A recycling method and recycling system for waste battery negative electrode waste

Abstract

The invention discloses a recycling method and recycling system for waste negative electrode waste of used batteries, and relates to the technical field of solid waste recycling. The method comprises the following steps: preliminarily crushing the waste negative electrode of used batteries, removing the shell and the diaphragm, and obtaining the coarse powder of the waste negative electrode; sending the coarse powder of the waste negative electrode into a pulverizer for further crushing, and classifying the coarse powder of the waste negative electrode with different particle sizes by a screening device to obtain fine powder of the waste negative electrode with different particle sizes; dividing the fine powder of the waste negative electrode into multiple levels according to the suspension degree by an airflow sorting method; extracting the fine powder of the secondary waste, and further dividing the levels by a spectral recognition method. The invention reduces the error rate of manual operation and improves the automation level of the recycling process.

Description

Technical Field

[0001] The invention relates to the technical field of solid waste recycling, and in particular to a recycling method and a recycling system for waste negative electrode materials of used batteries. Background Art

[0002] With the popularity of electronic devices and the rapid development of electric vehicles around the world, lithium-ion batteries have become the preferred power source in many fields due to their high energy density, long life and low self-discharge rate. However, with the increasing use of lithium-ion batteries, the problem of waste battery disposal has become increasingly prominent. The negative electrode waste of waste batteries contains a large amount of valuable materials such as graphite and silicon, but it may also contain harmful heavy metals and electrolyte solutions. If not handled properly, it will cause great harm to the environment and human health.

[0003] At present, the recycling technology of waste battery negative electrode waste is not yet fully mature. Traditional recycling methods such as landfill and incineration not only fail to effectively recover valuable materials, but also may cause secondary pollution to the environment. Therefore, developing an efficient, environmentally friendly and economically feasible method for recycling waste battery negative electrode waste has important practical significance and application prospects.

[0004] For example, a Chinese patent with a publication number of CN114959272A discloses a method for selectively recovering lithium from waste lithium-ion batteries, wherein waste lithium-ion battery materials including positive and negative electrodes are uniformly mixed with an acidic solution for acidification treatment, and no filtration is required after the acidification reaction is completed. A hydrothermal treatment is directly performed using a transition metal salt generated in situ in the solution. After the hydrothermal reaction is completed, filtration and separation are performed, and the valuable metal lithium enters the leaching solution while the transition metal is stored in the leaching residue in the form of oxides.

[0005] However, this design does not take into account that parameters such as temperature, pressure and reaction time during the hydrothermal treatment process have a significant impact on the leaching rate of lithium and the morphology of transition metals. Therefore, it is necessary to accurately control these parameters to ensure the stability and efficiency of the recycling process. To this end, the present invention provides a method and system for recycling waste negative electrode waste of used batteries. Summary of the invention

[0006] The object of the present invention is to provide a method and system for recycling waste negative electrode materials of used batteries to solve the existing problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for recycling waste negative electrode materials of waste batteries, comprising the following steps:

[0008] S1. Preliminarily crushing the negative electrode waste of used batteries, removing the shell and the diaphragm, and obtaining the coarse powder of the negative electrode waste;

[0009] S2, feeding the coarse negative electrode waste powder into a pulverizer for further pulverization, and classifying the coarse negative electrode waste powder of different particle sizes through a screening device to obtain fine negative electrode waste powder of different particle sizes;

[0010] S3, dividing the negative electrode waste fine powder into multiple levels according to the suspension degree by an airflow sorting method to obtain multi-level waste fine powder;

[0011] S4, extracting the multi-level waste fine powder output by S3, further identifying it through a spectral recognition method, and obtaining a tertiary silicon material, a mixed material and a tertiary graphite material;

[0012] S5, recycling the tertiary silicon material and the tertiary graphite material;

[0013] The grading of materials of different particle sizes by the screening device includes setting particle size data, including 5 microns, 10 microns, 15 microns, and 20 microns. When the particle size of the material belongs to (10 μm, 15 μm], it is classified as a primary graphite material. When the particle size of the material belongs to (15 μm, 20 μm], it is classified as a secondary graphite material. When the particle size of the material belongs to (0 μm, 5 μm], it is classified as a primary silicon material. When the particle size of the material belongs to (5 μm, 10 μm] microns, it is classified as a secondary silicon material.

[0014] The specific steps of the airflow sorting method include:

[0015] S31, setting the air flow velocity and the separator angle according to the particle size distribution of the negative electrode waste fine powder;

[0016] S32, setting the feed rate according to the processing capacity and condition of the equipment;

[0017] S33, dividing the primary silicon material and the primary graphite material into two parts according to the difference in suspension degree, taking the median of the fine powder suspension degree as the difference division value, and sending the fine powder with a suspension degree greater than the median of the fine powder suspension degree to the next step;

[0018] S34, increase the air flow velocity, further classify the fine powder obtained in S33, set the suspension thresholds Tsut1 and Tsut2, when the fine powder suspension is less than or equal to Tsut1, it is determined to be the first-level waste fine powder, when the fine powder suspension is less than Tsut2 and greater than Tsut1, it is determined to be the second-level waste fine powder, when the fine powder suspension is greater than or equal to Tsut2, it is determined to be the third-level waste fine powder;

[0019] S35, sending the secondary waste fine powder into S4;

[0020] The specific steps of S31 include:

[0021] S311, establishing a parameter allocation model, taking the support vector machine model as the main body, and taking the air flow velocity and separator angle corresponding to the historical silicon material classification and the historical graphite material classification as training data;

[0022] S312, assigning corresponding air flow velocities and separator angles to the primary silicon material, the secondary silicon material, the primary graphite material, and the secondary graphite material through the parameter allocation model;

[0023] The present invention is further improved in that the parameter allocation model includes setting a separation target, the separation target includes separation efficiency, purity and yield, setting operation constraints, the operation constraints include maximum gas flow rate and separator angle range; extracting the material particle size of all fine powders in the primary silicon material and the primary graphite material in step S2, the weight of the fine powder corresponding to each material particle size, and calculating the gas flow rate formula: Among them, d p Indicates particle size, m p represents the weight of fine powder corresponding to the particle size, and a, b, and c represent the set fitting coefficients; establishing a separator angle database, including calculating the separator angle corresponding to the historical particle size, calculating the average particle size, and extracting the separator angle corresponding to the particle size in the separator angle database;

[0024] The specific steps of S32 include:

[0025] S321, obtain the amount of material maq that the equipment can process per hour,

[0026] S322, obtain the environmental impact factor eif by installing an air temperature sensor and a particle sensor on the device; the specific steps include: evenly installing n1 temperature sensors and n2 particle sensors on the outer wall of the device to obtain the device temperature data set Tem φ =(Tem 1 ,Tem 2 ,...,Tem K ) and air particulate matter concentration datasets in Indicates The temperature value of the particle sensor is used to obtain the environmental impact factor of the device temperature value. Among them, β 1 represents the weight of temperature data, β 2 Indicates the weight of air particulate matter concentration data;

[0027] S323. Calculate the equipment energy efficiency impact factor een. The equipment energy efficiency impact factor een is calculated by dividing the ventilation volume of the airflow classifier by the power consumption of the equipment. The calculation formula is: Where P represents the ventilation efficiency of the air classifier, represents the power efficiency of the air classifier, n represents the number of power-consuming components in the air classifier, α i represents the weight of the power consuming component, w i represents the output power of the i-th power-consuming component in the airflow classifier, i∈[1,2,…,n];

[0028] S324, construct a feed quantity control algorithm to adjust the feed quantity in real time.

[0029] A further improvement of the present invention is that the feed rate control algorithm includes creating an evaluation function A set of feed rate data P(1) is randomly generated, and a parameter simulation algorithm is created. According to the historical feed rate and equipment performance, an evaluation function obtained by simulating the data in P(1) is obtained, P(1) is sorted from high to low, and its corresponding evaluation function sequence F_P(1) is obtained. An evaluation threshold TF is set, and the data in F_P(1) is traversed in sequence. The feed rate corresponding to the first evaluation function in the evaluation function sequence F_P(1) that is greater than TF is extracted as the feed rate parameter of the airflow classifier.

[0030] The present invention is further improved in that the spectrum recognition method comprises: placing the secondary waste powder in a sampler and laying it flat, setting a partition window with a side length of edg, dividing the secondary waste powder in the sampler into regions according to the partition window, collecting spectrum data of each region, and extracting the ultraviolet visible spectrum reflectance data set UVVis of the region. i =(UVVis 1 ,UVVis 2 ,…,UVVis p ), p represents the number of ultraviolet and visible light bands, and extracts the infrared band reflectance dataset lir of this area j =(lir 1 ,lir 2 ,…,lir q ), q represents the number of infrared bands, and the ultraviolet visible spectrum reflectance and infrared band reflectance are combined to obtain the spectrum evaluation index Extract max(FSpe ij ) as the level segmentation value, and set the level segmentation threshold T1_FSpe ij and T2_FSpe ij , will satisfy max(FSpe ij )≤T1_FSpe ij The area is divided into three levels of silicon materials, which will meet T1_FSpe ij <max(FSpe ij )≤T2_FSpe ijThe area is divided into mixed materials, which satisfies max(FSpe ij )>T1_FSpe ij The area of ​​graphite materials is divided into three levels.

[0031] In another aspect, the present invention provides a system for recycling waste negative electrode materials of used batteries, comprising:

[0032] The material crushing module is used to initially crush the negative electrode waste of used batteries, remove the shell and diaphragm, and obtain the coarse powder of the negative electrode waste;

[0033] The first classification module is used to send the coarse powder of the negative electrode waste to a pulverizer for further pulverization, and to classify the coarse powder of the negative electrode waste with different particle sizes through a screening device to obtain fine powder of the negative electrode waste with different particle sizes;

[0034] A second classification module is used to classify the negative electrode waste fine powder into multiple levels according to the suspension degree by airflow sorting method;

[0035] The third classification module is used to extract the secondary waste fine powder output by the second classification module and further classify it by using a spectral recognition method.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention firstly realizes efficient graded recycling of waste battery negative electrode waste by combining screening equipment, airflow sorting method and spectrum recognition technology. This multi-level classification method not only improves the recycling efficiency, but also ensures the quality and purity of the recycled materials.

[0038] 2. In the airflow sorting method, by considering the parameter allocation model, environmental influencing factors and equipment energy efficiency influencing factors, the airflow velocity and separator angle, as well as the feed amount, can be accurately controlled, thereby achieving accurate classification of negative electrode waste fine powders of different particle sizes and different suspension degrees;

[0039] 3. The present invention uses intelligent algorithms such as support vector machine models and spectral recognition methods to achieve intelligent identification and classification of waste battery negative electrode waste. This not only reduces the error rate of manual operation, but also improves the automation level of the recycling process;

[0040] 4. The recycling system of the present invention is scalable and customizable, and can be adjusted and optimized accordingly according to different recycling needs and processing capabilities to meet the recycling needs of different scales and types of waste battery negative electrode waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The present invention is a flow chart of a method for recycling waste negative electrode materials of used batteries;

[0042] Figure 2 The present invention is a framework diagram of a recycling system for waste negative electrode materials of used batteries. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0044] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0045] Example 1

[0046] Figure 1 A flow chart of a method for recycling negative electrode waste of used batteries disclosed in this embodiment is shown, and the steps are as follows:

[0047] S1. Preliminarily crushing the negative electrode waste of used batteries, removing the shell and the diaphragm, and obtaining the coarse powder of the negative electrode waste;

[0048] S2, feeding the coarse negative electrode waste powder into a pulverizer for further pulverization, and classifying the coarse negative electrode waste powder of different particle sizes through a screening device to obtain fine negative electrode waste powder of different particle sizes;

[0049] S3, dividing the negative electrode waste fine powder into multiple levels according to the suspension degree by airflow sorting method;

[0050] S4. Extract the secondary waste fine powder output by S3 and further grade it by spectral recognition method.

[0051] The grading of materials of different particle sizes by screening equipment includes setting particle size data, including 5 microns, 10 microns, 15 microns, and 20 microns. When the material particle size is 10-15 microns, it is classified as a primary graphite material; when the material particle size is 15-20 microns, it is classified as a secondary graphite material; when the material particle size is 0-5 microns, it is classified as a primary silicon material; and when the material particle size is 5-10 microns, it is classified as a secondary silicon material.

[0052] The specific steps of the airflow sorting method include:

[0053] S31, setting the air flow velocity and the separator angle according to the particle size distribution of the negative electrode waste fine powder;

[0054] S32, setting the feed rate according to the processing capacity and condition of the equipment;

[0055] S33, the first-level silicon material and the first-level graphite material are divided into two parts according to the difference in suspension degree, and the difference segmentation value is the median of the fine powder suspension degree, and the fine powder greater than the median of the fine powder suspension degree is sent to the next step; the purity of the final product can be further improved by separation by suspension degree difference. Since silicon and graphite have different densities and particle sizes, their suspension behaviors in the airflow will also be different, so the fine powder with the suspension degree in the first half is sent to the next step;

[0056] S34. Since only the first half is preliminarily classified in S33, for this part of fine powder, the larger the surface area of ​​the fine powder, the faster the chemical reaction rate may be, but it may also bring stability problems, such as easy agglomeration or oxidation. And very fine powder is easy to fly, causing dust pollution, increasing the difficulty of operation and safety hazards. In some application scenarios, too fine powder may not be suitable for use. For example, in battery manufacturing, too fine negative electrode materials may affect the compaction density and cycle performance of the electrode. The larger particle size of the overly coarse fine powder means that the surface area of ​​the fine powder per unit mass is smaller, which may reduce the rate of chemical reactions or the electrochemical performance of the battery. The coarser fine powder may form agglomerates when mixed or dispersed. In some application scenarios, the fine powder with a larger particle size may not meet the specification requirements. For example, when manufacturing high-capacity batteries, finer materials are required to increase the energy density. Therefore, the air flow velocity is increased, and the fine powder obtained in S33 is further graded, and the suspension thresholds Tsut1 and Tsut2 are set. When the fine powder suspension is less than or equal to Tsut1, it is determined to be a first-level waste fine powder. When the fine powder suspension is less than Tsut2 and greater than Tsut1, it is determined to be a second-level waste fine powder. When the fine powder suspension is greater than or equal to Tsut2, it is determined to be a third-level waste fine powder.

[0057] S35, sending the secondary waste fine powder into S4.

[0058] The specific steps of step S31 include:

[0059] S311, establishing a parameter allocation model, taking the support vector machine model as the main body, and taking the air flow velocity and separator angle corresponding to the historical silicon material classification and the historical graphite material classification as training data;

[0060] The parameter allocation model includes setting separation targets, including separation efficiency, purity and yield, setting operation constraints, including maximum gas flow rate and separator angle range; extracting the material particle size of all fine powders in the primary silicon material and the primary graphite material in step S2, the weight of fine powder corresponding to each material particle size, and calculating the gas flow rate formula: Among them, dp Indicates particle size, m p represents the weight of fine powder corresponding to the particle size, a, b, c represent the set fitting coefficients; establish a separator angle database, including the separator angle corresponding to the historical particle size, calculate the average particle size, and extract the separator angle corresponding to the particle size in the separator angle database.

[0061] S312, assigning corresponding air flow velocities and separator angles to the primary silicon material, the secondary silicon material, the primary graphite material, and the secondary graphite material through the parameter allocation model;

[0062] The specific steps of S32 include:

[0063] S321, obtain the amount of material maq that the equipment can process per hour,

[0064] S322, obtain the environmental impact factor eif by installing an air temperature sensor and a particle sensor on the device; the specific steps include: evenly installing n1 temperature sensors and n2 particle sensors on the outer wall of the device to obtain the device temperature data set Tem φ =(Tem 1 ,Tem 2 ,...,Tem K ) and air particulate matter concentration datasets in Indicates The temperature value of the particle sensor is used to obtain the environmental impact factor of the device temperature value. Among them, β 1 represents the weight of temperature data, β 2 Indicates the weight of air particulate matter concentration data;

[0065] S323. Calculate the equipment energy efficiency impact factor een. The equipment energy efficiency impact factor een is calculated by dividing the ventilation volume of the airflow classifier by the power consumption of the equipment. The calculation formula is: Where P represents the ventilation efficiency of the air classifier, represents the power efficiency of the air classifier, n represents the number of power-consuming components in the air classifier, α i represents the weight of the power consuming component, w i represents the output power of the i-th power-consuming component in the airflow classifier, i∈[1,2,…,n];

[0066] S324, construct a feed quantity control algorithm to adjust the feed quantity in real time.

[0067] The feed rate control algorithm includes creating an evaluation function A set of feed rate data P(1) is randomly generated, and a parameter simulation algorithm is created. According to the historical feed rate and equipment performance, an evaluation function is obtained by simulating the data in P(1). P(1) is sorted from high to low, and its corresponding evaluation function sequence F_P(1) is obtained. An evaluation threshold TF is set, and the data in F_P(1) is traversed in turn. The feed rate corresponding to the first evaluation function in the evaluation function sequence F_P(1) that is greater than TF is extracted as the feed rate parameter of the airflow classifier.

[0068] The spectrum recognition method comprises: placing the secondary waste fine powder in a sampler and laying it flat, setting a partition window with a side length of edg, dividing the secondary waste fine powder in the sampler into regions according to the partition window, collecting spectrum data of each region, and extracting an ultraviolet visible spectrum reflectance data set UVVis of the region. i =(UVVis 1 ,UVVis 2 ,…,UVVis p ), p represents the number of ultraviolet and visible light bands, and extracts the infrared band reflectance dataset lir of this area j =(lir 1 ,lir 2 ,…,lir q ), q represents the number of infrared bands, and the ultraviolet visible spectrum reflectance and infrared band reflectance are combined to obtain the spectrum evaluation index Extract max(FSpe ij ) as the level segmentation value, and set the level segmentation threshold T1_FSpe ij and T2_FSpe ij , will satisfy max(FSpe ij )≤T1_FSpe ij The area is divided into three levels of silicon materials, which will meet T1_FSpe ij <max(FSpe ij )≤T2_FSpe ij The area is divided into mixed materials, which satisfies max(FSpe ij )>T1_FSpe ij The area of ​​graphite materials is divided into three levels.

[0069] The threshold and weight may be set by default according to the present invention, or may be set by an operator.

[0070] Example 2

[0071] Figure 2 The framework diagram of a recycling system for waste negative electrode materials of used batteries of the present invention is shown. Based on the same inventive concept as that of Example 1, the present invention provides a recycling system for waste negative electrode materials of used batteries, comprising:

[0072] The material crushing module is used to initially crush the negative electrode waste of used batteries, remove the shell and diaphragm, and obtain the coarse powder of the negative electrode waste;

[0073] The first classification module is used to send the coarse powder of the negative electrode waste to a pulverizer for further pulverization, and to classify the coarse powder of the negative electrode waste with different particle sizes through a screening device to obtain fine powder of the negative electrode waste with different particle sizes;

[0074] A second classification module is used to classify the negative electrode waste fine powder into multiple levels according to the suspension degree by airflow sorting method;

[0075] A third classification module is used to extract the secondary waste fine powder output by the second classification module and further classify it by using a spectral recognition method;

[0076] The waste recycling module is used to recycle the tertiary silicon material and the tertiary graphite material.

[0077] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for recycling waste negative electrode materials of waste batteries, characterized in that: include: S1. Preliminarily crushing the negative electrode waste of used batteries, removing the shell and the diaphragm, and obtaining the coarse powder of the negative electrode waste; S2, feeding the coarse negative electrode waste powder into a pulverizer for further pulverization, and classifying the coarse negative electrode waste powder of different particle sizes through a screening device to obtain fine negative electrode waste powder of different particle sizes; S3, dividing the negative electrode waste fine powder into multiple levels according to the suspension degree by an airflow sorting method to obtain multi-level waste fine powder; S4, extracting the multi-level waste fine powder output by S3, further identifying it through a spectral recognition method, and obtaining a tertiary silicon material, a mixed material and a tertiary graphite material; S5, recycling the tertiary silicon material and the tertiary graphite material; The grading of materials of different particle sizes by the screening device includes setting particle size data, including 5 microns, 10 microns, 15 microns, and 20 microns. When the particle size of the material belongs to (10 μm, 15 μm], it is classified as a primary graphite material. When the particle size of the material belongs to (15 μm, 20 μm], it is classified as a secondary graphite material. When the particle size of the material belongs to (0 μm, 5 μm], it is classified as a primary silicon material. When the particle size of the material belongs to (5 μm, 10 μm] microns, it is classified as a secondary silicon material. The specific steps of the airflow sorting method include: S31, setting the air flow velocity and the separator angle according to the particle size distribution of the negative electrode waste fine powder; S32, setting the feed rate according to the processing capacity and condition of the equipment; S33, dividing the primary silicon material and the primary graphite material into two parts according to the difference in suspension degree, taking the median of the fine powder suspension degree as the difference division value, and sending the fine powder with a suspension degree greater than the median of the fine powder suspension degree to the next step; S34, increase the air flow velocity, further classify the fine powder obtained in S33, set the suspension thresholds Tsut1 and Tsut2, when the fine powder suspension is less than or equal to Tsut1, it is determined to be the first-level waste fine powder, when the fine powder suspension is less than Tsut2 and greater than Tsut1, it is determined to be the second-level waste fine powder, when the fine powder suspension is greater than or equal to Tsut2, it is determined to be the third-level waste fine powder; S35, sending the secondary waste fine powder into S4; The specific steps of S31 include: S311, establishing a parameter allocation model, taking the support vector machine model as the main body, and taking the air flow velocity and separator angle corresponding to the historical silicon material classification and the historical graphite material classification as training data; S312, assigning corresponding air flow velocities and separator angles to the primary silicon material, the secondary silicon material, the primary graphite material, and the secondary graphite material through the parameter allocation model; The parameter allocation model includes setting separation targets, including separation efficiency, purity and yield, setting operation constraints, including maximum gas flow rate and separator angle range; extracting the material particle size of all fine powders in the primary silicon material and the primary graphite material in step S2, the weight of fine powder corresponding to each material particle size, and calculating the gas flow rate formula: Among them, d p Indicates particle size, m p represents the weight of fine powder corresponding to the particle size, and a, b, and c represent the set fitting coefficients; establishing a separator angle database, including calculating the separator angle corresponding to the historical particle size, calculating the average particle size, and extracting the separator angle corresponding to the particle size in the separator angle database; The specific steps of S32 include: S321, obtain the amount of material maq that the equipment can process per hour, S322, obtain the environmental impact factor eif by installing an air temperature sensor and a particle sensor on the device; the specific steps include: evenly installing n1 temperature sensors and n2 particle sensors on the outer wall of the device to obtain the device temperature data set Tem φ =(Tem1,Tem2,...,Tem K ) and air particulate matter concentration datasets in Indicates The temperature value of the particle sensor is used to obtain the environmental impact factor of the device temperature value. Among them, β1 represents the weight of temperature data, and β2 represents the weight of air particle concentration data; S323. Calculate the equipment energy efficiency impact factor een. The equipment energy efficiency impact factor een is calculated by dividing the ventilation volume of the airflow classifier by the power consumption of the equipment. The calculation formula is: Where P represents the ventilation efficiency of the air classifier, represents the power efficiency of the air classifier, n represents the number of power-consuming components in the air classifier, α i represents the weight of the power consuming component, w i represents the output power of the i-th power-consuming component in the airflow classifier, i∈[1,2,…,n]; S324, construct a feed quantity control algorithm to adjust the feed quantity in real time.

2. The method for recycling negative electrode waste of used batteries according to claim 1, characterized in that: The feed rate control algorithm includes creating an evaluation function A set of feed rate data P(1) is randomly generated, and a parameter simulation algorithm is created. According to the historical feed rate and equipment performance, an evaluation function is obtained by simulating the data in P(1). P(1) is sorted from high to low, and its corresponding evaluation function sequence F_P(1) is obtained. An evaluation threshold TF is set, and the data in F_P(1) is traversed in turn. The feed rate corresponding to the first evaluation function in the evaluation function sequence F_P(1) that is greater than TF is extracted as the feed rate parameter of the airflow classifier.

3. The method for recycling negative electrode waste of used batteries according to claim 2, characterized in that: The spectrum recognition method comprises: placing the secondary waste fine powder in a sampler and laying it flat, setting a partition window with a side length of edg, dividing the secondary waste fine powder in the sampler into regions according to the partition window, collecting spectrum data of each region, and extracting an ultraviolet visible spectrum reflectance data set UVVis of the region. i =(UVVis1,UVVis2,…,UVVis p ), p represents the number of ultraviolet and visible light bands, and extracts the infrared band reflectance dataset lir of this area j =(lir1,lir2,…,lir q ), q represents the number of infrared bands, and the ultraviolet visible spectrum reflectance and infrared band reflectance are combined to obtain the spectrum evaluation index Extract max(FSpe ij ) as the level segmentation value, and set the level segmentation threshold T1_FSpe ij and T2_FSpe ij , will satisfy max(FSpe ij )≤T1_FSpe ij The area is divided into three levels of silicon materials, which will meet T1_FSpe ij <max(FSpe ij )≤T2_FSpe ij The area is divided into mixed materials, which satisfies max(FSpe ij )>T1_FSpe ij The area of ​​graphite materials is divided into three levels.

4. A system for recycling waste negative electrode materials of used batteries, used to implement a method for recycling waste negative electrode materials of used batteries as claimed in any one of claims 1 to 3, characterized in that: include: The material crushing module is used to initially crush the negative electrode waste of used batteries, remove the shell and diaphragm, and obtain the coarse powder of the negative electrode waste; The first classification module is used to send the coarse powder of the negative electrode waste to a pulverizer for further pulverization, and to classify the coarse powder of the negative electrode waste with different particle sizes through a screening device to obtain fine powder of the negative electrode waste with different particle sizes; A second classification module is used to classify the negative electrode waste fine powder into multiple levels according to the suspension degree by airflow sorting method; A third classification module is used to extract the secondary waste fine powder output by the second classification module and further classify it by using a spectral recognition method; The waste recycling module is used to recycle the tertiary silicon material and the tertiary graphite material.

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