical calculations and avoid flow field simulation distortion caused by mesh shape distortion. This ensures that smoother and more accurate physical field distribution data can be obtained when simulating gas-liquid-solid three-phase flow within a regular geometric body such as an airlift reactor.

[0047] Step S120: Boundary layer densification is performed on the gas distributor area, the guide tube wall and the gas-liquid interface area. The number of boundary layers is set to a preset number of boundary layers, the growth rate is between a preset minimum boundary layer growth rate and a preset maximum boundary layer growth rate, and the mesh size is refined to between a preset minimum boundary layer mesh size and a preset maximum boundary layer mesh size.

[0048] In step S120, for key regions where the physical quantity gradient changes drastically in the flow field, the flow details within the boundary layer are precisely captured. The gas distributor region is the source of bubble generation, the guide tube wall involves the frictional resistance between the fluid and the solid, and the gas-liquid interface is the core location where mass transfer occurs.

[0049] By refining the mesh in these regions and strictly controlling the growth rate and number of layers, we can precisely analyze the initial morphology of bubbles, wall shear forces, and turbulent dissipation processes at the phase interface, thus providing high-fidelity geometric data support for subsequent calculations of drag force and mass transfer rate.

[0050] Step S130: For regions where the solid concentration is higher than a preset solid concentration threshold, local mesh refinement is performed. The mesh size is between a preset minimum local mesh size and a preset maximum local mesh size. The mesh orthogonal quality is not lower than a preset orthogonal quality threshold, and the distortion is not higher than a preset distortion threshold.

[0051] In step S130, the particle distortion problem in the simulation of high solids content slurry is addressed, and the overall computational quality of the mesh is ensured. The lithium iron phosphate leaching process involves solid-liquid slurries with high solids content. Local refinement of the high-concentration region can effectively avoid the calculation deviation of solid particle motion trajectory caused by excessively coarse mesh, ensuring the accuracy of the solid-liquid two-phase flow simulation.

[0052] Meanwhile, by setting threshold limits for mesh orthogonality quality and distortion, low-quality mesh cells can be eliminated, preventing computational divergence or numerical oscillations caused by mesh distortion, thereby ensuring the stability and reliability of the entire numerical simulation process.

[0053] In some embodiments of this application, step S300 involves constructing a general governing equation, including the following steps: introducing interphase force source terms into the momentum equation and calculating the interphase force source terms using the Schiller-Naumann drag force model.

[0054] Specifically, the introduction of an interphase force source term into the momentum equation serves to realistically recreate the complex dynamic interaction mechanism between the gas, liquid, and solid phases from a mathematical and physical perspective. Within the Eulerian-Eulerian multiphase flow framework, the phases do not move independently but are coupled through momentum exchange. The interphase force source term acts as a bridge connecting the momentum equations of each phase, quantifying the dragging, pushing, and frictional effects between the dispersed phase (such as oxygen bubbles and lithium iron phosphate particles) and the continuous phase (dilute sulfuric acid solution). By adding this source term to the momentum conservation equation, the simulation system can accurately calculate the slip velocity of each phase due to density differences and relative motion, thereby precisely capturing the actual flow field distribution, phase holdup evolution, and turbulence modulation phenomena within the reactor. This is crucial for ensuring that the multiphase flow simulation conforms to physical reality.

[0055] Furthermore, the Schiller-Naumann drag model was used to calculate the interphase force source term, providing a widely validated and highly suitable quantitative calculation standard for the aforementioned momentum exchange under the specific process conditions. Drag is the most significant force affecting phase distribution and mixing efficiency in multiphase flow. The Schiller-Naumann model can accurately describe the pressure drag and frictional drag generated when fluid flows around particles, based on the Reynolds number range of the particles or bubbles.

[0056] In the lithium iron phosphate leaching system described in this application, this model can accurately calculate the resistance to the rise of oxygen bubbles in the liquid phase and the resistance to the settling or suspension of solid particles in the slurry. By introducing this classic semi-empirical formula, not only is the accuracy of the interphase momentum transfer calculation guaranteed, but the calculation precision and solution efficiency are also effectively balanced, laying a solid fluid dynamics foundation for the subsequent accurate prediction of oxygen utilization and lithium leaching rate.

[0057] In some embodiments of this application, the drag coefficient of the Schiller-Naumann drag model is calculated as a piecewise function, accurately capturing the interphase drag characteristics of the gas-liquid-solid three-phase system under complex flow conditions, thereby significantly improving the accuracy and reliability of numerical simulation. Specifically, this includes the following: (1) When the particle Reynolds number is less than or equal to the preset first Reynolds number threshold, the preset first drag coefficient calculation formula shall be adopted.

[0058] Specifically, this study aims to accurately describe the fluid dynamics characteristics of flow fields in low Reynolds number states (such as microbubbles or slow-moving settling particles). In local regions or the initial stages of a gaslift reactor, the relative velocity between particles and fluid is low, and fluid viscous forces dominate. Using calculation formulas adapted to the low Reynolds number range (typically including a viscous drag term inversely proportional to the Reynolds number and corresponding empirical correction terms), the Stokes drag experienced by microparticles or bubbles in viscous fluids and its correction effects can be accurately quantified. This effectively avoids the problems of distorted particle suspension height prediction or inaccurate bubble plume diffusion simulation caused by drag estimation errors in the low-velocity flow region, ensuring the simulation accuracy of the low-velocity flow field region within the reactor.

[0059] (2) When the particle Reynolds number is between the preset first Reynolds number threshold and the preset second Reynolds number threshold, the preset second drag coefficient calculation formula shall be adopted.

[0060] Specifically, as the gas velocity within the airlift reactor increases or the circulation speed within the guide tube accelerates, the particle Reynolds number increases significantly, inertial forces gradually become dominant, and the flow field structure becomes more complex. The second calculation formula used at this point introduces a power-law correction term for the high Reynolds number range, accurately capturing the changes in pressure differential drag generated when the fluid bypasses the particles and the altered drag characteristics caused by boundary layer separation. This treatment ensures that the simulation system can realistically reflect the interphase momentum exchange intensity of the reactor under high gas velocity and strong circulation conditions, providing a solid fluid dynamics foundation for accurately predicting the reactor's macroscopic mixing time, gas holdup distribution, and overall mass transfer efficiency.

[0061] In some embodiments of this application, step S300, constructing the general governing equation, further includes: introducing a gas-liquid mass transfer source term into the component transport equation, and calculating the gas-liquid mass transfer source term using the Licht-Stephan gas-liquid mass transfer model. The mass transfer equation of the Licht-Stephan gas-liquid mass transfer model includes a gas-liquid volumetric mass transfer coefficient. The value range of the gas-liquid volumetric mass transfer coefficient is from a preset minimum gas-liquid volumetric mass transfer coefficient threshold to a preset maximum gas-liquid volumetric mass transfer coefficient threshold.

[0062] Specifically, a gas-liquid mass transfer source term is introduced into the component transport equation to achieve precise quantification of the dynamic process of oxygen transfer from the gas phase to the liquid phase at the mathematical and physical level. In the oxygen leaching process of lithium iron phosphate, gaseous oxygen must first dissolve and diffuse into the dilute sulfuric acid liquid phase before it can participate in the subsequent oxidation reaction. The gas-liquid mass transfer source term, acting as a bridge connecting the gas-phase and liquid-phase component equations, calculates and reflects the oxygen mass transfer rate driven by the interphase concentration difference in real time.

[0063] By introducing this source term, the simulation system can not only track the flow of each phase, but also accurately capture the dissolution and consumption patterns of oxygen in the reactor, thus realistically reproducing the key impact of mass exchange between the gas and liquid phases on the overall reaction process, ensuring that the simulation results can objectively reflect the oxygen utilization rate and the distribution characteristics of dissolved oxygen in the liquid phase.

[0064] Furthermore, the Licht-Stephan gas-liquid mass transfer model was used to calculate the gas-liquid mass transfer source term, providing a quantitative calculation standard highly adapted to the bubble flow characteristics for the aforementioned mass transfer process. The Licht-Stephan model fully considers the interfacial renewal and turbulent disturbance effects during bubble rise in the liquid phase, and can accurately calculate the mass transfer rate at the gas-liquid interface based on local flow field parameters. The mass transfer equation of this model includes the key parameter of gas-liquid volumetric mass transfer coefficient, which comprehensively reflects the coupled influence of bubble specific surface area and liquid film mass transfer coefficient.

[0065] By limiting the range of the gas-liquid volumetric mass transfer coefficient to between the preset minimum gas-liquid volumetric mass transfer coefficient threshold and the preset maximum gas-liquid volumetric mass transfer coefficient threshold, the physical rationality of the calculation results can be effectively constrained, and the abnormality of the mass transfer coefficient caused by extreme fluctuations in the local flow field can be avoided. Thus, while ensuring the stability of the calculation, the distribution of mass transfer intensity in different regions of the reactor can be accurately captured, providing a reliable theoretical basis for optimizing the design of the gas distributor and improving the leaching efficiency.

[0066] In some embodiments of this application, step S300, constructing the general control equation, further includes: in the component transport equation, constructing a diffusion term in the solid-liquid mass transfer process based on Fick's law, wherein the value range of the solid-liquid mass transfer coefficient in the diffusion term of the solid-liquid mass transfer process is from a preset minimum solid-liquid mass transfer coefficient threshold to a preset maximum solid-liquid mass transfer coefficient threshold.

[0067] Specifically, a diffusion term for the solid-liquid mass transfer process is constructed in the component transport equation based on Fick's law. This precisely describes the physical process of the migration of reaction products such as lithium ions from the surface of lithium iron phosphate solid particles to the bulk liquid phase of dilute sulfuric acid from a microscopic perspective. Fick's law, as a fundamental theory of mass transfer, quantitatively reveals the spontaneous diffusion behavior of substances driven by concentration gradients.

[0068] By introducing this diffusion term into the governing equation, the simulation system can accurately calculate the diffusion flux caused by the ion concentration difference generated by the chemical reaction at the solid-liquid interface, thereby realistically reproducing the material stripping and dissolution kinetics on the surface of solid particles during leaching, ensuring the scientific validity and accuracy of the model's predictions of solid-phase conversion rate and liquid-phase ion concentration field distribution.

[0069] Furthermore, the range of solid-liquid mass transfer coefficient is limited to between a preset minimum solid-liquid mass transfer coefficient threshold and a preset maximum solid-liquid mass transfer coefficient threshold to ensure the physical rationality and computational stability of the numerical simulation process. The solid-liquid mass transfer coefficient is affected by various factors such as fluid viscosity, particle size, and turbulence intensity, and is prone to numerical oscillations in actual complex flow fields.

[0070] By setting reasonable upper and lower thresholds, the calculation results of the diffusion term can be effectively constrained, avoiding abnormal extreme values ​​of the mass transfer coefficient that violate physical principles due to extreme fluctuations in the local flow field or mesh discretization errors. This constraint mechanism not only prevents calculation divergence but also ensures that the solid-liquid mass transfer rate remains within the range that conforms to actual process conditions, providing reliable boundary conditions for subsequent accurate evaluation of leaching efficiency and optimization of reactor operating parameters.

[0071] In some embodiments of this application, the leaching reaction equation in step S400 specifically includes the following.

[0072] (1) The reaction rate of lithium iron phosphate reacting with dilute sulfuric acid to generate ferrous ions is positively correlated with the first reaction order power of the concentration of dilute sulfuric acid and the volume fraction of the solid phase.

[0073] Specifically, in the leaching process of lithium iron phosphate, the reaction rate is directly constrained by both the concentration of hydrogen ions in the liquid phase (i.e., the concentration of dilute sulfuric acid) and the number of reactive sites in the solid phase (i.e., the volume fraction of the solid phase). This positive correlation allows numerical simulations to accurately reflect the physicochemical phenomenon that the acidolysis reaction rate accelerates with increasing sulfuric acid concentration or solid particle content. This provides a mathematical description consistent with the basic principles of chemical reaction kinetics for accurately predicting the formation rate of ferrous ions and the dissolution process of lithium iron phosphate within the reactor.

[0074] (2) The calculation of the reaction rate of lithium iron phosphate reacting with dilute sulfuric acid to generate ferrous ions is based on the acidolysis reaction rate constant, the concentration of dilute sulfuric acid, the first reaction order, and the solid phase volume fraction.

[0075] Specifically, by using the acidolysis reaction rate constant as a proportionality constant and combining it with the first power of the reaction order of the dilute sulfuric acid concentration and the solid volume fraction, this calculation formula transforms the abstract chemical reaction mechanism into concrete numerical model source terms. This not only enables real-time dynamic solving of the acidolysis reaction rate but also ensures that the calculated reaction rate can adaptively adjust to changes in the concentration and phase fraction of the local flow field within the reactor. This precisely couples the microscopic chemical reaction process into the macroscopic flow field simulation, significantly improving the accuracy and reliability of the overall leaching process simulation.

[0076] (3) The range of the acid hydrolysis reaction rate constant at the preset reaction temperature is from the preset minimum acid hydrolysis reaction rate constant threshold to the preset maximum acid hydrolysis reaction rate constant threshold.

[0077] Specifically, the rate constant of acidolysis is affected by various factors such as temperature, catalyst, and material activity, and may fluctuate under complex actual operating conditions. By setting reasonable upper and lower thresholds, the calculated reaction rate can be effectively constrained, avoiding extreme values ​​that violate physical principles due to local parameter anomalies or numerical oscillations. This constraint mechanism not only prevents calculation divergence but also ensures that the acidolysis reaction kinetics remain within the range consistent with actual process experimental data, providing reliable kinetic boundary conditions for subsequent accurate evaluation of leaching efficiency and optimization of reactor operating parameters.

[0078] In some embodiments of this application, the oxidation reaction equation in step S400 specifically includes the following.

[0079] (1) The reaction rate of ferrous ions being oxidized by oxygen to ferric ions is positively correlated with the product of oxygen concentration, ferrous ion concentration and hydrogen ion concentration in the liquid phase.

[0080] Specifically, in the oxygen leaching system of lithium iron phosphate, the oxidation of ferrous ions is the core link in the entire reaction chain, and its reaction rate is directly limited by the synergistic effect of the oxidant (oxygen), the reaction substrate (ferrous ions), and the ambient acidity (hydrogen ions). This positive correlation allows numerical simulations to accurately reflect the combined constraints of oxygen dissolution, ferrous ion generation, and system acidity on the oxidation process, thus providing a mathematical description consistent with the basic principles of chemical kinetics for accurately predicting the rate of ferrous ion generation and the evolution of the overall redox environment within the reactor.

[0081] (2) The calculation of the reaction rate of ferrous ions being oxidized to ferric ions by oxygen is based on the oxidation reaction rate constant, the oxygen concentration in the liquid phase, the ferrous ion concentration, and the hydrogen ion concentration.

[0082] Specifically, by using the oxidation reaction rate constant as the core proportionality coefficient and the real-time concentrations of oxygen, ferrous ions, and hydrogen ions as dynamic variables, this calculation formula transforms the complex oxidation reaction mechanism into a numerical source term that can be solved by a computer. This not only enables real-time dynamic tracking of the oxidation reaction rate but also ensures that the calculated reaction rate can adaptively adjust to changes in the component concentrations of the local flow field within the reactor. This precisely couples the microscopic oxidation reaction process into the macroscopic flow field simulation, significantly improving the accuracy and reliability of the overall leaching process simulation.

[0083] (3) The oxidation reaction rate constant is calculated by the Arrhenius equation, and its value range at the preset reaction temperature is from the preset minimum oxidation reaction rate constant threshold to the preset maximum oxidation reaction rate constant threshold.

[0084] Specifically, by precisely quantifying the exponential effect of temperature on the oxidation reaction rate, the physical rationality and computational stability of the numerical simulation are ensured. The Arrhenius equation can scientifically reflect the physicochemical law that the reaction rate constant increases exponentially with increasing temperature, enabling the model to accurately capture the feedback regulation effect of the heat of reaction on the oxidation process.

[0085] Meanwhile, setting reasonable upper and lower thresholds can effectively constrain the calculated rate constant, avoiding abnormal extreme values ​​in the reaction rate that violate physical principles due to extreme fluctuations in the local temperature field or numerical calculation errors. This constraint mechanism not only prevents calculation divergence but also ensures that the oxidation reaction kinetics always remain within the range consistent with actual process experimental data, providing reliable kinetic boundary conditions for subsequent accurate evaluation of leaching efficiency and optimization of reactor thermal management parameters.

[0086] In some embodiments of this application, in step S300, the general control equation may further include an energy equation; in the energy equation, a reaction heat source term is introduced, which is calculated based on the reaction rate of the leaching reaction equation and the reaction rate of the oxidation reaction equation; the temperature boundary of the reaction system is set as a preset isothermal condition.

[0087] Specifically, a reaction heat source term is introduced into the energy equation to realistically reproduce the energy release and conversion patterns during the oxygen leaching process of lithium iron phosphate from a thermodynamic perspective. Both the leaching and oxidation reactions are chemical processes accompanied by heat changes. As a key source term in the energy conservation equation, the reaction heat source term can be used to calculate the heat generation rate of the system in real time based on the reaction rates of the leaching and oxidation reaction equations.

[0088] By introducing this source term, the numerical simulation system no longer treats the reactor as an isothermal flow device, but can accurately capture the temperature changes and heat distribution in local areas during the reaction process. This allows for a true reflection of the coupled effects of exothermic or endothermic effects on fluid properties, reaction kinetics, and mass transfer rates, providing a scientific thermodynamic calculation basis for assessing the potential role of reaction heat effects on leaching efficiency.

[0089] Furthermore, the heat source term was calculated based on the reaction rates of the leaching reaction equation and the oxidation reaction equation, realizing the dynamic coupling of chemical reaction kinetics and the energy conservation equation. The heat of reaction is not a constant value, but changes in real time with factors such as reactant concentration, temperature, and catalyst activity.

[0090] By using the reaction rate as the basis for calculating the heat source term, the model can accurately quantify the heat released or absorbed per unit time due to the breaking and formation of chemical bonds, ensuring that the spatiotemporal distribution of the heat source term is highly consistent with the actual chemical reaction process. This approach effectively avoids simulation bias caused by simplifying the heat of reaction to a constant value, enabling the numerical simulation results to truly reflect the dynamic evolution of the initial intense exothermic reaction or the later thermal equilibrium, significantly improving the accuracy of the thermo-chemical-fluid multiphysics coupling simulation.

[0091] Furthermore, setting the temperature boundary of the reaction system to a preset isothermal condition provides a stable thermodynamic environment constraint for numerical simulation, ensuring the physical rationality and engineering applicability of the calculation results. In actual industrial reactor operation, the reaction temperature is usually maintained stably through jacket cooling or an external heat exchange system to avoid excessively high or low temperatures affecting reaction selectivity and equipment safety.

[0092] Setting isothermal boundary conditions in the model can simulate this external thermal regulation mechanism, preventing the system temperature from rising indefinitely due to the accumulation of reaction heat, thus ensuring that the simulation process is always in a thermal equilibrium state that meets the actual process requirements. This boundary condition not only improves the stability of the numerical solution, but also provides a reliable benchmark for studying the relationship between flow field structure and reaction performance at constant operating temperatures, which helps to optimize the thermal management design and operating parameter control of the reactor.

[0093] In some embodiments of this application, step S500, setting boundary conditions, includes the following steps.

[0094] Step S511: Set the gas inlet boundary conditions: the inlet velocity is the preset minimum gas inlet velocity to the preset maximum gas inlet velocity, the temperature is the preset reaction temperature, and the oxygen mass fraction is the preset oxygen mass fraction threshold.

[0095] In step S511, a power source and material source that meet the actual process requirements are provided for the computational domain. The gas inlet velocity directly determines the gas holdup in the reactor, the upward kinetic energy of the bubble plume, and the overall internal circulation intensity. A reasonable velocity range setting can simulate the flow field characteristics under different aeration intensities. At the same time, specifying the inlet temperature and oxygen purity allows for precise control of the initial thermodynamic state entering the system and the oxidant supply, providing stable and reliable initial input conditions for subsequent accurate simulation of gas-liquid mass transfer and oxidation reactions.

[0096] Step S512: Set the liquid inlet boundary conditions: the inlet velocity is from the preset minimum liquid inlet velocity to the preset maximum liquid inlet velocity, the sulfuric acid concentration is from the preset minimum sulfuric acid concentration to the preset maximum sulfuric acid concentration, and the solid phase volume fraction is from the preset minimum solid phase volume fraction threshold to the preset maximum solid phase volume fraction threshold.

[0097] In step S512, the liquid phase environment and material load required for the leaching reaction are accurately recreated. The liquid inlet velocity affects the liquid phase replenishment rate and initial momentum, while the sulfuric acid concentration and solid volume fraction directly define the initial chemical potential of the reactants (hydrogen ions) and the initial concentration of the material to be treated (lithium iron phosphate particles) in the reaction system. This series of boundary conditions ensures that the simulation system can operate under a material environment consistent with actual production ratios, thus realistically reflecting the impact of different feeding conditions on leaching efficiency and reaction progress.

[0098] Step S513, set the wall boundary conditions: use a no-slip wall and a standard wall function, and keep the wall temperature constant at the preset reaction temperature.

[0099] Step S513 accurately describes the momentum exchange and heat transfer between the fluid and the solid wall of the reactor. The no-slip condition mandates that the fluid velocity close to the wall is zero, which aligns with the physical characteristics of viscous fluids and realistically simulates the wall's resistance effect on fluid flow and the formation of the boundary layer. Combined with standard wall functions, the numerical computation challenges of turbulent flow near the wall can be effectively addressed without extremely fine meshing near the wall, improving the accuracy of near-wall flow field simulation while maintaining computational efficiency.

[0100] In addition, by setting the wall temperature to a constant value, the thermal equilibrium state between the reactor wall and the environment or external temperature control system is simulated, providing a stable thermodynamic boundary constraint for the entire computational domain and preventing temperature field calculation distortion caused by unclear wall thermal boundary definition.

[0101] In some embodiments of this application, step S500 involves numerically solving and iterating the computational grid, including the following steps.

[0102] Step S521: The SIMPLE algorithm is used for pressure-velocity coupling.

[0103] In step S521, the SIMPLE algorithm (semi-implicit pressure correlation equation method) is used to perform pressure-velocity coupling, efficiently and stably solving the problem of pressure and velocity field coupling in incompressible or weakly compressible fluid flow. The SIMPLE algorithm uses a "guess-correction" iterative mechanism to first solve the momentum equation based on the estimated pressure field to obtain the velocity field, and then uses the pressure correction equation to force the mass conservation (continuity equation) to be satisfied, thereby correcting the pressure and velocity.

[0104] This method can effectively handle complex multiphase flow fields in airlift reactors. While ensuring computational accuracy, it significantly reduces the computational cost of solving large-scale equation systems simultaneously, providing a fundamental algorithmic guarantee for achieving steady-state or transient flow field simulation.

[0105] Step S522: Discretize the pressure term using the PRESTO! format, and discretize the momentum term, energy term, and volume fraction term using the second-order upwind format.

[0106] In step S522, the PRESTO! scheme is particularly suitable for handling flow problems with strong body forces, high swirl numbers, or highly distorted grids, accurately capturing the steep pressure gradients caused by bubble plumes and intense internal circulation within the reactor. Compared to the first-order scheme, the second-order upwind scheme has higher cutoff accuracy when discretizing convection terms such as momentum, energy, and volume fraction, effectively reducing numerical diffusion (false diffusion) phenomena, thus more realistically reproducing the fine structure of the velocity field, temperature field, and phase distribution within the reactor.

[0107] Step S523: Set the pressure relaxation factor to a preset minimum pressure relaxation factor to a preset maximum pressure relaxation factor, and the momentum relaxation factor to a preset minimum momentum relaxation factor to a preset maximum momentum relaxation factor.

[0108] In step S523, the convergence and stability of the numerical solution are ensured by adjusting the variable update magnitude during the iteration process. In separate solution algorithms such as SIMPLE, the equations are solved sequentially, and excessively large update step sizes can easily lead to computational divergence. By limiting the relaxation factor to a reasonable threshold range, it is equivalent to moderately "damping" the pressure and momentum corrections calculated in each iteration, preventing computational oscillations caused by drastic fluctuations in the local flow field, guiding the residuals to decrease smoothly, and thus gradually approaching the true physical convergence solution while ensuring computational robustness.

[0109] Step S524: Set the time step to a preset minimum time step to a preset maximum time step, and perform transient solution until the residual converges to below the preset residual convergence threshold.

[0110] In step S524, the gas-liquid-solid three-phase flow within the airlift reactor exhibits strong transient pulsations. A reasonable transient time step setting ensures sufficient resolution in the time dimension for the numerical simulation, thereby realistically reflecting the dynamic processes of bubble generation, rise, breakup, and particle suspension. Simultaneously, using residual convergence to a preset threshold as the criterion for iteration termination mathematically guarantees that the calculation results within each time step satisfy the mass, momentum, and energy conservation equations, ensuring the reliability and engineering reference value of the final simulation data.

[0111] In some embodiments of this application, reference is made to Figure 2 The method is applied to an airlift reactor, which is an internal circulation airlift reactor, consisting of a cylinder 101, a guide tube 102, a gas distributor 103, a gas-liquid separation zone 104, and a baffle 105. The guide tube divides the reactor into an upflow zone 201 (inside the guide tube) and a downflow zone 202 (outside the guide tube) for the lithium iron phosphate slurry. The gas distributor 103 is located below the bottom guide tube and adopts a microporous aeration structure (pore size 10 to 50 μm).

[0112] In some embodiments of this application, the leaching system involved is a gas-liquid-solid three-phase system, where the gas phase is pure oxygen generated by the oxidation of liquid oxygen, the liquid phase is a dilute sulfuric acid aqueous solution (concentration 3.7 mol / L), and the solid phase is lithium iron phosphate solid particles; the process parameters are set as follows: reaction temperature 60℃, slurry density 1.35 g / cm³, solid phase mass fraction 28%, and reaction pressure at atmospheric pressure; the core reaction is: oxygen oxidation of Fe²⁺. + Fe³ +It promotes the acid hydrolysis and leaching of lithium iron phosphate, which includes two core basic reactions: oxidation and leaching.

[0113] In some embodiments of this application, a specific implementation process of a numerical simulation method for enhancing oxygen leaching of lithium iron phosphate is provided, including the following steps.

[0114] (1) Establish a geometric model of the airlift reactor. The model adopts a two-dimensional axisymmetric form, which aims to ensure the calculation accuracy while taking into account the calculation efficiency. If higher accuracy is desired, a three-dimensional model can also be established, but it will face the problems of increased calculation volume and difficulty in convergence.

[0115] (2) Mesh generation was performed, with a focus on optimizing the mesh quality of the gas-liquid contact area, the guide tube wall, and the gas distributor area. A structured hexahedral mesh was used to improve computational convergence and accuracy, and to avoid numerical discretization errors caused by unstructured meshes. The reactor was divided into four regions: the upflow region, the downflow region, the gas distributor region, and the gas-liquid separation region, to facilitate subsequent local parameter adjustments.

[0116] The global mesh size is set to 2 to 5 mm. Boundary layer refinement is carried out in key areas such as the gas distributor, the wall of the guide tube, and the gas-liquid interface. Five boundary layers are set with a growth rate of 1.2, and the mesh size is refined to 0.5 to 1 mm. In areas with high solid content (28% solid mass fraction), the local mesh is refined to 1 to 2 mm to avoid distortion in the simulation of solid particles. Finally, the mesh quality is checked to ensure that the mesh orthogonality quality is not less than 0.8, the distortion is not greater than 0.2, and there are no negative meshes or mesh distortions to ensure computational stability.

[0117] (3) Activate the multiphase flow model and select the Euler-Euler multiphase flow model. This model is suitable for gas-liquid-solid three-phase flow simulation and can accurately describe the phase distribution characteristics of a high solid content (28%) system. Define three phases: gas phase (oxygen) as the main phase, liquid phase (dilute sulfuric acid aqueous solution) as the secondary phase, and solid phase (lithium iron phosphate particles) as the secondary phase. Set the liquid phase as the continuous phase and the gas and solid phases as the dispersed phases.

[0118] Table 1. Examples of physical property parameters for gas, liquid, and solid phases.

[0119] Referring to Table 1 above, the physical properties of the gas-liquid-solid three phases were set as follows in this simulation: the gas phase was oxygen, with a density of 1.29 kg / m³ and a dynamic viscosity of 2.1 × 10⁻⁶. -5 Pa·s, and its diffusion coefficient in the liquid phase was set to 2.0 × 10⁻⁶ Pa·s. -9 cm² / s; the liquid phase is a dilute sulfuric acid aqueous solution with a density set at 1200 kg / m³ and a dynamic viscosity of 1.8 × 10⁻⁶ cm² / s; -The specific heat capacity is 4.2 × 10³ Pa·s, the sulfuric acid concentration is 3.7 mol / L, the solid phase is lithium iron phosphate particles with a density of 3600 kg / m³ and an average particle size distribution between 50 and 100 μm.

[0120] (4) Activate the Species Transport model to simulate the concentration distribution and mass transfer process of each component in the system. Regarding component definition, a total of seven components are defined: gas phase component oxygen, and liquid phase components water, sulfuric acid, ferrous ions, ferric ions, lithium ions, and phosphate ions. Regarding component property settings, the molecular diffusion coefficients of each component in dilute sulfuric acid aqueous solution are specified, with the diffusion coefficients of ferrous ions, ferric ions, lithium ions, and phosphate ions being 1.0 × 10⁻⁶ respectively. -9 m² / s, 0.9×10 -9 m² / s, 1.5×10 -9 m² / s, 0.8×10 -9 m² / s; In terms of phase-component correlation, the gas phase is defined to contain only oxygen, the liquid phase to contain the other 6 components besides oxygen, and the solid phase does not participate in component transport but only participates in flow as a dispersed phase.

[0121] (5) The flow characteristics of the gas-liquid-solid three-phase system are described by the volume fraction equation, momentum equation, and energy equation. The general governing equations for each phase are as follows: 1. The volume fraction equation satisfies the following formula: The sum of the volume fractions of each phase is 1, i.e. ; For phase identification (g=gas phase, l=liquid phase, s=solid phase); for Phase volume fraction; for Phase density (kg / m³); Time (s); for Phase velocity vector (m / s); Operators help translate physical laws (such as conservation of mass and conservation of momentum) into mathematical equations that describe how matter flows from one grid cell to another. for Xiangdao The mass transfer rate of the phase (kg / (m³·s)) is not considered in this simulation because there is no interphase mass generation / consumption. .

[0122] 2. The momentum equation (conservation of momentum in each phase) satisfies the following formula: ; in, Static pressure (Pa); for Stress tensor of the phase (Pa); The acceleration due to gravity is 9.81 m / s². for phase and Interphase forces (N / m³) include drag, lift, and virtual mass forces, with drag being the primary force and the other forces being negligible. This represents the product of dyadic vectors.

[0123] Furthermore, the Schiller-Naumann drag model is adopted, which is suitable for scenarios where the Reynolds number of dispersed phase particles Re ≤ 1000, and the drag coefficient is... The calculation is as follows: ; in, The particle Reynolds number; The average particle size (m) of lithium iron phosphate particles. The dynamic viscosity of the liquid phase is (Pa·s). , denoted as the velocity vector (m / s) of the gas and liquid phases.

[0124] 3. In the energy equation, the temperature of each phase is conserved, and the reaction temperature is constant at 60℃. Therefore, it simplifies to a temperature constraint, satisfying the following formula: ;in, for The specific heat capacity of a phase at constant pressure (J / (kg·K)); for The corresponding temperature (K); for Thermal conductivity of the phase (W / (m·K)); for phase and Heat exchange rate between phases (W / m³); The heat of reaction is expressed as W / m³. This leaching reaction is a mild exothermic reaction, and the heat of reaction at 60°C is negligible. Furthermore, the temperature of each phase is set to 60℃ (333.15K) to simplify the calculation.

[0125] 4. Definition of gas-liquid mass transfer: Oxygen diffuses from the gas phase to the liquid phase. The Licht-Stephan gas-liquid mass transfer model is used to simulate the dissolution and diffusion process of oxygen in the liquid phase. The mass transfer equation is as follows: ; in, This represents the mass fraction of oxygen in the liquid phase. is the diffusion coefficient of oxygen in the liquid phase (m² / s); Given the gas-liquid volumetric mass transfer coefficient (1 / s), at 60℃ and normal pressure, and considering the microporous aeration conditions, set... ; The equilibrium mass fraction of oxygen in the liquid phase, calculated using Henry's Law, satisfies the following formula: ;in, The partial pressure of oxygen (Pa); Henry's Law (Pa·m³ / kg) represents the Henry's Law coefficient for oxygen in a dilute sulfuric acid system at 60°C. =7.8×10 6 Pa·m³ / kg.

[0126] 5. Definition of solid-liquid mass transfer: An acidolysis reaction occurs on the surface of lithium iron phosphate particles, and Fe²⁺… + The solid-liquid mass transfer process, from the solid surface to the liquid phase, is described by Fick's law, and the mass transfer equation is as follows: ; in, , Fe²⁺ on the solid surface and in the liquid phase, respectively. + The mass fraction; For Fe² + The diffusion coefficient (m² / s) at the solid surface is taken as 1.0 × 10⁻⁶. - ¹ 0 m² / s; Let be the solid-liquid mass transfer coefficient (m / s), taken as 5.0 × 10⁻⁶. -6 m / s; The solid-phase surface area (m² / m³) is calculated from the particle size: .

[0127] 6. Define the oxygen leaching process of lithium iron phosphate, which includes two core elementary reactions: the leaching reaction (lithium iron phosphate reacts with dilute sulfuric acid to produce Fe²⁺). + ) and oxidation reaction (Fe² + Oxidized by oxygen to Fe³ + The volumetric reaction model is used. Wherein: The leaching reaction (acidolysis of solid-phase lithium iron phosphate with dilute sulfuric acid) satisfies the following formula: ; ; in, The acidolysis reaction rate is expressed as mol / (m³·s). The rate constant of the acidolysis reaction (m) 2.4 / (mol 0.8·s), at 60℃, =2.5×10 -4 m 2.4 / (mol 0.8 ·s); The concentration of dilute sulfuric acid is mol / m³; the exponent 0.8 represents the reaction order of the acidolysis reaction with respect to the concentration of sulfuric acid. This represents the volume fraction of the solid phase.

[0128] Furthermore, the oxidation reaction (Fe²⁺ in the liquid phase) + (Oxidized by oxygen) satisfies the following formula: ; ; in, The oxidation reaction rate is expressed as mol / (m³·s). The oxidation reaction rate constant (m) 6 / (mol²·s)), calculated using the Arrhenius equation: ;in: Pre-exponential factor (m) 6 / (mol²·s)), A=1×10 8 m 6 / (mol²·s); Activation energy (J / mol) =32000 J / mol; is the gas constant (J / (mol·K)). =8.314 J / (mol·K); The reaction temperature is K. =333.15K (60℃), substituting this into the calculation, we get that at 60℃, ; , , oxygen in the liquid phase Fe² + H + The concentration (mol / m³).

[0129] (6) Based on the structural characteristics of the airlift reactor and the actual process parameters, the following boundary conditions are set to ensure that the simulation closely matches the actual operation: First, set the gas inlet location to the gas distributor inlet, at the annular boundary; set the phase to gas only (oxygen), with pure oxygen input; set the velocity to 0.05~0.2 m / s; set the temperature to 60℃ (333.15 K); and set the composition to... Quality fraction = 1.0.

[0130] Secondly, the liquid inlet was set at the bottom of the reactor, the liquid feed inlet; the phases were set as liquid and solid, with a solid volume fraction of 0.12 (corresponding to a mass fraction of 28%); the velocity was set to 0.02~0.05 m / s; the temperature was set to 60℃ (333.15 K); the liquid phase composition was H2SO4 concentration of 3.7 mol / L, and the remaining components (Fe²⁺) were... + Fe³ + (etc.) Initial concentration = 0.

[0131] Furthermore, the outlet position is set at the top of the reactor, at the outlet of the gas-liquid separation zone; the pressure is set to atmospheric pressure; the reflux is set to: reflux volume fraction = 0, to ensure smooth discharge of the gas, liquid, and solid phases; the components are set to free outlets, with each component naturally discharged according to its concentration distribution.

[0132] Then, the wall boundaries are set as the reactor cylinder wall and the guide tube wall: no slip wall, using the standard wall function; the temperature is a constant temperature boundary, set to 60℃ (333.15K).

[0133] Finally, the symmetry boundary is set as a two-dimensional axisymmetric model, which specifically includes the following: The solution algorithm adopts the SIMPLE algorithm (pressure-velocity coupling algorithm), which is suitable for steady-state / transient solutions of multiphase flow and has good convergence. The discretization scheme uses pressure discretization, such as the PRESTO! scheme (suitable for solving pressure fields in multiphase flows with high accuracy); the momentum, composition, and volume fraction discretization uses the second-order upwind scheme (reducing numerical discretization errors and improving computational accuracy). The pressure relaxation factor was set to 0.3–0.5; the momentum relaxation factor to 0.2–0.4; the component relaxation factor to 0.5–0.7; and the volume fraction relaxation factor to 0.2–0.3. Global initialization was adopted, with the initial temperature set at 60℃, the initial pressure at 101.325 kPa, the liquid phase volume fraction at 0.88, the solid phase volume fraction at 0.12, the gas phase volume fraction at 0, and the initial concentration of each reaction component (except H2SO4) at 0. Residual convergence is used, and all residuals (continuity, momentum, composition, volume fraction) are ≤1×10⁻⁶. -6 Mass conservation: Total mass flow rate error at inlet and outlet ≤ 1%; Transient solution: Time step set to 0.1~1s, iteration steps ≥ 1000 steps, until all parameters tend to stabilize.

[0134] In some embodiments of this application, the above examples are further described to provide a numerical simulation example of enhanced oxygen leaching of lithium iron phosphate, specifically including the following steps.

[0135] (1) Geometric modeling: A two-dimensional axisymmetric internal circulation airlift reactor model is established. The reactor cylinder diameter D = 1.0m, the guide tube diameter 0.7m, the total height 3.0m, the gas distributor width 5mm, and the micropore diameter 30μm.

[0136] (2) Mesh generation: ANSYS Meshing was used to generate structured hexahedral meshes with a global mesh size of 3 mm. The boundary layer of the gas distributor and the guide tube wall was refined (5 layers, growth rate 1.2). The mesh in the high solid content region was refined to 1.5 mm with a mesh orthogonality quality of 0.85 and a twist of 0.15. The mesh was exported in MSH format. (3) FLUENT basic settings: Start the double precision 2D solver, import the mesh and check that it is qualified, activate the Euler-Euler multiphase flow model, and define the three phases: gas phase (oxygen), liquid phase (dilute sulfuric acid aqueous solution), and solid phase (lithium iron phosphate particles); Phase properties and component settings: Set the physical properties of each phase according to the parameters in Table 1 above, activate the component transport model, define 7 components, and set the diffusion coefficient of each component; (4) Model and Equation Setup: Import the three-phase flow equation, mass transfer equation, and chemical reaction kinetic equation described in this invention, set the Schiller-Naumann drag model and the Licht-Stephan gas-liquid mass transfer model, and substitute them into the model. =2.5×10 - 4 m 2.4 / (mol 0.8 ·s), =3.8×10 - ³m 6 Parameters such as / (mol²·s); (5) Boundary conditions: gas inlet velocity 0.1 m / s, liquid inlet velocity 0.03 m / s, solid volume fraction 0.12, sulfuric acid concentration 2.0 mol / L, outlet atmospheric pressure, wall constant temperature 60℃; (6) Solution calculation: The SIMPLE algorithm, second-order upwind scheme, pressure relaxation factor 0.4, momentum relaxation factor 0.3, component relaxation factor 0.6, global initialization, transient solution, time step 0.5s, 1200 iterations, until the residual ≤1×10 -6 The mass conservation error is ≤0.8%.

[0137] Secondly, refer to Figure 3 This application provides a numerical simulation system for enhanced oxygen leaching of lithium iron phosphate, used to execute the aforementioned numerical simulation method for enhanced oxygen leaching of lithium iron phosphate, including a geometric modeling unit, a model configuration unit, a dynamic loading unit, a solution control unit, and a result output unit.

[0138] The geometric modeling unit is used to establish the geometric model of the airlift reactor and perform meshing operations, including boundary layer refinement and local mesh refinement. This transforms the physical reactor structure into a computer-recognizable geometric model. This unit not only establishes the internal circulation structural features, including the guide tube and microporous gas distributor, but also performs the crucial meshing operations.

[0139] By employing a structured hexahedral mesh and implementing boundary layer refinement and local mesh refinement for the gas distributor region, the guide tube wall, and the high solid concentration region, this element ensures that the computational mesh can accurately capture the geometric details of key physical phenomena such as bubble generation, interphase friction, and particle aggregation. This provides a high-precision spatial discretization basis for subsequent flow field calculations and effectively avoids numerical discretization errors and simulation distortions caused by mesh coarseness.

[0140] The model configuration unit is used to configure physical models, including the Eulerian-Eulerian multiphase flow model, component transport model, Schiller-Naumann drag model, and Licht-Stephan gas-liquid mass transfer model. Specifically, this unit treats the gas, liquid, and solid phases as an interpenetrating continuous medium by activating the Eulerian-Eulerian multiphase flow model, which is particularly suitable for describing dense fluid systems with a solid content of up to 28%.

[0141] Building upon this foundation, the unit incorporates a component transport model to track the distribution of seven key components and introduces the Schiller-Naumann drag model to accurately calculate interphase resistance. Simultaneously, it integrates the Licht-Stephan gas-liquid mass transfer model to quantify the dissolution and diffusion process of oxygen from the gas phase to the liquid phase. By inputting precise physical properties such as the density, viscosity, and particle size of oxygen, dilute sulfuric acid, and lithium iron phosphate particles, the unit imbues the model with a realistic physical identity, ensuring a high degree of consistency between the simulation environment and actual hydrometallurgical conditions.

[0142] The kinetics loading unit is used to import the leaching and oxidation reaction equations for lithium iron phosphate. By adding these reaction kinetic equations as source terms to the general governing equations, the simulation system no longer merely calculates fluid flow but can calculate in real time the consumption and generation of substances due to the chemical reaction. This allows the model to accurately predict the lithium-ion leaching rate, oxygen consumption, and heat of reaction, realistically reproducing the chemical reaction process where oxygen acts as an oxidant to enhance leaching, providing a core basis for evaluating leaching efficiency.

[0143] The solver control unit couples the geometric and physical models, and sets up the SIMPLE algorithm and a second-order upwind scheme to iteratively solve the coupled numerical model, thereby reducing numerical diffusion while maintaining computational accuracy. This unit also allows users to set relaxation factors for pressure, momentum, and components, guiding the residuals to decrease smoothly through repeated iterative solutions to the coupled numerical model. This process ensures that the mass, momentum, and energy conservation equations are satisfied at each time step, thus gradually approximating the true physical convergence solution in transient simulations, ultimately outputting stable flow field and reaction data.

[0144] The results output unit is used to output oxygen distribution cloud map, flow field velocity vector map, and lithium leaching rate change over time in the airlift reactor.

[0145] Specifically, the oxygen distribution cloud map inside the reactor is used to visually demonstrate the dissolution and distribution of the oxidant; the flow field velocity vector map is used to reveal the internal circulation path and mixing intensity of the fluid; and the curve of lithium leaching rate changing over time is used to reflect the progress and efficiency of the chemical reaction in real time. These visualization results intuitively reveal the mass transfer bottlenecks, mixing effects, and reaction kinetics characteristics within the reactor, providing scientific and quantitative theoretical support for the structural optimization and process parameter control of airlift reactors.

[0146] In summary, the numerical simulation method and system for enhanced oxygen leaching of lithium iron phosphate provided in this application have the following technical effects.

[0147] This solution effectively addresses the disconnect between existing simulation technologies and actual hydrometallurgical conditions by constructing a complete system encompassing geometric modeling, model configuration, dynamic loading, and solution control. Through the synergy of the Euler-Euler multiphase flow model and the Schiller-Naumann drag model, the system accurately recreates the environment of a high-density slurry with a solid content of 28%. Combined with the Licht-Stephan gas-liquid mass transfer model and core reaction kinetic equations, it achieves deep coupling of the flow field, concentration field, and chemical reaction field, significantly improving the accuracy of oxygen utilization and lithium leaching rate predictions.

[0148] Furthermore, this scheme utilizes a two-dimensional axisymmetric model combined with a local mesh refinement strategy, significantly reducing computational costs while ensuring solution stability through the SIMPLE algorithm and a second-order upwind scheme. Its output flow field vector diagram and mass concentration variation curves can intuitively reveal mass transfer bottlenecks and mixing characteristics within the reactor, providing quantitative theoretical support for the structural optimization of airlift reactors and the control of process parameters such as gas-liquid ratio and temperature. This effectively reduces industrial trial costs and significantly improves leaching efficiency.

[0149] It should be noted that in all specific embodiments of this application, all data processing activities related to user identity or personal characteristics, such as user information, user behavior data, historical data, and location information, will be conducted in accordance with the principles of legality, legitimacy, and necessity. All data collection, use, storage, and processing will be subject to compliance with applicable national and regional laws, regulations, and industry standards, and informed consent from users will be obtained in a clear and explicit manner before processing. For the processing of sensitive personal information, separate consent from users will be obtained through prominent means such as pop-up prompts and independent confirmation pages. If any processing conflicts with laws and regulations, the laws and regulations will prevail, and necessary data processing will only be carried out within the scope permitted by laws and regulations, ensuring that all data-based applications, analyses, and technical implementations are conducted within the scope permitted by laws and regulations.

[0150] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0151] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0152] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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 several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0154] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Additionally, computer-readable media can even be paper or other suitable media on which programs can be printed, for example, by optically scanning the paper or other media, then editing, interpreting, or, if necessary, processing it in a suitable manner to obtain the program electronically, and then storing it in computer memory.

[0155] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0156] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0157] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0158] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A numerical simulation method for enhancing oxygen leaching of lithium iron phosphate, characterized in that, The method is applied to an airlift reactor, and the method includes: Step S100: Establish a geometric model of the airlift reactor and perform mesh generation on the geometric model to obtain a computational mesh for numerical calculation. Step S200: Based on the computational grid, construct an Euler-Euler multiphase flow model and define the physical property parameters of the gas phase, liquid phase and solid phase therein; the gas phase is oxygen, the liquid phase is dilute sulfuric acid aqueous solution and the solid phase is lithium iron phosphate particles. Step S300: Construct general governing equations in the Euler-Euler multiphase flow model to describe the flow and heat and mass transfer laws between the phases; the general governing equations include volume fraction equations, momentum equations and component transport equations; Step S400: The core reaction kinetic equation for the oxygen leaching of lithium iron phosphate is coupled as a source term to the general governing equation to characterize the material transformation law in the reaction process; the core reaction kinetic equation includes the leaching reaction equation and the oxidation reaction equation; Step S500: Based on the general control equation coupled with the core reaction kinetic equation, set boundary conditions and solution parameters, perform numerical solution and iteration on the computational grid, and output the simulation results of the flow field, mass transfer and reaction process in the reactor.

2. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S100, the geometric model is meshed, specifically including: Structured hexahedral meshes are used for partitioning; Boundary layer densification is applied to the gas distributor area, the guide tube wall, and the gas-liquid interface area. The number of boundary layer layers is set to a preset number of boundary layer layers, the growth rate is between a preset minimum boundary layer growth rate and a preset maximum boundary layer growth rate, and the mesh size is refined to between a preset minimum boundary layer mesh size and a preset maximum boundary layer mesh size. For regions where the solid concentration is higher than a preset solid concentration threshold, local mesh refinement is performed, with the mesh size ranging from the preset minimum local mesh size to the preset maximum local mesh size. Among them, the orthogonal quality of the mesh is not lower than the preset orthogonal quality threshold, and the distortion is not higher than the preset distortion threshold.

3. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S300, the general governing equations are constructed, including: In the momentum equation, an interphase force source term is introduced, and the Schiller-Naumann drag force model is used to calculate the interphase force source term; the drag force coefficient of the Schiller-Naumann drag force model is limited to a piecewise function calculation: When the particle Reynolds number is less than or equal to the preset first Reynolds number threshold, the preset first drag coefficient calculation formula is used; When the particle Reynolds number is between a preset first Reynolds number threshold and a preset second Reynolds number threshold, the preset second drag coefficient calculation formula is used.

4. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S300, constructing the general governing equations further includes: In the component transport equation, a gas-liquid mass transfer source term is introduced, and the Licht-Stephan gas-liquid mass transfer model is used to calculate the gas-liquid mass transfer source term; the mass transfer equation of the Licht-Stephan gas-liquid mass transfer model includes the gas-liquid volumetric mass transfer coefficient. The range of the gas-liquid volumetric mass transfer coefficient is from the preset minimum gas-liquid volumetric mass transfer coefficient threshold to the preset maximum gas-liquid volumetric mass transfer coefficient threshold.

5. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S300, constructing the general governing equations further includes: In the component transport equation, a diffusion term for the solid-liquid mass transfer process is constructed based on Fick's law. In the diffusion term of the solid-liquid mass transfer process, the value range of the solid-liquid mass transfer coefficient is from a preset minimum solid-liquid mass transfer coefficient threshold to a preset maximum solid-liquid mass transfer coefficient threshold.

6. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S400, the leaching reaction equation is specifically as follows: The reaction rate of lithium iron phosphate reacting with dilute sulfuric acid to generate ferrous ions is positively correlated with the first reaction order power of the dilute sulfuric acid concentration and the solid phase volume fraction. The calculation of the reaction rate of lithium iron phosphate reacting with dilute sulfuric acid to generate ferrous ions is based on the acidolysis reaction rate constant, the concentration of dilute sulfuric acid, the first reaction order, and the solid phase volume fraction. The range of the acidolysis reaction rate constant at the preset reaction temperature is from the preset minimum acidolysis reaction rate constant threshold to the preset maximum acidolysis reaction rate constant threshold.

7. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S400, the oxidation reaction equation is specifically as follows: The reaction rate of ferrous ions being oxidized by oxygen to ferric ions is positively correlated with the product of the concentrations of oxygen, ferrous ions, and hydrogen ions in the liquid phase. The calculation of the reaction rate of ferrous ions being oxidized by oxygen to ferric ions is based on the oxidation reaction rate constant, the oxygen concentration in the liquid phase, the ferrous ion concentration, and the hydrogen ion concentration. The oxidation reaction rate constant is calculated using the Arrhenius equation, and its value ranges from the preset minimum oxidation reaction rate constant threshold to the preset maximum oxidation reaction rate constant threshold at the preset reaction temperature.

8. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S500, setting boundary conditions includes: Set the gas inlet boundary conditions: the inlet velocity is the preset minimum gas inlet velocity to the preset maximum gas inlet velocity, the temperature is the preset reaction temperature, and the oxygen mass fraction is the preset oxygen mass fraction threshold. Set the liquid inlet boundary conditions: the inlet velocity is from the preset minimum liquid inlet velocity to the preset maximum liquid inlet velocity, the sulfuric acid concentration is from the preset minimum sulfuric acid concentration to the preset maximum sulfuric acid concentration, and the solid phase volume fraction is from the preset minimum solid phase volume fraction threshold to the preset maximum solid phase volume fraction threshold. Set wall boundary conditions: Use a no-slip wall and standard wall function, and keep the wall temperature constant at the preset reaction temperature.

9. The numerical simulation method for enhanced oxygen leaching of lithium iron phosphate according to claim 1, characterized in that, In step S500, numerical solutions and iterations are performed on the computational grid, including: The SIMPLE algorithm is used for pressure-velocity coupling. The pressure term is discretized using the PRESTO! scheme, and the momentum, energy, and volume fraction terms are discretized using the second-order upwind scheme. The pressure relaxation factor is set to a preset minimum pressure relaxation factor to a preset maximum pressure relaxation factor, and the momentum relaxation factor is set to a preset minimum momentum relaxation factor to a preset maximum momentum relaxation factor. Set the time step from the preset minimum time step to the preset maximum time step, perform transient solution, until the residual converges to below the preset residual convergence threshold.

10. A numerical simulation system for enhanced oxygen leaching of lithium iron phosphate, used to execute the numerical simulation method for enhanced oxygen leaching of lithium iron phosphate as described in any one of claims 1 to 9, characterized in that, include: The geometric modeling unit is used to build the geometric model of the airlift reactor and perform mesh generation operations, including boundary layer refinement and local mesh refinement. The model configuration unit is used to configure physical models, including the Euler-Euler multiphase flow model, component transport model, Schiller-Naumann drag model, and Licht-Stephan gas-liquid mass transfer model. A kinetic loading unit is used to import the leaching reaction equation and oxidation reaction equation of lithium iron phosphate; The solution control unit is used to couple the geometric model with the physical model, and sets up the SIMPLE algorithm and the second-order upwind scheme to iteratively solve the numerical model obtained after coupling. The result output unit is used to output the oxygen distribution cloud map, the flow field velocity vector map, and the curve of lithium leaching rate changing with time in the airlift reactor.

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