A method and system for evaluating working conditions of a nickel smelting bath based on multi-physical field simulation

Abstract

This invention relates to the field of non-ferrous metal smelting technology, and more particularly to a method and system for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation. The method includes the following steps: establishing a spatial model of the molten pool based on the furnace dimensions, molten pool outline, and charge arrangement position of the nickel smelting furnace, forming molten pool structure data; performing temperature field simulation based on the molten pool structure data to obtain temperature distribution data at various spatial locations of the molten pool; performing melt thermal-driven flow simulation based on the temperature distribution data, recording the melt velocity and flow direction; performing nickel component migration simulation based on the melt velocity and flow direction data to obtain nickel component concentration data; and identifying nickel component enrichment regions based on the concentration distribution data. This invention achieves precise positioning of nickel component enrichment regions and smelting inhomogeneity regions based on non-ferrous metal smelting technology, improving the accuracy of evaluating abnormal molten pool conditions.

Description

Technical Field

[0001] This invention relates to the field of non-ferrous metal smelting technology, and in particular to a method and system for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation. Background Technology

[0002] In existing nickel smelting processes, the molten pool condition is typically assessed using methods such as temperature monitoring, melt sampling, and operational condition analysis. However, because the internal temperature field, melt flow, and nickel component migration within the molten pool are interconnected, it is easy to encounter problems in accurately determining localized temperature distributions, melt flow patterns, and nickel enrichment regions. While some technologies employ temperature field simulation or flow field analysis to study the smelting process, most focus on single-physics field analysis, lacking a synergistic analysis of temperature, flow, and nickel component migration processes. This makes it difficult to accurately identify inhomogeneous smelting regions and achieve dynamic assessment of the molten pool condition. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a method and system for evaluating the working conditions of nickel smelting pool based on multiphysics simulation, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for evaluating the operating conditions of a nickel smelting pool based on multiphysics simulation is provided, comprising the following steps: A spatial model of the molten pool is established based on the furnace body dimensions, molten pool outline, and charge arrangement of the nickel smelting furnace, thus generating molten pool structure data. Temperature field simulation is then performed based on the molten pool structure data to obtain temperature distribution data at various spatial locations within the molten pool. Based on the temperature distribution data, a thermally driven flow simulation of the melt was performed, recording the melt velocity and flow direction; based on the melt velocity and flow direction data, a nickel component migration simulation was performed to obtain nickel component concentration data. Identify nickel enrichment regions based on concentration distribution data; delineate smelting inhomogeneity regions within the nickel enrichment regions; and calculate the thermal uniformity and nickel component distribution balance within the smelting inhomogeneity regions. The working condition level of the uneven melting area is assessed based on the uniformity of the thermal field and the evenness of the nickel composition distribution, thus forming the working condition assessment result of the nickel smelting pool.

[0005] Preferably, this specification also provides a nickel smelting pool condition evaluation system based on multiphysics simulation, used to execute the nickel smelting pool condition evaluation method based on multiphysics simulation as described above. This nickel smelting pool condition evaluation system based on multiphysics simulation includes: The temperature field simulation module is used to establish a spatial model of the molten pool based on the furnace body dimensions, molten pool outline, and charge arrangement of the nickel smelting furnace, thereby generating molten pool structure data; and to perform temperature field simulation based on the molten pool structure data to obtain temperature distribution data at various spatial locations of the molten pool. The nickel component migration simulation module is used to simulate the thermally driven flow of melt based on temperature distribution data, and record the melt flow velocity and flow direction; it also simulates the migration of nickel components based on melt flow velocity and flow direction data to obtain nickel component concentration data. The region segmentation module is used to identify nickel-rich regions based on concentration distribution data; to segment non-uniform smelting regions within the nickel-rich regions; and to calculate the thermal uniformity and nickel component distribution balance within the non-uniform smelting regions. The operating condition assessment module is used to evaluate the operating condition level of the non-uniform melting area based on the uniformity of the thermal field and the degree of uniformity of nickel composition distribution, and to generate the operating condition assessment result of the nickel smelting pool.

[0006] The beneficial effects of this invention are as follows: (1) By establishing a spatial model of the molten pool in a nickel smelting furnace and conducting temperature field simulation in conjunction with the structure of the molten pool, the temperature distribution of each spatial location inside the molten pool is obtained, thus realizing the spatial characterization of the thermal state of the molten pool. This transforms the traditional method of judging the state of the molten pool by relying on single-point temperature detection into an analysis method based on the overall temperature field, thereby improving the accuracy of identifying the temperature state of the molten pool.

[0007] (2) By using temperature distribution to drive the thermal buoyancy and convection motion of the melt, the melt flow velocity and flow direction are obtained, and the convection migration and diffusion process of nickel components in the molten pool is simulated. This realizes the correlation analysis between temperature field, flow field and component field, and improves the characterization ability of nickel component migration state.

[0008] (3) By identifying nickel enrichment areas and combining the thermal field and nickel composition distribution characteristics within the enrichment areas to divide the smelting non-uniform areas, spatial positioning of the local smelting non-uniform state of the molten pool is realized, transforming the traditional method of analyzing only the overall state of the molten pool into a refined analysis method targeting local abnormal areas, thereby improving the ability to identify smelting non-uniform areas.

[0009] (4) By combining the uniformity of the thermal field in the non-uniform melting area and the degree of uniformity of nickel component distribution to classify the working conditions, a comprehensive judgment of the stable melting, slight deviation and serious deviation of the molten pool is realized. This transforms the traditional method of judging the working conditions based on a single temperature or component index into a comprehensive evaluation method based on the coupling characteristics of multiple physical fields, thereby improving the accuracy of the working condition evaluation of the nickel smelting pool. Attached Figure Description

[0010] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the steps of the nickel smelting pool condition evaluation method based on multiphysics simulation of the present invention; Figure 2This is a schematic diagram of the process for establishing the molten pool space model in this invention; Figure 3 This is a schematic diagram of the temperature field simulation process in this invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0012] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation, the method comprising the following steps: A spatial model of the molten pool is established based on the furnace body dimensions, molten pool outline, and charge arrangement of the nickel smelting furnace, thus generating molten pool structure data. Temperature field simulation is then performed based on the molten pool structure data to obtain temperature distribution data at various spatial locations within the molten pool. In one embodiment, taking a nickel smelting electric furnace as an example, a three-dimensional geometric model of the furnace body is established based on the furnace length, furnace width, and furnace height. The spatial range of the molten metal layer is determined by combining the actual molten pool surface, furnace bottom, and sidewall contours. The charge feeding position is mapped to the molten pool spatial model, forming molten pool structure data that includes the furnace body boundary, molten pool boundary, and the charge action area. Based on the molten pool structure data, the fluid calculation region is divided, and thermal boundary conditions for the furnace wall, furnace bottom, and molten pool surface are set. The heat conduction and convection heat transfer processes inside the molten pool are simulated to obtain the temperature values ​​of each calculation unit in the molten pool, and temperature distribution data is generated.

[0013] In another embodiment, assuming the established three-dimensional model of the nickel smelting furnace has a furnace length of 12m, a width of 6m, and a height of 4m, and a molten metal layer depth of 1.8m, it is divided into 6000 spatial calculation units. Simulations show that the internal temperature distribution of the molten pool ranges from 1320℃ to 1510℃, with the temperature near the charge feeding area at 1320℃ to 1380℃, the temperature in the central region of the molten pool at 1450℃ to 1510℃, and the temperature near the furnace wall at 1380℃ to 1430℃. This generates temperature distribution data containing the temperature values ​​of each spatial calculation unit. Based on the temperature distribution data, a thermally driven flow simulation of the melt was performed, recording the melt velocity and flow direction; based on the melt velocity and flow direction data, a nickel component migration simulation was performed to obtain nickel component concentration data. In one embodiment, the temperature gradient between different spatial locations within the molten pool is calculated based on temperature distribution data, and the direction of the molten thermal buoyancy is determined according to the temperature gradient. The direction of thermal buoyancy and the viscosity characteristics of the melt are introduced into the flow simulation to simulate the thermally driven convection process inside the melt, obtaining the velocity vector of each spatial calculation unit. The melt flow velocity and flow direction are determined based on the velocity vector, and the melt flow data is used as the transport conditions for the nickel component migration simulation to simulate the spatial migration of nickel components caused by melt convection and molecular diffusion, obtaining the nickel component concentration data of each spatial calculation unit.

[0014] In another embodiment, it is assumed that the internal temperature gradient of the molten pool is calculated to be 2.5–18.0 °C / m based on temperature distribution data, and thermal buoyancy drives the formation of a circulating convection from the bottom to the top of the molten pool. Simulations show that the melt velocity distribution is 0.05–0.42 m / s, with 0.05–0.12 m / s in the charge feeding area, 0.25–0.42 m / s in the center of the molten pool, and 0.08–0.18 m / s near the furnace wall. Using melt velocity and flow direction as conditions for nickel component migration, the calculated nickel component concentration range is 18.5–31.2%, with a concentration of 18.5–22.0% in the lower center of the molten pool, 24.0–27.5% in the sidewall recirculation area, and 29.0–31.2% in the locally enriched area, forming the nickel component concentration data. Identify nickel enrichment regions based on concentration distribution data; delineate smelting inhomogeneity regions within the nickel enrichment regions; and calculate the thermal uniformity and nickel component distribution balance within the smelting inhomogeneity regions. In one embodiment, continuous spatial regions with nickel concentrations higher than the average concentration of the molten pool are extracted based on nickel concentration data, and nickel enrichment regions are determined by combining the concentration gradient between adjacent calculation units. For nickel enrichment regions, the temperature distribution and melt flow state at the corresponding spatial locations are spatially correlated, and the temperature gradient and melt flow velocity differences within the regions are analyzed. Continuous regions with significant temperature changes and continuous nickel enrichment are classified as smelting inhomogeneous regions. The temperature dispersion and nickel concentration dispersion within the smelting inhomogeneous regions are calculated to generate thermal field uniformity data and nickel component distribution equilibrium data, respectively.

[0015] In another embodiment, assuming an average nickel concentration of 24.6% in the molten pool, continuous computational cells with nickel concentrations higher than 27.0% were identified as nickel-rich regions. Three such regions were identified, containing 18, 26, and 11 computational cells respectively. Analysis revealed that two of these regions exhibited significant temperature gradients and melt flow rate differences, leading to their classification as non-uniform melting region A and non-uniform melting region B. Region A had a temperature range of 1340–1425°C and a nickel concentration of 27.4–31.2%, while Region B had a temperature range of 1385–1460°C and a nickel concentration of 27.1–29.3%. The thermal uniformity and nickel concentration distribution balance of Regions A and B were calculated respectively.

[0016] The working condition level of the uneven melting area is assessed based on the uniformity of the thermal field and the evenness of the nickel composition distribution, thus forming the working condition assessment result of the nickel smelting pool.

[0017] In one embodiment, a joint operating condition discrimination relationship is established based on the thermal field uniformity and nickel component distribution balance of each smelting non-uniform region. Regions with high thermal field uniformity and balanced nickel component distribution are identified as stable smelting conditions; regions with deviations in either thermal field uniformity or nickel component distribution balance are identified as slightly deviated conditions; and regions with significant deviations in both thermal field uniformity and nickel component distribution balance are identified as severely deviated conditions. This generates operating condition level data corresponding to each smelting non-uniform region, and based on this, an evaluation result of the nickel smelting pool operating condition is generated.

[0018] In another embodiment, it is assumed that a joint evaluation result of thermal field uniformity and nickel component distribution equilibrium is established. The joint evaluation value is divided into three operating condition intervals: 0.80–1.00, 0.60–0.80, and 0–0.60, corresponding to stable smelting conditions, slightly deviated conditions, and severely deviated conditions, respectively. Calculations show that the thermal field uniformity of smelting non-uniform region A is 0.58, the nickel component distribution equilibrium is 0.64, and the joint evaluation value is 0.61, classifying it as a slightly deviated condition. The thermal field uniformity of smelting non-uniform region B is 0.43, the nickel component distribution equilibrium is 0.48, and the joint evaluation value is 0.45, classifying it as a severely deviated condition. The joint evaluation value for the remaining regions is 0.82–0.93, classifying them as stable smelting conditions. This forms the nickel smelting pool operating condition evaluation result.

[0019] Preferably, a spatial model of the molten pool is established based on the furnace dimensions, molten pool outline, and charge arrangement of the nickel smelting furnace, forming molten pool structural data including: The three-dimensional geometric boundary of the nickel smelting furnace is constructed based on the furnace length, furnace width, and furnace height in the furnace body dimensions, forming the furnace body geometric data; a molten pool spatial model is established based on the furnace body geometric data and the molten pool outline. In one embodiment, the length, width, and height of the nickel smelting furnace are spatially calibrated. A three-dimensional coordinate reference is established using the bottom surface of the furnace. The outer contour and internal spatial boundaries of the furnace are constructed according to the length, width, and height directions, forming the furnace body geometric data. The furnace bottom boundary, furnace wall boundary, and furnace top boundary in the furnace body geometric data are used to determine the furnace internal space range. Then, the molten pool surface contour and the furnace bottom contour are mapped onto the furnace body three-dimensional coordinate system to form the upper and lower boundaries and lateral boundaries of the molten pool. A closed molten pool spatial model is constructed based on the spatial connection relationship between the boundaries, and the internal space of the molten pool is divided into regions to form the molten pool spatial structure.

[0020] In another embodiment, assuming the nickel smelting furnace has a furnace length of 12m, a furnace width of 6m, and a furnace height of 4m, a three-dimensional coordinate system is established with the furnace bottom surface as Z=0m. The internal space of the furnace is divided into 0-12m along the length direction, 0-6m along the width direction, and 0-4m along the height direction. The bottom height of the molten pool is determined to be 0.4m, the molten pool surface height to be 2.2m, the effective length of the molten pool to be 10.8m, and the effective width to be 5.2m. Based on this, a molten pool spatial model is constructed within the geometric boundary of the furnace body. The interior of the molten pool is then divided into 12 length regions, 6 width regions, and 9 height regions, forming a total of 648 basic spatial regions used to describe the spatial structure of different locations within the molten pool.

[0021] The molten pool action area of ​​the molten metal layer is determined based on the molten pool spatial model; the furnace charge arrangement position is mapped to the molten pool action area to form molten pool structure data.

[0022] In one embodiment, the spatial range of the molten metal layer is determined based on the molten pool surface boundary, molten pool bottom boundary, and furnace wall boundary in the molten pool spatial model. The continuous spatial region below the molten pool surface, above the molten pool bottom, and within the lateral contour of the molten pool is defined as the molten pool action area. Subsequently, the furnace charge arrangement position is located using three-dimensional coordinates, and the furnace charge feeding position is transformed into the coordinate system of the molten pool spatial model. The positions of each furnace charge are spatially correlated with the molten pool action area. The corresponding furnace charge action position is determined based on the molten pool spatial area where the furnace charge is located, thereby forming molten pool structure data that includes the spatial range of the molten metal layer and the furnace charge arrangement relationship.

[0023] In another embodiment, assuming that the bottom height of the molten pool is 0.4m and the liquid level height is 2.2m as determined by the molten pool space model, the continuous area within the range of 0.4 to 2.2m in the height direction, 0 to 10.8m in the length direction, and 0.5 to 5.5m in the width direction is determined as the molten pool action area of ​​the metal melt layer. Three charge feeding positions are set in the charge arrangement area: the first charge position is (2.5m, 2.0m, 2.4m), the second charge position is (6.0m, 3.0m, 2.5m), and the third charge position is (9.5m, 4.2m, 2.3m). After mapping the three charge positions to the molten pool space model, the first charge position corresponds to the front area of ​​the molten pool, the second charge position corresponds to the middle area of ​​the molten pool, and the third charge position corresponds to the rear area of ​​the molten pool. According to the spatial correspondence between the charge position and the molten metal layer, the front charge action area, the middle charge action area, and the rear charge action area are formed. Finally, the molten pool structure data including the molten pool action area and the charge arrangement positions are obtained.

[0024] Preferably, temperature field simulation is performed based on the molten pool structure data to obtain temperature distribution data at various spatial locations of the molten pool, including: The fluid calculation region inside the molten pool is divided according to the molten pool structure data, and the thermal boundary position of the fluid calculation region is determined to form the thermal field calculation region data; the internal heat conduction boundary of the melt is set according to the thermal field calculation region data. In one embodiment, the internal space of the molten pool is divided into regions based on the molten pool spatial outline, the range of the molten metal layer, and the arrangement position of the furnace charge in the molten pool structure data. The continuous spatial region within the molten metal layer and located within the molten pool outline is defined as the fluid calculation region. The thermal boundary positions are determined along the contact positions between the fluid calculation region and the furnace bottom, furnace wall, molten pool surface, and furnace charge region, and corresponding thermal boundary regions are established according to different contact interfaces, forming thermal field calculation region data that includes the fluid calculation region and the thermal boundary positions. Based on the furnace bottom boundary, furnace wall boundary, molten surface boundary, and furnace charge boundary in the thermal field calculation region data, corresponding heat conduction boundaries are set respectively, so that a continuous heat transfer relationship is formed between each calculation region inside the melt and the external boundary, thus obtaining the internal heat conduction boundary of the melt.

[0025] In another embodiment, assuming the molten metal layer in the molten pool structure data has a length of 10.8m, a width of 5.2m, and a height of 1.8m, the interior of the molten metal layer is divided into 12 regions along the length, 6 regions along the width, and 9 regions along the height, forming a total of 648 fluid calculation regions. Based on the molten pool spatial boundary, the furnace bottom thermal boundary is determined to be located at Z=0.4m, the furnace wall thermal boundary is distributed at X=0m, X=10.8m, Y=0.5m, and Y=5.5m, and the molten pool surface thermal boundary is located at Z=2.2m; at the same time, three furnace charge contact areas are determined to be located near (2.5m, 2.0m), (6.0m, 3.0m), and (9.5m, 4.2m), respectively. The aforementioned boundary locations are mapped to 648 fluid calculation regions, forming four types of thermal boundary regions: furnace bottom, furnace wall, liquid surface, and furnace charge. Then, based on the contact relationship between each boundary region and the melt calculation region, heat conduction boundaries are set to form thermal field calculation region data containing 648 fluid calculation regions and multiple thermal boundary locations.

[0026] Based on the internal thermal conduction boundary of the melt, the internal thermal conduction and convection heat transfer process of the molten pool is simulated to form temperature distribution data at various spatial locations of the molten pool.

[0027] In one embodiment, the heat transfer relationship between adjacent fluid computational regions is determined based on the internal thermal conduction boundary of the melt, and the heat transfer process inside the melt is calculated according to the thermal boundary conditions of each region. The heat transfer direction is determined by the temperature difference between adjacent regions, and the temperature change of each region is calculated in combination with the internal thermal conduction process of the melt, forming thermal conduction temperature data. Subsequently, the heat exchange between high-temperature and low-temperature regions inside the melt is analyzed based on the temperature change data, and convective heat transfer is simulated in combination with the heat-carrying process generated by the melt flow, forming the temperature change results of each fluid computational region. After continuous calculation, the temperature change results of each spatial region are mapped back to the molten pool spatial model to form the temperature distribution data of each spatial location of the molten pool.

[0028] In another embodiment, it is assumed that the interior of the molten pool is divided into 648 fluid calculation regions, and the initial temperatures of the furnace bottom, furnace wall, and liquid surface regions are set to 1380℃, 1420℃, and 1460℃ respectively, based on the internal heat conduction boundaries of the melt. The initial temperature of the melt is set to 1400–1450℃ according to spatial location. Calculations show that the temperature near the furnace bottom increases from 1405℃ to 1432℃, the temperature near the furnace wall increases from 1410℃ to 1440℃, the temperature in the middle region of the molten pool changes from 1442℃ to 1468℃, and the temperature below the liquid surface changes from 1448℃ to 1475℃. Iterative temperature calculations are performed for different spatial locations, with the temperature distribution at the front of the molten pool being 1418–1445℃, in the middle 1440–1472℃, and at the rear 1428–1460℃; ultimately, spatial temperature distribution data of 1418–1475℃ is formed within the molten pool.

[0029] Preferably, the temperature distribution data at various spatial locations of the molten pool, based on the simulation of the internal thermal conduction boundary of the melt, includes: The heat transfer relationship between each calculation region of the molten pool is determined based on the internal heat conduction boundary of the melt; the heat transfer path from the high-temperature melt to the low-temperature region is identified based on the heat transfer relationship, and heat flux density distribution data is generated. In one embodiment, the computational regions of the molten pool are connected according to their spatial adjacency based on the internal thermal conductivity boundaries of the melt, and the heat transfer relationship between adjacent computational regions is determined based on the temperature transfer direction between them. The heat transfer direction of each adjacent region is continuously tracked, and the computational region with a higher temperature is designated as the heat output region, while the adjacent computational region with a lower temperature is designated as the heat receiving region, thereby determining the heat transfer path from the high-temperature melt to the low-temperature region. The heat transfer state per unit area is calculated along each heat transfer path, and the data is summarized according to the spatial location of the computational regions to form the heat flux density distribution data inside the molten pool.

[0030] In another embodiment, it is assumed that the interior of the molten pool is divided into 648 computational regions, where the temperature in the central region of the molten pool is 1460–1490°C, the temperature near the furnace wall is 1400–1435°C, the temperature near the furnace bottom is 1410–1440°C, and the temperature below the liquid surface is 1450–1480°C. Based on the temperature differences between adjacent computational regions, heat is transferred from the central high-temperature region to the furnace wall region, the furnace bottom region, and the region near the liquid surface. A first heat transfer path is formed from the central region to the furnace wall, a second heat transfer path is formed from the central region to the furnace bottom, and a third heat transfer path is formed from the central region to the liquid surface. The heat flux density corresponding to each path is calculated, where the heat flux density of the first heat transfer path is 8.5–15.2 kW / m², the second heat transfer path is 6.8–12.6 kW / m², and the third heat transfer path is 4.2–9.7 kW / m², thus forming heat flux density distribution data encompassing different spatial locations and different heat transfer intensities.

[0031] The heat transfer process inside the melt is analyzed based on the heat flux density distribution data to obtain temperature change data; the heat conduction and convection heat transfer processes inside the molten pool are iteratively solved based on the temperature change data to determine the temperature distribution data at each spatial location of the molten pool.

[0032] In one embodiment, based on the heat flux density distribution data, the direction and intensity of heat migration per unit time are analyzed along each heat transfer path. Regions with higher heat flux density are identified as areas of rapid heat migration, and the heat migration process is mapped to the corresponding molten pool computational region to obtain temperature change data for each computational region. Using this temperature change data as the temperature state for the next computational moment, the heat conduction process between adjacent computational regions within the molten pool is iteratively calculated. Simultaneously, convective heat transfer calculations are performed, incorporating the heat-carrying process caused by melt flow. The temperature state of each computational region is updated after each iteration until the temperature changes at adjacent iteration moments tend to stabilize, ultimately determining the temperature distribution data for each spatial location within the molten pool.

[0033] In another embodiment, assuming the initial temperature distribution of the 648 computational regions of the molten pool is 1410–1490°C, the heat migration from the central high-temperature region to the furnace wall region, furnace bottom region, and liquid surface region is calculated based on the heat flux density distribution data, with a temperature iteration time of 0.5 s. After the first iteration, the temperature in the furnace wall region increases from 1415°C to 1423°C, the furnace bottom region increases from 1420°C to 1427°C, and the central region of the molten pool decreases from 1475°C to 1469°C. After the second iteration, the furnace wall region temperature rises to 1430°C, the furnace bottom region rises to 1434°C, and the central region temperature drops to 1464°C. After the third iteration, the furnace wall region reaches 1435°C, the furnace bottom region reaches 1439°C, and the central region reaches 1461°C. Based on the above temperature changes, the convective heat transfer process of the melt is further introduced to obtain the following temperatures: front of the molten pool: 1428–1452℃; middle: 1455–1470℃; rear: 1436–1462℃; near the liquid surface: 1448–1465℃. This results in the temperature distribution data of each spatial location of the molten pool.

[0034] Preferably, the melt thermally driven flow simulation is performed based on temperature distribution data, and the melt flow velocity and flow direction are recorded, including: Identify temperature differences at different locations within the molten pool based on temperature distribution data, and calculate the temperature gradient using these differences; determine the direction of thermal buoyancy of the melt based on the melt temperature gradient; In one embodiment, based on the temperature distribution data of each spatial location in the molten pool, the spatial differences in temperature between adjacent calculation regions are compared, the temperature difference between adjacent locations is taken as the local temperature difference, and the temperature gradient in the corresponding direction is calculated according to the spatial distance between adjacent regions; the temperature gradient direction of each calculation region is correlated with the direction of melt density change, the direction of thermal buoyancy inside the melt is determined according to the temperature change relationship between high temperature region and low temperature region, and the thermal buoyancy direction of each calculation region is spatially correlated to form the thermal buoyancy direction distribution inside the molten pool.

[0035] In another embodiment, assuming four calculation positions A, B, C, and D are arranged along the length of the molten pool, with temperatures of 1420℃, 1450℃, 1480℃, and 1460℃ respectively, and a spatial distance of 1m between adjacent positions, the temperature gradient from A to B is 30℃ / m, the temperature gradient from B to C is 30℃ / m, and the temperature gradient from C to D is -20℃ / m. The temperature in the region from A to C continuously increases along the length, forming a localized high-temperature region at C. Therefore, the thermal buoyancy direction in this region mainly points towards the rising direction of the high-temperature melt. The temperature at D is lower than at C, forming a temperature-decreasing region from C to D, corresponding to a change in the thermal buoyancy direction. Three positions E, F, and G are set along the width of the molten pool, with temperatures of 1440℃, 1470℃, and 1435℃ respectively, to obtain the temperature gradient distribution in different directions, thereby determining the thermal buoyancy direction at multiple positions within the molten pool.

[0036] Based on the direction of thermal buoyancy of the melt and the preset melt viscosity characteristics, the convective motion between high-temperature melt and low-temperature melt is simulated to obtain melt convection field data; based on the melt convection field data, the velocity vector at each position is calculated, and the melt flow velocity and flow direction data are extracted from the velocity vector.

[0037] In one embodiment, the motion relationship between the high-temperature melt and the low-temperature melt inside the molten pool is established based on the direction of thermal buoyancy of the melt, and the flow resistance of the melt in different temperature regions is determined in combination with the preset melt viscosity characteristics. Using the direction of thermal buoyancy as the driving direction of melt motion, continuous simulation is performed on the rising of the high-temperature melt, the falling of the low-temperature melt, and the lateral compensation motion between adjacent regions to obtain melt flow field data. The three-dimensional motion components of each calculation position are extracted from the melt flow field data, and the velocity components in the three directions are combined into a velocity vector at the corresponding position. The melt velocity is determined according to the magnitude of the velocity vector, and the melt flow direction is determined according to the direction of the velocity vector, forming melt velocity and flow direction data.

[0038] In another embodiment, it is assumed that five computational regions (A, B, C, D, and E) are set inside the molten pool, with melt temperatures of 1420°C, 1450°C, 1480°C, 1465°C, and 1435°C, respectively, and corresponding melt viscosities of 0.82 Pa·s, 0.75 Pa·s, 0.68 Pa·s, 0.71 Pa·s, and 0.79 Pa·s, respectively. Based on the temperature gradient of each region, it is determined that the high-temperature melt in regions A to C moves upward and forms a reflux from region C to regions D and E. After convective motion simulation, the velocity components at positions A, B, C, D, and E are (0.06, 0.03, 0.12) m / s, (0.10, 0.05, 0.18) m / s, (0.04, -0.02, 0.26) m / s, (-0.08, -0.04, 0.15) m / s, and (-0.05, -0.03, 0.09) m / s, respectively. The corresponding melt velocities are 0.14 m / s, 0.21 m / s, 0.26 m / s, 0.17 m / s, and 0.11 m / s, with flow directions of upward-forward, upward-forward, mainly upward, downward-backward, and downward-backward, respectively. Mapping the velocity and direction at each position to the molten pool spatial model yields the melt velocity and flow direction data within the molten pool.

[0039] Preferably, determining the direction of melt thermal buoyancy based on the melt temperature gradient includes: The velocity component is extracted based on the melt temperature gradient to obtain melt velocity component data; the velocity vector at each spatial position is synthesized based on the melt velocity component data to determine the melt motion state; In one embodiment, the thermal driving direction of each spatial location within the molten pool is determined based on the melt temperature gradient. The melt motion state is decomposed along the length, width, and height directions of the molten pool, and the motion response in each direction is respectively taken as the velocity component in the length direction, width direction, and height direction. The three velocity components in each spatial location are calculated synchronously to form melt velocity component data. The three velocity components corresponding to the same spatial location are synthesized to obtain the three-dimensional velocity vector at that location. The melt motion state is determined according to the magnitude and direction of the velocity vector, forming the velocity vector distribution of each spatial location within the molten pool.

[0040] In another embodiment, five calculation locations (A, B, C, D, and E) are set inside the molten pool. The corresponding melt velocity components are obtained based on the temperature gradient at each location. Specifically, the velocity components at location A are (0.04, 0.02, 0.10) m / s, at location B (0.08, 0.03, 0.16) m / s, at location C (0.03, -0.02, 0.22) m / s, at location D (-0.06, -0.03, 0.13) m / s, and at location E (-0.04, -0.02, 0.08) m / s. The three-dimensional velocity vectors at each location are synthesized to obtain the melt velocity vectors at locations A, B, C, D, and E. Based on this, it is determined that the melt at locations A, B, and C mainly moves upwards, while the melt at locations D and E flows downwards. This forms melt motion state data that includes the direction of melt movement and spatial variation. The velocity amplitude at each spatial position is calculated based on the melt motion state to obtain melt velocity data; the melt motion direction is identified according to the melt velocity data and used as the direction of melt thermal buoyancy.

[0041] In one embodiment, the velocity amplitude at each spatial position is calculated based on the three-dimensional velocity vector in the melt's motion state, and the amplitude of the velocity vector is used as the melt flow velocity data at the corresponding position; the melt motion direction is identified according to the velocity amplitude change relationship between adjacent spatial positions, wherein the region where the velocity amplitude continuously increases is determined as the melt acceleration motion region, and the melt motion direction is determined in combination with the direction of the corresponding velocity vector; then the main melt motion direction driven by the temperature gradient is correlated with the direction of thermal buoyancy, and the main melt motion direction is used as the melt thermal buoyancy direction, thus forming melt flow velocity and thermal buoyancy direction data.

[0042] In another embodiment, assuming the velocity vectors corresponding to the five calculation positions A, B, C, D, and E are (0.04, 0.02, 0.10) m / s, (0.08, 0.03, 0.16) m / s, (0.03, -0.02, 0.22) m / s, (-0.06, -0.03, 0.13) m / s, and (-0.04, -0.02, 0.08) m / s, the calculated melt flow velocities are 0.11 m / s, 0.18 m / s, 0.22 m / s, 0.15 m / s, and 0.09 m / s, respectively. The velocity vectors at positions A, B, and C all have an upward height component, indicating that the melt in this region mainly moves upwards; the height velocity components at positions D and E decrease and the horizontal components reverse, indicating that a downward backflow occurs in this region. Based on the aforementioned melt movement direction, regions A, B, and C are defined as the upward direction of thermal buoyancy, and regions D and E are defined as the return direction of thermal buoyancy. This generates melt velocity data and melt thermal buoyancy direction data within the molten pool. Preferably, nickel component migration simulation is performed based on melt flow rate and flow direction data to obtain nickel component concentration data, including: Identify the convective migration path of nickel components within the molten pool based on melt flow velocity and flow direction data; determine the diffusion range of nickel components within the convective migration path; In one embodiment, based on the aforementioned melt velocity and flow direction data, the molten pool space is divided into multiple continuous flow regions, and the main migration direction of the nickel component is determined according to the velocity vector direction of adjacent spatial locations. Taking the initial charge region where the nickel component is located as the migration starting point, the movement trajectory of the nickel component is tracked region by region along the melt velocity vector direction. When the velocity direction between adjacent regions remains continuous, the corresponding spatial regions are connected to form a convection migration path for the nickel component. Subsequently, based on the melt flow state of adjacent regions on both sides of the convection migration path, the spatial range of the nickel component diffusion from the main migration path to the surrounding melt region is determined, and the diffusion range of the nickel component is expanded outward from the nickel component migration path as the center to form a nickel component diffusion range including the main migration path and its surrounding diffusion region.

[0043] In another embodiment, it is assumed that six consecutive spatial locations A, B, C, D, E, and F are selected as nickel component migration analysis points within the molten pool, with melt flow velocities of 0.18 m / s, 0.24 m / s, 0.31 m / s, 0.27 m / s, 0.21 m / s, and 0.16 m / s at each point, respectively. Point A is located in the charge feeding area. Based on the velocity vector direction at each point, the nickel component migrates sequentially from point A to points B, C, and D, and then branches off from point D to points E and F, thus forming a main migration path and a branch migration path. Diffusion analysis is performed on the spatial region surrounding the migration paths, revealing that the lateral diffusion widths of the nickel component along the main migration path are 0.35 m, 0.48 m, 0.62 m, and 0.55 m, respectively, and the diffusion widths along the branch paths are 0.28 m and 0.31 m, respectively. This determines the diffusion range of the nickel component and establishes a spatial correspondence between the convective migration path and the diffusion area of ​​the nickel component.

[0044] The diffusion range and convective migration path of nickel components are coupled, and the mass migration amount of nickel components is calculated. Based on the mass migration amount, the nickel component content is calculated to obtain nickel component concentration data.

[0045] In one embodiment, the diffusion range of nickel components is spatially superimposed with the convective migration path. The effective spatial region for nickel components to participate in convective migration is determined based on the overlap position. Combining the melt velocity, nickel component diffusion range, and cross-sectional area within each effective spatial region, the migration flux of nickel components at different migration locations is determined. The migration flux at each location is continuously integrated along the convective migration path to obtain the mass migration amount of nickel components within the corresponding spatial region. Subsequently, the nickel component mass migration amount and corresponding melt mass are converted to determine the nickel component content at each spatial location. The contents at different spatial locations are then continuously mapped to form nickel component concentration data.

[0046] In another embodiment, it is assumed that the determined convection migration path of the nickel component passes through four spatial regions A, B, C, and D sequentially, with effective diffusion ranges corresponding to cross-sectional areas of 0.42 m², 0.56 m², 0.68 m², and 0.51 m², respectively, and average melt velocities within these regions of 0.18 m / s, 0.24 m / s, 0.31 m / s, and 0.27 m / s, respectively. After coupled calculation of diffusion range and migration path, the nickel component migration fluxes in each region are 0.36 kg / s, 0.52 kg / s, 0.67 kg / s, and 0.48 kg / s, respectively. Mass accumulation is performed according to the migration durations of 20 s, 18 s, 15 s, and 12 s for each region, yielding nickel component mass migration amounts of 7.2 kg, 9.36 kg, 10.05 kg, and 5.76 kg, respectively. With melt masses of 420 kg, 510 kg, 620 kg and 480 kg in each region, the corresponding nickel component contents were calculated to be approximately 1.71%, 1.84%, 1.62% and 1.20%, respectively. This yielded nickel component concentration data distributed along the convection migration path of nickel components, characterizing the spatial migration and concentration changes of nickel components within the molten pool.

[0047] Preferably, the coupling of the nickel component diffusion range with the convective migration path and the calculation of the mass migration of the nickel component include: The overlapping locations of the diffusion range and the convection migration path of nickel components are extracted to form diffusion-convection overlap data; the migration flux of nickel components is calculated using the diffusion-convection overlap data; and the mass migration amount of nickel components is calculated based on the migration flux of nickel components.

[0048] In one embodiment, based on the spatial correspondence between the previously determined nickel component diffusion range and the convection migration path, the spatial regions located within the nickel component diffusion range and intersecting with the convection migration path are extracted. These regions are then connected according to their continuous positional relationship within the molten pool to form diffusion-convection overlap data. Subsequently, migration cross sections perpendicular to the nickel component convection migration direction are established at each overlap position. The migration flux of nickel component through the corresponding cross section is determined based on the area of ​​the migration cross section, the melt flow velocity, and the nickel component concentration within the overlap region. The nickel component migration flux of each migration cross section is calculated sequentially along the convection migration path, and mass accumulation is performed according to the spatial distance between adjacent migration positions and the migration duration to obtain the mass migration amount of nickel component corresponding to each overlap region.

[0049] In another embodiment, it is assumed that the convective migration path of the nickel component passes through four overlapping positions A, B, C, and D in sequence. The cross-sectional areas of the diffusion-convective overlap at each position are 0.36 m², 0.48 m², 0.55 m², and 0.42 m², respectively. The average melt velocities are 0.20 m / s, 0.26 m / s, 0.30 m / s, and 0.24 m / s, respectively, and the nickel component concentrations are 1.20%, 1.45%, 1.68%, and 1.52%, respectively. Based on the cross-sectional area, melt velocity, and nickel component concentration at each overlapping position, the nickel component migration flux is calculated to be 0.864 kg / s, 1.809 kg / s, 2.772 kg / s, and 1.532 kg / s, respectively. Assuming the effective migration times of nickel components at the four overlapping positions A, B, C, and D are 15s, 12s, 10s, and 8s, respectively, the corresponding mass migration amounts of nickel components are 12.96kg, 21.71kg, 27.72kg, and 12.26kg, respectively. After accumulating along the convection migration path, the total mass migration amount of nickel components along this migration path is 74.65kg, thus forming the nickel component mass migration data.

[0050] Preferably, the operating condition level of the smelting non-uniform region is assessed based on the uniformity of the thermal field and the evenness of the nickel composition distribution, forming the nickel smelting pool operating condition assessment results, including: Establish operating condition discrimination criteria based on the uniformity of the thermal field and the degree of uniformity of nickel composition distribution; evaluate the operating condition level of the non-uniform smelting area according to the operating condition discrimination criteria, including stable smelting conditions, slightly deviated conditions and severely deviated conditions. In one embodiment, the temperature distribution data within the aforementioned non-uniform smelting region is statistically analyzed, and the deviation between the temperature of each region and the average temperature of the region is calculated to form thermal field uniformity data. Simultaneously, the dispersion of nickel component concentration within the region is calculated based on nickel component concentration data at each spatial location to form nickel component distribution equilibrium data. The thermal field uniformity data and the nickel component distribution equilibrium data are correlated, and stable, deviating, and severely deviating judgment intervals are set according to the thermal field deviation and nickel component concentration dispersion, respectively, to form operating condition judgment criteria. Subsequently, the thermal field uniformity data and nickel component distribution equilibrium data of each non-uniform smelting region are input into the operating condition judgment criteria. When both the temperature distribution and nickel component concentration of a region are within the stable interval, the corresponding region is judged as a stable smelting condition; when one is within the deviation interval or both show slight deviations, it is judged as a slightly deviating condition; when both the temperature distribution and nickel component concentration exceed the severely deviating interval, it is judged as a severely deviating condition.

[0051] In another embodiment, the uneven melting region is divided into four regions: A, B, C, and D. The maximum temperature deviation and the degree of variation in nickel concentration in each region are used as the criteria for determining the operating condition. A stable melting condition is defined as a maximum temperature deviation not exceeding 15°C and a nickel concentration variation not exceeding 5%. A slightly deviated condition is defined as a maximum temperature deviation between 15°C and 30°C or a nickel concentration variation between 5% and 10%. A severely deviated condition is defined as a maximum temperature deviation exceeding 30°C and a nickel concentration variation exceeding 10%. Calculations show that region A has a maximum temperature deviation of 8°C and a nickel concentration variation of 3.2%, region B has 21°C and 6.8%, region C has 34°C and 13.5%, and region D has 27°C and 11.2%. Based on the operating condition judgment criteria, area A is judged as a stable smelting condition, area B is judged as a slightly deviated condition, area C is judged as a severely deviated condition, and area D is judged as a severely deviated condition because the temperature deviation is within the slightly deviated range and the nickel composition concentration variation exceeds 10%. This forms the operating condition level data for each smelting non-uniform area.

[0052] The smelting unevenness area was divided by the working condition level, and the working condition evaluation results of the nickel smelting pool were recorded.

[0053] In one embodiment, based on the operating condition level corresponding to each non-uniform smelting region, the location of regions in the molten pool spatial model is marked with a level. Regions with the same operating condition level and continuous spatial location are merged to form stable smelting regions, slightly deviated regions, and severely deviated regions. The spatial coordinates, area, operating condition level, and corresponding thermal field uniformity and nickel composition distribution balance of each region are correlated and recorded to form the nickel smelting molten pool operating condition evaluation result. Different operating condition regions are mapped to the molten pool structure model according to the spatial location of the molten pool to characterize the operating condition status of different regions during the nickel smelting process.

[0054] In another embodiment, it is assumed that the effective working area of ​​the molten pool is divided into 12 spatial regions, where A1–A5 are the front region, B1–B4 are the middle region, and C1–C3 are the rear region. Based on the operating condition level data, five regions, A1, A2, B1, B2, and C1, are determined to be stable smelting conditions; four regions, A3, A4, B3, and C2, are determined to be slightly deviated conditions; and three regions, A5, B4, and C3, are determined to be severely deviated conditions. After merging adjacent regions with the same operating condition level, five stable smelting regions, four slightly deviated regions, and three severely deviated regions are formed. The center coordinates, spatial range, and corresponding operating condition level of each region are recorded, thereby forming the nickel smelting pool operating condition evaluation result.

[0055] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0056] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for evaluating the operating conditions of a nickel smelting pool based on multiphysics simulation, characterized in that, Includes the following steps: A spatial model of the molten pool is established based on the furnace body dimensions, molten pool outline, and charge arrangement of the nickel smelting furnace, thus generating molten pool structure data. Temperature field simulation is then performed based on the molten pool structure data to obtain temperature distribution data at various spatial locations within the molten pool. Based on the temperature distribution data, a thermally driven flow simulation of the melt was performed, recording the melt velocity and flow direction; based on the melt velocity and flow direction data, a nickel component migration simulation was performed to obtain nickel component concentration data. Identify nickel enrichment regions based on concentration distribution data; The smelting non-uniform region is divided into areas where nickel components are enriched, and the thermal field uniformity and the degree of nickel component distribution uniformity in the smelting non-uniform region are calculated. The working condition level of the uneven melting area is assessed based on the uniformity of the thermal field and the evenness of the nickel composition distribution, thus forming the working condition assessment result of the nickel smelting pool.

2. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 1, characterized in that, Based on the furnace dimensions, molten pool outline, and charge arrangement of the nickel smelting furnace, a spatial model of the molten pool is established, resulting in molten pool structural data including: The three-dimensional geometric boundary of the nickel smelting furnace is constructed based on the furnace length, furnace width, and furnace height in the furnace body dimensions, forming the furnace body geometric data; a molten pool spatial model is established based on the furnace body geometric data and the molten pool outline. The molten pool action area of ​​the molten metal layer is determined based on the molten pool spatial model; the furnace charge arrangement position is mapped to the molten pool action area to form molten pool structure data.

3. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 1, characterized in that, Temperature field simulation was performed based on the molten pool structure data to obtain temperature distribution data at various spatial locations within the molten pool, including: The fluid calculation region inside the molten pool is divided according to the molten pool structure data, and the thermal boundary position of the fluid calculation region is determined to form the thermal field calculation region data; the internal heat conduction boundary of the melt is set according to the thermal field calculation region data. Based on the internal thermal conduction boundary of the melt, the internal thermal conduction and convection heat transfer process of the molten pool is simulated to form temperature distribution data at various spatial locations of the molten pool.

4. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 3, characterized in that, Based on the simulation of the internal thermal conduction boundary of the melt, the internal heat conduction and convection heat transfer processes of the molten pool are obtained, resulting in temperature distribution data at various spatial locations within the molten pool, including: The heat transfer relationship between each calculation region of the molten pool is determined based on the internal heat conduction boundary of the melt; the heat transfer path from the high-temperature melt to the low-temperature region is identified based on the heat transfer relationship, and heat flux density distribution data is generated. The heat transfer process inside the melt is analyzed based on the heat flux density distribution data to obtain temperature change data; the heat conduction and convection heat transfer processes inside the molten pool are iteratively solved based on the temperature change data to determine the temperature distribution data at each spatial location of the molten pool.

5. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 1, characterized in that, Based on temperature distribution data, a thermally driven melt flow simulation was performed, recording the melt velocity and flow direction, including: Identify temperature differences at different locations within the molten pool based on temperature distribution data, and calculate the temperature gradient using these differences; determine the direction of thermal buoyancy of the melt based on the melt temperature gradient; Based on the direction of thermal buoyancy of the melt and the preset melt viscosity characteristics, the convective motion between high-temperature melt and low-temperature melt is simulated to obtain melt convection field data; based on the melt convection field data, the velocity vector at each position is calculated, and the melt flow velocity and flow direction data are extracted from the velocity vector.

6. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 5, characterized in that, Determining the direction of melt thermal buoyancy based on the melt temperature gradient includes: The velocity component is extracted based on the melt temperature gradient to obtain melt velocity component data; the velocity vector at each spatial position is synthesized based on the melt velocity component data to determine the melt motion state; The velocity amplitude at each spatial position is calculated based on the melt motion state to obtain melt velocity data; the melt motion direction is identified according to the melt velocity data and used as the direction of melt thermal buoyancy.

7. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 1, characterized in that, Based on melt flow velocity and flow direction data, nickel component migration simulation was performed to obtain nickel component concentration data, including: Identify the convective migration path of nickel components within the molten pool based on melt flow velocity and flow direction data; determine the diffusion range of nickel components within the convective migration path; The diffusion range and convective migration path of nickel components are coupled, and the mass migration amount of nickel components is calculated. Based on the mass migration amount, the nickel component content is calculated to obtain nickel component concentration data.

8. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 7, characterized in that, The coupling of the diffusion range and convective migration path of nickel components, and the calculation of the mass migration of nickel components, include: The overlapping locations of the diffusion range and the convection migration path of nickel components are extracted to form diffusion-convection overlap data; the migration flux of nickel components is calculated using the diffusion-convection overlap data; and the mass migration amount of nickel components is calculated based on the migration flux of nickel components.

9. The method for evaluating the working conditions of a nickel smelting pool based on multiphysics simulation according to claim 1, characterized in that, The operating condition level of the non-uniform smelting area is assessed based on the uniformity of the thermal field and the evenness of the nickel composition distribution, forming the operating condition assessment results of the nickel smelting pool, including: Establish operating condition discrimination criteria based on the uniformity of the thermal field and the degree of uniformity of nickel composition distribution; evaluate the operating condition level of the non-uniform smelting area according to the operating condition discrimination criteria, including stable smelting conditions, slightly deviated conditions and severely deviated conditions. The smelting unevenness area was divided by the working condition level, and the working condition evaluation results of the nickel smelting pool were recorded.

10. A nickel smelting pool condition evaluation system based on multiphysics simulation, characterized in that, For executing the nickel smelting pool condition evaluation method based on multiphysics simulation as described in claim 1, the nickel smelting pool condition evaluation system based on multiphysics simulation includes: The temperature field simulation module is used to establish a spatial model of the molten pool based on the furnace body dimensions, molten pool outline, and charge arrangement of the nickel smelting furnace, thereby generating molten pool structure data; and to perform temperature field simulation based on the molten pool structure data to obtain temperature distribution data at various spatial locations of the molten pool. The nickel component migration simulation module is used to simulate the thermally driven flow of melt based on temperature distribution data, and record the melt flow velocity and flow direction; it also simulates the migration of nickel components based on melt flow velocity and flow direction data to obtain nickel component concentration data. The region segmentation module is used to identify nickel-rich regions based on concentration distribution data; to segment non-uniform smelting regions within the nickel-rich regions; and to calculate the thermal uniformity and nickel component distribution balance within the non-uniform smelting regions. The operating condition assessment module is used to evaluate the operating condition level of the non-uniform melting area based on the uniformity of the thermal field and the degree of uniformity of nickel composition distribution, and to generate the operating condition assessment result of the nickel smelting pool.

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