Efficient magnesium metal internal heat continuous production method

Abstract

The invention provides an efficient magnesium metal internal heat continuous production method. The method comprises the steps that heat source distribution data are obtained from an internal heat device, temperature values of all areas in the device are collected through a temperature sensor array, an initial heat source distribution diagram is generated, and specific positions and temperature ranges of a hot spot area and a non-uniform area are determined; according to a reaction kettle material selection scheme, wall thickness design parameters of a reaction kettle are calculated, stress distribution conditions in a high-temperature and high-pressure environment are simulated by adopting stress analysis software, and a wall thickness design scheme meeting safety standards is generated in combination with a thermal load demand value and material characteristics; according to the structure design scheme and the separation efficiency evaluation value of the separation equipment, the structure parameters of the refining equipment are optimized, the operability and the maintenance convenience of the equipment are analyzed by adopting an operation maintenance model, the maintenance period and the operation process are defined, and the production process scheme of the high-purity magnesium metal is generated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of internal heat continuous production of metal magnesium, and particularly relates to a high-efficiency internal heat continuous production method of metal magnesium. BACKGROUND

[0002] In the internal heat continuous production process of metal magnesium, the internal heat device as the core equipment, the optimization of its structural parameters directly affects the reaction efficiency and energy utilization rate. The design of the internal heat device needs to consider the uniform distribution of the heat source and the heat conduction efficiency to avoid local overheating or heat loss. The reaction kettle as the key equipment for material conversion, its internal structure needs to adapt to the high temperature and high pressure environment, while ensuring the continuity and stability of the material flow. The material selection and wall thickness design of the reaction kettle have important influence on the corrosion resistance and thermal stress distribution. The cooling system needs to be closely matched with the reaction kettle to ensure that the reaction products are rapidly cooled to avoid secondary reaction or product quality decline. The flow speed of the cooling medium and the design of the heat exchange area need to be accurately controlled to ensure the cooling efficiency while reducing energy consumption. The performance of the separation equipment directly affects the purity and yield of metal magnesium, and its structural parameters need to consider the physical properties of the material, such as density, viscosity and particle size, to achieve efficient separation. The design of the refining equipment as the last process needs to meet the production requirements of high-purity metal magnesium, while taking into account the operability and maintenance convenience of the equipment. The combination of process parameters of the whole set of equipment needs to be optimized through experiments and simulation analysis to achieve the balance of yield and quality. In actual production, the coordinated operation and parameter matching between the equipment are the key to ensure the stability of continuous production. SUMMARY

[0003] The present application provides a high-efficiency internal heat continuous production method of metal magnesium, mainly including: S1. Obtain the heat source distribution data from the internal heat device, use the temperature sensor array to collect the temperature values of each region inside, generate the initial heat distribution map, determine the location and temperature range of the hot spot area and non-uniform area; S2. Calculate the heat conduction efficiency based on the initial heat distribution map, simulate the heat conduction path by using the finite element analysis method, combine the material thermal conductivity coefficient and the environmental temperature boundary condition, judge the local overheating or heat loss area, generate the heat conduction efficiency evaluation value; S3. If the heat conduction efficiency evaluation value is lower than 80% of the preset threshold value, optimize the structural parameters of the internal heat device, adjust the layout and power distribution of the heating element, recalculate the improved heat conduction efficiency value and the updated heat distribution map; S4. According to the updated heat distribution map and the improved heat conduction efficiency value, determine the reaction kettle heat load demand value; combine the material corrosion resistance, thermal stress distribution characteristics and material performance database to match the best material, generate the reaction kettle material selection scheme; S5. Calculate the reactor wall thickness design parameters for the material selection scheme, simulate the stress distribution under high temperature and high pressure environment using stress analysis software, combine the heat load demand value and material characteristics to generate a wall thickness design scheme that meets the safety standards; S6. According to the wall thickness design scheme and the heat load demand value, obtain the fluid velocity and heat exchange area data of the cooling system; adopt fluid dynamics simulation to optimize the cooling medium flow path, generate an optimized fluid velocity and heat exchange area configuration scheme with the goal of improving heat exchange efficiency; S7. Based on the optimized fluid velocity and heat exchange area configuration scheme, adjust the separation equipment thermal management parameters, combine the material density, viscosity and particle size characteristics, use the separation efficiency model to determine the optimal separation conditions, generate the separation equipment structure design scheme and separation efficiency evaluation value; S8. According to the separation equipment structure design scheme and the separation efficiency evaluation value, optimize the structure parameters of the refining equipment, analyze the equipment operability and maintenance convenience using the operation and maintenance model, determine the maintenance cycle and operation process, and generate a high-purity magnesium production process scheme.

[0004] The technical scheme provided by the embodiment of the present application can include the following beneficial effects: The present application discloses a kind of efficient metal magnesium internal heat continuous production method, by optimizing the structure parameters of internal heating device, reactor, cooling system, separation equipment and refining equipment etc. core equipment, improve the efficiency and quality of metal magnesium production. The present application first adopts temperature sensor array to obtain the heat source distribution data of internal heating device, utilizes finite element analysis to optimize heat conduction efficiency, avoids local overheating or heat loss. For reactor, the corrosion resistance and thermal stress distribution of material are analyzed, and the optimal wall thickness design is determined using stress analysis software. Cooling system is optimized by fluid dynamics simulation to optimize the flow path of cooling medium, and the optimal flow velocity and heat exchange area combination are calculated. According to the material characteristics, the structure parameters of separation equipment are adjusted, and the optimal separation parameters are determined using separation efficiency model. The refining equipment is optimized by operation and maintenance model to optimize the structure design, improve the operability and maintenance convenience. The present application realizes the high efficiency, high quality and high stability of metal magnesium continuous production by the collaborative optimization of each equipment parameter, significantly improves the overall performance of production process. BRIEF DESCRIPTION OF DRAWINGS

[0005] Fig. 1 It is a flow chart of the efficient metal magnesium internal heat continuous production method of the present application.

[0006] Fig. 2 It is a schematic diagram of the efficient metal magnesium internal heat continuous production method of the present application.

[0007] Fig. 3 It is another schematic diagram of the efficient metal magnesium internal heat continuous production method of the present application. DETAILED DESCRIPTION

[0008] The technical solutions in the embodiments of the present application will be described clearly and in detail below with reference to the drawings in the embodiments of the present application. The described embodiments are only some of the embodiments of the present application.

[0009] As Figs. 1-3 , the embodiment of the efficient continuous production method of magnesium metal can specifically include: Step S101, obtaining heat source distribution data from the internal heating device, using a temperature sensor array acquisition device to collect the temperature values of each region inside the device, generating an initial heat source distribution map, and determining the specific location and temperature range of the hot spot area and the non-uniform area.

[0010] The temperature sensor array collects the temperature data of each region inside the internal heating device, generates initial heat source distribution information, and obtains the preliminary temperature distribution condition. According to the generated initial heat source distribution information, the temperature data is visually processed using image processing technology, a heat source distribution map is constructed, and the distribution characteristics of the hot spot area and the non-uniform area are determined. For the constructed heat source distribution map, the hot spot area and the non-uniform area are divided using a preset threshold range, and if the temperature value of a certain area exceeds the preset threshold range, it is marked as an abnormal area, and the specific location information of the abnormal area is obtained. Through further analysis of the specific location information of the abnormal area, the temperature range data of the abnormal area is obtained, and it is judged whether the temperature range meets the preset safety standard. According to the temperature range data after the judgment, the support vector machine algorithm is used to classify and process the abnormal area, and the characteristics of the hot spot area and the non-uniform area are distinguished to determine their respective distribution rules. The distribution rules of the hot spot area and the non-uniform area after classification processing are obtained, and the data integration is carried out according to the distribution rules to generate detailed heat source distribution characteristic information, and the final area division result is obtained. Through data storage and backup of the final area division result, the heat source distribution characteristic information is stored in a structured manner using a database management system, and it is judged whether the storage is complete. If data loss occurs during storage, an automatic recording mechanism is triggered to obtain complete data records.

[0011] For example, in the process of obtaining heat source distribution data from the internal heating device, first, an array of 100 temperature sensors is deployed to cover an area of 3.5m x 8m inside the device, each sensor is responsible for monitoring an area of 0.1 square meters, collecting temperature data, the data collection frequency is once per minute, lasting 24 hours, obtaining 1440 groups of data, each group of data contains 100 temperature values, the data is stored in the cloud database, and is marked with a timestamp. Subsequently, the initial heat source distribution map is generated using these data, the specific method is to estimate the temperature of the area not directly measured based on the collected temperature values through an interpolation algorithm (such as Kriging interpolation), to generate a heat distribution grid map with a resolution of 0.05 meters, the temperature range is set to 20°C to 100°C, and the color mapping from blue to red represents the temperature from low to high, the temperature values in the map are stored in the form of a two-dimensional matrix, and the matrix dimension is 200 x 200. Next, the specific location and temperature range of the hot spot area and the non-uniform area are determined, the analysis process is as follows: first, calculate the temperature gradient of each grid point, and mark the area with a gradient value greater than 5°C / m as a non-uniform area; Then, filter the grid points with a temperature value greater than 80°C, count the continuous distribution range, and if the continuous area is greater than 0.5 square meters, mark it as a hot spot area, and locate the hot spot center point coordinates through the algorithm, for example, a hot spot area center coordinate (3.2, 4.5) is found, with a temperature peak of 1200°C, covering an area of 0.8 square meters, and the non-uniform area is mainly distributed at the edge of the device, with a maximum temperature gradient of 8.2°C / m, covering an area of 1.2 square meters. The above data is used to generate a heat map and a gradient map through a visualization tool, and an analysis report is automatically output, which contains the coordinates, temperature range and area statistics of the hot spot and non-uniform area, for subsequent optimization design reference. To form a logical chain, the analysis results are associated with the device operating parameters, assuming that the temperature of the hot spot area is positively correlated with the equipment power, through regression analysis, it is found that when the power increases by 10%, the temperature of the hot spot area increases by about 5°C, thereby providing data support for power adjustment to ensure the stability of the system.

[0012] In step S102, the heat conduction efficiency of the internal heating device is calculated for the initial heat source distribution map, the finite element analysis method is used to simulate the heat conduction path, the material thermal conductivity coefficient and the environmental temperature are set as boundary conditions, it is judged whether there is a local overheating or heat loss area, and a heat conduction efficiency evaluation value is generated.

[0013] For the heat source distribution data, a finite element analysis method is used to construct a heat conduction model. The internal heating device is discretized by grid division to generate an initial heat conduction path distribution map. According to the initial heat conduction path distribution map, combined with material coefficients and environmental temperature as boundary conditions, the heat flow density distribution of each grid element is calculated to obtain the temperature field change data in the heat conduction process. Through the temperature field change data, the local overheating area and heat loss area are identified. If the temperature value of a certain grid element exceeds the preset threshold range, it is marked as an overheating area, and the abnormal area distribution information is output. For the abnormal area distribution information, the heat loss amount and the conduction efficiency value are calculated, the overall heat conduction efficiency is quantified by using the pre-established evaluation model, and the efficiency evaluation index is determined. According to the efficiency evaluation index, the heat flow transfer efficiency of each key node in the heat conduction path is analyzed. If the heat flow transfer efficiency of a certain node is lower than the preset standard, it is recorded as an inefficient node, and the inefficient node distribution data is obtained. Through the inefficient node distribution data, the grid parameters in the heat conduction model are adjusted, the temperature field distribution and the heat flow density are recalculated, and it is judged whether the optimized heat conduction efficiency reaches the preset target value. According to the optimized heat conduction efficiency data, the final heat source distribution adjustment scheme is generated, combined with the correction results of the local overheating and heat loss area, and the improvement direction of the heat conduction efficiency of the internal heating device is determined.

[0014] For example, for the calculation of the heat conduction efficiency of the initial heat source distribution map, the heat source distribution map is first converted into a three-dimensional grid model through digital modeling. Assuming that the center temperature of the heat source is 500 degrees Celsius and the distribution area is 0.5 square meters, the grid is divided using finite element analysis software, with a grid element size of 0.01 meters to ensure calculation accuracy. Next, set the material thermal conductivity to 50 W / (m·K) and the environmental temperature to 25 degrees Celsius as the boundary conditions, and perform numerical solution through the heat conduction equation ∂T / ∂t = α∇²T (where α is the thermal diffusivity, equal to the product of the thermal conductivity divided by the density and specific heat capacity, and the calculation is α = 0.02 m² / s). Using iterative algorithms such as the Jacobi method, set the convergence error to 0.001, calculate the temperature distribution of each node, and obtain the heat conduction path, where the heat decreases from the center to the edge, and the edge temperature is about 100 degrees Celsius. Then, analyze the temperature gradient to determine the local overheating area, assuming that the temperature of a certain node exceeds 400 degrees Celsius and the continuous area is greater than 0.05 square meters, it is marked as an overheating area. At the same time, calculate the heat loss by integrating the boundary heat flux density, and obtain the total loss of 200 W, accounting for 10% of the total heat input. Finally, generate the heat conduction efficiency evaluation value, define the efficiency as the ratio of output heat to input heat, calculate the efficiency as 0.85, and combine the overheating area ratio and heat loss ratio to obtain the comprehensive evaluation value of 0.8 (full score is 1.0), which is lower than 0.7 and needs to be optimized. Through the above process, a complete logical chain from modeling to evaluation is formed. If the heat conduction efficiency is insufficient, the subsequent optimization design module can be associated to automatically generate an improvement scheme, such as adjusting the material thermal conductivity to 60 W / (m·K) for secondary simulation to ensure system performance improvement.

[0015] Step S103, if the heat conduction efficiency evaluation value is lower than the preset threshold of eighty percent, based on the industry standard setting, the structure parameters of the internal heating device are optimized, the layout and power distribution of the heating element are adjusted, and the improved heat conduction efficiency value and the updated heat source distribution map are recalculated and generated.

[0016] The evaluation value data of the heat conduction efficiency is obtained, and is compared with the industry standard value and the preset threshold line to determine whether the evaluation value data of the heat conduction efficiency is lower than 80% of the preset threshold line. If the evaluation value data of the heat conduction efficiency is lower than 80% of the preset threshold line, the data of the structure parameter set is extracted for the inner heating device body, the optimization model established in advance is used to adjust the parameters to obtain the adjusted structure parameter set. According to the adjusted structure parameter set, the layout optimization method is used to rearrange the positions of the heating element groups in combination with the current state of the heating element groups to determine a new layout scheme. Through the new layout scheme, the power of the heating element groups is redistributed in combination with the power distribution strategy to obtain adjusted power distribution data. The finite element analysis method is used to recalculate the heat conduction efficiency value for the adjusted power distribution data and the new layout scheme to obtain updated efficiency evaluation data. According to the updated efficiency evaluation data, a new heat source distribution map is generated to determine whether the new heat source distribution map meets the requirements of the industry standard value. If the new heat source distribution map still does not meet the requirements of the industry standard value, the structure parameter set and the power distribution strategy are adjusted based on the updated efficiency evaluation data to obtain a final heat source distribution map that meets the standard.

[0017] For example, when evaluating the heat conduction efficiency, assume that the initial calculated efficiency evaluation value is 0.75, the industry standard preset threshold value is 1.0, and 80% of the threshold value, i.e. 0.8, is triggered to trigger the optimization process. The system first adjusts the structural parameters of the internal heating device based on the finite element analysis method (FEA) through the heat conduction simulation software, for example, adjusts the original heating element spacing from 5 centimeters to 3 centimeters to improve the heat distribution uniformity, and increases the thickness of the heat insulation layer from 2 millimeters to 3 millimeters to reduce heat loss, and the simulation calculation uses the heat conduction equation ∂T / ∂t = α∇²T, where α is the thermal diffusion coefficient, which is set to 0.002 m² / s, and the new heat flux density distribution is calculated. Then, the system automatically adjusts the layout and power distribution of the heating elements, and adjusts the originally uniformly distributed 10 heating elements (each with a power of 50W) to 6 elements in the center area with a power of 60W and 4 elements on the edge with a power of 40W to optimize the heat source concentration, and calculates the new power distribution matrix through the power distribution algorithm P_new = P_old * (1 +k * ΔT), where k is the adjustment coefficient 0.1 and ΔT is the temperature deviation. Then, the system re-runs the heat conduction simulation to generate an improved efficiency value, assuming that the new value is 0.85, which is 13.3% higher than the original value, and is close to the threshold requirement through thermodynamic analysis. Finally, the system generates an updated heat source distribution map using a visualization tool, uses a color temperature mapping algorithm to map the temperature range 1150-1250°C to a gradient color from blue to red, and the center area temperature is increased from the original 1150°C to 1200°C, and the edge temperature is stable at 12000°C. A high-resolution heat map is generated and stored in the database for subsequent analysis and reference. The above process is completed automatically by the system, forming a closed-loop logic from evaluation to optimization, and is associated with the device operation log to ensure that the optimized parameters are traceable, and if the efficiency is still not up to standard, the material thermal conductivity database can be further linked to screen better material parameters for continuous iterative optimization.

[0018] In step S104, the heat load demand value of the reaction kettle is determined according to the updated heat source distribution map and the improved heat conduction efficiency value, the material related data of the reaction kettle is obtained, the corrosion resistance and thermal stress distribution characteristics of the material are analyzed, the best material is matched by using the pre-established material performance database, and the reaction kettle material selection scheme is generated.

[0019] The heat load demand value of the reaction kettle is calculated through the heat source distribution data and the conduction efficiency data. The heat load distribution characteristics of the reaction kettle during operation are obtained by processing the related parameters of the heat source distribution and the conduction efficiency by using the pre-established load analysis model. According to the heat load distribution characteristics, the adaptability of the material under different heat load conditions is analyzed in combination with the material information of the reaction kettle. The stress distribution state of the material under the action of the heat load is simulated by using the finite element analysis method, and the thermal stress distribution characteristics of the material are determined. According to the thermal stress distribution characteristics, the corrosion resistance data of the material is obtained. The material performance index matched with the thermal stress distribution characteristics is extracted from the pre-established material performance database. It is judged whether the corrosion resistance data meets the preset threshold requirement. If not, other material data in the database is reselected to obtain the material performance combination meeting the condition. According to the material performance combination, the matching degree between the heat load demand and the material performance is analyzed. The correlation parameters of the material performance combination and the heat load demand are processed by using the data comparison tool to determine the optimal material matching scheme. According to the optimal material matching scheme, the heat load change trend of the reaction kettle under different operation scenarios is obtained. The stability of the material matching scheme under the change trend is analyzed by using the preset scene simulation tool. It is judged whether the adaptability requirement under multiple scenarios is met. If not, part of the parameters in the matching scheme is adjusted to obtain the updated material selection scheme. Through the updated material selection scheme, in combination with other auxiliary data in the material performance database, the optimization combination scheme of the reaction kettle under the specific heat load demand is generated. The fusion result of the selection scheme and the auxiliary data is processed by using the data integration tool to determine the final material optimization combination. According to the final material optimization combination, the adaptability analysis data of the reaction kettle under different heat source distribution conditions is generated. The adaptability analysis data is classified and archived by using the information processing module to obtain the comprehensive performance of the reaction kettle material under multiple operation environments.

[0020] For example, based on the updated heat source distribution map and the improved heat conduction efficiency value, first, the temperature distribution of each area in the reaction kettle is analyzed by the heat source distribution map. Assuming that the heat source distribution map shows that the temperature at the bottom of the reaction kettle is 1200°C, the temperature at the top is 1150°C, and the average temperature of the side wall is 1175°C, and the improved data of the heat conduction efficiency value is 0.85, the heat load demand value is calculated, and the formula Q=κ·A·ΔT is used, where κ is the heat conduction coefficient, A is the heat transfer area, and ΔT is the temperature difference between the bottom and the ambient temperature (25°C). Assuming that the heat transfer area is 2 square meters and the temperature difference is 275°C, the calculation result is Q=0.85×2×275=467.5 kW, and the heat load demand value is determined to be 467.5 kW. Then, the relevant data of the reaction kettle material is obtained, and the material performance database is called by the system to extract the corrosion resistance and thermal conductivity coefficient data of common materials such as stainless steel 316L and Hastelloy C276. Assuming that the corrosion resistance score of 316L is 8.5 (full score 10) and the thermal conductivity coefficient is 16.3 W / m·K, and the Hastelloy score is 9.2 and the thermal conductivity coefficient is 10.2 W / m·K. Subsequently, the corrosion resistance and thermal stress distribution characteristics of the material are analyzed, and the finite element analysis software is used to simulate the thermal stress distribution. The heat load of 467.5 kW and the temperature distribution data are inputted, and the thermal stress of 316L in the bottom area is 150 MPa and that of Hastelloy is 130 MPa. Combined with the corrosion resistance score, the performance of the two is comprehensively evaluated. The best material is matched by using the pre-established material performance database and the weighted scoring algorithm. The corrosion resistance weight is set to 0.6 and the thermal stress resistance weight is set to 0.4. The total score of 316L is 8.5×0.6+150 / 200×0.4=5.4, and the total score of Hastelloy is 9.2×0.6+130 / 200×0.4=5.78. It is concluded that Hastelloy is better. Finally, the reaction kettle material selection scheme is generated, and the system automatically outputs the report, recommending Hastelloy C276 as the best material, and attaching the heat load demand value of 467.5 kW, the thermal stress distribution data, and the scoring basis, forming a complete logical chain to ensure that the selected scheme is scientific and reasonable.

[0021] In step S105, the wall thickness design parameters of the reaction kettle are calculated for the reaction kettle material selection scheme. The stress analysis software is used to simulate the stress distribution under high temperature and high pressure environment, and the wall thickness design scheme that meets the safety standards is generated based on the heat load demand value and the material characteristics.

[0022] By constructing a digital model of the reaction kettle, boundary condition data related to high temperature and high pressure environments are obtained, and a finite element analysis method is used to perform preliminary stress distribution calculation on the model to obtain initial stress distribution results. According to the initial stress distribution results, matching analysis is performed on the material property data of the reaction kettle material, the corresponding mechanical property parameters are extracted from the pre-established material database, and the stress bearing range of the material under different environments is determined. If the stress bearing range of the material is lower than the maximum value in the initial stress distribution results, iterative calculation is performed by adjusting the wall thickness design parameters to obtain a new wall thickness value, and it is determined whether the safety standard requirements are met. If the adjusted wall thickness value still does not meet the safety standard, a reaction kettle material with higher strength characteristics is selected from the material database, the corresponding mechanical property parameters are obtained, and the new material applicability is determined. Through comprehensive analysis of the new material characteristics and thermal load data, the stress distribution under high temperature and high pressure environments is simulated again using the finite element analysis method to obtain updated stress distribution results. According to the updated stress distribution results, final parameter calculation is performed on the wall thickness design to obtain a wall thickness value that meets the safety standard, and the final design scheme is determined.

[0023] For example, the scheme for selecting the material and designing the wall thickness of the reactor is as follows. First, the material database is used to select a material suitable for a high-temperature and high-pressure environment, for example, 316L stainless steel, which has a yield strength of 205 MPa and is suitable for an environment with a temperature range of up to 1300°C. In combination with the requirement that the reactor has a design pressure of 10 MPa and a temperature of 1300°C, the initial wall thickness is calculated using a material mechanics algorithm, using the formula σ = PD / (2t) + C, where σ is the allowable stress of the material, which is 1.5 times the yield strength, i.e. 136.67 MPa, P is the design pressure, which is 10 MPa, D is the diameter of the reactor body, which is assumed to be 1.2 m, and C is the corrosion allowance, which is 0.002 m. The initial wall thickness t is calculated to be about 0.044 m, i.e. 44 mm. Then, the stress distribution is simulated using a finite element analysis software such as ANSYS. The reactor model is imported into the software, the boundary conditions are set to an internal pressure of 10 MPa and a temperature of 1200°C, the mesh is divided into hexahedral elements with a size of 0.01 m, and the simulation results show that the maximum stress is concentrated at the bottom of the reactor, which is 150 MPa, slightly higher than the allowable stress. Therefore, the wall thickness needs to be adjusted to 50 mm to reduce the stress to within 130 MPa to meet the safety standards. Subsequently, in combination with the heat load requirement value, for example, the reactor requires a heat input of 500 kW, the influence of the wall thickness on heat transfer is calculated using the heat conduction equation Q = kAΔT / L, where k is the thermal conductivity of 316L stainless steel, which is 16.3 W / (m·K), A is the heat transfer area, which is about 4.52 m², ΔT is the temperature difference between the inside and outside, which is 50°C, and L is the wall thickness, which is 0.05 m. The heat flow Q is calculated to be about 736 kW, which is higher than the requirement value, indicating that the wall thickness design is feasible in terms of heat load. Finally, the wall thickness design scheme is generated as 50 mm, and the material property parameters and simulation data are recorded in the design database, forming a complete logical chain from material selection to wall thickness optimization, ensuring that the design meets the safety standards, and avoiding human intervention through automatic calculation and simulation of the system, improving efficiency.

[0024] In step S106, according to the wall thickness design scheme and the heat load requirement value, the fluid velocity and heat exchange area data of the cooling system are obtained, and the flow path of the cooling medium water is optimized using a fluid dynamics simulation method to improve the heat exchange efficiency, and an optimized fluid velocity and heat exchange area configuration scheme is generated.

[0025] The initial cooling system parameters are obtained by collecting data of wall thickness design and heat load requirements, and the preliminary fluid velocity and heat exchange area reference values are determined. According to the collected parameters, a fluid dynamics simulation tool is used to model the flow path of the cooling medium, and a preliminary distribution diagram of the flow path is obtained. For the preliminary distribution diagram, the finite element analysis method is applied to calculate the fluid velocity distribution, and it is judged whether the fluid velocity meets the requirement of heat exchange efficiency. If the fluid velocity in some areas is lower than the preset threshold value, the flow path is adjusted locally, and the adjusted velocity distribution data is obtained. The fluid characteristics of the key area are extracted from the adjusted velocity distribution data, the optimization requirement of the heat exchange area is obtained, and the adjustment range of the heat exchange area is determined. According to the adjustment range, the heat exchange area is reconfigured, the overall performance of the cooling system is verified by using the iterative calculation method, and the optimized heat exchange area configuration result is obtained. Through the optimized configuration result, the flow path of the cooling medium is finally calibrated, and it is judged whether the flow path matches the fluid velocity and the heat exchange area. If there is deviation, the final flow path data is generated by local grid adjustment. After obtaining the final flow path data, the overall performance of the cooling system is simulated and verified in combination with the wall thickness design and the heat load requirement, and the final configuration scheme data is determined.

[0026] Exemplarily, in the design of the cooling system, first, data acquisition and analysis are performed based on the wall thickness design scheme and the heat load demand value. Assuming that the wall thickness is 5 mm and the heat load demand value is 500 kW, the initial heat exchange area is calculated by the heat conduction formula Q = kAAT / L, wherein k is the thermal conductivity coefficient, which is 50 W / (m·K), AT is the temperature difference, which is set to 20 degrees Celsius, and L is the wall thickness, which is 0.005 m. It is obtained that the initial heat exchange area A is about 5 square meters. Subsequently, combined with the fluid dynamics simulation method, the flow path of the cooling medium water is modeled by using the computational fluid dynamics software. The initial fluid velocity is set to 1.5 m / s. The flow field distribution is solved by the Navier-Stokes equation. The velocity gradient and pressure loss of the fluid in the pipeline are analyzed. It is found that the vortex exists at the bend angle of the fluid, which causes the heat exchange efficiency to decrease by about 10%. In view of this problem, the flow path design is optimized, and the bend radius of the pipeline is adjusted from 0.02 m to 0.04 m. The simulation results show that the vortex is reduced, the fluid velocity distribution is more uniform, the velocity is increased to 1.8 m / s after optimization, and the heat exchange efficiency is increased by about 15%. Further, through the thermal-fluid coupling analysis, combined with the heat exchange coefficient formula h = Nu·k / D (Nu is the Nusselt number, and D is the pipeline diameter, which is 0.05 m), the heat exchange coefficient after optimization is calculated to be 800 W / (m²·K). Thus, the heat exchange area is recalculated, and it is obtained that the optimized area can be reduced to 4.5 square meters, which meets the heat load demand and reduces the material cost by about 5%. Through the above simulation and iterative optimization, the configuration scheme of the fluid velocity of 1.8 m / s and the heat exchange area of 4.5 square meters is finally generated, which ensures the improvement of the heat exchange efficiency while taking into account the economy. The whole process is completed through the automatic calculation and analysis of the software, and the closed loop logic is formed between the data. From the initial design to the optimization adjustment, the numerical simulation is relied on to ensure the accuracy and reliability of the results.

[0027] In step S107, the heat management parameters of the separation equipment are adjusted according to the optimized fluid velocity and heat exchange area configuration scheme. Combined with the density, viscosity and particle size characteristics of the material, the best separation condition is determined by using the separation efficiency model to generate the structure design scheme of the separation equipment and the separation efficiency evaluation value.

[0028] By collecting initial data of fluid velocity and heat exchange area, combined with characteristic parameters of material density, material viscosity and particle size, a basic data set is constructed to obtain a comprehensive characteristic description of the material and equipment operation. According to the comprehensive characteristic description, a preliminary simulation is carried out by using a pre-established separation efficiency model to analyze the influence of fluid velocity and heat exchange area on separation efficiency and determine the preliminary adjustment range of thermal management parameters. In view of the preliminary adjustment range, combined with the material density and material viscosity characteristics, if the fluid velocity exceeds the preset threshold range, the heat exchange area is dynamically corrected to obtain the corrected heat distribution data, and it is judged whether the thermal management parameters meet the separation requirements. Through the corrected heat distribution data, the influence of particle size on separation efficiency is analyzed, and if the particle size distribution is uneven, the equipment parameters are adjusted to optimize the separation precision to obtain the adjusted equipment operation configuration. According to the adjusted equipment operation configuration, secondary simulation is carried out by using the separation efficiency model to analyze the correlation between structure design and efficiency evaluation, and determine the equipment parameter combination under the best separation condition. By using the determined equipment parameter combination, the structure design scheme of the separation equipment is generated, combined with the model analysis result, the final efficiency evaluation value is obtained, and the comprehensive verification of the separation efficiency is completed.

[0029] For example, to optimize the fluid velocity and heat exchange area configuration, first, the velocity distribution of the fluid in the separation device is simulated by computational fluid dynamics software, assuming an inlet flow rate of 2.5 meters per second and a pipe diameter of 0.3 meters. The velocity variation of the fluid at different cross sections is calculated using the Bernoulli equation and the continuity equation, and the optimal flow rate range is determined to be 2.0 to 3.0 meters per second. At the same time, the heat exchange area is optimized, and the heat exchanger area is set to 5 square meters. The heat transfer efficiency is calculated using the heat transfer equation Q = UAΔT, where U is the heat transfer coefficient and is taken as 200 watts per square meter·Kelvin, and ΔT is the temperature difference and is taken as 20 Kelvin. The heat transfer is calculated to be 20000 watts, ensuring that the fluid temperature is controlled within the appropriate separation range. Then, the thermal management parameters of the separation device are adjusted, and the temperature distribution during device operation is calculated using a thermal balance model. Assuming an ambient temperature of 25 degrees Celsius and an initial material temperature of 40 degrees Celsius, the heat loss is calculated to be 750 watts using the heat loss formula Q_loss = hAΔT (h is the convective heat transfer coefficient and is taken as 10 watts per square meter·Kelvin). The cooling system power is then adjusted to 800 watts to maintain temperature stability. Subsequently, the material characteristics are combined, assuming a material density of 1200 kilograms per cubic meter, a viscosity of 0.002 Pascal·seconds, and an average particle size of 50 microns. These parameters are input into the separation efficiency model, and the particle settling velocity is calculated using Stokes' law to be 0.001 meters per second. The separation time is optimized to be 30 seconds based on the flow field distribution in the device, and the optimal separation conditions are determined to be a flow rate of 2.2 meters per second and a temperature of 35 degrees Celsius. Finally, the separation device structure design scheme is generated, and the stress distribution of the device is simulated using finite element analysis software. The device wall thickness is set to 0.01 meters, and the material strength is 200 megapascals. The maximum stress is calculated to be 150 megapascals, meeting the safety requirements. At the same time, the separation efficiency evaluation value is generated, and the separation efficiency is calculated to be 77.6% using the separation efficiency formula η = 1 - exp(-kt) (k is the separation constant and is taken as 0.05, and t is the time 30 seconds). The design rationality is verified by combining the particle removal rate data. The above steps form a closed logic through numerical simulation and algorithm analysis, ensuring data-driven and automated processing of the entire process from fluid optimization to efficiency evaluation.

[0030] Step S108, according to the structure design scheme and separation efficiency evaluation value of the separation device, the structure parameters of the refining device are optimized, the operability and maintenance convenience of the device are analyzed by using the operation and maintenance model, the maintenance period and operation process are determined, and the production process scheme of high-purity magnesium is generated.

[0031] The initial structure parameters and performance evaluation results are obtained by analyzing the structure design data of the separation equipment, and the operation benchmark of the separation equipment in the current state is determined. According to the operation benchmark of the separation equipment, the structure parameters of the refining equipment are adjusted by using the pre-established optimization model to obtain an optimized structure configuration scheme. For the optimized structure configuration scheme, the operability and maintenance convenience of the equipment are simulated and analyzed by using the operation and maintenance model to determine the running stability of the equipment under different working conditions. If the running stability is lower than the preset threshold, the structure parameters are fine-tuned to obtain adjusted parameter combinations, and the final structure design scheme of the equipment is determined. According to the final structure design scheme, combined with the simulation results of the operation and maintenance model, the planning data of the maintenance cycle and the operation process are generated to obtain the guidance scheme for the operation and maintenance of the equipment. Through the guidance scheme, combined with the actual demand of the production process, the production process of high-purity magnesium metal is digitally simulated to determine the matching degree of the process under different parameters. If the matching degree reaches the preset standard, the simulation results are integrated with the optimized structure parameters to determine the production process scheme of high-purity magnesium metal.

[0032] For example, in the process of optimizing the structure parameters of the refining equipment and generating a high-purity metal magnesium production process scheme, first, based on the structural design scheme of the separation equipment, the finite element analysis software is used to simulate and optimize the key components of the equipment, such as the diameter and height of the separation tower. Assuming that the initial design diameter is 2.5 meters and the height is 10 meters, it is found through simulation that the stress concentration area is at the bottom of the tower. After optimization, the diameter is adjusted to 2.8 meters and the height is adjusted to 9.5 meters, reducing the stress peak value by about 15% and improving the structural stability. At the same time, combined with the separation efficiency evaluation value (for example, the separation efficiency is 92.5%), the optimized efficiency can be improved to 94.3% through regression analysis algorithm. The calculation formula is efficiency improvement rate = (new efficiency - old efficiency) / old efficiency * 100%. Then, the operability and maintenance convenience are analyzed using the operation and maintenance model. The equipment operation data is collected using a big data analysis platform, and the operating parameters such as temperature control at 850°C ± 5°C and pressure at 1.2 MPa are set. Based on historical failure data (assuming an average failure interval of 500 hours), the maintenance cycle is predicted to be 450 hours with an error range of ± 10 hours through Monte Carlo simulation algorithm, and an automatic operation process is generated. Through the PLC control system, real-time monitoring and adjustment of temperature and pressure are realized to ensure that the operation has no manual intervention. Subsequently, combined with the optimized parameters and maintenance cycle, a high-purity metal magnesium production process scheme is generated. The purity of the raw magnesium ore is set to 98.2%, and after treatment by the refining equipment, the target purity reaches 99.9%. The processing time per ton of raw material is calculated to be 6.5 hours using the mass balance algorithm, and the yield is 85%. The process stability is verified through process simulation software with an error control within 0.5%. To form a logical chain, the maintenance cycle is associated with the production plan. If the yield decreases to 80% within the maintenance cycle, the system automatically adjusts the raw material input to 90% of the original plan to ensure production continuity. The above steps are all realized through information technology for automatic processing, and the data analysis and optimization results are fed back to the central control system in real time to form a closed-loop management to ensure efficient operation of the process.

[0033] The above is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields is also included in the patent protection scope of the present application.

Claims

1. A high-efficiency metal magnesium production method, characterized by, The method comprises the following steps: S1. Obtain heat source distribution data from the internal heating device, collect temperature values of each region inside by using a temperature sensor array, generate an initial heat distribution map, determine the location and temperature range of hot spot regions and non-uniform regions; S2. Calculate heat conduction efficiency based on the initial heat distribution map, simulate heat conduction paths by using a finite element analysis method, judge local overheating or heat loss regions in combination with material thermal conductivity and environmental temperature boundary conditions, and generate heat conduction efficiency evaluation values; S3. If the heat conduction efficiency evaluation values are lower than 80% of a preset threshold value, optimize the structure parameters of the internal heating device, adjust the layout and power distribution of heating elements, and recalculate to generate improved heat conduction efficiency values and an updated heat distribution map; S4. Determine a reaction kettle heat load demand value according to the updated heat distribution map and the improved heat conduction efficiency values, match the best material in combination with material corrosion resistance, thermal stress distribution characteristics and a material performance database, and generate a reaction kettle material selection scheme; S5. Calculate reaction kettle wall thickness design parameters for the material selection scheme, simulate stress distribution under a high-temperature and high-pressure environment by using a stress analysis software, generate a wall thickness design scheme meeting safety standards in combination with the heat load demand value and material characteristics; S6. Obtain fluid velocity and heat exchange area data of a cooling system according to the wall thickness design scheme and the heat load demand value, simulate and optimize cooling medium flow paths by using fluid dynamics, generate an optimized fluid velocity and heat exchange area configuration scheme with the aim of improving heat exchange efficiency; S7. Adjust separation equipment thermal management parameters based on the optimized fluid velocity and heat exchange area configuration scheme, determine the best separation conditions by using a separation efficiency model in combination with material density, viscosity and particle size characteristics, and generate a separation equipment structure design scheme and separation efficiency evaluation values; S8. Optimize purification equipment structure parameters according to the separation equipment structure design scheme and the separation efficiency evaluation values, analyze equipment operability and maintenance convenience by using an operation and maintenance model, clarify the maintenance cycle and operation process, and generate a high-purity magnesium production process scheme.

2. The method of claim 1, wherein, The step S1 comprises: generating initial heat distribution information by collecting temperature data through a temperature sensor array; visualizing temperature data to construct a heat distribution map by using image processing technology; dividing hot spot regions and non-uniform regions based on a preset threshold range, marking abnormal regions with temperature exceeding the limit; analyzing whether the temperature range of the abnormal region meets safety standards; classifying abnormal regions by using a support vector machine algorithm to distinguish the distribution law of hot spot regions and non-uniform regions; and integrating the distribution law to generate heat source distribution characteristic information.

3. The method of claim 1, wherein, The step S2 comprises: constructing a heat conduction model by using a finite element analysis, generating an initial heat conduction path distribution map through grid discretization; calculating grid element heat flux density distribution and temperature field change data in combination with boundary conditions; identifying and marking local overheating regions and heat loss regions; calculating heat loss and conduction efficiency values, quantifying overall heat conduction efficiency through an evaluation model; analyzing the heat flow transfer efficiency of key nodes of the heat conduction path, and recording low-efficiency nodes; adjusting grid parameters to recalculate the temperature field distribution, and generating a heat source distribution adjustment scheme.

4. The method of claim 1, wherein, The step S3 comprises: comparing the heat conduction efficiency evaluation value with the preset threshold value; if below 80%, extracting the internal heating device structure parameter set, adjusting the parameters through the optimization model; rearranging the heating element position by using the layout optimization method; re-distributing the heating element power in combination with the power distribution strategy; re-calculating the heat conduction efficiency value through the finite element analysis; and cyclically adjusting the structure parameter set and the power distribution until the industry standard is met.

5. The method of claim 1, wherein, The step S4 comprises: calculating the reactor heat load demand value through the heat source distribution and the conduction efficiency data; simulating the stress distribution state of the material under the heat load by using the finite element analysis; matching the material performance index from the material performance database to verify whether the corrosion resistance meets the threshold value; analyzing the heat load demand and the material performance matching degree to determine the optimal material scheme; and verifying the stability of the material under multiple operation scenarios by using the scene simulation tool.

6. The method of claim 1, wherein, The step S5 comprises: constructing a digital model of the reactor, setting high-temperature and high-pressure boundary conditions; calculating the initial stress distribution result by using the finite element analysis; matching the material stress bearing range, and if below the maximum stress value, iteratively adjusting the wall thickness parameter; if still not meeting the safety standard, re-selecting a high-strength material and updating the stress distribution simulation; and calculating the wall thickness value meeting the safety standard according to the final stress distribution result.

7. The method of claim 1, wherein, The step S6 comprises: collecting the wall thickness design and the heat load demand data to determine the fluid velocity and the heat exchange area benchmark value; establishing a cooling medium flow path model by using the fluid dynamics simulation tool; adjusting the flow path in the area below the threshold value by analyzing the fluid velocity distribution through the finite element analysis; extracting the fluid characteristics of the key area to optimize the heat exchange area configuration; and iteratively verifying the overall efficiency of the cooling system to generate the final flow path and heat exchange area matching scheme.

8. The method of claim 1, wherein, The step S7 comprises: constructing a data set in combination with the fluid velocity, the heat exchange area and the material characteristic parameters; simulating and analyzing the influence of the fluid parameters on the separation efficiency by using the separation efficiency model; dynamically correcting the heat exchange area to meet the separation demand; adjusting the equipment parameters based on the particle size distribution to optimize the separation precision; determining the best equipment parameter combination through secondary simulation to generate the separation efficiency evaluation value.

9. The method of claim 1, wherein, The step S8 comprises: determining the operation benchmark according to the separation equipment structure parameters and the efficiency evaluation result; adjusting the refining equipment structure parameters through the optimization model; simulating and analyzing the equipment operability and maintenance convenience by using the operation and maintenance model; fine-tuning the structure parameters if the operation stability is below the threshold value; generating the maintenance cycle and operation process guidance scheme; and verifying the production process matching degree through digital simulation to integrate and generate the production process scheme.

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