A ternary lithium and lithium iron phosphate mixed recovery optimization method and system

Abstract

The present application relates to the technical field of battery recycling, and in particular to a ternary lithium and lithium iron phosphate mixed recovery optimization method and system. The method comprises obtaining the electromagnetic characteristic set data of the mixed black powder of the current batch under different frequency electromagnetic waves, including dielectric response data and conductivity response data; comparing the electromagnetic characteristic set data with the material characteristic reference library to determine the material state of the mixed black powder of the current batch; using the material state of the mixed black powder of the current batch to correct the black powder proportion estimation model to obtain the corrected black powder proportion estimation model; and determining the recovery scheme for the mixed black powder of the current batch based on the corrected black powder proportion estimation model. The present application aims to solve the problem that the existing mixed recovery method cannot accurately determine the material proportion during the recovery process of ternary lithium and lithium iron phosphate mixed black powder, resulting in poor wet metallurgical treatment effect and low recovery purity.

Description

Technical Field

[0001] This invention relates to the field of battery recycling technology, specifically to an optimized method and system for the mixed recycling of ternary lithium and lithium iron phosphate. Background Technology

[0002] In the recycling process of used batteries, the recycling process for hybrid batteries containing ternary lithium and lithium iron phosphate materials includes steps such as discharging, dismantling, and crushing before entering the hydrometallurgical processing stage. The effectiveness of hydrometallurgical processing is extremely sensitive to the specific composition ratio of the black powder (a mixture of positive and negative electrode active materials) obtained after crushing.

[0003] However, the introduction of novel carbon-based conductive additives into lithium iron phosphate formulations alters the electrical properties of the lithium iron phosphate material itself, thereby affecting the overall conductivity reference level of the mixed black powder. Since subtle changes in material formulations are typically not covered by routine incoming material inspections at recycling plants, existing mixing optimization methods are designed and calibrated based on the physical properties of the old formulation materials. This makes it impossible to accurately distinguish whether conductivity changes stem from a genuine change in material proportions or from alterations in the conductivity of the lithium iron phosphate material itself. This results in continuously outputting systematically biased proportion estimation data, making it impossible to accurately determine material proportions. This directly impacts the setting of key process parameters in the hydrometallurgical leaching stage, such as the acid concentration of the leachate and the reaction temperature. Consequently, the actual leaching reaction results deviate significantly from expectations; it becomes difficult to effectively separate valuable metals such as nickel, cobalt, and lithium from impurities such as iron; and the purity of the recovered nickel, cobalt, and lithium products decreases significantly. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized method and system for the mixed recycling of ternary lithium and lithium iron phosphate, which solves the problem that existing mixed recycling methods cannot accurately determine the material ratio during the recycling of mixed ternary lithium and lithium iron phosphate black powder, resulting in poor hydrometallurgical treatment and low recycling purity.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an optimized method for the mixed recycling of ternary lithium and lithium iron phosphate, comprising the following steps:

[0006] Before the current batch of mixed black powder of ternary lithium and lithium iron phosphate enters the hydrometallurgical pretreatment stage, acquire the electromagnetic property set data of the current batch of mixed black powder under electromagnetic waves of different frequencies, including dielectric response data and conductivity response data.

[0007] The electromagnetic property set data is compared with the material property reference library to determine the material state of the current batch of mixed black powder;

[0008] By using the material state of the current batch of mixed black powder, the black powder ratio estimation model is corrected to obtain the corrected black powder ratio estimation model.

[0009] Based on the revised black powder ratio estimation model, a recovery plan for the mixed black powder in the current batch is determined.

[0010] Furthermore, the step of obtaining electromagnetic property set data, including dielectric response data and conductivity response data, of the current batch of mixed lithium iron phosphate black powder under different frequency electromagnetic waves before the current batch of mixed black powder enters the hydrometallurgical pretreatment stage includes:

[0011] Before the current batch of mixed black powder of ternary lithium and lithium iron phosphate enters the hydrometallurgical pretreatment stage, local bulk density data and initial data of electromagnetic properties including dielectric response data and conductivity response data of the current batch of mixed black powder are obtained.

[0012] The initial data of the electromagnetic property set is corrected using the local packing density data to obtain the electromagnetic property set data.

[0013] Further, the step of correcting the initial data of the electromagnetic property set using the local packing density data to obtain the electromagnetic property set data includes:

[0014] The local packing density data is processed by frequency segmentation to obtain multiple frequency sub-interval data.

[0015] Determine the sensitivity between each frequency sub-interval data and the corresponding frequency in the initial data of the electromagnetic property set;

[0016] The preset correction factor is adjusted using each of the aforementioned sensitivities to obtain the surface effect stripping factor for each frequency;

[0017] The initial data of the electromagnetic property set is corrected using the surface effect stripping factor for each frequency to obtain the electromagnetic property set data.

[0018] Further, the step of comparing the electromagnetic property set data with a material property reference library to determine the material state of the current batch of mixed black powder includes:

[0019] Identify the key electrical parameters in each frequency sub-interval within the electromagnetic property set data;

[0020] The key electrical parameters of each frequency sub-range are compared with the material property reference library to obtain the parameter comparison results;

[0021] If the parameter comparison results indicate that there are key electrical parameters in the material property reference library, then the material state of the current batch of mixed black powder is determined.

[0022] If the parameter comparison results indicate that the key electrical parameters are not found in the material property reference library, an early warning will be issued.

[0023] Furthermore, after determining the recovery plan for the current batch of mixed black powder based on the modified black powder ratio estimation model, the method further includes:

[0024] The current batch of mixed black powder is sampled and analyzed to obtain the material ratio and lithium iron phosphate formulation data of the mixed black powder;

[0025] The material ratios and lithium iron phosphate formulation data were matched with the recovery scheme for the current batch of mixed black powder to obtain the data matching results.

[0026] If the data matching results indicate that the difference index is met, then the recovery plan for the mixed black powder in the current batch will be executed;

[0027] If the data matching results indicate that the discrepancy index is not met, a solution warning will be issued.

[0028] Further, the steps of sampling and analyzing the current batch of mixed black powder to obtain the material ratio and lithium iron phosphate formulation data of the mixed black powder include:

[0029] Obtain multiple trace samples, sampling locations, and time information at different time points and spatial locations of the current batch of mixed black powder before it enters the hydrometallurgical pretreatment stage;

[0030] Elemental analysis was performed on multiple trace samples to confirm the content data of key elements in each trace sample;

[0031] Based on the key element content data, sampling location and time information, a dynamic distribution map of the material properties and formulation changes in the mixed black powder was determined;

[0032] Based on the dynamic distribution map of the mixed black powder including material properties and formulation changes, the material ratio and lithium iron phosphate formulation data of the mixed black powder are obtained.

[0033] Furthermore, the step of performing elemental analysis on multiple trace samples to confirm the content data of key elements in each trace sample includes:

[0034] Based on multiple trace samples, the particle size distribution, surface roughness, and compaction information of each trace sample were confirmed.

[0035] Based on the particle size distribution, surface roughness, and compaction information of each trace sample, the spectral acquisition parameters, including laser energy, detector integration time, and sample excitation position, are adjusted.

[0036] Based on the spectral acquisition parameters, the content data of key elements in each trace sample were confirmed.

[0037] Furthermore, based on the key element content data, sampling location, and time information, the step of determining the dynamic distribution map of material properties and formulation changes in the mixed black powder includes:

[0038] Fluctuation analysis was performed on the content data of the key elements to identify local outlier points that exceeded the preset fluctuation range;

[0039] By cross-validating local outlier points using historical batch data, it can be determined that the local outlier points include anomalies of real material changes and noise interference.

[0040] Based on the key element content data, the local outlier points of the anomaly type are processed to obtain the processed key element content data.

[0041] Based on the processed key element content data, sampling location and time information, a dynamic distribution map of the material properties and formulation changes in the mixed black powder was determined.

[0042] Furthermore, the step of revising the black powder ratio estimation model using the material state of the current batch of mixed black powder to obtain the revised black powder ratio estimation model includes:

[0043] The material state of the current batch of mixed black powder is verified to obtain the material state of the mixed black powder that has passed the verification.

[0044] By using the material state of the qualified mixed black powder, the black powder ratio estimation model is corrected to obtain the corrected black powder ratio estimation model.

[0045] This invention also provides an optimized recycling system for a mixture of ternary lithium and lithium iron phosphate, the system comprising:

[0046] The data acquisition module is used to acquire electromagnetic property set data, including dielectric response data and conductivity response data, of the current batch of mixed black powder of ternary lithium and lithium iron phosphate under different frequency electromagnetic waves before the current batch of mixed black powder enters the hydrometallurgical pretreatment stage.

[0047] The state comparison module is used to compare the electromagnetic property set data with the material property reference library to determine the material state of the current batch of mixed black powder.

[0048] The model correction module is used to correct the black powder ratio estimation model by utilizing the material state of the current batch of mixed black powder, and obtain the corrected black powder ratio estimation model.

[0049] The scheme determination module is used to determine the recovery scheme for the current batch of mixed black powder based on the modified black powder ratio estimation model.

[0050] Compared with the prior art, the optimized method and system for the mixed recycling of ternary lithium and lithium iron phosphate of the present invention has the following advantages:

[0051] This invention obtains electromagnetic property data of mixed black powder under different frequencies of electromagnetic waves before the hydrometallurgical pretreatment stage and compares it with a material property reference library to accurately determine the material state of the current batch of mixed black powder. Based on the material state, the black powder ratio estimation model is corrected to obtain a more accurate black powder ratio estimation result, and an optimized recovery scheme is determined accordingly. By dynamically correcting the estimation model, this application can avoid the propagation of systematically biased estimation data downstream, thereby ensuring the accuracy of process parameter settings in the hydrometallurgical leaching stage, effectively preventing side reactions caused by excessively high acid concentration and abnormal increases in iron ion concentration, ultimately significantly improving the recovery purity of valuable metals such as nickel, cobalt, and lithium, and reducing additional purification costs. Attached Figure Description

[0052] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0053] Figure 1 This is a flowchart of an optimized method for the mixed recycling of ternary lithium and lithium iron phosphate according to the present invention.

[0054] Figure 2 This is a structural block diagram of an optimized recycling system for ternary lithium and lithium iron phosphate mixtures according to the present invention.

[0055] In the diagram: 210, Data Acquisition Module; 220, State Comparison Module; 230, Model Correction Module; 240, Scheme Determination Module.

[0056] The implementation and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0058] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0059] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms, and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If a combination of technical solutions is contradictory or impossible to implement, such a combination should be considered nonexistent and not within the scope of protection claimed by this invention.

[0060] Existing waste battery recycling methods rely on macroscopic physical properties (such as conductivity or magnetic susceptibility) to estimate the material ratio when processing mixed ternary lithium and lithium iron phosphate powder. However, due to the continuous evolution of lithium iron phosphate formulations (especially carbon-based conductive additives) in the upstream battery manufacturing supply chain, existing technologies cannot accurately distinguish whether changes in conductivity stem from actual changes in the material ratio or from changes in the conductivity of the lithium iron phosphate material itself. Consequently, it is difficult to accurately determine the material ratio, resulting in systematically biased ratio estimation data from existing technologies. This, in turn, affects the setting of process parameters in the hydrometallurgical leaching stage, leading to a decrease in the purity of recovered valuable metals.

[0061] To further understand the content, features, and effects of this invention, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings:

[0062] Please see Figure 1 This invention provides an optimized method for the mixed recycling of ternary lithium and lithium iron phosphate, comprising the following steps:

[0063] S100. Before the current batch of mixed lithium-ion and lithium iron phosphate black powder enters the hydrometallurgical pretreatment stage, acquire the electromagnetic property set data, including dielectric response data and conductivity response data, of the current batch of mixed black powder under different frequency electromagnetic waves. The mixed lithium-ion and lithium iron phosphate black powder refers to a powdery substance formed by mixing ternary lithium battery cathode materials and lithium iron phosphate battery cathode materials after pretreatment such as crushing and sieving, which may contain other components such as conductive agents and binders. The hydrometallurgical pretreatment stage typically refers to the preparation stage before the black powder enters chemical treatments such as acid leaching and alkaline leaching; its treatment effect is crucial for the subsequent extraction of valuable metals. The electromagnetic property set data refers to the sum of dielectric response data and conductivity response data exhibited by the mixed black powder under the action of electromagnetic waves at different frequencies, reflecting the internal structure, composition, and conductivity properties of the material. This step involves placing the mixed black powder sample in an electromagnetic field and using a broadband dielectric spectrometer or impedance analyzer to scan within a preset frequency range (e.g., from 100 Hz to 1 GHz), recording the dielectric constant (real and imaginary parts) and conductivity at each frequency point. The data can be collected and stored as a dataset of electromagnetic properties. Alternatively, time-domain reflectometry (TDR) or microwave resonant cavity methods can be used to indirectly derive dielectric and conductivity response data by measuring the propagation or resonance characteristics of electromagnetic waves in the black powder sample.

[0064] S200. The electromagnetic property set data is compared with the material property reference library to determine the material state of the current batch of mixed black powder. The material property reference library is a pre-established database that stores standard electromagnetic property data of ternary lithium and lithium iron phosphate materials with different formulations and proportions under different frequencies of electromagnetic waves, used for comparison with the real-time acquired black powder data. Specifically, this step can input the acquired electromagnetic property set data into a pattern recognition algorithm, which is pre-trained on known material state data in the material property reference library. By calculating the similarity between the feature vector of the current data and the feature vectors of each known material state in the reference library, the best-matching material state can be determined. Another implementation method can also use a machine learning model, such as a support vector machine (SVM) or neural network, taking the electromagnetic property set data as input and outputting the material state of the current batch of black powder, such as a standard lithium iron phosphate formulation, a novel carbon-based conductive lithium iron phosphate formulation, or an abnormal material.

[0065] S300. Using the material state of the current batch of mixed black powder, the black powder ratio estimation model is corrected to obtain a corrected black powder ratio estimation model. The black powder ratio estimation model is a pre-set mathematical model used to estimate the relative proportions of ternary lithium and lithium iron phosphate based on the physical or electrical properties of the black powder. If the material state identification result indicates that the current batch of black powder contains lithium iron phosphate with a novel carbon-based conductive agent, the parameters related to the conductivity of lithium iron phosphate in the black powder ratio estimation model can be adjusted according to preset correction rules. This adjustment can be linear shift, multiplicative factor adjustment, or more complex nonlinear function correction. Alternatively, based on the identified material state, the model most suitable for the current material state can be selected from multiple preset black powder ratio estimation models as the corrected model.

[0066] S400. Based on the revised black powder ratio estimation model, determine the recovery plan for the current batch of mixed black powder. Specifically, the revised model in this step will output a more accurate estimated ratio of ternary lithium to lithium iron phosphate. Based on the ratio estimate, a preset recovery plan database can be consulted to select the hydrometallurgical leaching process parameters that best match the ratio, such as acid concentration, reaction temperature, and reaction time. Alternatively, the revised model can be directly used as input to a decision support system. This system dynamically generates customized recovery plans based on the estimated ratio and material state, including but not limited to the type and concentration of leaching agent, reaction conditions, and preliminary strategies for subsequent separation and purification.

[0067] This invention acquires a set of electromagnetic property data, including dielectric and conductivity response data, of the current batch of mixed black powder under different frequencies of electromagnetic waves before it enters the hydrometallurgical pretreatment stage. This allows for a more comprehensive and detailed characterization of the electromagnetic properties of the black powder. The performance of dielectric and conductivity response data at different frequencies reflects the microstructure, composition, and distribution and type of conductive agents in the material, thus providing richer information than a single macroscopic conductivity value. By comparing the acquired set of electromagnetic property data with a material property reference library, this application can accurately determine the material state of the current batch of mixed black powder, such as identifying whether it contains lithium iron phosphate with novel carbon-based conductive agents. Once the material state is accurately identified, it can be used to correct the black powder ratio estimation model. This correction can involve parameter adjustment, model selection, or algorithm optimization, aiming to eliminate estimation biases introduced by changes in material formulation, so that the corrected model can output more accurate material ratio estimates. Finally, based on the corrected black powder ratio estimation model, a recycling plan for the current batch of mixed black powder can be determined. With improved accuracy in estimating proportions, the determined recovery schemes (such as acid concentration and reaction temperature in the hydrometallurgical leaching stage) become more precise, thus avoiding deviations in process parameter settings caused by incorrect estimations. Therefore, it effectively avoids unexpected side reactions, reduces abnormal increases in iron ion concentration in the leachate, and ensures the efficiency and product purity of subsequent metal separation and precipitation processes. Specifically, this application significantly improves the accuracy of material proportion estimation during mixed black powder recovery by introducing multi-frequency electromagnetic characteristic data for material state identification and thereby revising the black powder proportion estimation model, thus optimizing the development of the recovery scheme and ensuring the efficient and high-purity recovery of valuable metals.

[0068] In some embodiments of this application, the step of obtaining electromagnetic property set data, including dielectric response data and conductivity response data, of the current batch of mixed black powder containing ternary lithium and lithium iron phosphate under different frequency electromagnetic waves before the current batch of mixed black powder enters the hydrometallurgical pretreatment stage includes:

[0069] Before the current batch of mixed lithium iron phosphate and ternary lithium iron phosphate black powder enters the hydrometallurgical pretreatment stage, local bulk density data and initial electromagnetic property set data including dielectric response and conductivity response data of the current batch of mixed black powder are acquired. Local bulk density data can be understood as the mass or compactness information of black powder per unit volume measured at different locations and / or time points in the black powder flow using appropriate measuring equipment (e.g., sensors based on optical, acoustic, or weighing principles) before the mixed black powder enters the hydrometallurgical pretreatment stage. This data aims to reflect the physical packing state of the black powder in a local area. Initial electromagnetic property set data refers to the raw dielectric response and conductivity response data directly acquired by electromagnetic wave detection equipment without considering or completely eliminating the influence of local bulk density.

[0070] The initial data of the electromagnetic property set is corrected using the local packing density data to obtain the electromagnetic property set data. The correction process refers to the systematic modification or adjustment of the initial electromagnetic property set data based on the acquired local packing density data. Its purpose is to eliminate or reduce the interference caused by changes in local packing density on the electromagnetic response data, so that the processed electromagnetic property set data can more accurately reflect the intrinsic material properties of the mixed black powder, rather than the apparent influence of its physical packing state. In practical applications, the correction process can employ various methods, such as establishing an empirical model based on historical data, or using a physical model for parameter compensation, using local packing density as an input variable to normalize or calibrate the original dielectric constant and conductivity.

[0071] Specifically, when the current batch of mixed black powder enters the hydrometallurgical pretreatment stage via the conveying system, multiple non-contact sensors, such as laser rangefinders or image recognition systems, can be installed along the conveying path to acquire the surface morphology and volume distribution of the black powder in real time. Combined with the mass flow rate data of the black powder, the local bulk density data of different regions can be estimated. Simultaneously, in the same region where the black powder flows, electromagnetic wave detectors transmit and receive electromagnetic waves at preset frequencies to acquire the original dielectric response and conductivity response data, i.e., the initial data of the electromagnetic property set. Subsequently, the local bulk density data is input into a pre-established correction model. The correction model can be a model trained based on machine learning algorithms or a mathematical model based on physical principles. Its function is to adjust the initial data of the electromagnetic property set based on the measured local bulk density. For example, when a local bulk density deviation from the standard value is detected, the model will compensate or normalize the original dielectric constant and conductivity data according to its internal mapping relationship to eliminate the interference of bulk density on the electromagnetic response. Ultimately, a more accurate set of electromagnetic properties data was obtained after correction, which was then compared with a material property reference library to achieve accurate determination of the state of the mixed black powder material.

[0072] The proposed solution involves first acquiring the local bulk density data of the current batch of mixed black powder during the acquisition of electromagnetic property set data, and then combining this data with the initial electromagnetic property set data for correction. The local bulk density of the mixed black powder directly affects its internal porosity, interparticle contact area, and effective dielectric constant and conductivity for electromagnetic wave propagation. Without this correction, black powder from different batches or different regions within the same batch, even with the same chemical composition and material ratio, may exhibit different electromagnetic response characteristics due to differences in bulk density, leading to misjudgments of the material state. By using the local bulk density data to correct the initial electromagnetic property set data, the electromagnetic response deviation caused by changes in physical packing state can be effectively eliminated or compensated. Therefore, the acquired electromagnetic property set data can more accurately reflect the true material composition and properties of the mixed black powder, providing more reliable and accurate input data for subsequent material state determination and recycling scheme formulation.

[0073] In some embodiments of this application described above, the step of correcting the initial data of the electromagnetic property set using the local packing density data to obtain the electromagnetic property set data includes:

[0074] The local packing density data is processed by frequency segmentation to obtain multiple frequency sub-intervals. This frequency segmentation divides the wideband electromagnetic response data into several narrower frequency ranges, allowing for a more precise analysis of the impact of local packing density on the electromagnetic wave response at different frequencies. Therefore, multiple frequency sub-intervals are obtained, each representing the local packing density characteristics of the mixed black powder within a specific frequency range.

[0075] Determine the sensitivity of each frequency sub-interval data point to the corresponding frequency in the initial electromagnetic property set. This step assesses the impact of variations in local packing density on dielectric and conductivity response data within each frequency sub-interval. This sensitivity can be quantified by establishing mathematical models, such as regression analysis or machine learning algorithms, to quantify the correlation between local packing density and electromagnetic response. Higher sensitivity indicates a greater impact of local packing density on the initial electromagnetic property set data within that frequency sub-interval.

[0076] By adjusting a preset correction factor based on each sensitivity level, a surface effect stripping factor for each frequency is obtained. Specifically, the preset correction factor is initially set based on experience or a theoretical model to eliminate or reduce the influence of local packing density on the initial data of the electromagnetic property set. By combining the sensitivity of each frequency sub-interval, the preset correction factor can be dynamically adjusted so that it can more accurately reflect the influence of local packing density on surface effects at different frequencies. Therefore, a surface effect stripping factor for each frequency can be obtained, which is specifically used to strip away surface effects caused by differences in local packing density.

[0077] The initial data of the electromagnetic property set is corrected using the surface effect stripping factor for each frequency to obtain electromagnetic property set data. Specifically, the correction process involves performing mathematical operations, such as multiplication or subtraction, between the initial data of the electromagnetic property set and the corresponding surface effect stripping factor to eliminate or reduce measurement errors and surface effects caused by local packing density, thereby obtaining more realistic and accurate electromagnetic property set data.

[0078] This embodiment's solution performs frequency segmentation processing on local packing density data and determines the sensitivity of each frequency sub-interval to the initial electromagnetic property set data. This allows for a refined identification of the differences in the impact of local packing density on the electromagnetic response at different frequencies. Through refined sensitivity assessment, preset correction factors can be precisely adjusted, thereby generating surface effect stripping factors for each frequency. This effectively eliminates or reduces surface effects caused by local packing density inhomogeneity, ensuring that the acquired electromagnetic property set data more accurately reflects the intrinsic material properties of the mixed black powder, rather than its physical packing state.

[0079] In some embodiments of this application described above, the step of comparing the electromagnetic property set data with a material property reference library to determine the material state of the current batch of mixed black powder includes:

[0080] The key electrical parameters in each frequency sub-interval of the electromagnetic property dataset are determined. Specifically, this step involves extracting specific electrical indicators that characterize the composition, structure, or physicochemical properties of the mixed black powder material from the original dielectric response and conductivity response data. Key electrical parameters may include, but are not limited to, the peak dielectric constant at a specific frequency, the inflection point of the loss factor, the rate of change of conductivity, or the integral value within a specific frequency range. This aims to simplify the complex original electromagnetic property dataset into more representative and discriminative feature vectors, facilitating subsequent accurate comparisons.

[0081] The key electrical parameters for each frequency sub-interval are compared with a material property reference library to obtain the parameter comparison results. This step involves matching the extracted key electrical parameters with the key electrical parameters of known materials pre-stored in the material property reference library. This comparison process can be implemented using various algorithms, such as distance-based similarity matching, pattern recognition, and machine learning classifiers. The material property reference library typically contains typical key electrical parameters and corresponding material state information for different types of ternary lithium and lithium iron phosphate black powders at different mixing ratios.

[0082] If the parameter comparison results indicate the existence of key electrical parameters in the material property reference library, the material state of the current batch of mixed black powder is determined. Specifically, this step involves a high degree of match between the electromagnetic properties of the current batch of mixed black powder and a known material state in the reference library, thereby accurately identifying its specific material composition or mixing ratio. For example, if the comparison results show that the key electrical parameters of the current black powder match the characteristics of 70% ternary lithium and 30% lithium iron phosphate in the reference library, then the material state of the current batch can be determined to be that ratio.

[0083] If the parameter comparison results indicate that the key electrical parameters are not found in the material property reference library, an early warning will be issued. This early warning mechanism aims to identify abnormal or unknown batches of mixed black powder. For example, when the comparison results show that the key electrical parameters of the current black powder do not match any known material state in the reference library, an automatic early warning will be issued, prompting the operator that the material state of the current batch of black powder cannot be accurately identified and may require manual verification or additional testing to avoid errors in subsequent recycling processes due to misjudgment.

[0084] Specifically, after acquiring the electromagnetic property set data of the mixed black powder, the data is first analyzed. Within the frequency sub-range of 100kHz to 1MHz, the specific peak frequency and peak amplitude of the dielectric constant are identified as key electrical parameters. For example, if a dielectric constant peak is detected at 250kHz, this peak frequency and amplitude are then compared with a material property reference library. If an entry exists in the reference library showing that a black powder mixed with ternary lithium NCM811 and lithium iron phosphate in a 7:3 ratio has this dielectric constant peak at 250kHz, then the material state of the current batch of mixed black powder is determined to be a 7:3 mixture of ternary lithium NCM811 and lithium iron phosphate. However, if the comparison results show that the key electrical parameters of the current batch of black powder (e.g., an abnormal dielectric constant valley at 300 kHz with no matching item in the reference library) do not match any known material state in the reference library, an early warning will be issued immediately, prompting the operator that the material state of the current batch of black powder is abnormal or unknown, and further manual testing or analysis is required to prevent incorrect recycling procedures from being implemented.

[0085] This embodiment extracts key electrical parameters from electromagnetic property datasets in each frequency sub-interval, transforming complex raw data into refined feature information, thereby improving data processing efficiency and comparison accuracy. This allows subsequent comparisons with a material property reference library to focus more on differences in the essential properties of the materials. By clearly distinguishing the presence or absence of comparison results and introducing an early warning mechanism, this application can effectively identify abnormal batches where the material state cannot be accurately determined, avoiding blind subsequent processing under incomplete or mismatched information, thus significantly improving the robustness and reliability of the entire recycling optimization method.

[0086] In some embodiments of this application described above, after determining the recovery plan for the current batch of mixed black powder based on the modified black powder ratio estimation model, the method further includes:

[0087] The current batch of mixed black powder is sampled and analyzed to obtain the material ratio and lithium iron phosphate formulation data. This step directly measures the actual composition of the current batch of mixed black powder through physical sampling and laboratory analysis. For example, X-ray fluorescence spectroscopy (XRF) and inductively coupled plasma optical emission spectroscopy (ICP-OES) can be used to accurately obtain the content of key elements such as lithium, cobalt, nickel, manganese, iron, and phosphorus in the mixed black powder, thereby calculating the actual ratio of ternary lithium materials to lithium iron phosphate materials and the specific formulation of lithium iron phosphate (such as the purity or specific doping of LiFePO4). The aim is to obtain more accurate real material data than model estimations.

[0088] The material proportions and lithium iron phosphate formulation data are matched with the recovery scheme for the current batch of mixed black powder to obtain data matching results. This step compares the actual material data obtained through sampling analysis with the material parameters implied in the recovery scheme determined based on the modified model. Recovery schemes are typically optimized for mixed black powder with specific proportions and formulations; therefore, this step aims to assess the consistency between the actual materials and the scheme design objectives. Data matching may involve quantitative comparisons of key element proportions, material type percentages, and specific formulation characteristics.

[0089] If the data matching results indicate that the difference criteria are met, then the recovery plan for the mixed black powder in the current batch will be executed.

[0090] If the data matching results indicate a failure to meet the difference criteria, a warning is issued. This step involves determining whether the recovery plan is applicable to the current batch of mixed black powder based on the data matching results. The "difference criteria" can be understood as a preset allowable deviation range or threshold, used to measure the acceptable difference between the actual data and the plan's design objectives. If the difference between the actual data and the plan's design objectives is within the acceptable range, the plan is considered effective and can continue to be implemented. Conversely, if the difference exceeds the preset criteria, it indicates that the current plan may not be suitable for this batch of material, and a warning needs to be issued so that operators can further inspect, analyze, or adjust the recovery plan to avoid potential process problems or resource waste.

[0091] Specifically, based on the revised black powder ratio estimation model, a recovery scheme for a 70:30 ratio of ternary lithium to lithium iron phosphate mixed black powder was determined. After the scheme was determined, a sampling and analysis step was triggered to verify its applicability. Specifically, an automated sampling device collected multiple trace samples from the current batch of mixed black powder at different time points and spatial locations. These samples were then sent to online or offline analysis equipment, such as laser-induced breakdown spectroscopy (LIBS) or X-ray diffraction (XRD), to accurately measure the content of key elements such as lithium, cobalt, nickel, manganese, iron, and phosphorus in each sample, determining the actual material ratio and lithium iron phosphate formulation data for the current batch of mixed black powder. For example, the analysis results showed that the actual ratio of the current batch of mixed black powder was 65:35 (ternary lithium:lithium iron phosphate), and the purity of the lithium iron phosphate formulation was slightly lower than expected. At this point, the actual data was matched with the parameters corresponding to the previously determined 70:30 recovery scheme. A preset difference index might stipulate that the deviation in material ratio should not exceed ±5%. In this example, the actual ratio of 65:35 deviates from the target ratio of 70:30 by 5%, which is just at the boundary of the difference indicator. If this deviation is determined to be within the difference indicator (e.g., a 5% deviation is allowed), the original recovery plan will be executed. However, if the actual ratio is 60:40, the deviation reaches 10%, exceeding the difference indicator, a plan warning will be issued immediately, prompting operators that the current recovery plan may not be fully applicable to this batch of materials and that the recovery plan needs to be reassessed or adjusted. For example, it may be necessary to adjust the acid leaching conditions of the hydrometallurgical pretreatment or subsequent separation parameters to adapt to the actual material composition, thereby ensuring recovery efficiency and product quality.

[0092] This embodiment effectively overcomes the limitations of relying solely on model estimations by introducing an independent verification process after the recycling plan is determined. Specifically, by conducting actual sampling and analysis of the current batch of mixed black powder, more accurate material ratios and lithium iron phosphate formulation data can be obtained than model estimates. This data is then used to verify the determined recycling plan. This allows for the identification of potential deviations in the estimation model or unexpected variations in batch materials, ensuring a high degree of consistency between the selected recycling plan and the actual material characteristics. When unacceptable differences are found between the actual materials and the plan's design objectives, a timely warning is issued, preventing the blind execution of recycling plans that could lead to low recycling efficiency, poor product quality, or even equipment damage. This significantly improves the reliability and adaptability of the recycling process.

[0093] In some embodiments of this application described above, the step of sampling and analyzing the current batch of mixed black powder to obtain the material ratio and lithium iron phosphate formulation data of the mixed black powder includes:

[0094] This step involves acquiring multiple trace samples of the current batch of mixed black powder at different time points and spatial locations before it enters the hydrometallurgical pretreatment stage. The sampling locations and time information are recorded. Before the mixed black powder enters the hydrometallurgical pretreatment process, sampling is conducted at different time points and spatial locations to ensure that the acquired samples fully represent the actual composition and distribution of the entire batch of mixed black powder. Sampling locations can include different depths of black powder accumulation, edges, and central areas. The time information records the specific moment each sample was collected, aiming to capture any potential non-uniformity of the black powder within the batch.

[0095] Elemental analysis was performed on multiple trace samples to confirm the content of key elements in each sample. This step involves chemical composition analysis of each acquired trace sample to accurately determine the content of key elements. Key elements typically include lithium, nickel, cobalt, manganese, iron, and phosphorus, to determine the ratio of ternary lithium materials and lithium iron phosphate materials, as well as the lithium iron phosphate formulation. Elemental analysis can be performed using various techniques, such as inductively coupled plasma optical emission spectroscopy (ICP-OES), X-ray fluorescence spectroscopy (XRF), or atomic absorption spectroscopy (AAS), with the aim of obtaining precise microscopic composition data.

[0096] Based on the key element content data, sampling location, and time information, a dynamic distribution map of the material properties and formulation changes in the mixed black powder is determined. This step combines the key element content data of each trace sample with its corresponding sampling location and time information to construct a two-dimensional or three-dimensional distribution map that reflects the dynamic changes in the material properties and formulation of the mixed black powder throughout the batch. This dynamic distribution map can intuitively show the differences in black powder composition in different regions and at different time points, such as the local enrichment areas of ternary lithium and lithium iron phosphate, and the changes in the iron and phosphorus ratio in lithium iron phosphate. Its purpose is to comprehensively understand the macroscopic homogeneity or non-homogeneity of the black powder.

[0097] Based on a dynamic distribution map of the mixed black powder, which includes variations in material properties and formulation, the material ratios and lithium iron phosphate formulation data of the mixed black powder are obtained. This step involves processing and analyzing the dynamic distribution map to extract the average material ratio (e.g., the overall ratio of ternary lithium to lithium iron phosphate) and the average lithium iron phosphate formulation data for the entire batch of mixed black powder. This may involve operations such as weighted averaging, statistical analysis, or regional integration on the data in the distribution map, the purpose of which is to provide accurate input data for subsequent recycling schemes.

[0098] This embodiment obtains trace samples of the mixed black powder at different time points and spatial locations before the hydrometallurgical pretreatment stage, and performs detailed elemental analysis on them, thereby constructing a dynamic distribution map reflecting the material properties and formulation changes of the black powder. Due to the comprehensive sampling and analysis mechanism, the acquisition of material proportions and lithium iron phosphate formulation data for the mixed black powder is more accurate and reliable.

[0099] In some embodiments of this application described above, the step of performing elemental analysis on multiple trace samples to confirm the content data of key elements in each trace sample includes:

[0100] Based on multiple trace samples, the particle size distribution, surface roughness, and compaction information of each trace sample were confirmed. Specifically, particle size distribution refers to the distribution of particle sizes in the trace sample, which affects the effective area and depth of laser-sample interaction; surface roughness refers to the unevenness of the trace sample surface, which affects laser reflection, scattering, and the uniformity of the sample excitation area; compaction information refers to the density of the trace sample after compaction, which affects the porosity and thermal conductivity of the sample, and thus affects the laser ablation and plasma excitation processes. All physical properties have a significant impact on the accuracy of spectral analysis.

[0101] Based on the particle size distribution, surface roughness, and compaction information of each trace sample, spectral acquisition parameters, including laser energy, detector integration time, and sample excitation position, are adjusted. These spectral acquisition parameters can be understood as a series of settings used to control the spectrometer's operating state during elemental analysis, specifically including laser energy, detector integration time, and sample excitation position. Adjusting the laser energy aims to ensure the sample is effectively excited without excessive ablation or oversaturation; adjusting the detector integration time aims to optimize the signal-to-noise ratio of the acquired signal; and adjusting the sample excitation position aims to achieve precise control based on sample homogeneity or the analytical needs of specific regions.

[0102] Based on the spectral acquisition parameters, the content data of key elements in each trace sample were confirmed.

[0103] Specifically, elemental analysis can be performed using laser-induced breakdown spectroscopy (LIBS). First, particle size distribution data of the trace sample is acquired using a laser diffraction particle size analyzer. Surface roughness is measured using an optical profilometer, and the compaction degree of a known volume of compacted sample is measured by gravimetric analysis. For example, if a trace sample is found to have fine particles and high surface roughness, the laser energy can be reduced accordingly to avoid excessive ablation and self-absorption effects, and the detector integration time can be appropriately increased to capture weaker signals. Conversely, if the sample compaction degree is high, the laser energy may need to be increased to ensure sufficient ablation depth. The adjusted spectral acquisition parameters are then applied to LIBS analysis to accurately confirm the content data of key elements such as lithium, iron, cobalt, nickel, manganese, and phosphorus in the trace sample.

[0104] This embodiment's approach first confirms the particle size distribution, surface roughness, and compaction information of each trace sample before performing elemental analysis, thereby gaining a comprehensive understanding of the sample's physical properties. Given that physical properties directly influence the generation and acquisition of spectral signals, based on the confirmed particle size distribution, surface roughness, and compaction information, spectral acquisition parameters, such as laser energy, detector integration time, and sample excitation location, can be specifically adjusted. Because the spectral acquisition parameters are optimized to adapt to the specific physical state of each trace sample, the subsequent spectral acquisition process can minimize interference from matrix effects, surface effects, and sample inhomogeneities, thereby ensuring higher accuracy and reliability of the acquired key element content data.

[0105] In some embodiments of this application described above, the step of determining the dynamic distribution map of material properties and formulation changes in the mixed black powder based on the key element content data, sampling location, and time information includes:

[0106] Fluctuation analysis is performed on the key element content data to identify local outliers that exceed a preset fluctuation range. Specifically, fluctuation analysis can be understood as performing statistical analysis on the changing trends of key element content data in time or space, such as by calculating moving averages, standard deviations, or using time series analysis methods, to identify data points that significantly deviate from the overall trend. The preset fluctuation range can be set based on historical experience data, process requirements, or statistical principles, for example, as the interval between the average value and three standard deviations. Data points exceeding this range are preliminarily identified as local outliers.

[0107] Cross-validation of local outliers using historical batch data helps determine whether they fall into two categories: genuine material changes and noise interference. This involves comparing identified outliers from the current batch with similar data from previous batches. For example, a historical anomaly pattern library can be established, recording different types of anomalies (such as localized enrichment of genuine materials, sensor malfunctions, and uneven sampling) and their corresponding electrochemical or physical characteristics. By comparing these patterns, it can be determined whether the current outlier is due to genuine localized material changes (e.g., a high ternary lithium content in a certain area) or simply caused by measurement noise or occasional interference. Cross-validation helps avoid misjudgments and ensures the accuracy of subsequent processing.

[0108] Based on the key element content data, local outliers of the aforementioned anomaly types are processed to obtain processed key element content data. Specifically, the processing of local outliers varies depending on the type of outlier. For example, if an outlier is determined to be noise interference, it can be corrected or removed using methods such as smoothing filtering, median filtering, or data interpolation. If an outlier is determined to be a genuine local change in the material, it may be necessary to retain the data or assign it special weight when generating a dynamic distribution map to accurately reflect the true distribution of the material. The processed key element content data is purified and corrected, and can more accurately reflect the true material properties and formulation changes of the mixed black powder.

[0109] Based on the processed key element content data, sampling location, and time information, a dynamic distribution map of the material properties and formulation changes in the mixed black powder is determined. Specifically, based on the processed key element content data, sampling location, and time information, a more accurate and reliable dynamic distribution map of the material properties and formulation changes in the mixed black powder can be constructed. This dynamic distribution map can intuitively display the content distribution of different materials (such as ternary lithium and lithium iron phosphate) and the concentration changes of formulation components (such as key elements such as nickel, cobalt, manganese, and phosphorus) in the mixed black powder, as well as the dynamic evolution of these properties at different time points and spatial locations.

[0110] Specifically, during trace elemental analysis of a batch of mixed black powder, the content of the critical element nickel at a specific sampling point (e.g., a location in the middle of the batch) suddenly showed a much higher value than the surrounding areas. Using this raw data directly might incorrectly indicate a high concentration of ternary lithium enrichment in the dynamic distribution map, thus affecting subsequent recovery strategies. According to the proposed solution, firstly, fluctuation analysis is performed on the critical element content data of all sampling points. By determining the moving average and standard deviation of the nickel content data, it was found that the nickel content at this specific sampling point exceeded a preset fluctuation range (e.g., exceeding the average plus three times the standard deviation), and was therefore identified as a local outlier. Next, this local outlier was cross-validated using historical batch data. For example, by querying a historical database, it was found that such a high nickel content had never occurred before at the same sampling location or in similar batches, and the spectral characteristics of this outlier were more consistent with typical noise signals than with true material enrichment characteristics. Therefore, this outlier was identified as a noise interference type. Subsequently, based on this anomaly type, the local outlier was processed. For example, median filtering can be used to smooth the nickel content data at this sampling point, or interpolation can be performed using the average value of its adjacent sampling points to eliminate the influence of noise. Finally, based on the processed key element content data (i.e., the corrected nickel content data), combined with sampling location and time information, a dynamic distribution map of the mixed black powder's material properties and formulation variations is generated. This dynamic distribution map will no longer display the false nickel content peak, but will more accurately reflect the true distribution of nickel in the mixed black powder, thus providing a reliable basis for determining subsequent recycling schemes.

[0111] This embodiment effectively addresses the interference of noise and outliers in the raw data on the accuracy of the dynamic distribution map by introducing a refined processing mechanism for key element content data. By performing fluctuation analysis on the key element content data, local outliers deviating from the normal range can be preliminarily identified, laying the foundation for subsequent precise processing. Secondly, cross-validation of these local outliers using historical batch data distinguishes which anomalies represent genuine material changes. This avoids erroneous smoothing or removal of genuine material changes and prevents noisy data from misleading subsequent analysis. Finally, targeted processing measures are taken based on the specific type of outlier, such as filtering noise or specially marking genuine material changes, resulting in purer and more accurate key element content data. Due to this layered and intelligent anomaly handling mechanism, the final generated dynamic distribution map of the mixed black powder more realistically and accurately reflects the actual distribution and changes of the material.

[0112] In some embodiments of this application described above, the step of correcting the black powder ratio estimation model using the material state of the current batch of mixed black powder to obtain a corrected black powder ratio estimation model includes:

[0113] The material state of the current batch of mixed black powder is verified to obtain a qualified material state. Verification of the material state of the current batch of mixed black powder can be understood as a secondary verification or reliability assessment of the already determined material state. Specifically, the verification process may include, but is not limited to: comparing the material state of the current batch with that of similar materials in historical batches, verifying it against preset quality control standards, or cross-validating it by introducing additional independent measurement methods. Its purpose is to identify and eliminate potentially inaccurate or unreliable material state data caused by abnormal data acquisition, environmental interference, or minor differences between material batches. Verification ensures that the material state data used for subsequent model correction has been rigorously screened and confirmed, thereby improving the quality of data input.

[0114] By using the material state of the qualified mixed black powder, the black powder ratio estimation model is corrected to obtain the corrected black powder ratio estimation model.

[0115] Specifically, during the processing of mixed black powder, its material state is initially determined using electromagnetic property data. However, slight vibrations during transportation may cause minor changes in local bulk density, potentially affecting the accuracy of the electromagnetic property data and introducing uncertainty into the initially determined material state. This application employs the following verification method: comparing the initially determined material state with the historical production records of the batch of black powder and the typical material state range of similar materials. For example, if a key dielectric response parameter deviates from the historical average by more than a preset threshold, a warning will be issued. Further, additional auxiliary tests such as local bulk density measurements or X-ray fluorescence spectroscopy analysis can be performed on the batch of black powder to obtain independent verification data. If the auxiliary test results differ significantly from the initially determined material state, it indicates a potential error in the initial material state. Through verification, if the initially determined material state is found to be abnormal or does not meet preset standards, it can be corrected or re-evaluated until a qualified mixed black powder material state is obtained. For example, if the verification result indicates an abnormality in a dielectric response data point, the abnormal value can be corrected based on the auxiliary test data, or, if the data is confirmed to be unreliable, it can be marked as invalid and a re-measurement triggered. Only after the material condition has been confirmed to be accurate and reliable is it used to correct the black powder ratio estimation model. This ensures the quality of the input data for model correction, avoids deviations in the recycling scheme caused by inaccurate data, and thus improves recycling efficiency and economic benefits.

[0116] This embodiment effectively improves the robustness and accuracy of the entire recycling optimization method by adding a material state verification step before correcting the black powder ratio estimation model using the material state of the mixed black powder. Specifically, after the material state of the mixed black powder is determined, it is not immediately used for model correction, but is first sent to the verification process. During this verification process, any factors that may cause distortion of the material state data, such as sensor drift, instantaneous environmental disturbances, or local anomalies in the material, can be detected and dealt with in a timely manner. Because the material state is rigorously verified, the material state data input into the black powder ratio estimation model is ensured to be highly reliable and accurate, enabling the model to be corrected based on real and valid material information. This avoids model deviations caused by inaccurate data and provides a solid data foundation for the subsequent determination of the recycling scheme.

[0117] Based on any of the above embodiments, please refer to the optimized method for the mixed recycling of ternary lithium and lithium iron phosphate. Figure 2 The present invention also provides a mixed recycling optimization system for ternary lithium and lithium iron phosphate, the system comprising a data acquisition module 210, a state comparison module 220, a model correction module 230, and a scheme determination module 240.

[0118] The data acquisition module 210 is used to acquire electromagnetic characteristic set data, including dielectric response data and conductivity response data, of the current batch of mixed black powder of ternary lithium and lithium iron phosphate under different frequency electromagnetic waves before the current batch of mixed black powder enters the hydrometallurgical pretreatment stage.

[0119] The state comparison module 220 is used to compare the electromagnetic property set data with the material property reference library to determine the material state of the current batch of mixed black powder.

[0120] The model correction module 230 is used to correct the black powder ratio estimation model by utilizing the material state of the mixed black powder in the current batch, so as to obtain the corrected black powder ratio estimation model.

[0121] The scheme determination module 240 is used to determine the recovery scheme for the current batch of mixed black powder based on the modified black powder ratio estimation model.

[0122] In this embodiment, the data acquisition module 210 acquires electromagnetic characteristic data of the mixed black powder under different frequencies of electromagnetic waves, and the state comparison module 220 compares the data with a material characteristic reference library to accurately identify the material state of the current batch of mixed black powder. This allows the system to effectively identify subtle changes in the lithium iron phosphate formulation. Once the material state is accurately identified, the model correction module 230 can use this material state to specifically correct the black powder ratio estimation model, thereby eliminating estimation biases introduced by changes in the material formulation. Therefore, the corrected model can output more accurate material ratio estimates. Finally, the scheme determination module 240 determines the recovery scheme for the current batch of mixed black powder based on the corrected black powder ratio estimation model. Because the accuracy of the estimated ratio is significantly improved, the determined recovery scheme (e.g., acid concentration and reaction temperature in the hydrometallurgical leaching stage) will be more precise, thus avoiding deviations in process parameter settings caused by incorrect estimations.

[0123] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention specification.

Claims

1. An optimized method for the mixed recovery of ternary lithium and lithium iron phosphate, characterized in that, Includes the following steps: Before the current batch of mixed black powder of ternary lithium and lithium iron phosphate enters the hydrometallurgical pretreatment stage, acquire the electromagnetic property set data of the current batch of mixed black powder under electromagnetic waves of different frequencies, including dielectric response data and conductivity response data. The electromagnetic property set data is compared with the material property reference library to determine the material state of the current batch of mixed black powder; By using the material state of the current batch of mixed black powder, the black powder ratio estimation model is corrected to obtain the corrected black powder ratio estimation model. Based on the revised black powder ratio estimation model, a recovery plan for the mixed black powder in the current batch is determined.

2. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 1, characterized in that, The step of obtaining electromagnetic property set data, including dielectric response data and conductivity response data, of the current batch of mixed lithium iron phosphate black powder under different frequency electromagnetic waves before the current batch of mixed black powder enters the hydrometallurgical pretreatment stage includes: Before the current batch of mixed black powder of ternary lithium and lithium iron phosphate enters the hydrometallurgical pretreatment stage, local bulk density data and initial data of electromagnetic properties including dielectric response data and conductivity response data of the current batch of mixed black powder are obtained. The initial data of the electromagnetic property set is corrected using the local packing density data to obtain the electromagnetic property set data.

3. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 2, characterized in that, The steps for correcting the initial data of the electromagnetic property set using the local packing density data to obtain the electromagnetic property set data include: The local packing density data is processed by frequency segmentation to obtain multiple frequency sub-interval data. Determine the sensitivity between each frequency sub-interval data and the corresponding frequency in the initial data of the electromagnetic property set; The preset correction factor is adjusted using each of the aforementioned sensitivities to obtain the surface effect stripping factor for each frequency; The initial data of the electromagnetic property set is corrected using the surface effect stripping factor for each frequency to obtain the electromagnetic property set data.

4. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 1, characterized in that, The steps for determining the material state of the current batch of mixed black powder by comparing the electromagnetic property set data with the material property reference library include: Identify the key electrical parameters in each frequency sub-interval within the electromagnetic property set data; The key electrical parameters of each frequency sub-range are compared with the material property reference library to obtain the parameter comparison results; If the parameter comparison results indicate that there are key electrical parameters in the material property reference library, then the material state of the current batch of mixed black powder is determined. If the parameter comparison results indicate that the key electrical parameters are not found in the material property reference library, an early warning will be issued.

5. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 1, characterized in that, After determining the recovery plan for the current batch of mixed black powder based on the modified black powder ratio estimation model, the method further includes: The current batch of mixed black powder is sampled and analyzed to obtain the material ratio and lithium iron phosphate formulation data of the mixed black powder; The material ratios and lithium iron phosphate formulation data were matched with the recovery scheme for the current batch of mixed black powder to obtain the data matching results. If the data matching results indicate that the difference index is met, then the recovery plan for the mixed black powder in the current batch will be executed; If the data matching results indicate that the discrepancy index is not met, a solution warning will be issued.

6. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 5, characterized in that, The steps for sampling and analyzing the current batch of mixed black powder to obtain the material ratio and lithium iron phosphate formulation data of the mixed black powder include: Obtain multiple trace samples, sampling locations, and time information at different time points and spatial locations of the current batch of mixed black powder before it enters the hydrometallurgical pretreatment stage; Elemental analysis was performed on multiple trace samples to confirm the content data of key elements in each trace sample; Based on the key element content data, sampling location and time information, a dynamic distribution map of the material properties and formulation changes in the mixed black powder was determined; Based on the dynamic distribution map of the mixed black powder including material properties and formulation changes, the material ratio and lithium iron phosphate formulation data of the mixed black powder are obtained.

7. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 6, characterized in that, The steps of performing elemental analysis on multiple trace samples to confirm the content data of key elements in each trace sample include: Based on multiple trace samples, the particle size distribution, surface roughness, and compaction information of each trace sample were confirmed. Based on the particle size distribution, surface roughness, and compaction information of each trace sample, the spectral acquisition parameters, including laser energy, detector integration time, and sample excitation position, are adjusted. Based on the spectral acquisition parameters, the content data of key elements in each trace sample were confirmed.

8. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 6, characterized in that, Based on the key element content data, sampling location, and time information, the steps for determining the dynamic distribution map of material properties and formulation changes in the mixed black powder include: Fluctuation analysis was performed on the content data of the key elements to identify local outlier points that exceeded the preset fluctuation range; By cross-validating local outlier points using historical batch data, it can be determined that the local outlier points include anomalies of real material changes and noise interference. Based on the key element content data, the local outlier points of the anomaly type are processed to obtain the processed key element content data. Based on the processed key element content data, sampling location and time information, a dynamic distribution map of the material properties and formulation changes in the mixed black powder was determined.

9. The optimized method for the mixed recovery of ternary lithium and lithium iron phosphate according to claim 1, characterized in that, The step of correcting the black powder ratio estimation model by utilizing the material state of the current batch of mixed black powder to obtain the corrected black powder ratio estimation model includes: The material state of the current batch of mixed black powder is verified to obtain the material state of the mixed black powder that has passed the verification. By using the material state of the qualified mixed black powder, the black powder ratio estimation model is corrected to obtain the corrected black powder ratio estimation model.

10. An optimized recycling system for ternary lithium and lithium iron phosphate mixtures, characterized in that, The system includes: The data acquisition module is used to acquire electromagnetic property set data, including dielectric response data and conductivity response data, of the current batch of mixed black powder of ternary lithium and lithium iron phosphate under different frequency electromagnetic waves before the current batch of mixed black powder enters the hydrometallurgical pretreatment stage. The state comparison module is used to compare the electromagnetic property set data with the material property reference library to determine the material state of the current batch of mixed black powder. The model correction module is used to correct the black powder ratio estimation model by utilizing the material state of the mixed black powder in the current batch, and obtain the corrected black powder ratio estimation model. The scheme determination module is used to determine the recovery scheme for the current batch of mixed black powder based on the modified black powder ratio estimation model.

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