Control method and system for improving regeneration utilization rate of waste aluminum material

Abstract

The invention relates to the technical field of metal smelting, and discloses a control method and system for improving the regeneration utilization rate of waste aluminum materials. The method comprises the following steps: acquiring aluminum scrap components through spectral analysis, establishing a database and performing heat treatment; burdening is optimized based on a database, and temperature field feeding is controlled; applying an electromagnetic field to form directional flow, and adjusting frequency and intensity; filtering, degassing, deironing and detecting purity adjusting parameters in sequence; collecting temperature stress data during solidification, and adjusting cooling and vibration; and detecting the quality of the finished product and performing correlation analysis with process parameters to optimize the production process. Through multi-source data acquisition, correlation analysis and feedback optimization, accurate control over the waste aluminum material recycling process is achieved, and therefore the quality stability of a secondary aluminum product is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of metal smelting, and in particular to a control method and system for improving the recycling rate of waste aluminum materials. Background Art

[0002] At present, the recycling of scrap aluminum has become an important part of the aluminum industry, which is of great significance for energy conservation, emission reduction and resource recycling. The existing scrap aluminum recycling process mainly includes sorting, pretreatment, smelting, purification, casting and other processes. Among them, the composition of scrap aluminum is detected by spectral analysis, the surface coating is removed by heat treatment in a quenching furnace, the smelting is carried out by electromagnetic stirring, and the melt is purified by filtering, degassing, magnetic field separation and other methods, and finally crystallization casting is carried out. These are all commonly used process methods.

[0003] However, the existing technology has the following shortcomings: there are many types of scrap aluminum materials, the composition is complex, and there is a lack of effective correlation analysis and coordinated control between the parameters of each process. In particular, in key processes such as heat treatment, batching, smelting, purification and casting, they are often controlled separately and are not related to each other, which makes it difficult to optimize process parameters and cannot form a complete process control system. At the same time, due to the lack of data collection and analysis of the entire process, it is also difficult to achieve continuous optimization of process parameters, which affects the quality stability and resource utilization efficiency of recycled aluminum. Summary of the invention

[0004] The present application provides a control method and system for improving the recycling rate of waste aluminum materials, which is used to establish a collaborative control method throughout the entire process of heat treatment, batching, smelting, purification and casting. Through the collection, correlation analysis and feedback optimization of multi-source data, precise control of the recycling process of waste aluminum materials can be achieved, thereby ensuring the quality stability of recycled aluminum products.

[0005] In a first aspect, the present application provides a control method for improving the recycling rate of waste aluminum materials, the control method for improving the recycling rate of waste aluminum materials comprising:

[0006] The scrap aluminum material is subjected to component detection by a spectrum analyzer to obtain scrap aluminum material component data, and a component information database is established based on the scrap aluminum material component data; the scrap aluminum material is subjected to heat treatment in a quenching furnace, and the exhaust gas component is monitored in real time by an infrared spectrometer to establish a coating decomposition curve, and the heat treatment termination time point is determined based on the coating decomposition curve to obtain the pretreated scrap aluminum material and heat treatment process parameters;

[0007] Based on the component information database, according to the chemical composition of the pretreated scrap aluminum material and the target product specification requirements, the batching ratio parameters are calculated through the batching optimization algorithm; according to the batching ratio parameters and the temperature field distribution characteristics in the furnace, the batching time sequence and the batching process control instructions are generated; according to the batching time sequence, the pretreated scrap aluminum material is put into the smelting furnace to obtain the scrap aluminum material to be melted;

[0008] Applying an electromagnetic field of a specific frequency to the scrap aluminum material to be melted to form a melt directional flow field; generating an electromagnetic field dynamic adjustment instruction according to the temperature distribution of the melt directional flow field; controlling the frequency and intensity parameters of the electromagnetic field based on the electromagnetic field dynamic adjustment instruction to form a molten scrap aluminum material with uniform temperature and a smelting process control strategy;

[0009] The molten aluminum scrap is passed through a ceramic filter, an ultrasonic degassing device and a magnetic field gradient separation device in sequence to remove impurity particles, dissolved gas and iron-based impurities; melt purity data is obtained through an online detection device, and a filtering parameter adjustment instruction is generated according to the melt purity data to obtain purified molten aluminum scrap and a purification process control strategy;

[0010] The purified molten waste aluminum material is injected into a crystallizer, and solidification process data is collected through a temperature sensor and a strain sensor; a casting parameter dynamic adjustment instruction is generated according to the solidification process data; the secondary cooling water volume, the crystallizer vibration frequency and the casting speed are controlled based on the casting parameter dynamic adjustment instruction to obtain a regenerated aluminum ingot and a casting process control strategy;

[0011] The recycled aluminum ingots are subjected to spectral analysis, metallographic analysis and mechanical property testing to obtain quality inspection data; the quality inspection data are correlated with the heat treatment process parameters, batching process control instructions, smelting process control strategy, purification process control strategy and casting process control strategy to generate a process parameter optimization database; and production process optimization instructions are generated according to the process parameter optimization database.

[0012] In a second aspect, the present application provides a control system for improving the recycling rate of waste aluminum materials, and the control system for improving the recycling rate of waste aluminum materials comprises:

[0013] The detection module is used to detect the composition of the scrap aluminum material through a spectrum analyzer to obtain the composition data of the scrap aluminum material, and establish a composition information database according to the composition data of the scrap aluminum material; heat treat the scrap aluminum material in a quenching furnace, monitor the exhaust gas composition in real time through an infrared spectrometer, establish a coating decomposition curve, determine the heat treatment termination time point according to the coating decomposition curve, and obtain the pretreated scrap aluminum material and heat treatment process parameters;

[0014] A calculation module is used to calculate the batching ratio parameters through a batching optimization algorithm based on the component information database, according to the chemical composition of the pretreated scrap aluminum material and the target product specification requirements; generate a batching placement sequence and a batching process control instruction according to the batching ratio parameters and the temperature field distribution characteristics in the furnace; and put the pretreated scrap aluminum material into the smelting furnace according to the batching placement sequence to obtain the scrap aluminum material to be melted;

[0015] A generation module is used to apply an electromagnetic field of a specific frequency to the scrap aluminum material to be melted to form a melt directional flow field; generate an electromagnetic field dynamic adjustment instruction according to the temperature distribution of the melt directional flow field; and control the frequency and intensity parameters of the electromagnetic field based on the electromagnetic field dynamic adjustment instruction to form a molten scrap aluminum material with uniform temperature and a smelting process control strategy;

[0016] An adjustment module is used to sequentially pass the molten aluminum scrap through a ceramic filter, an ultrasonic degassing device, and a magnetic field gradient separation device to remove impurity particles, dissolved gases, and iron-based impurities; obtain melt purity data through an online detection device, generate a filtering parameter adjustment instruction according to the melt purity data, and obtain purified molten aluminum scrap and a purification process control strategy;

[0017] A control module is used to inject the purified molten aluminum waste into a crystallizer, collect solidification process data through a temperature sensor and a strain sensor; generate a casting parameter dynamic adjustment instruction according to the solidification process data; and control the secondary cooling water volume, the crystallizer vibration frequency and the casting speed based on the casting parameter dynamic adjustment instruction to obtain a regenerated aluminum ingot and a casting process control strategy;

[0018] The association module is used to perform spectral analysis, metallographic analysis and mechanical property testing on the recycled aluminum ingot to obtain quality inspection data; associate the quality inspection data with the heat treatment process parameters, batching process control instructions, smelting process control strategy, purification process control strategy, and casting process control strategy to generate a process parameter optimization database; and generate production process optimization instructions based on the process parameter optimization database.

[0019] In the technical solution provided by the present application, a complete data collection and analysis system is established in the process of recycling waste aluminum materials, and intelligent control of the whole process is realized. The waste aluminum materials are detected by the spectrum analyzer and a component information database is established. The coating decomposition process is monitored in real time with an infrared spectrometer to ensure the precise control of the pretreatment process; in the batching link, optimization calculations are performed based on the component information database, and the batching timing is generated in combination with the temperature field distribution characteristics, which improves the accuracy and uniformity of the batching; by applying an electromagnetic field of a specific frequency to the melt and adjusting the electromagnetic field parameters in real time, the uniform control of the melt temperature field is achieved; the three-stage purification process of ceramic filter, ultrasonic degassing device and magnetic field gradient separation device is adopted, and the real-time monitoring of the online detection device is coordinated to significantly improve the purity of the melt; in the casting process, the precise control of the solidification process is achieved through the coordinated monitoring of temperature sensors and strain sensors, and the stability of the ingot quality is ensured; finally, by conducting comprehensive quality inspections on the recycled aluminum ingots, and correlating the inspection data with the parameters of each process, a process parameter optimization database is established, and continuous optimization of the production process is achieved. The entire solution significantly improves the recycling efficiency of scrap aluminum and the consistency of product quality by combining data-driven and intelligent control. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 A schematic diagram of an embodiment of a control method for improving the recycling rate of waste aluminum materials in an embodiment of the present application;

[0022] Figure 2 This is a schematic diagram of an embodiment of a control system for improving the recycling rate of waste aluminum materials in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application embodiment provides a control method and system for improving the recycling rate of scrap aluminum. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the control method for improving the recycling rate of waste aluminum materials includes:

[0025] Step S101, detecting the composition of the scrap aluminum material by a spectrum analyzer to obtain the composition data of the scrap aluminum material, and establishing a composition information database according to the composition data of the scrap aluminum material; heat treating the scrap aluminum material in a quenching furnace, monitoring the exhaust gas composition in real time by an infrared spectrometer, establishing a coating decomposition curve, determining the heat treatment termination time point according to the coating decomposition curve, and obtaining the pretreated scrap aluminum material and heat treatment process parameters;

[0026] Step S102: Based on the component information database, the batching ratio parameters are calculated by the batching optimization algorithm according to the chemical composition of the pretreated scrap aluminum and the target product specification requirements; the batching timing and batching process control instructions are generated according to the batching ratio parameters and the temperature field distribution characteristics in the furnace; the pretreated scrap aluminum is put into the smelting furnace according to the batching timing to obtain the scrap aluminum to be melted;

[0027] Step S103, applying an electromagnetic field of a specific frequency to the aluminum scrap to be melted to form a melt directional flow field; generating an electromagnetic field dynamic adjustment instruction according to the temperature distribution of the melt directional flow field; controlling the frequency and intensity parameters of the electromagnetic field based on the electromagnetic field dynamic adjustment instruction to form a molten aluminum scrap with uniform temperature and a smelting process control strategy;

[0028] Step S104, passing the molten aluminum scrap through a ceramic filter, an ultrasonic degassing device and a magnetic field gradient separation device in sequence to remove impurity particles, dissolved gases and iron-based impurities; obtaining melt purity data through an online detection device, generating a filtering parameter adjustment instruction according to the melt purity data, and obtaining the purified molten aluminum scrap and a purification process control strategy;

[0029] Step S105, injecting the purified molten waste aluminum into the crystallizer, collecting solidification process data through the temperature sensor and the strain sensor; generating a casting parameter dynamic adjustment instruction according to the solidification process data; controlling the secondary cooling water volume, the crystallizer vibration frequency and the casting speed based on the casting parameter dynamic adjustment instruction, and obtaining the regenerated aluminum ingot and the casting process control strategy;

[0030] Step S106, perform spectral analysis, metallographic analysis and mechanical property test on the recycled aluminum ingot to obtain quality inspection data; correlate the quality inspection data with heat treatment process parameters, batching process control instructions, smelting process control strategy, purification process control strategy, and casting process control strategy to generate a process parameter optimization database; generate production process optimization instructions based on the process parameter optimization database.

[0031] It is understandable that the execution subject of the present application may be a control system for improving the recycling rate of waste aluminum materials, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0032] Specifically, the composition of the scrap aluminum is detected by a spectrometer. The spectrometer generates a characteristic spectrum by exciting the light source to make the atoms of the material undergo energy level transitions, thereby identifying the type and content of elements in the material. When detecting scrap aluminum, the spectrometer collects spectral data at five different positions on the surface of the scrap aluminum to obtain the content values ​​of elements such as aluminum, silicon, copper, zinc, magnesium, and manganese. These values ​​are normalized to form a scrap aluminum component characteristic matrix and stored in the component information database. For scrap aluminum with surface coating, heat treatment is required in a quenching furnace. The quenching furnace heats the scrap aluminum to 480-520°C, and the infrared spectrometer collects the vibration absorption spectrum data of organic molecules in the exhaust gas in real time. By performing spectral analysis on the exhaust gas component data, the decomposition rate of the coating organic matter is calculated, and the coating decomposition curve is drawn. When the decomposition rate of the organic matter measured three times in a row is lower than 0.1% / min, it indicates that the coating has been fully decomposed, and the heat treatment termination time point is determined.

[0033] After obtaining the pretreated scrap aluminum, intelligent batching is performed based on the composition information database. The batching optimization algorithm first compares the pretreated scrap aluminum with the target product specifications according to the main element content, secondary element content, and impurity element content to generate composition difference data. Based on the composition difference data, the scrap aluminum is divided into three categories: main material, auxiliary material, and regulating material, and the initial batching ratio is obtained by weighted calculation. Combined with the temperature field distribution characteristics in the smelting furnace, the batching ratio is iteratively optimized until the difference in batching quality calculated from two iterations is less than the set threshold. According to the optimized batching ratio parameters, the feeding point positions are sorted according to the temperature field gradient to generate the batching delivery sequence.

[0034] After the scrap aluminum to be melted is put into the smelting furnace, an electromagnetic field with a frequency of 20-60Hz is applied for electromagnetic stirring. The electromagnetic force measurement device collects electromagnetic force distribution data in real time, calculates the force field distribution parameters, and forms a directional flow field of the melt. The temperature sensor array monitors the temperature in the flow field in different regions and constructs a temperature distribution matrix. By calculating the temperature gradient value and the flow field velocity distribution, the uneven temperature field area is located and the temperature field compensation data is generated. Based on the compensation data, the electromagnetic field frequency and intensity adjustment required for each area are calculated, and the multi-channel controller converts it into an excitation signal of the electromagnetic coil to achieve uniform control of the melt temperature field. The purification of molten scrap aluminum adopts a three-stage purification process. First, the impurity particles are removed by the ceramic filter, and the pressure difference and flow rate changes before and after the filtration are monitored in real time to calculate the degree of clogging of the filter pores. Then, the ultrasonic degassing device is used to remove the dissolved gas, and the ultrasonic power, frequency and bubble size distribution data are collected. Finally, the magnetic field gradient separation device is used to remove iron impurities, and the impurity migration trajectory is calculated in combination with the melt flow rate. The online detection device comprehensively analyzes these three sets of data, generates a filter parameter adjustment instruction, and realizes the linkage control of the three purification devices.

[0035] The purified molten waste aluminum is injected into the crystallizer for casting. Multiple temperature sensor measuring points and strain sensors are arranged on the wall of the crystallizer to collect temperature and stress values ​​and generate a temperature-stress distribution matrix. The collected data is Fourier transformed, and the vibration resonance frequency range of the crystallizer is determined by spectrum analysis. At the same time, the temperature gradient value of the solidification area is calculated, and a linear mapping relationship is established with the cooling water flow rate, and the crystal growth rate is determined in combination with the advancement speed of the solidification interface. After these parameters are prioritized, they are converted into control quantities for secondary cooling water volume, crystallizer vibration frequency, and casting speed. Finally, the quality of the recycled aluminum ingots is tested, including spectral analysis, metallographic analysis, and mechanical property testing. The component content, grain size, strength and other data obtained by the test are respectively correlated and analyzed with the control parameters of each process of heat treatment, batching, smelting, purification, and casting to generate a comprehensive correlation matrix of process quality. By classifying and storing the process parameters, establishing a parameter optimization index table, calculating the parameter adjustment direction and priority order, and forming a production process optimization instruction.

[0036] For example, a batch of scrap aluminum was measured by spectral analysis to have an aluminum content of 94.5%, a silicon content of 4.2%, and a copper content of 0.8%, which belongs to A356 alloy recycled material. During the heat treatment process, the infrared spectrometer detected that the characteristic absorption peak intensity of the CH bond and C=O bond in the tail gas continued to decrease, and the heat treatment was completed when the decomposition rate dropped to 0.08% / min. During the batch optimization, the batch was used as the main material, and the scrap with a low silicon content was adjusted. The ratio was determined after three iterative calculations. During smelting, the temperature sensor detected that the local temperature difference reached 15°C. By adjusting the electromagnetic field frequency in this area from 35Hz to 45Hz, the temperature field tended to be uniform. During the purification process, the filter screen was replaced when the pressure drop increased by 30%, the ultrasonic power was automatically adjusted according to the bubble size distribution, and the magnetic field strength was dynamically adjusted with the change of iron content. During casting, the mapping coefficient of the temperature gradient value and the cooling water flow was 0.8, and the solidification process was controlled accordingly. Through the correlation analysis of quality data and each process parameter, a process parameter optimization system covering the entire process was established.

[0037] In the embodiment of the present application, by establishing a complete data collection and analysis system in the process of recycling waste aluminum, intelligent control of the whole process is realized. The waste aluminum is detected by a spectrometer and a component information database is established. The coating decomposition process is monitored in real time with an infrared spectrometer to ensure accurate control of the pretreatment process; in the batching link, optimization calculations are performed based on the component information database, and the batching timing is generated in combination with the temperature field distribution characteristics, which improves the accuracy and uniformity of the batching; by applying an electromagnetic field of a specific frequency to the melt and adjusting the electromagnetic field parameters in real time, the uniformization control of the melt temperature field is achieved; a three-stage purification process of a ceramic filter, an ultrasonic degassing device and a magnetic field gradient separation device is adopted, and the real-time monitoring of the online detection device is coordinated to significantly improve the purity of the melt; in the casting process, the precise control of the solidification process is achieved through the coordinated monitoring of temperature sensors and strain sensors, and the stability of the ingot quality is ensured; finally, by conducting comprehensive quality inspections on the recycled aluminum ingots, and correlating the inspection data with the parameters of each process, a process parameter optimization database is established, and continuous optimization of the production process is achieved. The entire solution significantly improves the recycling efficiency of scrap aluminum and the consistency of product quality by combining data-driven and intelligent control.

[0038] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0039] (1) Scan and detect the five positions of the waste aluminum material through a spectrum analyzer to obtain the element content value of the waste aluminum material and generate the composition data of the waste aluminum material;

[0040] (2) Classifying the types of scrap aluminum materials according to the scrap aluminum material composition data, normalizing the scrap aluminum material composition data, and generating scrap aluminum material composition characteristic data;

[0041] (3) Establishing a component information database based on the component characteristic data of the scrap aluminum materials to form a component characteristic matrix of the scrap aluminum materials;

[0042] (4) placing the waste aluminum material in a quenching furnace for heating, collecting tail gas component data through an infrared spectrometer, and calculating the organic matter decomposition value based on the tail gas component data;

[0043] (5) drawing a coating decomposition curve based on the organic matter decomposition value, and recording the heat treatment termination time point when the organic matter decomposition value is less than a preset threshold;

[0044] (6) Correlating the coating decomposition curve with the process parameters to generate heat treatment process parameters, marking the treated scrap aluminum material, and obtaining the pretreated scrap aluminum material.

[0045] Specifically, the composition of the scrap aluminum is tested. The surface of the scrap aluminum is scanned and tested using a spectrometer. The basic principle of the spectrometer is to use a high-temperature arc to excite the sample surface, so that the atoms in the material are excited and electronically transitioned, generating characteristic spectral lines. The spectrometer separates these characteristic spectral lines through a spectroscopic system, and then the detector measures the intensity of each spectral line to determine the content of each element in the material. In actual testing, five positions at least 50 mm apart on the surface of the scrap aluminum are selected for scanning, and each position is measured three times repeatedly. The average value is taken as the element content value of the point, and a 5×N element content data matrix is ​​obtained, where N is the number of element types detected.

[0046] The obtained scrap aluminum composition data is normalized to eliminate the influence of the difference in the numerical range of different element contents. The normalization process uses the maximum and minimum value normalization method to map the content value of each element to the interval [0,1]. For the element content value of each detection position, the minimum value of the element at all positions is subtracted, and then divided by the difference between the maximum and minimum values ​​of the element content. The normalized data is more convenient for subsequent data analysis and type classification. At the same time, according to the normalized element content characteristics and combined with the aluminum alloy grade standard, the scrap aluminum is divided into different types, such as 1 series pure aluminum, 2 series aluminum copper, 5 series aluminum magnesium, etc. When establishing the composition information database, the normalized scrap aluminum composition characteristic data is classified and stored according to the alloy type. Each record in the database contains information such as scrap aluminum number, sampling location, original element content value, normalized value, alloy type, etc., forming a multi-dimensional scrap aluminum composition characteristic matrix. This structured storage method facilitates the subsequent rapid retrieval and call of specific types of scrap aluminum data.

[0047] For scrap aluminum with coating on the surface, it is necessary to perform heat treatment in a quenching furnace to remove the coating. The quenching furnace is a heat treatment equipment that can accurately control the temperature, with a heating chamber, a temperature control system and an exhaust gas collection system. During the heat treatment process, an infrared spectrometer is installed on the exhaust gas collection pipeline to monitor the organic components in the exhaust gas in real time. The infrared spectrometer is based on the principle of molecular vibration absorption. Different chemical bonds (such as CH bonds, C=O bonds, CO bonds, etc.) have characteristic absorption peaks at specific wave numbers. The degree of decomposition of the coating can be determined by measuring the intensity changes of these characteristic peaks. The coating decomposition curve is drawn based on the exhaust gas component data collected by the infrared spectrometer. First, the absorbance value of the key characteristic peak is obtained, and the decomposition rate of the organic matter at each time point is calculated. The decomposition rate refers to the ratio of the change in the intensity of the characteristic peak per unit time to the initial intensity, which reflects the dynamic process of coating decomposition. When the decomposition rate at multiple consecutive time points is lower than the preset threshold (usually 0.1% / min), it indicates that the coating has been basically decomposed completely, and the time point recorded at this time is the end time point of the heat treatment.

[0048] The generation of heat treatment process parameters is the result of correlation analysis between the coating decomposition curve and process parameters such as furnace temperature curve, heating rate, and holding time. By analyzing the decomposition of the coating under different heat treatment conditions, the corresponding relationship between the coating type, thickness and the optimal heat treatment process parameters is established. At the same time, the waste aluminum after heat treatment is marked, and the heat treatment process parameters it has experienced are recorded, and it enters the next process as the pre-treated waste aluminum.

[0049] For example, when processing a batch of aluminum alloy door and window waste, the composition is detected at five locations by a spectrometer. Spectral analysis shows that the main elements are aluminum, magnesium, and silicon, of which the aluminum content is between 94-96%, the magnesium content is between 0.4-0.6%, and the silicon content is between 0.2-0.3%. At the same time, a small amount of copper, manganese and other elements are also detected. After normalizing these raw data, combined with the element content characteristics, it is determined to be 6063 aluminum alloy. Since the surface of this batch of waste materials has a fluorocarbon paint coating, heat treatment is required. During the heat treatment process, the infrared spectrometer monitors the characteristic peaks of the tail gas in real time, and obvious absorption peaks are observed at the CF bond (1150cm^-1) and the C=O bond (1730cm^-1). As the heat treatment proceeds, the intensity of these characteristic peaks gradually decreases, and the coating decomposition curve is drawn by calculating the decomposition rate. When the rate of change of the characteristic peak intensity drops below the preset threshold, the termination time of the heat treatment is determined, and the temperature, time and other parameters recorded during the entire heat treatment process are stored in the database as heat treatment process parameters for this type of waste aluminum material. At the same time, the treated waste is marked to complete the pretreatment process.

[0050] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0051] (1) extracting the main element content, secondary element content, and impurity element content values ​​of the pretreated scrap aluminum material from the composition information database, and comparing the main element content, secondary element content, and impurity element content values ​​with the target composition range in the target product specification requirements to generate composition difference data;

[0052] (2) Based on the composition difference data, the pre-treated scrap aluminum materials are divided into three categories: main materials, auxiliary materials, and adjustment materials. The three categories of scrap aluminum materials are weighted and calculated to generate the initial batching ratio value;

[0053] (3) numerically calculating the initial batching ratio and the temperature field distribution characteristics in the smelting furnace, iteratively optimizing the batching ratio parameters, and outputting the batching ratio parameters when the difference in batching quality obtained by two iterative calculations is less than a set threshold;

[0054] (4) constructing a feeding sequence matrix based on the batching ratio parameters, sorting the feeding point positions according to the temperature field gradient according to the feeding sequence matrix, and generating a batching time sequence;

[0055] (5) According to the real-time correlation calculation between the batching timing and the temperature field distribution characteristics of the smelting furnace, the time interval, feeding weight and feeding speed of each batch of feeding are determined to form the batching process control instructions;

[0056] (6) The pretreated scrap aluminum is placed in a designated temperature zone in the smelting furnace according to the batching process control instructions, thereby completing the batching process of the scrap aluminum to be melted.

[0057] Specifically, the data in the composition information database are extracted and analyzed. The main element content refers to the elements with a content of more than 1% in the alloy, including aluminum (Al) as a matrix element, and copper (Cu), silicon (Si), magnesium (Mg), zinc (Zn) and other major alloying elements; the secondary element content refers to the elements with a content between 0.1% and 1% in the alloy, such as manganese (Mn), chromium (Cr), etc. Although these elements have a low content, they have an important impact on the performance of the alloy; the impurity element content refers to the elements with a content of less than 0.1%, such as iron (Fe), nickel (Ni), etc. The data extraction process first needs to establish an element content classification standard, and classify and organize the element content values ​​stored in the database according to three levels: main elements, secondary elements, and impurity elements. The database query uses SQL statements for multi-condition screening to ensure that the extracted data is complete and accurate. The comparison of the element content value with the target product specification requirements adopts an item-by-item comparison method to calculate the deviation value between the actual content of each element and the target content, and mark the direction of the deviation (higher or lower than the target value). These deviation values ​​constitute a composition difference data matrix. Each row in the matrix represents an element, and each column contains four parameters: actual content, target content, deviation value, and deviation direction, providing basic data support for subsequent batching optimization. Scrap aluminum materials are classified and weighted based on composition difference data. The criterion for selecting the main material is to comprehensively consider the deviation values ​​of all main elements. When the absolute sum of the deviation values ​​is the smallest, the batch of scrap is classified as the main material. The main material plays a basic role in batching, and it is necessary to ensure that its main element content is basically consistent with the target requirements. The selection of auxiliary materials is relatively loose, allowing certain deviations in individual elements, but the overall composition structure should be similar to the target product. Adjustment materials are mainly used to adjust the content of specific elements. Usually, scrap materials with particularly high or low content of certain elements are selected, and the insufficient or excessive content of elements in the main and auxiliary materials is compensated by adjusting the proportion of the material. The quality characteristics of the three types of waste materials need to be considered in weighted calculations. The weight coefficient is set based on a large amount of production practice data. The main material determines the basic performance of the product, so its weight is usually set to 0.6-0.7; the auxiliary material is used as a supplement to the main material, and its weight is set to 0.2-0.3; the adjustment material is used for fine-tuning, and its weight is generally 0.1-0.2. The initial batching ratio value is obtained through matrix calculation, which reflects the proportional relationship of the three types of waste materials in the total batching amount.

[0058] The temperature field distribution characteristics in the smelting furnace are obtained by a temperature monitoring network composed of multiple thermocouple measurement points. The layout of the thermocouples needs to take into account the furnace structure and the melt flow characteristics to ensure that the temperature conditions in each area can be accurately reflected. The temperature field data includes the real-time temperature value of each measurement point and the temperature gradient between adjacent measurement points. These data are updated in real time through the data acquisition system. The iterative optimization process first performs the first round of calculations based on the initial batching ratio value to obtain a set of batching parameters, and then substitutes this set of parameters into the temperature field distribution model to predict the impact of different batching schemes on the smelting process. Each iteration will generate a new batching quality value, calculate the difference between the results of two adjacent iterations, and when the difference is less than the set threshold (usually 0.5% of the total batching amount), it is considered that the optimal batching scheme has been achieved. This process must not only ensure that the element content meets the requirements, but also consider factors such as smelting efficiency and energy consumption.

[0059] The feeding sequence matrix is ​​a multidimensional data structure that contains information such as the type, weight, and placement of each batch of feeding. The number of rows in the matrix corresponds to the number of feeding batches, and the number of columns contains various parameters of the feeding. The temperature field gradient is an important basis for sorting the feeding point positions. The temperature gradient reflects the uniformity of the temperature distribution in the furnace. A smaller temperature gradient means that the temperature distribution in the area is more uniform and more suitable as a feeding point. The sorting process first calculates the temperature gradient value of each possible feeding point position, and then conducts a comprehensive evaluation based on the actual operating conditions (such as the feeding port position, the range of motion of the robotic arm, etc.) to determine the optimal placement position for each batch of feeding. This process requires the establishment of a mathematical model to convert the temperature field distribution data into an operational feeding sequence instruction.

[0060] The data in the feeding sequence matrix is ​​correlated with the real-time temperature field distribution to calculate the specific control parameters. The time interval is determined based on the recovery of the molten pool temperature after each batch of feeding, and sufficient melting time must be guaranteed; the feeding weight is determined by the capacity of the furnace and the heat balance calculation, which must ensure production efficiency while avoiding excessive feeding that causes a sharp drop in temperature; the feeding speed needs to consider the shape and size of the scrap and the receiving capacity of the molten pool, and optimize the control by establishing a speed-temperature response model. All these parameters need to meet the actual process requirements and be converted into executable digital instructions.

[0061] The location, time, weight and speed of each batch of feeding are precisely controlled to ensure that the actual operation is consistent with the optimization plan. The temperature changes of the molten pool should be monitored in real time during the operation. If abnormal fluctuations occur, the feeding parameters of subsequent batches need to be adjusted in time. After the entire batching process is completed, the scrap aluminum to be melted is recorded and marked to provide accurate raw material information for subsequent smelting processes.

[0062] For example, when producing a batch of 7075 aluminum alloy for aviation structural parts, recycled aluminum scrap is fully utilized. First, the composition data of candidate scraps are extracted from the database, which contains multiple batches of scraps from different sources. Through composition analysis and comparison, a batch of aircraft skin scraps are selected as the main material (the content of the main elements is very close to the target), two batches of profile scraps of different models are selected as auxiliary materials (the composition structure is similar but some elements are slightly deviated), and a batch of scraps with high zinc content is used as adjustment materials. The initial proportion is determined according to the weighted calculation results, and then the optimization calculation is performed in combination with the temperature field distribution data measured in real time in the smelting furnace. After many iterations, the optimal batching scheme is determined, and detailed feeding execution instructions are generated. In the actual feeding process, the feeding position is selected strictly according to the temperature field distribution characteristics to ensure the stability of the molten pool temperature after each batch of feeding, and the batching process is successfully completed.

[0063] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0064] (1) applying an electromagnetic field with a frequency range of 20-60 Hz to the aluminum scrap to be melted, collecting electromagnetic force distribution data through an electromagnetic force measuring device, calculating force field distribution parameters based on the electromagnetic force distribution data, and generating a melt directional flow field;

[0065] (2) The temperature in the melt directional flow field is collected in different regions through a temperature sensor array, and real-time temperature values ​​of multiple temperature measurement points are obtained to construct a temperature distribution matrix to generate the temperature distribution of the melt directional flow field;

[0066] (3) Calculating the temperature gradient value inside the melt based on the temperature distribution matrix, combining the flow field velocity distribution of the melt directional flow field, positioning and marking the uneven temperature field area, and generating temperature field compensation data;

[0067] (4) Calculating the electromagnetic field intensity by region based on the temperature field compensation data, determining the electromagnetic field frequency adjustment amount and intensity adjustment amount required for each region, and forming an electromagnetic field dynamic adjustment instruction;

[0068] (5) converting the electromagnetic field dynamic adjustment instruction into an excitation signal of the electromagnetic coil through a multi-channel controller, adjusting the frequency and intensity parameters of the electromagnetic field in real time, and generating electromagnetic field control data;

[0069] (6) The electromagnetic field control data is correlated with the smelting temperature curve to form a molten scrap aluminum material with uniform temperature. The frequency and intensity parameters of the electromagnetic field and the temperature distribution data obtained by real-time collection and analysis are integrated to generate a smelting process control strategy.

[0070] Specifically, an electromagnetic field is applied to the scrap aluminum to be melted. The electromagnetic field is generated by a special induction coil, and the coil structure adopts a water-cooled design and can withstand high temperature environments. The electromagnetic field frequency range is controlled between 20-60Hz, and this frequency range can generate electromagnetic forces suitable for stirring aluminum liquid. The electromagnetic force measurement device includes a Hall sensor array for measuring the electromagnetic field intensity distribution; a magnetic flux sensor for monitoring magnetic field changes; and a force sensor for measuring the electromagnetic force acting on the melt. The data collected by these sensors are signal conditioned and digitally processed to form electromagnetic force distribution data. The electromagnetic force distribution data contains information on the magnitude, direction and spatial distribution of the force. These data are processed by a special algorithm to calculate the force field distribution parameters. The force field distribution parameters include electromagnetic force density, force field gradient, torque distribution, etc. These parameters directly determine the flow characteristics of the melt. Based on these parameters, a prediction model for the directional flow field of the melt is established for temperature field control.

[0071] After determining the basic force field distribution, the melt temperature needs to be accurately monitored and controlled. The temperature sensor array uses high-precision platinum-rhodium thermocouples, which are distributed in a three-dimensional grid in the furnace. The spacing between each temperature measurement point is determined according to the furnace size and control accuracy requirements, usually between 100-150mm. This arrangement ensures the spatial resolution of the temperature field measurement. Each thermocouple is equipped with a signal conditioning circuit, including cold end compensation, signal amplification and anti-interference processing, to ensure the reliability of the measurement data. The temperature data is collected in a time-division multiplexing manner, and each temperature measurement point is scanned one by one through a multi-way switch. The sampling frequency can be adjusted according to the process requirements, and is generally set to 1-2Hz. The collected raw temperature data is processed by digital filtering to remove high-frequency noise and outliers, and then reconstructed into a temperature distribution matrix according to the spatial position information. This matrix contains the real-time temperature information of each position of the melt and is the basic data for subsequent temperature field analysis.

[0072] The calculation of the temperature gradient value is achieved by numerically analyzing the temperature distribution matrix. The three-dimensional finite difference method is used to calculate the temperature change rate of each spatial point in three orthogonal directions. At the same time, based on the fluid movement generated by the electromagnetic force, a melt flow field model is established to calculate the flow velocity distribution in each area. The temperature gradient data and the flow field data are comprehensively analyzed to identify areas with uneven temperature fields. The criteria for determining uneven temperature fields include excessive local temperature gradients, excessive temperature fluctuations, and asymmetric temperature distribution. For the identified abnormal areas, their spatial position, temperature deviation value, duration and other information are recorded. This information constitutes the temperature field compensation data, which is used to guide subsequent electromagnetic field parameter adjustments.

[0073] Based on the temperature field compensation data, the electromagnetic field strength is optimized and calculated in different regions. This process adopts the following mathematical model:

[0074]

[0075] Where: ΔE ij represents the electromagnetic field intensity adjustment required for region (i, j); Represents the temperature gradient value of region (i, j); V ij represents the flow field velocity in region (i, j); δ ij represents the temperature compensation coefficient of region (i, j); α, β, γ are weight coefficients;

[0076] Then calculate the frequency adjustment parameter according to the electromagnetic field strength adjustment amount:

[0077] Δf ij =μ·ΔE ij +v·R ij +ω·P ij

[0078] in:

[0079] Δf ij represents the frequency adjustment amount of region (i, j); R ij represents the thermal resistance coefficient of region (i, j); P ij represents the power density of region (i, j); μ, v, ω are the adjustment coefficients.

[0080] The design of the multi-channel controller adopts a modular structure. Each control channel contains an independent signal processing unit, a D / A converter and a power amplifier. After receiving the electromagnetic field dynamic adjustment command, the controller first performs parameter analysis and converts the adjustment command into specific control parameters. Then the analog control signal is generated through the D / A converter, and the electromagnetic coil is driven after power amplification. Each control channel has a real-time feedback function, which can monitor the actual parameters of the output signal to ensure control accuracy. All parameter changes generated during the control process will be recorded, including the frequency adjustment process, field strength change curve, temperature response characteristics, etc. These data constitute the electromagnetic field control data.

[0081] Finally, it is necessary to correlate and analyze the electromagnetic field control data with the melting temperature curve. The melting temperature curve includes the temperature changes during the entire melting process, which is divided into a heating section, a constant temperature section, and a cooling section. In the heating section, the focus is on analyzing the temperature rise rate and the temperature response characteristics of each area; in the constant temperature section, the main focus is on the uniformity and stability of the temperature field; in the cooling section, the temperature drop rate needs to be controlled. By analyzing these data, the control effect is evaluated and the control strategy is continuously optimized. The formed melting process control strategy includes a parameter configuration plan to guide the production of subsequent batches.

[0082] For example, when processing a batch of aviation aluminum alloy scrap, an electromagnetic field is first applied at a base frequency of 20 Hz, and the electromagnetic force distribution is measured through the Hall sensor array to obtain the initial force field distribution data. The temperature sensor array detection shows that the temperature in the middle area of ​​the melt is 15°C lower than the surrounding area. By calculating the three-dimensional temperature gradient and flow field velocity distribution, the specific location where stirring needs to be strengthened is located. According to the temperature field compensation data, it is calculated that the electromagnetic field frequency in this area needs to be increased to 35 Hz and the field strength needs to be increased by about 20%. After receiving these adjustment instructions, the multi-channel controller gradually adjusts the excitation current of the coils in each area through 12 independent control channels. The temperature field changes are continuously monitored throughout the process. When the temperature field uniformity meets the requirements, all control parameters are recorded to form a process control plan.

[0083] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0084] (1) passing the molten aluminum scrap through a ceramic filter, collecting the pressure difference value and flow rate value before and after filtration, calculating the degree of pore blockage of the filter according to the change trend of the pressure difference value and the flow rate value, and generating filter status data;

[0085] (2) degassing the melt by an ultrasonic degassing device, collecting ultrasonic power, frequency, and bubble size distribution data, performing statistical analysis on the bubble size distribution data, and generating bubble distribution characteristic data;

[0086] (3) collecting magnetic field intensity distribution data based on the magnetic field gradient separation device, calculating the migration trajectory of iron-based impurities in combination with the melt flow rate, and generating impurity separation efficiency data;

[0087] (4) Inputting the filter status data, bubble distribution characteristic data, and impurity separation efficiency data into the online detection device, collecting the impurity content, gas content, and iron content values, and forming melt purity data;

[0088] (5) Calculating the filter replacement time point, ultrasonic power adjustment amount, and magnetic field strength adjustment amount based on the melt purity data, and generating a filter parameter adjustment instruction;

[0089] (6) The filter parameter adjustment instructions are converted into control signals, and the three purification devices are adjusted in a coordinated manner to obtain the purified molten waste aluminum material. The filter status data, bubble distribution characteristic data, and impurity separation efficiency data are integrated to generate a purification process control strategy.

[0090] Specifically, in the purification process of waste aluminum recycling, the molten waste aluminum needs to be filtered through a ceramic filter. The ceramic filter is a filtering device made of porous ceramic material, which has the characteristics of high temperature resistance, corrosion resistance and high mechanical strength. The pore size of the filter is generally between 30-50 microns, which can effectively intercept non-metallic inclusions and oxide particles in the melt. During the filtering process, the pressure sensor monitors the pressure difference on both sides of the filter in real time, and the flow sensor monitors the flow rate change of the melt. The pressure difference value reflects the resistance condition of the filter, and the flow rate value reflects the passing capacity of the melt. The change trend of these two parameters directly indicates the degree of blockage of the filter. By establishing a pressure difference-flow rate relationship curve and calculating the slope change rate of the curve, the working state of the filter can be accurately judged. When the slope change exceeds the preset threshold, it means that the filter begins to be significantly blocked and needs to be replaced in time. All these monitoring data and calculation results constitute the filter status data. The ultrasonic degassing device is a device that uses high-frequency sound waves to produce cavitation effect in the melt, prompting the dissolved gas to precipitate and form bubbles. The operating frequency of the ultrasonic generator is generally in the range of 18-25kHz, and the power can be adjusted according to the melt volume. During the degassing process, it is necessary to monitor the actual output power and operating frequency of the ultrasonic wave, and capture the formation and floating process of the bubble through a special bubble observation window and a high-speed camera system. The bubble size distribution data is obtained through an image processing algorithm, including parameters such as the average diameter, number density, and size distribution of the bubble. These raw data are statistically analyzed to generate bubble distribution characteristic data, which is used to evaluate the degassing effect and guide the adjustment of process parameters.

[0091] The magnetic field gradient separation device uses the force of a strong magnetic field to separate iron-based impurities. The device consists of a superconducting magnet and a magnetic field gradient control unit, which can generate a magnetic field with controllable intensity and gradient. The magnetic field intensity distribution data is collected in real time through the Hall sensor array, and combined with the flow velocity of the melt, the movement trajectory of the iron-based impurities in the magnetic field can be calculated. The impurity separation efficiency data includes information such as the magnetic field intensity distribution in the separation area, the movement trajectory of the impurities, and the separation time.

[0092] The online detection device integrates a variety of detection methods, including spectral analysis, gas content analysis, and magnetic impurity detection. The device receives the status data of the above three links, and performs real-time sampling and analysis to obtain the values ​​of impurity content, dissolved gas content, and iron content in the melt. After normalization, these data form standardized melt purity data.

[0093] Based on the melt purity data, the purification process parameters are optimized and calculated using the following mathematical model:

[0094]

[0095] Where: Treplace The time to replace the filter; D p is the equivalent diameter of particle impurities; Q f is the filtration capacity coefficient; ΔP is the pressure loss increment; P0 is the initial pressure loss; R c is the congestion rate; λ1, λ2, λ3 are weight coefficients.

[0096] Ultrasonic power adjustment calculation formula:

[0097]

[0098] Where: W adjust is the power regulation amount; G t is the target gas content; G c is the current gas content;

[0099] is the gas content change rate; G e is the gas content error; ξ1, ξ2, ξ3 are control parameters.

[0100] Magnetic field strength adjustment calculation formula:

[0101]

[0102] Where: H adjust is the magnetic field strength adjustment; F e is the iron content; V m is the melt flow rate; is the magnetic field gradient; θ1, θ2, θ3 are the adjustment coefficients.

[0103] Finally, the calculated adjustment parameters are converted into equipment control signals to achieve the coordinated operation of the three purification devices. All parameters and data of the entire purification process are recorded to form a process control strategy.

[0104] For example, when processing a batch of aviation aluminum alloy waste, the pressure difference on both sides of the filter screen was monitored by the pressure sensor and gradually increased from the initial 0.2MPa to 0.35MPa, while the flow rate decreased by 20%. Based on these data, it was calculated that the blockage rate of the filter screen reached the warning value. During the ultrasonic degassing process, a large number of bubbles with a diameter of 0.5-2mm were recorded by the bubble observation system, and statistical analysis showed that the bubble size distribution was normally distributed. The magnetic field gradient separation device operates at a magnetic field intensity of 1.5T. By tracking the migration trajectory of iron-based impurities, the optimal separation area position is determined. The online detection data shows that the purity of the melt is gradually improved. When each indicator reaches the target value, the process parameter combination is recorded as a reference for processing similar working conditions. The entire purification process obtains a high-purity aluminum melt that meets the requirements through the coordinated cooperation of three devices.

[0105] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0106] (1) Injecting the purified molten aluminum scrap into the crystallizer, collecting temperature values ​​at multiple temperature measuring points arranged on the wall of the crystallizer by temperature sensors, combining the stress values ​​collected by the strain sensor to generate a temperature-stress distribution matrix, and performing time series analysis on the temperature-stress distribution matrix to obtain solidification process data;

[0107] (2) Performing Fourier transform processing on the solidification process data to obtain the temperature fluctuation spectrum and the stress fluctuation spectrum, determining the crystallizer vibration resonant frequency range through spectrum analysis, and generating vibration frequency adjustment data;

[0108] (3) Calculating the temperature gradient value of the solidification area based on the temperature-stress distribution matrix, linearly mapping the temperature gradient value with the cooling water flow rate, and generating secondary cooling water flow adjustment data;

[0109] (4) Calculating the solidification interface advancement speed according to the temperature-stress distribution matrix, determining the crystal growth rate in combination with the temperature gradient value, and generating casting speed adjustment data;

[0110] (5) integrating the vibration frequency adjustment data, the secondary cooling water volume adjustment data, and the casting speed adjustment data to form a casting parameter dynamic adjustment instruction, and prioritizing the casting parameter dynamic adjustment instructions;

[0111] (6) The sorted casting parameter dynamic adjustment instructions are converted into control quantities, and the secondary cooling water volume, crystallizer vibration frequency, and casting speed are linked to obtain regenerated aluminum ingots. The temperature-stress distribution matrix, vibration frequency adjustment data, secondary cooling water volume adjustment data, and casting speed adjustment data are integrated to generate a casting process control strategy.

[0112] Specifically, the temperature and stress are monitored in real time. The temperature sensor uses platinum-rhodium thermocouples, which are arranged in a grid along the wall of the crystallizer with a grid spacing of 50-100mm to form a dense array of temperature measurement points. Each temperature measurement point collects temperature values ​​in real time with a sampling frequency of 10Hz. The strain sensor uses high-temperature strain gauges, which are also arranged in a grid on the wall of the crystallizer to measure the thermal stress generated during the solidification process. The temperature and stress data are collected synchronously and paired according to the spatial position information to construct a temperature-stress distribution matrix. This matrix is ​​a three-dimensional data structure, and each spatial point contains a temperature value and a corresponding stress value. The matrix is ​​analyzed in time series to record the change of temperature and stress at each point over time, so as to grasp the dynamic characteristics of the entire solidification process. The solidification process data needs to be processed by Fourier transform to convert the time domain signal into a frequency domain signal. Fast Fourier transform (FFT) is performed on the temperature and stress data respectively to obtain the corresponding spectrum diagram. The temperature fluctuation spectrum reflects the fluctuation characteristics of the temperature field, and the stress fluctuation spectrum reflects the structural response characteristics. By analyzing the overlapping range of the two spectra, the resonant frequency range of the crystallizer vibration is determined. The resonant frequency refers to the vibration frequency that can effectively promote crystal growth and improve solidification structure, usually in the range of 5-15Hz. The vibration frequency adjustment data includes parameters such as the base frequency, frequency variation range and adjustment step size.

[0113] The temperature gradient of the solidification area is calculated based on the temperature-stress distribution matrix. The temperature gradient calculation adopts the three-dimensional finite difference method to calculate the temperature change rate in the radial, axial and circumferential directions respectively. The temperature gradient value directly affects the formation of the solidification structure. A larger temperature gradient is conducive to the formation of a fine and uniform grain structure. The cooling water flow rate is associated with the temperature gradient value through a linear mapping relationship. The mapping relationship takes into account factors such as heat transfer coefficient, water temperature and flow channel structure. The adjustment data of the secondary cooling water volume includes the water distribution ratio of each cooling zone, the flow rate variation range and the response time. The calculation of the solidification interface advancement speed is based on the spatiotemporal evolution law of the temperature field. The position of the solidification front is determined by tracking the movement of the isothermal surface, and the advancement speed is calculated in combination with the time interval. The crystal growth rate needs to comprehensively consider factors such as temperature gradient, supercooling and solute distribution. The casting speed is a key parameter for controlling the solidification process and must match the solidification interface advancement speed to avoid defects such as cracks and segregation.

[0114] The integration of various adjustment data adopts multi-objective optimization method to establish the objective function:

[0115]

[0116] Among them: F(x) is the comprehensive optimization goal; wi is the weight coefficient of each parameter; fi(x) is each sub-objective function; n is the number of optimization parameters.

[0117] Priority sorting takes into account the coupling relationship between parameters and process requirements, usually taking temperature control as the primary goal, followed by vibration parameters, and finally casting speed. The sorted adjustment instructions are executed step by step in the established order, and the process response is observed after each parameter adjustment to ensure that the process is stable and controllable.

[0118] The control quantity conversion needs to consider the equipment characteristics and process limitations. The secondary cooling water volume is controlled by adjusting the valve opening, the vibration frequency is adjusted by the frequency converter, and the casting speed is controlled by the traction motor. All control parameters and process data are recorded to form a casting process control strategy.

[0119] For example, when producing a certain type of aviation aluminum alloy, 48 temperature measuring points and 36 stress measuring points were arranged on the wall of the crystallizer. The temperature data showed that the temperature gradient reached 15°C / cm in the early stage of solidification, and the stress data reflected obvious thermal strain accumulation. Through FFT analysis, it was found that the main fluctuation frequencies of temperature and stress were concentrated in the range of 8-12Hz, and the vibration frequency of the crystallizer was set to 10Hz accordingly. The mapping relationship between temperature gradient and cooling water flow rate shows that when the temperature gradient increases by 5°C / cm, the cooling water flow rate needs to be increased by 0.2m 3 / h. The calculation of the solidification interface advancement speed shows that under given process conditions, the optimal casting speed should be controlled within the range of 1.2-1.5m / min. After these parameters were optimized and integrated, a set of process control solutions was formed to successfully produce recycled aluminum ingots with uniform structure and stable performance.

[0120] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0121] (1) Sampling and analyzing the recycled aluminum ingots, obtaining component content values ​​through spectral analysis, obtaining grain size values ​​through metallographic analysis, and obtaining strength values ​​through mechanical property testing to form quality inspection data;

[0122] (2) Perform time series matching of quality inspection data and heat treatment process parameters, calculate correlation coefficients, and generate heat treatment-quality correlation data; perform correspondence analysis on quality inspection data and batching process control instructions to generate batching-quality correlation data; perform data mapping on quality inspection data and smelting process control strategies to generate smelting-quality correlation data; perform numerical correlation between quality inspection data and purification process control strategies to generate purification-quality correlation data; perform feature matching on quality inspection data and casting process control strategies to generate casting-quality correlation data;

[0123] (3) Perform weighted fusion on heat treatment-quality correlation data, batching-quality correlation data, smelting-quality correlation data, purification-quality correlation data, and casting-quality correlation data to generate a comprehensive process quality correlation matrix;

[0124] (4) Building a process parameter optimization database based on the process quality comprehensive correlation matrix, classifying and storing the process parameters, and generating a parameter optimization index table;

[0125] (5) Calculate the adjustment direction of each process parameter according to the parameter optimization index table, determine the parameter adjustment priority, and generate a parameter adjustment strategy matrix;

[0126] (6) Convert the parameter adjustment strategy matrix into control quantities and generate production process optimization instructions.

[0127] Specifically, standardized sampling and analysis are carried out. The sampling positions include the head, middle and tail of the aluminum ingot, and 3-5 sample points are taken in each area according to the principle of equal spacing. When these samples are spectrally analyzed, the German SPECTROMAXX spectrometer is used, which can detect the content of more than 20 elements at the same time. Each sample point needs to be measured repeatedly many times, and the average value is taken as the element content value after removing the outliers. Metallographic analysis uses a metallographic microscope to observe the microstructure of the sample, measures the grain size through professional image analysis software, and calculates the average grain size. The mechanical properties test uses a universal testing machine to process the sample according to the standard size, and conducts tensile and compression mechanical properties tests to obtain performance indicators such as yield strength, tensile strength, and elongation. The quality inspection data obtained is correlated with the control parameters of each process link. The first is the timing matching of the heat treatment process parameters, establishes a corresponding relationship between the sampling position of the sample and the corresponding heat treatment period, and calculates the correlation coefficient between each process parameter (such as heat treatment temperature, time, heating rate, etc.) and the performance index. The corresponding analysis of the batching process control instructions focuses on the relationship between the raw material ratio and the uniformity of the composition, and establishes the mapping relationship between the input ratio and the composition of various types of waste materials. The data mapping of the smelting process control strategy examines the influence of electromagnetic stirring parameters and temperature control parameters on the uniformity of the melt. The numerical correlation of the purification process control strategy mainly analyzes the control effect of filtration, degassing, iron removal and other processes on the impurity content. The feature matching of the casting process control strategy focuses on the corresponding relationship between solidification parameters and organizational properties.

[0128] The weighted fusion process of associated data adopts a multi-level analysis method. The associated data of each process link is weighted according to the degree of influence on quality, a judgment matrix is ​​constructed, and the eigenvalues ​​and eigenvectors are calculated to obtain the weight coefficients of each process parameter. Through weighted superposition, a comprehensive association matrix reflecting the process-quality relationship of the entire process is generated. This matrix contains the complex associations between process parameters, process variables and quality indicators. The construction of the process parameter optimization database adopts a hierarchical structure. The first layer is the basic parameter layer, which contains the standard process parameters of each process; the second layer is the adjustment parameter layer, which records the allowable range of variation of the parameters; the third layer is the optimization parameter layer, which stores historical optimization experience. The parameter optimization index table achieves fast query and call by establishing multi-dimensional indexes such as parameter code, process number, and influencing factors.

[0129] The generation of parameter adjustment strategy is based on parameter sensitivity analysis. The influence of parameter adjustment is determined by calculating the partial derivatives of quality indicators on each parameter. The priority of parameter adjustment is sorted, taking into account the coupling relationship between parameters and process constraints. The parameter adjustment strategy matrix contains specific information such as adjustment direction, adjustment step, and adjustment order. Finally, the strategy matrix is ​​converted into specific control instructions. Each control instruction contains three elements: execution object, execution parameter, and execution condition, forming a standardized production process optimization instruction set.

[0130] For example, when producing a batch of 7075 aluminum alloy recycled aluminum ingots for aviation, spectral analysis found that the zinc content of the middle sample was slightly lower than the standard requirement. Metallographic analysis showed that the grain size distribution was not uniform and the tensile strength also fluctuated slightly. Through the correlation analysis of process data, it was found that this was related to the timing of the addition of high-zinc content waste during the batching process, the intensity of electromagnetic stirring during the smelting process, and the cooling rate during the casting process. According to the results of the correlation analysis, the batches of high-zinc content waste were adjusted, the duration of electromagnetic stirring was increased, and the distribution of secondary cooling water was optimized to ensure that the product quality met the expected requirements. The complete parameter combination of this optimization process is recorded in the database as a reference for similar working conditions.

[0131] The above describes the control method for improving the recycling rate of waste aluminum in the embodiment of the present application. The following describes the control system for improving the recycling rate of waste aluminum in the embodiment of the present application. Figure 2 In the embodiment of the present application, one embodiment of the control system for improving the recycling rate of waste aluminum materials includes:

[0132] The detection module is used to detect the composition of the scrap aluminum material through a spectrum analyzer to obtain the composition data of the scrap aluminum material, and establish a composition information database according to the composition data of the scrap aluminum material; heat treat the scrap aluminum material in a quenching furnace, monitor the exhaust gas composition in real time through an infrared spectrometer, establish a coating decomposition curve, determine the heat treatment termination time point according to the coating decomposition curve, and obtain the pretreated scrap aluminum material and heat treatment process parameters;

[0133] A calculation module is used to calculate the batching ratio parameters through a batching optimization algorithm based on the component information database, according to the chemical composition of the pretreated scrap aluminum material and the target product specification requirements; generate a batching placement sequence and a batching process control instruction according to the batching ratio parameters and the temperature field distribution characteristics in the furnace; and put the pretreated scrap aluminum material into the smelting furnace according to the batching placement sequence to obtain the scrap aluminum material to be melted;

[0134] A generation module is used to apply an electromagnetic field of a specific frequency to the scrap aluminum material to be melted to form a melt directional flow field; generate an electromagnetic field dynamic adjustment instruction according to the temperature distribution of the melt directional flow field; and control the frequency and intensity parameters of the electromagnetic field based on the electromagnetic field dynamic adjustment instruction to form a molten scrap aluminum material with uniform temperature and a smelting process control strategy;

[0135] An adjustment module is used to sequentially pass the molten aluminum scrap through a ceramic filter, an ultrasonic degassing device, and a magnetic field gradient separation device to remove impurity particles, dissolved gases, and iron-based impurities; obtain melt purity data through an online detection device, generate a filtering parameter adjustment instruction according to the melt purity data, and obtain purified molten aluminum scrap and a purification process control strategy;

[0136] A control module is used to inject the purified molten aluminum waste into a crystallizer, collect solidification process data through a temperature sensor and a strain sensor; generate a casting parameter dynamic adjustment instruction according to the solidification process data; and control the secondary cooling water volume, the crystallizer vibration frequency and the casting speed based on the casting parameter dynamic adjustment instruction to obtain a regenerated aluminum ingot and a casting process control strategy;

[0137] The association module is used to perform spectral analysis, metallographic analysis and mechanical property testing on the recycled aluminum ingot to obtain quality inspection data; associate the quality inspection data with the heat treatment process parameters, batching process control instructions, smelting process control strategy, purification process control strategy, and casting process control strategy to generate a process parameter optimization database; and generate production process optimization instructions based on the process parameter optimization database.

[0138] Through the cooperation of the above components, a complete data collection and analysis system is established in the process of recycling waste aluminum, and intelligent control of the whole process is realized. The composition of waste aluminum is detected by a spectrometer and a composition information database is established. The coating decomposition process is monitored in real time with an infrared spectrometer to ensure the precise control of the pretreatment process. In the batching process, optimization calculations are performed based on the composition information database, and the batching timing is generated in combination with the temperature field distribution characteristics, which improves the accuracy and uniformity of the batching. The temperature field of the melt is uniformly controlled by applying an electromagnetic field of a specific frequency to the melt and adjusting the electromagnetic field parameters in real time. The three-stage purification process of ceramic filter, ultrasonic degassing device and magnetic field gradient separation device is adopted, and the purity of the melt is significantly improved with the real-time monitoring of the online detection device. In the casting process, the precise control of the solidification process is achieved through the coordinated monitoring of temperature sensors and strain sensors, ensuring the stability of the ingot quality. Finally, by conducting comprehensive quality inspections on the recycled aluminum ingots and correlating the inspection data with the parameters of each process, a process parameter optimization database is established to achieve continuous optimization of the production process. The entire solution significantly improves the recycling efficiency of scrap aluminum and the consistency of product quality by combining data-driven and intelligent control.

[0139] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for improving the recycling rate of waste aluminum, characterized in that: The control method for improving the recycling rate of waste aluminum materials comprises: The scrap aluminum material is subjected to component detection by a spectrum analyzer to obtain scrap aluminum material component data, and a component information database is established based on the scrap aluminum material component data; the scrap aluminum material is subjected to heat treatment in a quenching furnace, and the exhaust gas component is monitored in real time by an infrared spectrometer to establish a coating decomposition curve, and the heat treatment termination time point is determined based on the coating decomposition curve to obtain the pretreated scrap aluminum material and heat treatment process parameters; Based on the component information database, according to the chemical composition of the pretreated scrap aluminum material and the target product specification requirements, the batching ratio parameters are calculated through the batching optimization algorithm; according to the batching ratio parameters and the temperature field distribution characteristics in the furnace, the batching time sequence and the batching process control instructions are generated; according to the batching time sequence, the pretreated scrap aluminum material is put into the smelting furnace to obtain the scrap aluminum material to be melted; Applying an electromagnetic field of a specific frequency to the scrap aluminum material to be melted to form a melt directional flow field; generating an electromagnetic field dynamic adjustment instruction according to the temperature distribution of the melt directional flow field; controlling the frequency and intensity parameters of the electromagnetic field based on the electromagnetic field dynamic adjustment instruction to form a molten scrap aluminum material with uniform temperature and a smelting process control strategy; The molten aluminum scrap is passed through a ceramic filter, an ultrasonic degassing device and a magnetic field gradient separation device in sequence to remove impurity particles, dissolved gas and iron-based impurities; melt purity data is obtained through an online detection device, and a filtering parameter adjustment instruction is generated according to the melt purity data to obtain purified molten aluminum scrap and a purification process control strategy; The purified molten waste aluminum material is injected into a crystallizer, and solidification process data is collected through a temperature sensor and a strain sensor; a casting parameter dynamic adjustment instruction is generated according to the solidification process data; the secondary cooling water volume, the crystallizer vibration frequency and the casting speed are controlled based on the casting parameter dynamic adjustment instruction to obtain a regenerated aluminum ingot and a casting process control strategy; The recycled aluminum ingots are subjected to spectral analysis, metallographic analysis and mechanical property testing to obtain quality inspection data; the quality inspection data are correlated with the heat treatment process parameters, batching process control instructions, smelting process control strategy, purification process control strategy and casting process control strategy to generate a process parameter optimization database; and production process optimization instructions are generated according to the process parameter optimization database.

2. The control method for improving the recycling rate of waste aluminum according to claim 1 is characterized in that: The waste aluminum material is subjected to component detection by a spectrum analyzer to obtain component data of the waste aluminum material, and a component information database is established according to the component data of the waste aluminum material; the waste aluminum material is subjected to heat treatment in a quenching furnace, and the exhaust gas component is monitored in real time by an infrared spectrometer to establish a coating decomposition curve, and the heat treatment termination time point is determined according to the coating decomposition curve, so as to obtain the pretreated waste aluminum material and heat treatment process parameters, including: Scan and detect the five points of the scrap aluminum material through a spectrum analyzer to obtain the element content value of the scrap aluminum material and generate the scrap aluminum material composition data; Classifying the types of waste aluminum materials according to the waste aluminum material composition data, normalizing the waste aluminum material composition data, and generating waste aluminum material composition characteristic data; Establishing the component information database based on the waste aluminum component characteristic data to form a waste aluminum component characteristic matrix; The waste aluminum material is placed in the quenching furnace for heating, the exhaust gas component data is collected by the infrared spectrometer, and the organic matter decomposition value is calculated according to the exhaust gas component data; Draw the coating decomposition curve based on the organic matter decomposition value, and record the heat treatment termination time point when the organic matter decomposition value is less than a preset threshold; The coating decomposition curve is associated with the process parameters to generate the heat treatment process parameters, and the treated scrap aluminum material is marked to obtain the pretreated scrap aluminum material.

3. The control method for improving the recycling rate of waste aluminum according to claim 2 is characterized in that: Based on the component information database, according to the chemical composition of the pre-treated scrap aluminum material and the target product specification requirements, the batching ratio parameters are calculated through the batching optimization algorithm; according to the batching ratio parameters and the temperature field distribution characteristics in the furnace, the batching timing and batching process control instructions are generated; The pretreated scrap aluminum material is put into a smelting furnace according to the batching time sequence to obtain the scrap aluminum material to be melted, including: Extracting the main element content, secondary element content, and impurity element content values ​​of the pretreated scrap aluminum material from the composition information database, and comparing the main element content, secondary element content, and impurity element content values ​​with the target composition range in the target product specification requirements to generate composition difference data; Based on the component difference data, the pre-treated scrap aluminum materials are divided into three categories: main materials, auxiliary materials, and adjustment materials, and weighted calculations are performed on the three categories of scrap aluminum materials to generate initial batching ratio values; Numerical calculation is performed based on the initial batching ratio value and the temperature field distribution characteristics in the smelting furnace, and the batching ratio parameters are iteratively optimized, and the batching ratio parameters are output when the difference in batching quality obtained by two iterative calculations is less than a set threshold; Constructing a feeding sequence matrix based on the batching ratio parameters, sorting the feeding point positions according to the temperature field gradient according to the feeding sequence matrix, and generating the batching time sequence; According to the real-time correlation calculation between the batching timing and the temperature field distribution characteristics of the smelting furnace, the time interval, the weight and the speed of each batch of batching are determined to form the batching process control instructions; The pretreated scrap aluminum material is put into the designated temperature zone position in the smelting furnace according to the batching process control instruction to complete the batching process of the scrap aluminum material to be melted.

4. The control method for improving the recycling rate of waste aluminum according to claim 3 is characterized in that: The electromagnetic field of a specific frequency is applied to the waste aluminum material to be melted to form a melt directional flow field; and a dynamic adjustment instruction of the electromagnetic field is generated according to the temperature distribution of the melt directional flow field; Based on the electromagnetic field dynamic adjustment instruction, the frequency and intensity parameters of the electromagnetic field are controlled to form a molten waste aluminum material with uniform temperature and a smelting process control strategy, including: Applying an electromagnetic field with a frequency range of 20-60 Hz to the scrap aluminum material to be melted, collecting electromagnetic force distribution data through an electromagnetic force measuring device, calculating force field distribution parameters according to the electromagnetic force distribution data, and generating the melt directional flow field; The temperature in the melt directional flow field is collected by region through a temperature sensor array, real-time temperature values ​​of multiple temperature measurement points are obtained, a temperature distribution matrix is ​​constructed, and the temperature distribution of the melt directional flow field is generated; Calculating the temperature gradient value inside the melt according to the temperature distribution matrix, positioning and marking the temperature field non-uniform area in combination with the flow field velocity distribution of the melt directional flow field, and generating temperature field compensation data; Based on the temperature field compensation data, the electromagnetic field strength is calculated by region, the electromagnetic field frequency adjustment amount and intensity adjustment amount required for each region are determined, and the electromagnetic field dynamic adjustment instruction is formed; The electromagnetic field dynamic adjustment instruction is converted into an excitation signal of the electromagnetic coil by a multi-channel controller, and the frequency and intensity parameters of the electromagnetic field are adjusted in real time to generate electromagnetic field control data; The electromagnetic field control data is correlated with the smelting temperature curve to form the molten scrap aluminum material with uniform temperature, and the frequency and intensity parameters of the electromagnetic field and the temperature distribution data obtained by real-time collection and analysis are integrated to generate the smelting process control strategy.

5. The control method for improving the recycling rate of waste aluminum according to claim 4 is characterized in that: The molten aluminum scrap is passed through a ceramic filter, an ultrasonic degassing device and a magnetic field gradient separation device in sequence to remove impurity particles, dissolved gas and iron impurities; melt purity data is obtained through an online detection device, and a filtering parameter adjustment instruction is generated according to the melt purity data to obtain the purified molten aluminum scrap and a purification process control strategy, including: Passing the molten aluminum waste through the ceramic filter, collecting the pressure difference value and flow rate value before and after filtration, calculating the degree of pore blockage of the filter according to the change trend of the pressure difference value and the flow rate value, and generating filter status data; Degassing the melt by the ultrasonic degassing device, collecting ultrasonic power, frequency, and bubble size distribution data, performing statistical analysis on the bubble size distribution data, and generating bubble distribution characteristic data; The magnetic field intensity distribution data is collected based on the magnetic field gradient separation device, and the migration trajectory of iron-based impurities is calculated in combination with the melt flow rate to generate impurity separation efficiency data; Inputting the filter screen state data, bubble distribution characteristic data, and impurity separation efficiency data into the online detection device, collecting the impurity content, gas content, and iron content values, and forming the melt purity data; Calculating the filter replacement time point, ultrasonic power adjustment amount, and magnetic field strength adjustment amount based on the melt purity data to generate the filter parameter adjustment instruction; The filtering parameter adjustment instruction is converted into a control signal, and the three purification devices are adjusted in linkage to obtain the purified molten waste aluminum material, and the filter status data, bubble distribution characteristic data, and impurity separation efficiency data are integrated to generate the purification process control strategy.

6. The control method for improving the recycling rate of waste aluminum according to claim 5 is characterized in that: The purified molten aluminum waste is injected into a crystallizer, and solidification process data is collected through a temperature sensor and a strain sensor; and a casting parameter dynamic adjustment instruction is generated according to the solidification process data; Based on the casting parameter dynamic adjustment instruction, the secondary cooling water volume, the crystallizer vibration frequency and the casting speed are controlled to obtain the regenerated aluminum ingot and the casting process control strategy, including: The purified molten aluminum scrap is injected into a crystallizer, and temperature values ​​are collected at multiple temperature measuring points arranged on the wall of the crystallizer by the temperature sensor, and a temperature-stress distribution matrix is ​​generated in combination with the stress values ​​collected by the strain sensor, and a time series analysis is performed on the temperature-stress distribution matrix to obtain the solidification process data; Performing Fourier transform processing on the solidification process data to obtain a temperature fluctuation spectrum and a stress fluctuation spectrum, determining a crystallizer vibration resonance frequency range through spectrum analysis, and generating vibration frequency adjustment data; Calculating the temperature gradient value of the solidification region based on the temperature-stress distribution matrix, linearly mapping the temperature gradient value with the cooling water flow rate, and generating secondary cooling water flow adjustment data; Calculating the solidification interface advancement speed according to the temperature-stress distribution matrix, determining the crystal growth rate in combination with the temperature gradient value, and generating casting speed adjustment data; Integrate the vibration frequency adjustment data, the secondary cooling water volume adjustment data, and the casting speed adjustment data to form the casting parameter dynamic adjustment instruction, and prioritize the casting parameter dynamic adjustment instructions; The sorted casting parameter dynamic adjustment instructions are converted into control quantities, and the secondary cooling water volume, crystallizer vibration frequency, and casting speed are linked and adjusted to obtain the regenerated aluminum ingots. The temperature-stress distribution matrix, vibration frequency adjustment data, secondary cooling water volume adjustment data, and casting speed adjustment data are integrated to generate the casting process control strategy.

7. The control method for improving the recycling rate of waste aluminum according to claim 6 is characterized in that: The regenerated aluminum ingot is subjected to spectral analysis, metallographic analysis and mechanical property test to obtain quality inspection data; the quality inspection data is correlated with the heat treatment process parameters, batching process control instructions, smelting process control strategy, purification process control strategy and casting process control strategy to generate a process parameter optimization database; Generating production process optimization instructions according to the process parameter optimization database includes: Sampling and analyzing the recycled aluminum ingots, obtaining component content values ​​through spectral analysis, obtaining grain size values ​​through metallographic analysis, and obtaining strength values ​​through mechanical property testing to form the quality inspection data; The quality inspection data is matched with the heat treatment process parameters in time series, and the correlation coefficient is calculated to generate heat treatment-quality correlation data; the quality inspection data is analyzed correspondingly with the batching process control instructions to generate batching-quality correlation data; the quality inspection data is mapped with the smelting process control strategy to generate smelting-quality correlation data; the quality inspection data is numerically associated with the purification process control strategy to generate purification-quality correlation data; the quality inspection data is feature matched with the casting process control strategy to generate casting-quality correlation data; Performing weighted fusion on the heat treatment-quality correlation data, batching-quality correlation data, smelting-quality correlation data, purification-quality correlation data, and casting-quality correlation data to generate a comprehensive process quality correlation matrix; Building the process parameter optimization database based on the process quality comprehensive association matrix, classifying and storing the process parameters, and generating a parameter optimization index table; Calculate the adjustment direction of each process parameter according to the parameter optimization index table, determine the parameter adjustment priority, and generate a parameter adjustment strategy matrix; The parameter adjustment strategy matrix is ​​converted into a control quantity to generate the production process optimization instruction.

8. A control system for improving the recycling rate of waste aluminum, used to implement the control method for improving the recycling rate of waste aluminum as described in any one of claims 1 to 7, characterized in that: The control system for improving the recycling rate of waste aluminum materials comprises: The detection module is used to detect the composition of the scrap aluminum material through a spectrum analyzer to obtain the composition data of the scrap aluminum material, and establish a composition information database according to the composition data of the scrap aluminum material; heat treat the scrap aluminum material in a quenching furnace, monitor the exhaust gas composition in real time through an infrared spectrometer, establish a coating decomposition curve, determine the heat treatment termination time point according to the coating decomposition curve, and obtain the pretreated scrap aluminum material and heat treatment process parameters; A calculation module is used to calculate the batching ratio parameters through a batching optimization algorithm based on the component information database, according to the chemical composition of the pretreated scrap aluminum material and the target product specification requirements; generate a batching placement sequence and a batching process control instruction according to the batching ratio parameters and the temperature field distribution characteristics in the furnace; and put the pretreated scrap aluminum material into the smelting furnace according to the batching placement sequence to obtain the scrap aluminum material to be melted; A generation module is used to apply an electromagnetic field of a specific frequency to the scrap aluminum material to be melted to form a melt directional flow field; generate an electromagnetic field dynamic adjustment instruction according to the temperature distribution of the melt directional flow field; and control the frequency and intensity parameters of the electromagnetic field based on the electromagnetic field dynamic adjustment instruction to form a molten scrap aluminum material with uniform temperature and a smelting process control strategy; An adjustment module is used to sequentially pass the molten aluminum scrap through a ceramic filter, an ultrasonic degassing device, and a magnetic field gradient separation device to remove impurity particles, dissolved gases, and iron-based impurities; obtain melt purity data through an online detection device, generate a filtering parameter adjustment instruction according to the melt purity data, and obtain purified molten aluminum scrap and a purification process control strategy; A control module is used to inject the purified molten aluminum waste into a crystallizer, collect solidification process data through a temperature sensor and a strain sensor; generate a casting parameter dynamic adjustment instruction according to the solidification process data; and control the secondary cooling water volume, the crystallizer vibration frequency and the casting speed based on the casting parameter dynamic adjustment instruction to obtain a regenerated aluminum ingot and a casting process control strategy; The association module is used to perform spectral analysis, metallographic analysis and mechanical property testing on the recycled aluminum ingot to obtain quality inspection data; associate the quality inspection data with the heat treatment process parameters, batching process control instructions, smelting process control strategy, purification process control strategy, and casting process control strategy to generate a process parameter optimization database; and generate production process optimization instructions based on the process parameter optimization database.

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