Method for selectively leaching valuable metals from spent lithium battery cathode material

Abstract

This application provides a method for selective leaching of valuable metals from cathode materials of spent lithium-ion batteries. By analyzing the microstructure of the particles using scanning electron microscopy, a three-dimensional model is constructed to simulate the acid leaching dissociation behavior. Combined with real-time concentration monitoring and sequence modeling to predict retention risk areas, the acid permeation path and leaching parameters are dynamically adjusted to form a stepwise leaching control scheme, ultimately achieving selective release of cobalt first, then nickel, and finally manganese. The core innovation of this invention lies in combining microstructure analysis with numerical simulation and real-time feedback to precisely control the leaching process, significantly improving the separation efficiency of valuable metals and reducing impurity interference, providing systematic and efficient technical support for the resource recycling of spent lithium-ion batteries.

Description

Technical Field

[0001] This invention relates to the field of lithium battery recycling technology, and in particular to a method for selective leaching of valuable metals from the cathode material of waste lithium batteries. Background Technology

[0002] The recycling of cathode materials from spent lithium-ion batteries is a crucial area for promoting the circular economy of new energy batteries. Its core lies in the efficient separation and extraction of valuable metals such as cobalt, nickel, and manganese. These metals directly determine the economic value of the recycled materials and the efficiency of resource reuse; therefore, achieving their efficient extraction has become a key focus for the industry.

[0003] Current acid leaching methods typically involve immersing the waste cathode material entirely in an acid solution. However, due to the complex internal structure of cathode material particles, the distribution and bonding states of different metal elements within the particles vary significantly, leading to inconsistent release sequences and rates under acidic conditions. Cobalt tends to preferentially dissolve from the outer layer of the particles or specific crystal defects, while nickel and manganese are released more slowly due to deeper encapsulation or lattice stability. This uneven dissociation behavior makes it difficult to control the order of metal release in traditional one-time leaching. This can easily result in nickel and manganese beginning to dissolve in large quantities before cobalt has been fully leached, or excessive impurities contaminating the solution in the later stages of cobalt leaching, leading to decreased separation purity.

[0004] More importantly, the difference in dissociation sequence caused by this microstructure directly affects the precise control of the leaching process. Different regions on the particle surface and inside have different resistance to acid penetration. Acid tends to preferentially penetrate rapidly into certain regions along paths with less resistance, while other regions encased in dense structures penetrate slowly or even remain confined. As a result, after leaching has progressed to a certain stage, cobalt in some regions has dissolved in large quantities, but nickel and manganese in other regions have not yet been released due to insufficient contact with the acid. At the same time, the dissolved cobalt may redeposit or co-precipitate with the later-released nickel and manganese in the solution due to subsequent changes in acid concentration. This interaction between the asynchronous release and uneven local acid penetration makes selective separation between metals extremely difficult, resulting in an imbalance in the proportion of target metals in the leachate, increased impurity content, and a heavier burden on subsequent purification steps.

[0005] Therefore, how to overcome the contradiction between the uneven metal dissociation sequence caused by the differences in the microstructure of the cathode material and the uneven acid penetration path during the acid leaching process, and achieve the gradual release of cobalt, nickel and manganese in the expected order, has become a key problem that urgently needs to be solved for the selective leaching of valuable metals from waste lithium battery cathode materials. Summary of the Invention

[0006] To address the technical problems mentioned in the background section, this invention provides a method for selective leaching of valuable metals from waste lithium-ion battery cathode materials, comprising: S1, acquiring a microstructure image of the waste lithium-ion battery cathode material, and determining the spatial distribution characteristics of the cobalt-rich surface region and the nickel-manganese-rich inner region based on the microstructure image; S2, constructing a three-dimensional model of the particles, simulating the dissociation behavior of each metal component under acid leaching conditions, and obtaining the initial dissociation sequence and rate differences; S3, determining whether it is necessary to regulate the dissociation priority based on the initial dissociation sequence and rate differences, and if so, generating a dissociation priority sequence of cobalt preferential release and nickel-manganese delayed release based on the metal bond characteristics of the cobalt-rich surface region and the nickel-manganese-rich inner region; S4, during the acid leaching process... S5. Obtain real-time concentration data of each metal ion in the leachate, and predict the risk area of ​​nickel and manganese component retention in subsequent leaching stages based on the real-time concentration data and the dissociation priority sequence; S6. Adjust the penetration path of acid inside the particles to optimize the leaching parameters and obtain an optimized stepwise leaching control scheme; S7. Simulate the concentration changes of each metal ion and impurity ion during the leaching process, and make feedback corrections to the leaching conditions for the next batch based on the concentration changes; S8. Integrate the spatial distribution characteristics, the three-dimensional model of the particles, the initial dissociation sequence, the dissociation priority sequence, the risk area of ​​retention, the optimized stepwise leaching control scheme, and the feedback correction conditions to form a complete selective leaching process flow.

[0007] Optionally, step S1 includes: step S11, dividing the microstructure image into regions using a pixel-level segmentation model; step S12, extracting the area ratio, number of layers, and nesting relationship of each region after segmentation; and step S13, determining the spatial distribution characteristics of the surface cobalt-rich region and the inner nickel-manganese-rich region based on the area ratio, number of layers, and nesting relationship.

[0008] Optionally, step S2 includes: step S21, importing the three-dimensional model of the particles into the finite element analysis environment; step S22, setting the boundary conditions and material property parameters of the acid leaching system; step S23, calculating the dissociation time series of each metal bond under acid leaching conditions through the finite element analysis environment; and step S24, determining the initial dissociation sequence of cobalt, nickel, and manganese and the rate differences at each stage based on the dissociation time series.

[0009] Optionally, step S3 includes: step S31, extracting the bond energy characteristics and coordination environment parameters of the surface cobalt-rich region and the inner nickel-manganese-rich region; step S32, classifying the inner and outer regions according to the bond energy characteristics and coordination environment parameters; and step S33, generating a dissociation priority sequence based on the classification results and the initial dissociation order, with cobalt being the first to be released, followed by nickel, and then manganese.

[0010] Optionally, step S4 includes: step S41, using the real-time concentration data, current leaching time, and released proportion as input sequences; step S42, processing the input sequences through a sequence modeling network; step S43, outputting the leaching probability distribution of nickel and manganese components in each region within a future time window; and step S44, determining the spatial location of the risk area for nickel and manganese component retention based on the leaching probability distribution.

[0011] Optionally, step S5 includes: step S51, determining the local area that needs enhanced penetration based on the spatial coordinates of the risk area; step S52, increasing the acid flow rate allocation weight of the local area; step S53, reducing the acid allocation ratio of the non-risk area; and step S54, replanning the acid penetration path inside the particles based on the adjusted allocation ratio.

[0012] Optionally, step S6 includes: step S61, when the simulation shows that the concentration of impurity ions in a certain leaching stage exceeds the preset range, shortening the duration of the stage or reducing the acid concentration in the stage; step S62, when the target metal shows a recovery rate lower than the set value in the simulation, increasing the number of subsequent leaching stages or increasing the temperature of the corresponding stage; step S63, regenerating the stepwise leaching control scheme for the next batch based on the corrected parameters.

[0013] Optionally, step S7 includes: step S71, establishing the temporal and spatial correspondence between the output data of each step; step S72, organizing the output data of each step into a unified process control parameter table through data fusion; and step S73, generating an executable selective leaching process flow file based on the process control parameter table.

[0014] The technical solution provided by this invention has the following beneficial effects:

[0015] This invention discloses a method for selective leaching of valuable metals from waste lithium-ion battery cathode materials. Addressing the challenge of achieving efficient selective separation of valuable metals such as cobalt, nickel, and manganese in waste lithium-ion battery cathode materials due to uneven dissociation order and rate caused by differences in microstructure, this invention proposes a logically coherent solution. By analyzing the particle microstructure using scanning electron microscopy, this invention constructs a three-dimensional model to simulate acid leaching dissociation behavior. Combined with real-time concentration monitoring and sequence modeling to predict retention risk areas, it dynamically adjusts the acid penetration path and leaching parameters to form a step-by-step leaching control scheme, ultimately achieving selective release of cobalt first, followed by nickel, and then manganese. The core innovation of this invention lies in combining microstructure analysis with numerical simulation and real-time feedback to precisely control the leaching process, significantly improving the separation efficiency of valuable metals and reducing impurity interference, providing systematic and efficient technical support for the resource recycling of waste lithium-ion batteries. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for selective leaching of valuable metals from waste lithium battery cathode materials according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0018] like Figure 1 As shown, this invention provides a method for selective leaching of valuable metals from waste lithium-ion battery cathode materials. Specifically, it may include: S1, acquiring a microstructure image of the waste lithium-ion battery cathode material, and determining the spatial distribution characteristics of the cobalt-rich surface region and the nickel-manganese-rich inner region based on the microstructure image. Waste lithium-ion battery cathode materials typically refer to nickel-cobalt-manganese-lithium oxide particulate materials obtained from the dismantling and recycling of retired power batteries or consumer batteries after multiple charge-discharge cycles. During long-term use, these particles often form core-shell structures or gradient distribution structures of varying degrees due to electrochemical reactions, phase transitions, and the dissolution and redeposition of transition metals. The surface region is usually enriched in cobalt, while the inner region is relatively enriched in nickel and manganese. This difference in elemental distribution directly affects the release sequence and rate of each metal during subsequent acid leaching. Specifically, the cathode material powder, after pretreatment (i.e., crushing, sorting, calcination, or mechanochemical activation), is first uniformly dispersed on a conductive adhesive to prepare a sample suitable for scanning electron microscopy observation. High-resolution microstructure images are acquired using backscattered electron imaging mode or a surface scanning mode combined with energy dispersive spectroscopy. In these images, brighter regions typically correspond to enriched areas of elements with higher average atomic numbers. Since cobalt has a higher atomic number than nickel and manganese, cobalt-rich surface regions often exhibit brighter ring-like or shell-like features in backscattered images. In one embodiment, the acquired microstructure images are preprocessed, including contrast stretching, noise filtering, and edge enhancement, to improve the accuracy of subsequent segmentation. Then, a pixel-level segmentation model is used to divide the image into regions. This pixel-level segmentation model can be a semantic segmentation network trained on a convolutional neural network, supervised by a large dataset of labeled microscopic images of waste cathode materials. It can effectively distinguish between cobalt-rich shells, nickel-manganese-rich cores, and potentially present cracks, voids, and impurities.

[0019] Optionally, this step may further include: step S11, dividing the microstructure image into regions using a pixel-level segmentation model.

[0020] After segmentation, each pixel is assigned a category label. For example, category 1 represents the surface cobalt-rich region, category 2 represents the inner nickel-manganese-rich region, category 3 represents the porous or cracked region, and category 0 represents the background. Step S12: Extract the area ratio, number of layers, and nesting relationship of each segmented region.

[0021] The area ratio is obtained by statistically analyzing the percentage of each type of pixel in the total particle projection area. The number of layers is determined by analyzing the inclusion relationship of connected components. For example, if the cobalt-rich region completely encloses the inner nickel-manganese-rich region, it is determined to be a single-layer core-shell structure; if there are multiple rings with gradually changing brightness, it may be determined to be a multi-layer gradient structure. The nesting relationship records which regions are contained by which regions. For example, the inner nickel-manganese-rich region is completely contained by the surface cobalt-rich region, or some regions have exposed nickel-manganese-rich cores. Step S13: Determine the spatial distribution characteristics of the surface cobalt-rich region and the inner nickel-manganese-rich region based on the area ratio, number of layers, and nesting relationship.

[0022] In one possible implementation, if the surface cobalt-rich region accounts for more than 25% of the total area and exhibits a complete shell, it is classified as a strong core-shell distribution; if the surface cobalt-rich region accounts for less than 12% of the total area and exhibits a patchy, discontinuous distribution, it is classified as a weakly enriched distribution; if multiple nested layers exist simultaneously and the thickness is uneven, it is further labeled as a gradient-inhomogeneous distribution. These spatial distribution characteristics will directly serve as important prior knowledge for subsequent 3D particle reconstruction. It should be noted that different batches of waste batteries exhibit significant differences in microstructure due to variations in operating conditions, cycle count, and cathode formulation. For example, particles obtained from retired ternary batteries with high-nickel systems may have a thinner surface cobalt-rich region and a lower degree of cobalt enrichment, while particles recovered from early batteries with a more balanced nickel-cobalt-manganese ratio often have a thicker and more continuous surface cobalt-rich layer. Therefore, in practice, it is recommended to collect microscopic images of at least 20-50 typical particles from the same batch of material, perform statistical analysis, and then use representative distribution characteristics for subsequent modeling to improve the universality of the process. S2. Construct a three-dimensional model of the particles to simulate the dissociation behavior of each metal component under acid leaching conditions, obtaining the initial dissociation sequence and rate differences. The purpose of constructing the three-dimensional particle model is to extrapolate the spatial distribution information obtained from the two-dimensional microscopic images to the actual three-dimensional internal structure of the particles, providing a geometric basis for subsequent numerical simulations of acid penetration and metal dissociation. In one embodiment, firstly, based on multiple two-dimensional cross-sectional images of the particles obtained in step S1, a three-dimensional stereoscopic model is generated using stereoscopic microscopic reconstruction technology or statistical inversion methods. Specifically, multiple backscattered images taken at different tilt angles of the same particle can be selected, or statistical learning can be performed on the cross-sections of multiple similar particles to infer the probability distribution of parameters such as the thickness of the cobalt-rich shell, the core size, and the interface curvature in three-dimensional space. Then, the particles are discretized into a three-dimensional mesh using a voxelization method. Each voxel is assigned corresponding material properties according to its region, for example, the cobalt-rich region is labeled as the high-cobalt phase, and the inner layer is labeled as the high-nickel-manganese phase. At the same time, the approximate local chemical composition of each voxel is recorded. In another possible implementation, when only a small number of high-quality cross-sectional images are available, a parametric modeling strategy can be used. For example, assuming the particles are approximately spherical or ellipsoidal, the overall equivalent diameter is first determined. Then, the shell thickness and core radius are deduced based on the area ratio. A certain degree of random perturbation is then introduced to simulate the irregularity and local thickness fluctuations of real particles, thereby generating multiple representative three-dimensional digital twins of the particles. Once the three-dimensional particle model is obtained, the physicochemical behavior simulation of the acid leaching process can be carried out.

[0023] Optionally, this step also includes: step S21, importing the particle three-dimensional model into the finite element analysis environment.

[0024] The finite element analysis environment needs to support multiphysics coupling calculations, and be able to simultaneously consider the seepage of acid in porous media, diffusion of chemical components, interfacial chemical reactions, and solid-phase dissolution kinetics. Step S22: Set the boundary conditions and material property parameters of the acid leaching system.

[0025] Boundary conditions include the initial concentration and temperature of the acid in the leaching container, the convective mass transfer coefficient corresponding to the stirring intensity, and the contact conditions between the outer surface of the particles and the acid. Material property parameters include the porosity and permeability of each phase, the reaction rate constants, activation energies, and dissolution equilibrium constants of different metal-oxygen bonds in a given acidic environment. These parameters can be derived partly from experimental data reported in the literature and partly from fitting small-scale leaching experiments. Step S23 involves calculating the dissociation time series of each metal bond under acid leaching conditions using the finite element analysis environment.

[0026] During the calculation, the model tracks the change curves of the dissolution fractions of target metals such as cobalt, nickel, and manganese within each voxel over time, while simultaneously recording the position of the reaction front at key interfaces. By statistically analyzing a large number of voxels, the trends of the cumulative dissolution percentages of cobalt, nickel, and manganese over time can be obtained. Step S24: Based on the dissociation time series, the initial dissociation sequence of cobalt, nickel, and manganese and the rate differences at each stage are determined.

[0027] For example, in a typical simulation, under conditions of 2 mol / L sulfuric acid, 60°C, and a liquid-to-solid ratio of 10:1, the cobalt dissolution fraction in the cobalt-rich surface region reached approximately 65% ​​within the first 15 minutes, while the dissolution fractions of nickel and manganese in the core were only 8% and 5%, respectively. In the subsequent 30-60 minute phase, cobalt continued to be released slowly, while nickel began to dissolve at an accelerated rate, and manganese maintained a low rate until the manganese dissolution rate significantly increased after 90 minutes. This time-dissolution fraction curve clearly shows the initial dissociation order: cobalt first, then nickel, and finally manganese, with significant differences in the average dissolution rate at each stage. In another embodiment, when the particles exhibit a thicker cobalt-rich shell and a higher core density, the simulation results may show that cobalt is released rapidly in the first 20 minutes before entering a plateau phase, while nickel and manganese show almost no significant dissolution during this stage, only beginning to be released in large quantities after the acid gradually penetrates to the core interface through diffusion. In this case, the initial dissolution order is still cobalt-first, but the lag time for nickel and manganese is longer, and the rate difference is more significant. This difference suggests that stronger kinetic control methods are needed to maintain selectivity in actual processes. S3: Based on the initial dissociation sequence and rate differences, determine whether dissociation priority needs to be controlled; if so, generate a dissociation priority sequence based on the metal bond characteristics of the surface cobalt-rich region and the inner nickel-manganese-rich region, with cobalt releasing preferentially and nickel and manganese releasing later. Not all waste cathode materials naturally possess sufficient dissociation selectivity. When simulation results show that the difference in release rates of cobalt, nickel, and manganese in the initial stage of acid leaching is less than a threshold, for example, the difference in the dissolution fraction of the three is less than 15% within the first 30 minutes, or nickel and manganese show obvious co-dissolution before cobalt release is complete, it is considered that the initial dissociation sequence has insufficient selectivity and requires active control of the dissociation priority. The basis for determining whether control is needed mainly comes from the dissociation time series curves obtained in step S44, quantified by comparing the separation degree, crossover time points, and slope differences in the early stages of each metal dissolution curve.

[0028] In one embodiment, if the dissolution rate of cobalt exceeds 70% within the first 30% of the total leaching time, while the dissolution rates of nickel and manganese are both below 20% during the same period, the natural selectivity is considered good, and the initial dissociation sequence can be directly adopted. Conversely, if the dissolution rates of the three are relatively close within the first 30% of the time, for example, cobalt 45%, nickel 32%, and manganese 28%, it is determined that an additional priority control strategy needs to be introduced. When it is determined that control is needed, the metallic bond characteristics of the surface cobalt-rich region and the inner nickel-manganese-rich region need to be further analyzed.

[0029] Optionally, this step also includes: step S31, extracting the bond energy characteristics and coordination environment parameters of the surface cobalt-rich region and the inner nickel-manganese-rich region.

[0030] Bond energy characteristics mainly refer to the average bond energy and bond length distribution of metal-oxygen bonds, while coordination environment parameters include the average coordination number of transition metals, the degree of oxygen octahedral distortion, and the degree of local symmetry breaking. These parameters can be obtained through first-principles calculations combined with experimental characterization data, such as using X-ray absorption fine structure spectroscopy to infer the coordination number and bond length, and using density functional theory to calculate the bond dissociation energy under different chemical environments. Step S32: Classify the stability of the inner and outer regions based on the aforementioned bond energy characteristics and coordination environment parameters.

[0031] Under acidic conditions, regions with lower bond energies and less stable coordination environments are more prone to proton attack and metal ion desorption. For example, in the surface cobalt-rich region, due to long-term cycling, some cobalt is in a higher oxidation state or has more defects, resulting in a relatively low average bond energy of the cobalt-oxygen bond and greater octahedral coordination distortion, thus leading to poor stability. In contrast, the inner nickel-manganese-rich region has a higher nickel-manganese ratio and a relatively complete structure, resulting in stronger overall metal-oxygen bonds and higher stability. By setting threshold ranges for bond energy and coordination distortion, different regions within the particle can be divided into high-stability, medium-stability, and low-stability regions. Step S33: Based on the classification results and the initial dissociation order, a dissociation priority sequence is generated, with cobalt being the first to release, followed by nickel, and manganese being the last.

[0032] In one embodiment, combining bond stability classification with the initial time series obtained from simulation, the dissociation priority is explicitly defined as follows: the first priority is the low-stability cobalt-rich phase on the surface, the second priority is the metastable phase dominated by nickel in the transition zone, and the third priority is the high-stability manganese-rich phase in the core. This priority sequence respects the inherent bond strength differences in the material and provides a clear control target for subsequent leaching path optimization through explicit ordering. In another possible implementation, when there is a significant multilayer structure inside the particles, the priority sequence can be further refined. For example, the high-defect cobalt phase on the outermost layer can be listed as the highest priority, the low-defect cobalt phase on the second outermost layer as the second highest priority, the nickel-rich region on the outer edge of the core as the third priority, and the manganese-rich region in the center of the core as the lowest priority, thus forming a more refined graded release sequence. S4, during acid leaching, real-time concentration data of each metal ion in the leachate is obtained, and the risk areas of nickel and manganese component retention in subsequent leaching stages are predicted based on the real-time concentration data and the dissociation priority sequence. In actual acid leaching operations, the change in the concentration of metal ions in the leachate is an important indicator reflecting the metal dissociation behavior inside the particles. By monitoring these concentration data in real time, the release progress of target metals such as cobalt, nickel, and manganese can be dynamically tracked. Combined with the dissociation priority sequence generated in the preceding steps, it can be determined whether there are deviations from the expected selective release. Real-time concentration data acquisition typically relies on online analytical instruments, such as plasma atomic emission spectrometry (PISA) or UV-Vis spectrophotometers, to continuously sample and analyze the leachate, recording the concentration values ​​of each metal ion at regular time intervals. Specifically, an automatic sampling device is installed in the acid leaching reaction tank, extracting a small amount of leachate sample every 5 or 10 minutes and sending it to the analytical instrument for rapid determination of the concentrations of metal ions such as cobalt, nickel, and manganese. Simultaneously, process parameters such as leaching time, acid replenishment volume, and stirring rate at the sampling time are recorded to facilitate subsequent analysis of the correlation between concentration changes and process conditions. The obtained concentration data is typically stored in time series format. For example, at the 10th minute after leaching begins, the cobalt ion concentration is 0.8 g / L, the nickel ion concentration is 0.2 g / L, and the manganese ion concentration is 0.1 g / L; at the 20th minute, the cobalt ion concentration rises to 1.5 g / L, the nickel ion concentration is 0.4 g / L, and the manganese ion concentration is 0.15 g / L. These data reflect the dynamic process of metal release. In one embodiment, the real-time concentration data is compared and analyzed with the dissociation priority sequence generated in step S3. The dissociation priority sequence clarifies the ideal release order: cobalt is released first, followed by nickel, and manganese is released last. Therefore, if the rate of increase in cobalt ion concentration in the real-time concentration data is significantly higher than that of nickel and manganese, and the increase in nickel ion concentration occurs earlier than that of manganese ions, the current leaching process is considered to meet the expected selectivity. Conversely, if the nickel or manganese ion concentration rises rapidly before the expected proportion of cobalt release, it indicates the existence of an unexpected co-dissolution phenomenon, requiring further prediction of potential retention risks in subsequent stages.

[0033] Optionally, this step further includes: Step S41, using the real-time concentration data, current leaching time, and released percentage as input sequences. The released percentage is calculated by comparing the real-time concentration value with the theoretical total soluble metal content. For example, if the total cobalt content in the particles is 10 g / L, and the current real-time concentration of cobalt ions in the leachate is 5 g / L, then the released percentage of cobalt is 50%. These input sequences comprehensively reflect the current progress of the leaching process. Step S42, processing the input sequences using a sequence modeling network.

[0034] Sequence modeling networks are predictive tools based on time series analysis, whose core lies in capturing the temporal patterns and trends of concentration changes. Specifically, this network extracts features from historical concentration data, learns the growth patterns of each metal ion concentration over time, and, combined with the current leaching time and the proportion already released, infers the release behavior of each metal over a future period. The network's training data comes from a large number of historical leaching experimental records, ensuring its predictive ability is applicable to different particle structures and process conditions. Step S43 outputs the probability distribution of nickel and manganese component dissolution in each region within the future time window.

[0035] The future time window is typically set to the following 30 minutes or 1 hour, and the dissolution probability distribution indicates the likelihood of nickel and manganese components being dissolved at different spatial locations within the particle during this time period. For example, the prediction result may show that the nickel dissolution probability in the core center region of the particle is only 20%, while the nickel dissolution probability near the outer edge of the core is 60%, and the manganese dissolution probability in all regions is less than 30%. Step S44: Determine the spatial location of the risk area for nickel and manganese component retention based on the dissolution probability distribution. Regions with a dissolution probability below the dissolution probability threshold, such as 40%, are marked as high-risk retention areas because the nickel and manganese components in these regions may be difficult to release effectively in subsequent leaching stages. The spatial location is clearly marked by corresponding to the voxel grid of the particle's three-dimensional model, indicating the specific coordinate range of the high-risk area. For example, the spherical center region with a depth of 70% to 90% of the particle radius is marked as a high-risk area for nickel and manganese retention. In one possible implementation, the dissolution probability threshold and time window length are adjusted according to the particle characteristics of different batches of waste cathode materials. For example, for particles with a thick cobalt-rich outer shell and a dense core, the leaching probability threshold can be reduced to 30% to identify potential retention areas earlier; while for particles with a loose structure and a more exposed core, the leaching probability threshold can be increased to 50% to avoid excessive intervention in the leaching process. This flexible adjustment helps improve the targeting and practicality of the prediction. S5, the acid penetration path within the particles is adjusted to optimize the leaching parameters, resulting in an optimized stepwise leaching control scheme. After identifying high-risk areas for nickel-manganese component retention, targeted measures need to be taken to enhance the acid's penetration into these areas to promote the release of retained metals. The adjustment of the acid penetration path is mainly achieved by changing the flow distribution of the acid within the reaction tank and the diffusion conditions within the particles.

[0036] Optionally, this step also includes: step S51, determining the local area requiring enhanced penetration based on the spatial coordinates of the lingering risk area.

[0037] The spatial coordinates are derived from the high-risk areas marked in step S44. For example, if the high-risk area is located at the center of the particle's core, then the ability of the acid to penetrate deep into the particle needs to be enhanced; if the high-risk area is located in an asymmetrical core region on one side of the particle, then the acid needs to be directed to that side. Step S52: Increase the acid flow rate allocation weight in the local area.

[0038] By adjusting the local rotation speed of the agitator in the reaction tank or adding a directional jet device, the flow velocity of the acid near the high-risk area is increased, thereby enhancing the scouring and mass transfer effect on that area. For example, the rotation speed of the agitator blades at the bottom of the reaction tank near the projection position of the high-risk area is increased by 20%, or the acid is directionally sprayed into that area through a micro-nozzle. Step S53: Reduce the acid distribution ratio in the non-risk area.

[0039] For the surface area where cobalt has been fully released or the outer area with a high probability of dissolution, the acid flow rate and contact time should be appropriately reduced to avoid excessive leaching leading to increased dissolution of impurity ions. For example, the stirring intensity corresponding to the non-risk area can be reduced by 10%, or the acid supply to that area can be reduced by using a layered distribution device. Step S54: The acid penetration path inside the particles is replanned according to the adjusted distribution ratio.

[0040] The redesigned path prioritizes high-risk areas for acid penetration, ensuring that the acid preferentially passes through the pore and crack networks of these areas to gradually dissolve the retained nickel and manganese components. For example, the preferential penetration channel of acid from the particle surface to the high-risk core region is simulated in the particle's three-dimensional model. Combined with the flow field distribution within the reaction tank, the acid injection point and flow rate distribution are optimized. In one embodiment, for batches with complex internal particle structures, a multi-stage penetration path planning can be introduced. For example, in the initial stage of leaching, the acid path primarily targets the cobalt-rich surface region; in the middle stage, the path gradually shifts to the nickel-rich region at the outer edge of the core; and in the later stage, it concentrates on the high-risk manganese-rich region at the center of the core. This staged path planning better matches the priority sequence of metal release. Based on the adjusted penetration path, leaching parameters are further optimized, including acid concentration, temperature, stirring rate, leaching time, and other process conditions, forming a step-by-step leaching control scheme. For example, a higher acid concentration and temperature are used in the initial stage to accelerate cobalt release; the acid concentration is reduced and the stirring rate is increased in the middle stage to promote nickel release; and the leaching time is appropriately extended and acid penetration in the core region is directionally enhanced in the later stage, resulting in a control scheme that includes multiple time periods and corresponding parameter combinations. This optimization scheme can significantly improve the selective separation efficiency of each metal. In another possible implementation, a personalized stepwise leaching control scheme is formulated for the differences in microstructure of different particle batches. For example, for particles with obvious core-shell structures, a short-time high-intensity leaching can be used in the initial stage to quickly remove the surface cobalt, followed by mild conditions to avoid premature co-dissolution of nickel and manganese; while for particles with gradient structures, a gradual parameter adjustment can be used to gradually increase the depth of acid penetration. This targeted optimization can effectively cope with the diversity of material structures. S6, simulate the concentration changes of each metal ion and impurity ion during the leaching process, and make feedback corrections to the leaching conditions of the next batch based on the concentration changes. The optimized stepwise leaching control scheme needs to be fully verified before practical application to ensure that it can achieve the expected selectivity under different conditions. Numerical simulation can predict the concentration trends of metal ions and impurity ions at each stage without large-scale experiments, providing a basis for subsequent process improvements. Specifically, the control scheme obtained in step S5 is input into the aforementioned finite element analysis environment, and the leaching process under stepwise control conditions is simulated by combining the three-dimensional particle model and acid leaching kinetic parameters. The simulation includes the cumulative growth curves of cobalt, nickel, and manganese ion concentrations at each time period, as well as the possible dissolution of impurity ions such as aluminum and iron. The simulation results are presented in the form of time-concentration curves. For example, in the first stage (10 minutes), the cobalt ion concentration rises rapidly to 1.2 g / L, while the nickel and manganese ion concentrations remain below 0.1 g / L, and the impurity ion concentration is below 0.05 g / L. In the second stage, the nickel ion concentration gradually rises to 0.8 g / L, the manganese ion concentration remains low, and the impurity ion concentration increases slightly.

[0041] Optionally, this step also includes: step S61, when the simulation shows that the concentration of impurity ions in a certain leaching stage exceeds a preset range, shortening the duration of that stage or reducing the acid concentration in that stage.

[0042] For example, if the impurity ion concentration in the second stage exceeds 0.2 g / L, the duration of that stage is shortened from 30 minutes to 20 minutes, or the acid concentration is reduced from 2.0 mol / L to 1.5 mol / L to reduce impurity leaching. In step S62, when the target metal shows a recovery rate lower than the set value in the simulation, the number of subsequent leaching stages is increased or the temperature of the corresponding stage is raised.

[0043] For example, if the nickel recovery rate is only 40% after the second stage, lower than the expected 60%, a third stage lasting 15 minutes is added, and the temperature of this stage is increased from 60°C to 70°C to enhance the leaching kinetics of nickel. Step S63: Regenerate the stepwise leaching control scheme for the next batch based on the revised parameters.

[0044] The revised scheme comprehensively considers the metal recovery ratio and impurity control requirements. For example, the acid concentration in the first stage is adjusted to 1.8 mol / L, and the duration is 15 minutes; the temperature in the second stage is increased to 65°C, and the duration is 25 minutes; a third stage is added to promote manganese release under mild conditions. This feedback correction mechanism can continuously optimize process parameters and improve the adaptability of different batches of materials. In one embodiment, for batches of waste cathode materials with high impurity content, the timing of the peak dissolution of impurity ions can be specifically monitored in the simulation, and the acid strength can be reduced or a chelating agent can be introduced in advance at the corresponding stage to inhibit impurity release. This forward-looking adjustment helps to avoid the burden of subsequent separation and purification steps in actual operation. S7, the spatial distribution characteristics, the particle three-dimensional model, the initial dissociation sequence, the dissociation priority sequence, the retention risk area, the optimized stepwise leaching control scheme, and the feedback correction conditions are correlated and integrated to form a complete selective leaching process flow. To ensure the systematicness and operability of the entire selective leaching method, the key data and schemes generated in the aforementioned steps need to be uniformly integrated to form a structured and executable process flow document. This process document not only guides the processing of the current batch of materials, but also serves as a knowledge base to provide a reference for the process design of subsequent batches of materials.

[0045] Optionally, this step may also include: step S71, establishing the temporal and spatial correspondence between the output data of each step.

[0046] Temporal correspondence refers to the order in which data are generated and their time dependence. For example, spatial distribution characteristics and the 3D particle model are the basis for initial analysis, while the determination of dissociation priority sequence and retention risk areas depends on real-time concentration data and simulation results. Spatial correspondence refers to the association between data and specific regions within the particles. For example, the spatial coordinates of retention risk areas correspond one-to-one with the voxel grids in the 3D particle model, and the optimized permeation path is directly related to the spatial distribution of risk areas. Step S72 involves organizing the output data from each step into a unified process control parameter table through data fusion.

[0047] The process control parameter table is stored in tabular or database form, including parameters such as particle microstructure characteristics, metal dissociation sequence, leaching stage division, acid concentration, temperature, stirring rate, and duration for each stage, as well as acid penetration weight allocation for different regions. For example, the table records that the first stage is for the surface cobalt-rich region, with an acid concentration of 2.0 mol / L, a duration of 15 minutes, and a penetration weight of 70%; the second stage is for the nickel-rich region at the outer edge of the core, with an acid concentration of 1.5 mol / L, a duration of 25 minutes, and a penetration weight of 60%. Step S73: Generate an executable selective leaching process flow file based on the process control parameter table.

[0048] This document is output in a standardized format, containing complete operational steps and parameter settings from raw material pretreatment, microstructure analysis, model building, leaching parameter optimization to feedback correction. For example, the document details the process for processing a batch of high-core-shell structure particles: the first stage uses high-intensity leaching to strip the surface cobalt; the second stage switches to milder conditions to release nickel; and the third stage extends the leaching time to dissolve the core manganese. It also records the expected metal recovery ratio and impurity control targets for each stage. In one possible implementation, the process flow document can also embed automated control logic to directly drive the operation of the leaching equipment. For example, by importing the document into the control system of the reaction tank, the system automatically adjusts the acid injection volume, stirring rate, and temperature changes according to the parameter table in the document, achieving unattended step-by-step leaching operation. This automated integration can significantly improve the accuracy and consistency of process execution. In another embodiment, parameter adjustment ranges can be reserved in the process flow document for waste cathode materials from different sources. For example, for retired battery materials in high-nickel systems, the document specifies that the acid concentration in the first stage can fluctuate between 1.8 and 2.2 mol / L, and the duration can be selected between 10 and 20 minutes to accommodate the uncertainty of material properties. This flexible design makes the process flow document more widely applicable. It should be noted that the generation of the process flow document is not static, but is continuously updated and improved with feedback data from each batch of leaching experiments. For example, when processing a new batch of materials, if it is found that the core manganese release ratio is consistently lower than expected, a high-temperature and high-pressure assisted leaching stage can be added to the document, and the relevant parameters and effects can be recorded for reference in subsequent batches. This continuous iteration mechanism can continuously improve the maturity and stability of the selective leaching process. In one embodiment, to further verify the effectiveness of the process flow document, a complete leaching process can be run on a small-scale test device, and the actual metal recovery ratio and impurity content can be recorded and compared with the expected targets in the document. If the actual results deviate from the expectations, for example, the cobalt recovery ratio is more than 10% lower than expected, it is necessary to go back to the particle 3D model construction or dissociation sequence simulation steps to check whether there are unreasonable parameter settings, and adjust the relevant content in the process flow document accordingly. This verification and correction cycle helps ensure the reliability of the process flow documentation in actual production. In another possible implementation, for large-scale industrial applications, a scheduling strategy for multi-batch parallel processing can be incorporated into the process flow documentation. For example, when processing multiple batches of material simultaneously in the same reaction tank, the acid resources and leaching time can be allocated based on the differences in particle structure between batches to avoid interference between batches. This scheduling design can effectively improve equipment utilization and production efficiency, supporting industrial deployment.

[0049] If the technical solution of this application involves the collection, storage, use, processing, transmission, provision, disclosure, or deletion of personal information, the products using this technical solution have clearly and understandably informed the users of the personal information processing rules before processing personal information, and have obtained the individuals' voluntary consent in accordance with the law. If the technical solution of this application involves sensitive personal information (such as biometrics, religious beliefs, specific identities, medical and health information, financial accounts, and location tracking), the products using this solution have obtained the individuals' separate consent before processing sensitive personal information, and have also met the requirement of "express consent," ensuring that individuals make authorization decisions voluntarily based on full knowledge.

[0050] Specific implementation methods include, but are not limited to, the following: setting up clear and prominent signs at personal information collection devices such as cameras and sensors to inform relevant personnel that they have entered the scope of personal information collection and that their personal information will be collected and processed. If an individual voluntarily enters the collection scope after being informed, it is deemed that they have agreed to the collection of their personal information; or using obvious icons, text descriptions, or other means on the terminal device or system interface for personal information processing to inform them of the rules for personal information processing, and obtaining the individual's explicit authorization through interactive methods such as pop-up prompts, check confirmation boxes, or asking the individual to upload their personal information themselves.

[0051] The aforementioned personal information processing rules should include, but are not limited to, the name and contact information of the personal information processor, the specific purpose of personal information processing, the processing method, the types of personal information processed, the retention period, and the methods and procedures for individuals to exercise their relevant rights.

[0052] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for selective leaching of valuable metals from waste lithium battery cathode materials, characterized in that, include: S1. Obtain a microstructure image of the cathode material from a spent lithium battery, and determine the spatial distribution characteristics of the cobalt-rich surface region and the nickel-manganese-rich inner region based on the microstructure image; S2. Construct a three-dimensional model of the particles to simulate the dissociation behavior of each metal component under acid leaching conditions, and obtain the initial dissociation sequence and rate differences; S3. Determine whether the dissociation priority needs to be adjusted based on the initial dissociation sequence and rate differences. If so, generate a dissociation priority sequence with cobalt preferential release and nickel-manganese delayed release based on the metal bond characteristics of the cobalt-rich surface region and the nickel-manganese-rich inner region; S4. Obtain real-time concentration data of each metal ion in the leachate during acid leaching, and predict the risk area of ​​nickel-manganese component retention in subsequent leaching stages based on the real-time concentration data and the dissociation priority sequence. S5, Adjust the penetration path of the acid solution inside the particles to optimize the leaching parameters and obtain an optimized stepwise leaching control scheme; S6, Simulate the concentration changes of various metal ions and impurity ions during the leaching process, and make feedback corrections to the leaching conditions for the next batch based on the concentration changes; S7, Integrate and correlate the spatial distribution characteristics, the three-dimensional model of the particles, the initial dissociation sequence, the dissociation priority sequence, the retention risk area, the optimized stepwise leaching control scheme, and the feedback correction conditions to form a complete selective leaching process flow.

2. The method as described in claim 1, characterized in that, Step S1 includes: Step S11, dividing the microstructure image into regions using a pixel-level segmentation model; Step S12, extracting the area ratio, number of layers, and nesting relationship of each region after segmentation; Step S13, determining the spatial distribution characteristics of the surface cobalt-rich region and the inner nickel-manganese-rich region based on the area ratio, number of layers, and nesting relationship.

3. The method as described in claim 1, characterized in that, Step S2 includes: Step S21, importing the three-dimensional model of the particles into the finite element analysis environment; Step S22, setting the boundary conditions and material property parameters of the acid leaching system; Step S23, calculating the dissociation time series of each metal bond under acid leaching conditions through the finite element analysis environment; Step S24, determining the initial dissociation sequence of cobalt, nickel, and manganese and the rate differences of each stage based on the dissociation time series.

4. The method as described in claim 1, characterized in that, Step S3 includes: Step S31, extracting the bond energy characteristics and coordination environment parameters of the surface cobalt-rich region and the inner nickel-manganese-rich region; Step S32, classifying the stability of the inner and outer regions according to the bond energy characteristics and coordination environment parameters; Step S33, generating a dissociation priority sequence based on the classification results and the initial dissociation order, with cobalt being the first to be released, followed by nickel, and then manganese.

5. The method as described in claim 1, characterized in that, Step S4 includes: Step S41, taking the real-time concentration data, current leaching time and released ratio as input sequences; Step S42, processing the input sequences through a sequence modeling network; Step S43, outputting the leaching probability distribution of nickel and manganese components in each region within a future time window; Step S44, determining the spatial location of the risk area for nickel and manganese component retention based on the leaching probability distribution.

6. The method as described in claim 1, characterized in that, Step S5 includes: Step S51, determining the local area that needs enhanced penetration based on the spatial coordinates of the risk area; Step S52, increasing the acid flow rate allocation weight of the local area; Step S53, reducing the acid allocation ratio of the non-risk area; Step S54, replanning the acid penetration path inside the particles based on the adjusted allocation ratio.

7. The method as described in claim 1, characterized in that, Step S6 includes: Step S61, when the simulation shows that the concentration of impurity ions in a certain leaching stage exceeds the preset range, shortening the duration of the stage or reducing the acid concentration in the stage; Step S62, when the target metal shows a recovery rate lower than the set value in the simulation, increasing the number of subsequent leaching stages or increasing the temperature of the corresponding stage; Step S63, regenerating the step-by-step leaching control scheme for the next batch based on the corrected parameters.

8. The method as described in claim 1, characterized in that, Step S7 includes: Step S71, establishing the temporal and spatial correspondence between the output data of each step; Step S72, organizing the output data of each step into a unified process control parameter table through data fusion; Step S73, generating an executable selective leaching process flow file based on the process control parameter table.

Images