Numerical simulation method for nickel pressurized stirring reaction kettle leaching process

Abstract

The invention provides a numerical simulation method for the leaching process of a nickel pressurized stirred tank reactor, which comprises the following steps: creating a geometric model of the nickel pressurized stirred tank reactor, adopting a structured-unstructured mixed grid division mode for the geometric model, and performing grid division on a computational domain of the geometric model to obtain a grid file; constructing a multiphase flow numerical model in the reaction kettle; setting process conditions corresponding to the nickel leaching process in the grid file, and coupling and solving the multiphase flow numerical model to obtain simulation data of flow field characteristic indexes in the reaction kettle in the nickel leaching process; constructing a solid model which is scaled down in equal proportion, and scaling the stirring speed and the gas flow based on a similarity criterion; carrying out a water simulation experiment, and measuring entity experiment data of flow field characteristic indexes; comparing the simulation data with the entity experiment data to correct parameters of the multiphase flow numerical model; different process conditions are set in batches, the corrected multiphase flow numerical model is solved in a coupling mode, and simulation data of the leaching process of the reaction kettle under the different process conditions are obtained. The simulation precision can be improved.

Description

Technical Field

[0001] This invention belongs to the field of nickel metallurgical pressure leaching technology, specifically relating to a numerical simulation method for nickel pressure stirring reactor leaching process that combines numerical simulation and physical verification. Background Technology

[0002] Pressure acid leaching is the mainstream process for extracting nickel and cobalt from laterite nickel ore, with the core equipment being a pressure-stirred reactor. The high temperature, high pressure, and strong acid environment inside the reactor creates complex phase movements, and the fluid characteristics directly affect the nickel leaching efficiency and equipment lifespan.

[0003] Current research methods for leaching processes in reactors have shortcomings: traditional numerical simulation methods yield results that deviate significantly from industrial realities, and traditional physical experiments require the construction of industrial-grade equipment, which is costly, time-consuming, and difficult to verify under multiple operating conditions.

[0004] Therefore, in view of the shortcomings of the existing technology, it is necessary to design a new numerical simulation method for the nickel pressurized stirred reactor leaching process to solve the problems of low accuracy and high cost of traditional technology. Summary of the Invention

[0005] This invention aims to address the problems of traditional numerical simulations neglecting two-phase flow interaction and lacking physical verification, and provides a numerical simulation method for the leaching process of nickel in a pressurized stirred reactor, thereby improving simulation accuracy and guiding industrial optimization.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, this application provides a numerical simulation method for the leaching process of nickel in a pressurized stirred reactor, comprising:

[0008] Geometric modeling and mesh generation: A geometric model of a nickel pressurized stirred reactor, including the reactor body, stirring system, cooling coils, and supporting I-beams, is created in 3D modeling software. The geometric model is then imported into mesh generation software, and a hybrid structured-unstructured mesh generation method is used to mesh the computational domain of the geometric model to obtain the mesh file.

[0009] A multiphase flow numerical model was constructed: Based on the three-phase flow regime of gas, liquid, and solid in the reactor, a multiphase flow numerical model was constructed. The multiphase flow numerical model includes a heterogeneous flow model, a drag force model, and a turbulence model. Among them, the heterogeneous flow model is used to describe the three-phase flow of gas, liquid, and solid in the reactor, the drag force model is used to calculate the interaction forces between the gas and liquid phases and between the liquid and solid phases in the reactor, and the turbulence model is used to characterize the turbulence characteristics of the three-phase flow field in the reactor.

[0010] Parameter setting and model solving: Set the process conditions corresponding to the nickel leaching process in the mesh file; based on the set process conditions, couple and solve the multiphase flow numerical model to obtain the simulation data of the flow field characteristics in the reactor during the nickel leaching process;

[0011] Solid model validation: Construct a scaled-down solid model and scale the stirring speed and gas flow rate based on similarity criteria; conduct water simulation experiments to simulate sulfuric acid solution and air to simulate oxygen, and measure the solid experimental data of flow field characteristic indicators;

[0012] Model correction: By comparing the simulation data and the actual experimental data of the flow field characteristic index, the parameters of the multiphase flow numerical model are corrected; after the correction is completed, different process conditions are set in batches, and the corrected multiphase flow numerical model is solved in a coupled manner to obtain the simulation data (i.e., numerical simulation results) of the reactor leaching process under different process conditions.

[0013] In one possible implementation, the structured-unstructured hybrid meshing method is used to mesh the computational domain of the geometric model. This includes: for regions with gentle flow field changes, such as the middle of the reactor body, a hexahedral structured mesh is used to ensure computational efficiency; for regions with intense flow field shear and complex geometry, such as the periphery of the agitator, the walls, and the cooling coils, a tetrahedral unstructured mesh is used and local refinement is applied.

[0014] In one possible implementation, the mesh quality satisfies orthogonality ≥ 0.8 and distortion ≤ 0.2.

[0015] In one possible implementation, the meshing further includes: determining the optimal mesh size through mesh convergence analysis: based on the calculation deviation threshold of the target physical quantity, iteratively adjusting the mesh density and carrying out numerical calculations under multiple corresponding mesh sizes; when the calculation deviation of the target physical quantity caused by the change in mesh density meets the preset accuracy requirements, the corresponding mesh size is selected as the optimal mesh scheme.

[0016] The above scheme employs a hexahedral structured mesh to ensure computational efficiency in regions with gentle flow field changes, such as the middle of the vessel. For regions with intense flow field shear and complex geometry, such as the vicinity of the agitator, the wall surface, and the cooling coil, a tetrahedral unstructured mesh is used with local refinement. At the same time, the mesh quality is controlled to meet the requirements of orthogonality ≥ 0.8 and distortion ≤ 0.2. Finally, the optimal mesh size is determined through mesh convergence analysis, balancing the accuracy and efficiency of numerical computation.

[0017] In one possible implementation, the heterogeneous flow model adopts the Eulerian-Eulerian heterogeneous flow model, the drag force model adopts the Grace drag force model, and the turbulence model adopts the Realizable k-ε turbulence model.

[0018] In one possible implementation, the coupled solution of the multiphase flow numerical model includes: using the coupled solution algorithm in AnsysFluent to iteratively solve the multiphase flow numerical model, wherein the convergence criterion for the iterative solution is that the residual is less than a preset threshold and the number of steady-state calculation steps is greater than or equal to a preset number of steps.

[0019] In one possible implementation, the process conditions settings include setting inlet and outlet conditions, boundary conditions, process conditions, and physical property parameters; wherein, setting inlet and outlet conditions includes setting all flow channel inlets as velocity inlets, the top of the vessel as a pressure outlet, and the bottom slag outlet as a velocity outlet; wherein, the physical property parameters are dynamically adjusted with pressure and temperature.

[0020] In one possible implementation, scaling the stirring speed and gas flow rate based on similarity criteria includes: scaling the stirring speed based on the Froude number similarity criterion and scaling the gas flow rate based on the ventilation criterion similarity criterion.

[0021] In one possible implementation, the flow field characteristics include flow field uniformity and gas holdup;

[0022] In one possible implementation, the parameters of the multiphase flow numerical model are corrected in the model correction step as follows: if the gas holdup error in the flow field characteristic simulation data and the actual experimental data is greater than a preset gas holdup error threshold, the parameters of the drag model are adjusted; when the flow field uniformity deviation is greater than a preset flow field uniformity deviation threshold, the parameters of the turbulence model are optimized.

[0023] In one possible implementation, the preset gas holdup error threshold is 5%, and the preset flow field uniformity deviation threshold is 10%.

[0024] The beneficial effects of this invention are:

[0025] (1) This invention innovatively combines multiphase flow numerical model with physical experiment to solve the problem that traditional flow field simulation ignores the complex effects of multiphase flow and lacks physical verification. The average relative error between the simulation results and the water simulation experiment results is <5%, and the accuracy is significantly improved compared with the existing technology (average relative error 7%), realizing a precise fit between flow field simulation and physical verification.

[0026] (2) After the multiphase flow numerical model is corrected, different process conditions are set in batches, and the corrected multiphase flow numerical model is solved in a coupled manner to obtain simulation data of the leaching process in the reactor under different process conditions. This allows for the quantification of the influence of process conditions (stirring speed, impeller type, gas inlet velocity, etc.) on the leaching process, and thus the determination of the optimal process conditions. In the experiment, the optimal process conditions were determined to be: stirring speed 80 RPM, gas inlet velocity 1 m / s, and a straight-sloping combined impeller, which can increase the nickel leaching rate to 92%, significantly optimizing the reaction efficiency and product performance.

[0027] (3) It can provide core data support and theoretical guidance for reactor structure optimization, intelligent control of process conditions and high efficiency of metallurgical processes, and has outstanding engineering application value;

[0028] (4) By taking advantage of the visualization of water simulation experiments, dynamic flow field characterization and quantitative analysis of key areas such as the impeller wake zone, circulation channel and potential dead zone in the co-precipitation vessel can be realized, accurately revealing the coupling mechanism between the flow field structure and the leaching reaction, filling the technical gap of traditional simulation that can only output numerical results and lacks intuitive mechanism support.

[0029] (5) Construct a closed-loop optimization system of “simulation calculation - physical experiment - model correction”. Through water simulation experiments, real flow field data are obtained and the parameters of the multiphase flow numerical model are corrected in reverse, reducing the number of simulation iterations by more than 30%. While ensuring accuracy, the calculation efficiency is significantly improved, solving the problems of long time consumption and lag in parameter control in traditional flow field simulation. Attached Figure Description

[0030] Figure 1 A schematic flowchart of a numerical simulation method for a nickel pressurized stirred reactor leaching process provided by the present invention;

[0031] Figure 2 Perspective view of the geometric model of the reactor;

[0032] Figure 3 A schematic diagram of grid division provided for this invention (with denser grid in the stirring impeller area).

[0033] Figure 4 The velocity contour plot is shown under optimal process conditions (stirring speed 80 RPM, gas inlet velocity 1 m / s).

[0034] Figure 5 This is a comparison chart of gas holdup under optimal process conditions in simulation and physical verification experiments. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be further described in detail below with reference to the embodiments and accompanying drawings.

[0036] It should be noted that the terms "comprising" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0037] Furthermore, the terms "installation," "setup," "equipped with," "connection," "linked," and "socketing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or an internal connection between two devices, components, or parts. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0038] This invention proposes a numerical simulation method for the leaching process of nickel in a pressurized stirred reactor, belonging to the field of nickel metallurgical pressurized leaching technology. It addresses the problems of traditional numerical simulations neglecting the interaction between gas and liquid two-phase flows, the coupling effects of process conditions, and the lack of physical verification, leading to large deviations between simulation results and actual leaching processes. The method includes: 1) constructing a geometric model of the reactor, including a stirring system, cooling coils, and support structures, in modeling software, and importing it into mesh generation software to generate a refined mesh file; 2) establishing a multiphase flow numerical model based on the gas-liquid two-phase flow interaction mechanism, coupling a drag model and a turbulence model; 3) setting process conditions such as stirring speed and gas inlet velocity, as well as multiphase physical property parameters, and solving the numerical model; 4) constructing a 1:175 scaled-down solid model, conducting water simulation experiments based on similarity criteria, verifying and correcting the simulation results, and finally obtaining the flow field characteristics and leaching efficiency of the reactor under different operating conditions. This invention improves simulation accuracy and provides support for reactor optimization design and intelligent metallurgy.

[0039] The following will refer to Figure 1 A specific implementation method according to this application is described.

[0040] Example 1:

[0041] This invention designs a numerical simulation method for the nickel pressurized stirred reactor leaching process that combines simulation and physical modeling, addressing the problems of low accuracy and high cost associated with traditional techniques. By constructing a geometric model and a multiphase flow numerical model, this application can quantify the coupling effects of impeller type, number of layers, and process conditions to determine optimal operating conditions. Combining physical experiments to verify simulation results and correct the parameters of the simulation model reduces the deviation between the numerical simulation results and industrial realities. Employing a miniature physical model reduces the cost of verifying simulation results and ensures consistency between the miniature physical model and the prototype based on similarity criteria.

[0042] Combination Figure 1-5 This implementation method is described as follows: Figure 1 As shown in the embodiments of this application, a numerical simulation method for the leaching process of nickel in a pressurized stirred reactor is provided, including:

[0043] S1. Geometric Modeling and Meshing: Create a geometric model of a nickel pressurized stirred reactor, including the reactor body, stirring system, cooling coils, and supporting I-beams, in 3D modeling software. Import the geometric model into meshing software and use a hybrid structured-unstructured meshing method to mesh the computational domain of the geometric model to obtain the mesh file.

[0044] In some embodiments, geometric models can be constructed using SolidWorks software, such as... Figure 2 As shown. The geometric model is based on an industrial 200m... 3 The prototype is a titanium reactor. The reactor body adopts a cylindrical structure with an inner diameter of 3800mm, a straight section length of 17500mm, and elliptical end caps (short radius 950mm) at both ends. The stirring system has six-bladed straight impellers (diameter 1200mm) in chambers 1-3 and three-bladed inclined impellers (tilt angle 45°) in chambers 4-5, with a blade spacing of 500mm. The cooling coil is a double-layered sleeve cooling coil (pipe diameter 40mm) supported by four sets of I-beams (section 100×100mm).

[0045] In some embodiments, Ansys Fluent (Fluent Meshing) software can be used for hybrid mesh generation. For example... Figure 3 As shown. The model was imported into Ansys Fluent software, and a hybrid mesh was used: the impeller, cooling coil and wall area (the area of ​​severe fluid shear) were meshed with tetrahedral unstructured mesh, and the middle part of the vessel was meshed with hexahedral structured mesh.

[0046] In one possible implementation, the structured-unstructured hybrid meshing method is used to mesh the computational domain of the geometric model. This includes: for regions with gentle flow field changes, such as the middle of the reactor body, a hexahedral structured mesh is used to ensure computational efficiency; for regions with intense flow field shear and complex geometry, such as the periphery of the agitator, the walls, and the cooling coils, a tetrahedral unstructured mesh is used and local refinement is applied.

[0047] In some embodiments, when dividing the computational domain of the reactor, for regions with relatively gentle flow field changes, such as the middle of the reactor body, the feature size of a single mesh cell is controlled between 10 mm and 80 mm. For regions with intense flow field shear, such as around the agitator, on the wall, and in the cooling coil, local mesh refinement (smaller size) is performed on top of this to balance computational accuracy and efficiency.

[0048] In some embodiments, the mesh quality satisfies orthogonality ≥ 0.8 and distortion ≤ 0.2. Orthogonality ≥ 0.8 and distortion ≤ 0.2 are two key indicators for evaluating the geometric quality of mesh elements, directly determining the reliability of numerical computation. Orthogonality describes the perpendicularity of the edges of the mesh elements; the closer the value is to 1, the closer the mesh is to a regular hexahedron (structured mesh) or a regular tetrahedron (unstructured mesh). Orthogonality ≥ 0.8 means a regular mesh shape, effectively reducing numerical discretization errors during computation and avoiding computational deviations caused by mesh distortion. Distortion describes the degree to which mesh elements deviate from the ideal shape; the closer the value is to 0, the better the mesh shape. Distortion ≤ 0.2 prevents excessive stretching, compression, or deformation of the mesh, ensuring the stability of solving fluid control equations such as momentum and continuity equations, and avoiding computational divergence.

[0049] In summary, this set of parameters is a rigid quality standard for reactor mesh generation, which ensures both the accuracy of flow field calculations and the efficiency of engineering calculations. It is a prerequisite for subsequent numerical simulations to accurately reflect the actual flow field state.

[0050] In some embodiments, grid independence verification is performed, i.e., the optimal grid size is determined through grid convergence analysis: based on the calculation deviation threshold of the target physical quantity (such as flow field velocity), the grid density is iteratively adjusted and numerical calculations are carried out under multiple corresponding grid sizes. When the calculation deviation of the target physical quantity caused by the change in grid density meets the preset accuracy requirements, the grid size that balances calculation accuracy and efficiency is selected as the optimal grid scheme. For example, when the number of grids increases from 7 million to 10 million, the flow field velocity calculation deviation is <3%, and the optimal number of grids is determined to be 7 million.

[0051] S2. Constructing a multiphase flow numerical model: Based on the flow regime of the gas-liquid-solid three-phase flow in the reactor (the interaction between them), a multiphase flow numerical model is constructed. The multiphase flow numerical model includes a heterogeneous flow model, a drag force model, and a turbulence model. Among them, the heterogeneous flow model is used to describe the gas-liquid-solid three-phase flow in the reactor, the drag force model is used to calculate the interaction forces between the gas-liquid phase and between the liquid and solid phases in the reactor, and the turbulence model is used to characterize the turbulence characteristics of the three-phase flow field in the reactor.

[0052] From the perspective of "motion state", the overall form of material flow inside the reactor is a three-phase flow field. The three phases are: liquid phase (sulfuric acid solution), solid phase (nickel ore particles), and gas phase (oxygen). The flow, mixing, and interaction of the three types of substances under stirring, such as rising bubbles, suspended particles, and dragging between liquid phases, can be described by a multiphase flow model to describe the motion law of each phase.

[0053] From the perspective of "material composition", the core phases involved in the leaching process in the reactor can be combined into a mixed slurry-oxygen two-phase system. The first phase (continuous phase) is the mixed slurry, which is essentially a mixed system of "liquid phase (saturated sulfuric acid solution) + solid phase (latite nickel ore particles)". In this application, the solid particles are uniformly dispersed in the liquid phase, which is often regarded as a "pseudo-single-phase" slurry in engineering. The second phase (discrete phase) is oxygen, which serves as the gaseous medium required for the reaction and is dispersed in the slurry in the form of bubbles.

[0054] Heterogeneous flow models can employ the Euler-Euler heterogeneous flow model, whose governing equations include a two-phase continuity equation and a momentum equation, with the momentum equation introducing interphase force terms. For the liquid phase, the continuity equation is expressed as:

[0055] ;

[0056] The momentum equation is expressed as follows:

[0057] ;

[0058] In the formula: , , These represent the volume fraction, density, and velocity of the liquid phase, respectively. For interphase forces, where the subscript is... For liquid phase identification, For identifying phases other than liquid phase.

[0059] The drag force model can be the Grace drag force model, which calculates the drag force coefficient using the Tomiyama drag force coefficient calculation model, and the We number is introduced for fitting and optimization. The expression is:

[0060] ;

[0061] In the formula: For bubble Reynolds number, For Euler number, This is the Euler number correlation fitting function.

[0062] The turbulence model can be a Realizable k-ε turbulence model, with modifications to the turbulent viscosity calculation to incorporate streamline curvature effects. The kinetic energy equation is calculated as follows:

[0063] ;

[0064] In the formula: The density of the fluid (sulfuric acid solution / slurry). For turbulent kinetic energy, For time, The velocity vector of the fluid. The molecular dynamic viscosity of the fluid. For fluid viscosity; and All are spatial coordinates, subscripts , This is a representation of tensors, corresponding to different directions in three-dimensional space; The average velocity gradient generated by kinetic energy; The change in buoyancy is caused by kinetic energy; The turbulent kinetic energy dissipation rate; This is the source term of the kinetic energy equation.

[0065] Turbulent dissipation equation

[0066] ;

[0067] in, This is the source term of the turbulent dissipation equation; Turbulent kinetic energy dissipation rate The Prandtl number (empirical constant) for turbulence; and These are all empirical constants; in some embodiments, they are... =0.43、 =1.9、 =1.0、 =1.2.

[0068] S3. Parameter setting and model solution: Set the process conditions corresponding to the nickel leaching process in the mesh file; based on the set process conditions, couple and solve the multiphase flow numerical model to obtain the simulation data of the flow field characteristics in the reactor during the nickel leaching process.

[0069] In some embodiments, the mesh file can be loaded using Ansys Fluent (Fluent Meshing) software, and then the parameters can be set to solve the single-tank reactor model.

[0070] In some embodiments, the process condition settings include setting inlet and outlet conditions, boundary conditions, process conditions, and physical property parameters, and may also include setting initial conditions and control parameters.

[0071] In some embodiments, inlet and outlet conditions are set, including setting the flow channel inlet (gas-liquid two-phase inlet, including slurry inlet and oxygen inlet) as a velocity inlet, setting the top of the vessel body as a pressure outlet, and setting the bottom slag outlet as a velocity outlet.

[0072] The reactor is a pressure leaching device (pressure 1.2~1.6MPa). A pressure outlet at the top of the reactor stabilizes the internal pressure. By setting the outlet pressure value (matching the working pressure inside the reactor), sudden pressure fluctuations due to outlet flow rate variations are avoided, ensuring a stable gas-liquid-solid three-phase flow field. Simultaneously, it provides an orderly discharge channel for oxygen (gas phase), conforming to the conventional design logic of pressure-driven reactors. The bottom slag outlet discharges the slag (solid phase) after the leaching reaction. Its velocity outlet allows for precise control of the slag discharge rate, preventing slag accumulation and blockage at the bottom. It also matches the flow state of the slurry (continuous phase flow), ensuring overall flow field continuity and meeting the process requirement of "orderly discharge of liquid-solid mixed phases."

[0073] In some embodiments, inlet and outlet conditions are set, including setting wall conditions: the reactor wall and the cooling coil are non-slip boundaries, and the agitator is a rotating boundary.

[0074] In some embodiments, the process conditions are set, including setting the stirring speed to 40~100 RPM and the gas inlet velocity to 0.5~2 m / s. Here, RPM is an abbreviation for Revolutions Per Minute.

[0075] In some embodiments, physical property parameters are set, including defining physical property parameters such as mixed slurry and oxygen.

[0076] In some embodiments, the physical properties are dynamically adjusted with pressure (1.2~1.6MPa) and temperature (160~180℃) to improve the fit between the simulation results and actual industrial conditions, while ensuring the accuracy and reliability of process optimization.

[0077] In some embodiments, the sulfuric acid solution has a density of 1400 kg / m³ and a viscosity of ≤50 mPa·s, and the laterite nickel ore particles have a density of 1600~2200 kg / m³.

[0078] In some embodiments, defining physical property parameters may include: setting the sulfuric acid solution density to 1400 kg / m³ and viscosity to 30 mPa·s, setting the laterite nickel ore particle density to 1800 kg / m³ and particle size to 0.5~2 mm, and setting the oxygen density to 5.6 kg / m³.

[0079] In some embodiments, the convergence criterion for iterative solution is that the residual is less than a preset threshold (e.g., 10). -6 The steady-state calculation steps are greater than or equal to the preset number of steps (e.g., 3000 steps).

[0080] The gas-liquid-solid three-phase flow within the reactor follows the momentum and continuity equations of fluid mechanics. Pressure and velocity are coupled variables—velocity distribution affects pressure distribution, and conversely, pressure distribution determines velocity changes—and cannot be solved independently. Using coupled solution algorithms in Ansys Fluent (such as SIMPLE and SIMPLEC), a "prediction-correction" iterative process gradually reconciles the calculated pressure and velocity results, achieving a pressure-velocity coupled solution. This ensures that both satisfy the momentum and continuity equations simultaneously, avoiding convergence issues.

[0081] In the convergence criterion for iterative solutions, residuals are an indicator that measures the deviation between the numerical calculation results and the theoretical solutions of the equations. A larger residual indicates a greater deviation from the true solution; a smaller residual indicates a closer approximation to the true flow field state. The convergence criterion is the standard for determining whether the calculation can be stopped. Setting the residual to be less than a preset threshold means that the calculation is considered convergent and the results reliable only when the calculation deviations of each physical quantity (such as velocity, pressure, and phase volume fraction) decrease below the preset threshold. If the calculation stops before the residual reaches this threshold, the obtained flow field velocity, gas holdup, and other data will have significant errors and cannot be used for subsequent process optimization analysis. This application simulates the flow field state during the stable operation of the reactor (i.e., the flow field parameters do not change with time). Steady-state calculations require multiple iterations to gradually approximate the true solution. Setting the number of steady-state calculation steps to be greater than or equal to a preset number of steps allows sufficient convergence time for the iterative calculations. Because the flow field inside the reactor is complex (strong shear, multiphase mixing), the residual decreases rapidly in the first few hundred iterations, but to reduce it to the high accuracy required by the preset threshold, thousands of iterations are often needed. Setting a minimum number of iterations avoids "false convergence" (stopping before the residuals reach a threshold) caused by insufficient iterations, ensuring the validity of the calculation results. This set of parameters is a "precision assurance measure" for numerical simulation. Through reasonable algorithms and strict convergence criteria, the simulation results can truly reflect the actual flow field state inside the reactor.

[0082] After solving, key data are extracted, such as the flow field uniformity (proportion of high-speed region), gas holdup (area of ​​region >0.05) and turbulent dissipation rate (peak value around the impeller) at different stirring speeds. Figure 4 This is a velocity contour plot of the flow field under optimal process conditions (stirring speed 80 RPM, gas inlet velocity 1 m / s). The figure shows the simulation results of the gas-liquid-solid three-phase flow field inside the reactor. The color scale on the right (Velocity Magnitude) represents the fluid velocity amplitude (unit: m / s), with colors ranging from dark blue (0 m / s) to red (3 m / s) corresponding to increasing velocities. As can be seen from the figure, the area near the stirrer exhibits a high red-yellow value region (velocity close to 3 m / s), indicating that the rotation of the stirrer exerts a strong shearing effect on the surrounding fluid, driving it to move at high speed. The upper and middle regions of the reactor are predominantly blue-green (velocities 0.5~1.5 m / s), reflecting the circulating flow characteristics of the fluid within the reactor. This result is consistent with the phenomenon observed in the physical verification where "particles around the stirrer move violently, while particles in the middle of the reactor are uniformly dispersed," verifying the accuracy of the simulation model.

[0083] S4. Validation of the physical model: Construct a scaled-down physical model and scale the stirring speed and gas flow rate based on similarity criteria; conduct a water simulation experiment using water to simulate sulfuric acid solution and air to simulate oxygen, and measure the physical experimental data of the flow field characteristic indicators.

[0084] In some embodiments, a solid model is constructed at a 1:175 scale based on the parameters of an industrial reactor. The reactor body is made of acrylic (5mm wall thickness), the agitator is made of ABS (acrylonitrile-butadiene-styrene) resin, and the cooling coil is made of copper wire (4mm diameter); the inner diameter is 21.7cm, and the agitator diameter is 6.86cm (corresponding to a prototype of 1200mm).

[0085] In some embodiments, the physical model is equipped with auxiliary devices: a speed-regulating motor (such as a brushless speed-regulating motor, 0~500RPM), a glass rotor flow meter (such as an LZB-3WB glass rotor flow meter, 0~1L / min), and a camera (such as a high-speed camera system, ISO=4000).

[0086] In the experimental system of the pressurized acid leaching reactor for laterite nickel ore, a brushless speed-regulating motor (0~500RPM), an LZB-3WB glass rotor flowmeter (0~1L / min), and a camera (ISO=4000) work together to respectively achieve stirring speed control, gas flow rate measurement, and detailed flow field acquisition, providing support for precise control of experimental conditions and data verification. The brushless speed-regulating motor, as the power source of the stirring system, drives the stirring paddle to rotate and precisely adjusts its speed. Its 0~500RPM speed range covers the required stirring speed range (e.g., 40~100RPM). By changing the speed, it controls the shear intensity and turbulence of the flow field inside the reactor, thereby affecting the uniformity of gas-liquid-solid three-phase mixing and the gas holdup distribution. Simultaneously, the brushless motor operates stably with minimal speed fluctuations, ensuring a constant speed under the same operating conditions and avoiding experimental data deviations due to speed fluctuations. The glass rotor flowmeter, as a gas delivery metering device, can accurately measure and control the flow rate of gas (e.g., oxygen) entering the reactor. The 0~1L / min flow rate range adapts to the low-flow gas delivery requirements of experiments. Real-time flow rate is intuitively read via rotor height, ensuring consistency between the gas input and experimental settings. This provides accurate variable parameters for studying the "influence of gas inlet velocity on leaching efficiency" and guarantees that the gas-liquid ratio meets experimental design requirements. The camera, as a flow field visualization data acquisition device, plays a crucial role in capturing the dynamic details of the gas-liquid-solid three-phase flow field within the reactor. The high ISO=4000 sensitivity setting enhances image brightness and clarity in low-light conditions or when photographing high-speed moving media (such as bubbles or particles), preventing loss of flow field details due to insufficient light. By capturing images of bubble morphology and particle suspension, it provides intuitive experimental evidence for subsequent numerical model verification (such as comparing gas holdup calculation results). The clear division of labor and synergistic effect of these three components construct a complete experimental support system of "operating condition control—parameter measurement—data acquisition," ensuring the accuracy and reliability of experimental data.

[0087] In some embodiments, experimental parameters (including stirring speed and gas flow rate) are scaled based on similarity criteria. In some embodiments, the stirring speed is scaled based on the Froude number similarity criterion (165~412 RPM), and the gas flow rate is scaled based on the ventilation number similarity criterion (0.15~0.62 L / min). For example, according to the Froude number... The prototype's 80 RPM corresponds to the model's 330 RPM. , In the formula The speed of the agitator. The characteristic length (the inner diameter of the reactor or the diameter of the agitator, which serves as the similarity benchmark between the prototype and the model) It is the acceleration due to gravity; The rotational speed of the prototype agitator. The rotational speed of the model's stirring paddle. The length of the prototype feature. For model feature length, The length similarity ratio between the prototype and the model ( This means the prototype feature length is 175 times that of the model. Gas flow rate is based on ventilation standard. The prototype inlet velocity of 1 m / s corresponds to a model velocity of 0.3 L / min. In the formula This represents the gas volumetric flow rate. The speed of the agitator. This refers to the diameter (characteristic length) of the agitator. This represents the volumetric flow rate corresponding to the prototype gas inlet. The gas volumetric flow rate is shown in the figure. A comparison of gas holdup under optimal process conditions between simulation and physical verification experiments is shown in the figure below. Figure 5 As shown in the figure, Phase 3 represents the third phase, which in multiphase flow simulation refers to a specific material phase, such as the gas phase. The left side of the figure shows the multiphase flow field simulation results under the optimal process conditions (the color scales correspond to the volume percentage of "Phase 3," with values ​​from 0 to 0.4 representing the gas phase percentage from low to high); the right side shows the physical verification diagram under the same process conditions (the dispersed particles in the diagram correspond to the "Phase 3" medium in the simulation). The flow field distribution characteristics of both are consistent, verifying the reliability of this simulation model.

[0088] This step employs a water-simulation visualization experimental method, constructing an experimental setup geometrically similar to an industrial co-precipitation reactor using water as the medium. Visualization techniques such as high-speed cameras and particle image velocimetry (PIV) are used to dynamically capture the flow field in key areas within the reactor, including the impeller wake, circulation channels, and potential dead zones. Furthermore, image recognition and data processing algorithms can be combined to qualitatively characterize and quantitatively analyze flow field parameters such as fluid velocity distribution, streamline trajectories, and turbulent kinetic energy intensity within these areas. This approach leverages the transparency and flow characteristics of water to replicate the flow field within an industrial reactor, solving the problem of direct observation of the flow field and providing intuitive experimental data support for subsequent numerical model verification and process optimization.

[0089] S5. Model Correction: Compare the simulation data and physical experimental data of the flow field characteristic indicators to correct the parameters of the multiphase flow numerical model; after correction, set different process conditions in batches, couple and solve the corrected multiphase flow numerical model to obtain simulation data of the reactor leaching process under different process conditions.

[0090] In some embodiments, the flow field characteristic indicators include flow field uniformity and gas holdup; in other embodiments, the flow field characteristic indicators may also include one or more of the following: flow field velocity distribution, turbulent dissipation rate, bubble distribution, and bubble trajectory.

[0091] In the model correction step, the parameters of the multiphase flow numerical model are corrected as follows: if the gas holdup error in the flow field characteristic simulation data and the actual experimental data is greater than the preset gas holdup error threshold, the parameters of the drag model (such as the drag coefficient of the Grace drag model) are adjusted; when the flow field uniformity deviation is greater than the preset flow field uniformity deviation threshold, the parameters of the turbulence model (such as the empirical constants of the Realizable k-ε turbulence model) are optimized.

[0092] In some embodiments, the preset gas holdup error threshold is 5%, and the preset flow field uniformity deviation threshold is 10%.

[0093] It should be understood that the numbers S1 to S5 above are only used to distinguish and facilitate the expression of different steps, and do not necessarily constitute a restriction on the execution order between the steps.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A numerical simulation method for the leaching process of nickel in a pressurized stirred reactor, characterized in that, The method includes the following steps: Geometric modeling and mesh generation: A geometric model of a nickel pressurized stirred reactor, including the reactor body, stirring system, cooling coils, and supporting I-beams, is created in 3D modeling software. The geometric model is then imported into mesh generation software, and a hybrid structured-unstructured mesh generation method is used to mesh the computational domain of the geometric model to obtain the mesh file. A multiphase flow numerical model was constructed: Based on the three-phase flow regime of gas, liquid, and solid in the reactor, a multiphase flow numerical model was constructed. The multiphase flow numerical model includes a heterogeneous flow model, a drag force model, and a turbulence model. Among them, the heterogeneous flow model is used to describe the three-phase flow of gas, liquid, and solid in the reactor, the drag force model is used to calculate the interaction forces between the gas and liquid phases and between the liquid and solid phases in the reactor, and the turbulence model is used to characterize the turbulence characteristics of the three-phase flow field in the reactor. Parameter setting and model solving: Set the process conditions corresponding to the nickel leaching process in the mesh file; based on the set process conditions, couple and solve the multiphase flow numerical model to obtain the simulation data of the flow field characteristics in the reactor during the nickel leaching process; Solid model validation: Construct a scaled-down solid model and scale the stirring speed and gas flow rate based on similarity criteria; conduct water simulation experiments to simulate sulfuric acid solution and air to simulate oxygen, and measure the solid experimental data of flow field characteristic indicators; Model correction: By comparing the simulation data and the actual experimental data of the flow field characteristic index, the parameters of the multiphase flow numerical model are corrected; after the correction is completed, different process conditions are set in batches, and the corrected multiphase flow numerical model is solved in a coupled manner to obtain simulation data of the reactor leaching process under different process conditions.

2. The method according to claim 1, characterized in that, The method of using a hybrid structured-unstructured mesh generation approach to mesh the computational domain of the geometric model includes: for regions with gentle flow field changes within the reactor, a hexahedral structured mesh is used to ensure computational efficiency; for regions with intense flow field shearing and complex geometry within the reactor, a tetrahedral unstructured mesh is used with local refinement.

3. The method according to claim 2, characterized in that, The mesh quality satisfies orthogonality ≥ 0.8 and distortion ≤ 0.

2.

4. The method according to claim 3, characterized in that, The meshing also includes: determining the optimal mesh size through mesh convergence analysis: based on the calculation deviation threshold of the target physical quantity, iteratively adjusting the mesh density and carrying out numerical calculations under multiple mesh sizes. When the calculation deviation of the target physical quantity caused by the change in mesh density meets the preset accuracy requirements, the corresponding mesh size is selected as the optimal mesh scheme.

5. The method according to claim 1, characterized in that, The heterogeneous flow model adopts the Euler-Euler heterogeneous flow model, the drag force model adopts the Grace drag force model, and the turbulence model adopts the Realizable k-ε turbulence model.

6. The method according to claim 1, characterized in that, The coupled solution of the multiphase flow numerical model includes: using the coupled solution algorithm in Ansys Fluent to iteratively solve the multiphase flow numerical model, with the convergence criterion for the iterative solution being that the residual is less than a preset threshold and the number of steady-state calculation steps is greater than or equal to a preset number of steps.

7. The method according to claim 1, characterized in that, The process conditions settings include setting inlet and outlet conditions, boundary conditions, process conditions, and physical property parameters; wherein, setting inlet and outlet conditions includes setting all flow channel inlets as velocity inlets, the top of the vessel as a pressure outlet, and the bottom slag outlet as a velocity outlet; wherein, the physical property parameters are dynamically adjusted with pressure and temperature.

8. The method according to claim 1, characterized in that, The scaling of stirring speed and gas flow rate based on similarity criteria includes: scaling the stirring speed based on the Froude number similarity criterion and scaling the gas flow rate based on the ventilation criterion similarity criterion.

9. The method according to any one of claims 1 to 8, characterized in that, The flow field characteristic indicators include flow field uniformity and gas holdup. In the model correction step, the parameters of the multiphase flow numerical model are corrected as follows: if the gas holdup error in the flow field characteristic simulation data and the actual experimental data is greater than the preset gas holdup error threshold, the parameters of the drag model are adjusted; when the flow field uniformity deviation is greater than the preset flow field uniformity deviation threshold, the parameters of the turbulence model are optimized.

10. The method according to claim 9, characterized in that, The preset gas holdup error threshold is 5%, and the preset flow field uniformity deviation threshold is 10%.

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