A method and system for identifying the properties of solid waste raw materials for lithium-ion battery recycling

Abstract

The present invention relates to the recycling and utilization of lithium-ion batteries, and in particular to a method and system for identifying the properties of solid waste raw materials for lithium-ion battery recycling. When the present invention conducts solid waste identification on lithium-ion battery recycling raw materials, it first obtains the X-ray fluorescence spectrum element spectrum and X-ray diffraction spectrum of the material, extracts characteristic parameters related to the material composition, and adopts a machine learning method to combine the advantages of X-ray fluorescence spectrum and diffraction spectrum to more accurately determine the main components of the material, thereby solving the problem of single instrument analysis in the prior art. Then, a machine learning method is used to determine the source properties of the material, select the applicable product quality standard based on the main components of the material, and further determine whether the material is solid waste based on whether the material meets the requirements of the product quality standard, thereby obtaining results more quickly and accurately.

Description

Technical Field

[0001] The present invention relates to the recycling of lithium-ion batteries, and in particular to a method and system for identifying the properties of solid waste raw materials for lithium-ion battery regeneration. Background Art

[0002] With the rapid development of the new energy vehicle and electronics industries, the lithium-ion battery industry is accelerating, driving demand for upstream electrode materials. As the scale of power battery use continues to expand and the development cycle of power batteries continues to lengthen, the number of scrapped power batteries continues to grow rapidly. This scrap volume will continue to grow as battery installed capacity increases. The abundance of valuable metals such as nickel, cobalt, and lithium in power batteries is far higher than in their original ore. For example, one ton of 622-type ternary cathode material, commonly found in the market, contains approximately 70 kg of lithium metal, 350 kg of nickel metal, and 120 kg of cobalt metal. Therefore, recycling and reusing spent power batteries can alleviate the contradiction between my country's economic development and resource constraints, as well as the contradiction between economic development and environmental protection.

[0003] The "General Standard for the Identification of Solid Waste" (GB 34330-2017) is an important basis for identifying the properties of imported solid waste, providing general guidelines for this process. However, recycled raw materials from used lithium-ion batteries have unique characteristics, such as diverse sources, varying recycling processes, and complex composition. They possess both raw material and product attributes, making identification criteria ambiguous. There is still disagreement and controversy regarding whether these recycled raw materials are resources or solid waste. Current solid waste identification practices overly rely on the expertise of the identification personnel, requiring them to be familiar with relevant policies, testing methods, and the technical background of lithium-ion batteries. This results in low identification efficiency and is prone to inconsistent conclusions. Commonly used methods for solid waste identification include X-ray fluorescence spectroscopy and X-ray diffraction analysis. However, X-ray fluorescence spectroscopy, for example, is significantly affected by matrix effects when performing quantitative analysis. X-ray diffraction analysis can only provide qualitative analysis and is unable to perform quantitative and comprehensive analysis of polycrystalline materials. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for identifying the properties of solid waste as raw materials for lithium-ion battery recycling, which are used to solve the problems existing in the prior art.

[0005] The present invention provides a method for identifying the properties of solid waste as a raw material for lithium-ion battery recycling, which is characterized by comprising:

[0006] S1: Collect material data, analyze the composition of the material, and determine the main components of the material;

[0007] The collecting of material data includes obtaining an X-ray fluorescence spectrum element spectrum and an X-ray diffraction spectrum of the material;

[0008] Extracting spectral characteristic parameters based on the X-ray fluorescence spectrum elemental spectrum of the material; extracting diffraction characteristic parameters based on the X-ray diffraction pattern;

[0009] Inputting the spectral characteristic parameters and the diffraction characteristic parameters into a pre-trained random forest recognition model to analyze the composition of the material and determine the main components of the material;

[0010] S2: Determination of material source attributes;

[0011] S3: Obtain product quality standards applicable to recycled raw materials;

[0012] S4: Determine whether it is solid waste.

[0013] Preferably, in S1, collecting material data, analyzing the composition of the material, and determining the main components of the material include:

[0014] An X-ray fluorescence spectrometer is used to obtain the X-ray fluorescence elemental spectrum; an X-ray diffraction pattern is obtained using an X-ray diffractometer.

[0015] Preferably, the extracting of spectral characteristic parameters and the extracting of diffraction characteristic parameters include: extracting the concentration ratio of key elements and the total amount of heavy metals; extracting the peak intensity ratio and lattice parameters.

[0016] Preferably, the pre-trained random forest recognition model includes: selecting 3N historical identification materials, including main component confirmation information; obtaining 3N historical identification materials' X-ray fluorescence spectrum elemental spectra, extracting key element concentration ratios and total heavy metal amounts, obtaining 3N historical identification materials' X-ray diffraction patterns, extracting peak intensity ratios and lattice parameters; taking 2N historical identification materials and corresponding main component confirmation information, using key element concentration ratios, total heavy metal amounts, peak intensity ratios, lattice parameters, and main component confirmation information as training data for the random forest recognition model, and establishing a random forest recognition training model; taking the remaining N historical identification materials and corresponding main component confirmation information, using key element concentration ratios, total heavy metal amounts, peak intensity ratios, and lattice parameters as test data, verifying the random forest recognition training model, and obtaining a pre-trained random forest recognition model.

[0017] Preferably, in S2, determining the source attributes of the material includes: determining the content of impurity elements using inductively coupled plasma atomic emission spectrometry and ion selective electrode method; and determining the source of the material based on the appearance of the material, the main components of the material and the content of impurity elements in the material.

[0018] Preferably, in said S3, obtaining the product quality standard applicable to the recycled raw materials includes: obtaining the product quality standard applicable to the recycled raw materials based on the source and main components of the materials.

[0019] Preferably, in said S4, determining whether it is solid waste includes: determining whether the material is solid waste according to the product quality standard of the recycled raw material.

[0020] According to another aspect of the present invention, a system for identifying the properties of solid waste raw materials for lithium-ion battery recycling is provided. The system adopts the method for identifying the properties of solid waste raw materials for lithium-ion battery recycling according to any one of claims 1 to 7, and the system comprises:

[0021] The material main component confirmation module is used to collect material data, analyze the composition of the material, and determine the main components of the material to be tested;

[0022] A material source attribute determination module is used to determine the source of the material based on the appearance of the material, the main components of the material and the content of impurity elements in the material;

[0023] A judgment standard acquisition module is used to obtain the product quality standard applicable to the recycled raw material based on the source and main components of the material;

[0024] The solid waste determination module is used to determine whether a material is solid waste based on the product quality standards of recycled raw materials.

[0025] Preferably, the material main component confirmation module includes: a material data acquisition module for acquiring material data, wherein the material data includes the X-ray fluorescence spectrum element spectrum and the X-ray diffraction spectrum of the material.

[0026] Preferably, the material composition analysis module includes: a material data processing module for performing data processing operations on the material data; wherein the material data processing includes extracting key element concentration ratios and total heavy metal content based on the X-ray fluorescence elemental spectrum of the material; and extracting peak intensity ratios and lattice parameters based on the X-ray diffraction pattern. A random forest recognition model establishment module is used to establish a random forest recognition model.

[0027] The embodiments of the present invention have the following technical effects:

[0028] When identifying solid waste from recycled lithium-ion battery raw materials, the present invention first obtains the material's X-ray fluorescence elemental spectrum and X-ray diffraction pattern, extracting characteristic parameters related to the material's composition. Using machine learning, the advantages of X-ray fluorescence spectroscopy and diffraction patterns are combined to more accurately determine the material's primary components, resolving the limitations of single-instrument analysis in existing technologies. Machine learning is then used to determine the material's source attributes, selecting applicable product quality standards based on the material's primary components. Further, whether the material meets the product quality standards determines whether it is solid waste, resulting in faster and more accurate results. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 This is a flow chart of a method for identifying the properties of solid waste raw materials for lithium-ion battery recycling provided by an embodiment of the present invention.

[0031] Figure 2 This is a schematic diagram of the appearance of one of the samples provided in an embodiment of the present invention.

[0032] Figure 3 It is a module diagram of the system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0034] Attachment Figure 1 A flow chart showing a method for identifying the properties of solid waste raw materials for lithium-ion battery recycling is provided, including:

[0035] S1: Collect material data, analyze the composition of the material, and determine the main components of the material;

[0036] Material data is collected to obtain the material's X-ray fluorescence elemental spectrum and X-ray diffraction pattern. Analyzing and identifying the chemical composition of the sample is the most fundamental requirement for identification. X-ray fluorescence spectrometry (XRF) is typically used to analyze the elemental composition of inorganic samples. It can reveal all elements with a content above 0.01%, excluding light elements with lower atomic numbers in the periodic table (usually located after nitrogen). X-ray diffractometer (XRD) is used to analyze the physical structure of the sample. Both methods are very practical and effective for analyzing and identifying the overall chemical properties of the sample and can effectively determine the primary components of the sample. If necessary, other testing methods such as energy spectrum analysis and thermogravimetric analysis can also be used.

[0037] In this embodiment, an X-ray fluorescence spectrum and an X-ray diffraction spectrum are obtained for the material to be tested, and the concentration ratio of key elements and the total amount of heavy metals are extracted based on the X-ray fluorescence spectrum. For example, if the material is nickel-cobalt-manganese hydroxide, the Ni / (Co+Mn) concentration ratio and the total weight of heavy metals (ΣCo+Ni+Mn…) can be extracted based on the X-ray fluorescence spectrum. The peak intensity ratio and lattice parameters are extracted based on the X-ray diffraction spectrum. For example, if the material is nickel-cobalt powder, the peak intensity ratio of the (003) peak / (104) peak and the c / a value can be extracted based on the X-ray diffraction spectrum.

[0038] Among them, a pre-trained random forest recognition model is selected. During training, 3N historical identification materials are selected, including confirmation information of the main components; X-ray fluorescence spectra of 3N historical identification materials are obtained to extract the concentration ratio of key elements and the total amount of heavy metals; and X-ray diffraction patterns of 3N historical identification materials are obtained to extract the peak intensity ratio and lattice parameters;

[0039] Take 2N historical identification materials and corresponding main component confirmation information, use the key element concentration ratio, total heavy metal amount, peak intensity ratio, lattice parameter, and main component confirmation information as the training data of the random forest recognition model, and establish a random forest recognition training model;

[0040] Take the remaining N pieces of historical identification materials and the corresponding main component confirmation information, use the key element concentration ratio, total heavy metal amount, peak intensity ratio, and lattice parameters as test data, verify the random forest recognition training model, and obtain the pre-trained random forest recognition model.

[0041] The key element concentration ratio, total heavy metal content, peak intensity ratio, and lattice parameters of the material to be tested are input into the pre-trained real-time forest recognition model to confirm the main components of the material. For example, it can be confirmed to be lithium nickel cobalt manganese oxide, nickel cobalt hydroxide, lithium carbonate, nickel hydroxide, cobalt carbonate, cobalt hydroxide, etc.

[0042] S2: Material source attribute determination: Determine the material source based on the main components of the material and the appearance of the material.

[0043] For example, after step S1, it is determined that the main component of the material named lithium nickel cobalt manganese oxide is lithium nickel cobalt manganese oxide. Figure 2 Observe the attached Figure 2 The material appears as a gray-black powder with no apparent abnormalities, including no obvious inclusions. The sample's primary components are nickel, cobalt, and manganese. The sample's appearance, chemical composition, and phase structure are consistent with those of lithium nickel cobalt manganese oxide, with no abnormalities in impurities or harmful elements. This indicates that the material is a lithium-ion battery intermediate, not a recycled raw material for lithium-ion batteries.

[0044] For example, after step S1, it is determined that the main components of the material named nickel cobalt powder are LiCoO2, LiNi 0.333 Co 0.333 Mn 0.333 O2 and graphite. The content of iron, copper, aluminum, lead, cadmium, arsenic, chromium and fluorine in the sample was determined by inductively coupled plasma atomic emission spectrometry and ion selective electrode method. The sample is a gray-black powder with uniform color, no odor, no agglomeration and no obvious inclusions. The sample mainly contains lithium-ion battery positive electrode materials LiCoO2, LiNi 0.333 Co 0.333 Mn 0.333 O2 and graphite components of the negative electrode material, as well as aluminum as the positive electrode current collector and copper as the negative electrode current collector. The sample's compositional characteristics are consistent with those of recycled material from crushed and reclaimed waste lithium-ion batteries. It is inferred that the sample is recycled material obtained by crushing, screening, and removing impurities from various types of waste lithium-ion batteries. This means that the material is recycled raw material for lithium-ion batteries.

[0045] In S3, the applicable product quality standards for recycled raw materials are obtained, including: obtaining the applicable product quality standards for recycled raw materials based on the source and main components of the materials. Due to their different sources, power battery intermediates and recycled raw materials are subject to different provisions of the "General Rules for the Identification of Solid Waste" (GB 34330-2017) (hereinafter referred to as the "General Rules"). Power battery intermediates are considered "target products" of industrial production, and their solid waste identification should be determined in accordance with Article 4 of the General Rules. Power battery recycled raw materials derived from solid waste such as scrapped power batteries, components, and raw materials are considered "products produced from solid waste," and their solid waste identification should be determined in accordance with Article 5.2 of the General Rules. Recycled raw materials should comply with national, local, or industry-wide product quality standards. Product quality standards for recycled raw materials include GB / T 45203-2024, YS / T 1460-2021, YS / T 1552-2022, and YS / T 1228-2018.

[0046] In S4, determining whether the material is solid waste includes: determining whether the material is solid waste based on the product quality standard of the recycled raw material.

[0047] For example, the sample's source, main components, and hazardous element content are applicable to GB / T 45203-2024. Analysis showed that the sample's main element content met the requirements of GB / T 45203-2024, but the hazardous element content exceeded the limits specified in the standard, indicating that the sample did not meet the requirements of GB / T 45203-2024. According to the "General Rules for the Identification of Solid Waste" (GB 34330-2017), the sample was classified as solid waste.

[0048] Example 2, as attached Figure 3 As shown, the present invention also provides a system for identifying the properties of solid waste raw materials for lithium-ion battery recycling, the system adopts the method for identifying the properties of solid waste raw materials for lithium-ion battery recycling of embodiment 1, and the system includes:

[0049] The material main component confirmation module is used to collect material data and determine the main components of the material to be tested;

[0050] The material data acquisition module is used to collect material data, and the material data includes the X-ray fluorescence spectrum element spectrum of the material and the X-ray diffraction spectrum of the material.

[0051] A material data processing module, used for performing data processing operations on the material data;

[0052] The material data processing includes extracting the key element concentration ratio and the total amount of heavy metals based on the X-ray fluorescence spectrum element spectrum of the material; and extracting the peak intensity ratio and lattice parameters based on the X-ray diffraction pattern.

[0053] Random forest recognition model building module is used to build random forest recognition models.

[0054] A material source attribute determination module is used to determine the content of impurity elements using inductively coupled plasma atomic emission spectrometry and ion selective electrode method; and to determine the source of the material based on the content of the impurity elements, the main components of the material, and the appearance of the material;

[0055] A judgment standard acquisition module is used to obtain the product quality standard applicable to the recycled raw material based on the source and main components of the material;

[0056] The solid waste determination module is used to determine whether a material is solid waste based on the product quality standards of recycled raw materials.

[0057] Example 3: The present invention also provides an electronic device, including one or more processors and a memory.

[0058] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0059] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement a method for identifying the properties of solid waste of lithium-ion battery recycled raw materials in any embodiment of the present application described above and / or other desired functions. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0060] In one example, the electronic device may further include an input device and an output device, these components interconnected via a bus system and / or other connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, etc. The output device may output various information to the outside, including warning information, braking force, etc. The output device may include, for example, a display, a speaker, a printer, a communication network, and remote output devices connected thereto.

[0061] Of course, for the sake of simplicity, components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0062] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to implement the functions of a method for identifying the properties of solid waste of lithium-ion battery recycled raw materials provided in any embodiment of the present application.

[0063] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0064] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor implements a method for identifying the properties of solid waste of lithium-ion battery recycled raw materials provided in any embodiment of the present application.

[0065] The computer-readable storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying the properties of solid waste used as raw material for lithium-ion battery recycling, characterized in that: include: S1: Collect material data, analyze the composition of the material, and determine the main components of the material; The collecting of material data includes obtaining an X-ray fluorescence spectrum element spectrum and an X-ray diffraction spectrum of the material; Extracting spectral characteristic parameters based on the X-ray fluorescence spectrum elemental spectrum of the material; wherein the extracted spectral characteristic parameters include extracting the concentration ratio of key elements and the total amount of heavy metals; Extracting diffraction characteristic parameters based on the X-ray diffraction pattern; wherein the extracted diffraction characteristic parameters include extracting peak intensity ratio and lattice parameters; Inputting the spectral characteristic parameters and the diffraction characteristic parameters into a pre-trained random forest recognition model to analyze the composition of the material and determine the main components of the material; The pre-trained random forest recognition model includes: Select 3N historical identification materials, including confirmation information of main components; Obtaining 3N copies of X-ray fluorescence elemental spectra of the historically identified materials, extracting the key element concentration ratio and the total amount of heavy metals; obtaining 3N copies of X-ray diffraction patterns of the historically identified materials, extracting the peak intensity ratio and the lattice parameters; Taking 2N copies of the historically identified materials and the corresponding main component confirmation information, using the key element concentration ratio, the total amount of heavy metals, the peak intensity ratio, the lattice parameter, and the main component confirmation information as training data for a random forest recognition model, and establishing a random forest recognition training model; Taking the remaining N pieces of the historically identified materials and the corresponding main component confirmation information, using the key element concentration ratio, the total amount of heavy metals, the peak intensity ratio, and the lattice parameter as test data, verifying the random forest recognition training model to obtain a trained random forest recognition model; S2: Determine the source attributes of the material; S3: Obtain product quality standards applicable to recycled raw materials; S4: Determine whether the material is solid waste.

2. The method for identifying the properties of solid waste as a raw material for lithium-ion battery recycling according to claim 1, characterized in that: In S1, collecting material data and analyzing the composition of the material include: An X-ray fluorescence spectrometer is used to obtain the X-ray fluorescence elemental spectrum; an X-ray diffraction pattern is obtained using an X-ray diffractometer.

3. The method for identifying the properties of solid waste as a raw material for lithium-ion battery recycling according to claim 1, characterized in that: In S2, the material source attribute determination includes: Using inductively coupled plasma atomic emission spectrometry and ion selective electrode method to determine the impurity element content of the material; The source of the material is determined based on the appearance of the material, the main components of the material and the content of impurity elements in the material.

4. The method for identifying the properties of solid waste as a raw material for lithium-ion battery recycling according to claim 1, wherein: In S3, the product quality standards applicable to the recycled raw materials are obtained, including: Based on the source and main components of the material, obtain the product quality standards applicable to the recycled raw materials.

5. The method for identifying the properties of solid waste used as raw material for lithium-ion battery recycling according to claim 1, wherein: In S4, determining whether the material is solid waste includes: Determine whether the material is solid waste based on the product quality standard of the recycled raw material.

6. A system for identifying the properties of solid waste used as raw materials for lithium-ion battery recycling, characterized in that: The system adopts the method for identifying the properties of solid waste of lithium-ion battery recycled raw materials according to any one of claims 1 to 5, and the system comprises: The material main component confirmation module is used to collect material data, analyze the composition of the material, and determine the main components of the material; A material source attribute determination module includes determining the content of impurity elements using inductively coupled plasma atomic emission spectrometry or ion selective electrode method; obtaining the appearance of the material; and determining the source of the material based on the appearance of the material, the main components of the material, and the content of impurity elements in the material; A judgment standard acquisition module is used to obtain the product quality standard applicable to the recycled raw material based on the source and main components of the material; The solid waste determination module is used to determine whether the material is solid waste based on the product quality standard used for the recycled raw materials.

7. The lithium-ion battery recycled raw material solid waste property identification system according to claim 6, characterized in that: The material main component confirmation module includes: The material data acquisition module is used to acquire material data, wherein the material data includes the X-ray fluorescence spectrum element spectrum of the material and the X-ray diffraction spectrum of the material.

8. The lithium-ion battery recycled raw material solid waste property identification system according to claim 7, characterized in that: The material main component confirmation module includes: A material data processing module, used for performing data processing operations on the material data; The material data processing includes extracting the key element concentration ratio and the total amount of heavy metals based on the X-ray fluorescence spectrum of the material; and extracting the peak intensity ratio and lattice parameters based on the X-ray diffraction pattern. Random forest recognition model building module is used to build random forest recognition models.

Images