Method for efficiently recovering iron phosphate from waste lithium batteries

Abstract

This invention discloses a method for efficiently recycling iron phosphate from spent lithium batteries. The method includes: comprehensively analyzing the core characteristics of iron phosphate in spent lithium batteries, constructing a parameter linkage system throughout the entire recycling process, deeply integrating resource characteristics with process control parameters such as separation and leaching, and achieving precise adaptation and coordinated operation of each process step through a dynamic control mechanism and real-time feedback mechanism. This method dynamically adjusts process execution parameters to flexibly address the characteristic differences of different batches of spent lithium batteries, effectively solving the problems of blindly setting process parameters, poor adaptability, and the disconnect between parameter optimization and process monitoring in traditional technologies. It enhances the selectivity of iron phosphate separation, improves product purity and recycling efficiency, and the technical process is scientifically sound, easy to operate, and widely applicable. It ensures full resource utilization while reducing consumption and emissions, balancing resource recycling and environmental protection requirements.

Description

Technical Field

[0001] This invention relates to the field of battery recycling technology, and in particular to a method for efficiently recycling iron phosphate from waste lithium batteries. Background Technology

[0002] With the rapid development of the new energy industry, lithium iron phosphate batteries are increasingly used in transportation, energy storage, and other fields, resulting in an explosive growth in the number of waste batteries. As a core component of battery electrodes, iron phosphate is rich in key resources. Its effective recycling can not only compensate for the insufficient extraction of primary resources but also reduce the pollution of soil and water bodies by harmful substances in waste batteries, meeting the dual requirements of resource recycling and environmental protection. The core problem currently facing the recycling field is that the occurrence state of iron phosphate in waste lithium batteries varies significantly. The recycling process involves multiple interrelated technological steps, requiring precise control of various key parameters to improve recycling efficiency while ensuring product quality. This poses a severe challenge to the systematic and coordinated nature of existing recycling technologies.

[0003] Existing technologies for recycling iron phosphate from spent lithium batteries have two prominent drawbacks: First, they lack a comprehensive and accurate analysis of the iron phosphate resources in spent lithium batteries, failing to effectively link the resource's inherent characteristics with the control parameters of subsequent separation processes. This results in highly arbitrary process parameter settings, making it impossible to dynamically adjust them according to the actual conditions of different batches of spent batteries, thus affecting the stability and adaptability of the recycling process. Second, the optimization of process parameters and real-time status monitoring during the recycling process are disconnected, failing to achieve dynamic feedback between parameter optimization and process monitoring. This makes it impossible to promptly correct deviations that occur during process execution, leading to poor leaching and separation effects, making it difficult to achieve the expected product purity standards, and also reducing overall recycling efficiency. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method for efficiently recycling iron phosphate from waste lithium batteries.

[0005] The technical solution adopted in this invention is a method for efficiently recycling iron phosphate from waste lithium batteries, comprising the following steps: S1, quantitatively characterizing the content distribution, active state, and occurrence morphology of iron phosphate in waste lithium batteries using a waste lithium battery resource potential assessment model, and simultaneously collecting parameters such as battery electrode thickness, iron phosphate crystal particle size, elemental molar ratio, and reactive site density; S2, constructing the initial input dataset for a phosphorus-iron separation thermodynamic control model based on the parameters obtained in S1, and determining the thermodynamic equilibrium range by controlling the system temperature gradient, pH gradient, and reaction interfacial tension parameters; S3, employing a multi-objective optimization algorithm for leaching efficiency to optimize the leaching agent concentration, liquid-solid ratio, and other parameters. The stirring rate and reaction time are synergistically controlled in multiple dimensions to form a dynamic optimization scheme; S4, the phosphate-iron separation process is started based on the optimized parameters output from S3, and the separation effect of phosphate and impurities is enhanced by adjusting the mass transfer coefficient, diffusion rate and phase interface contact area of ​​the reaction system in real time; S5, the pH value of the leachate, the precipitation rate of phosphate, the concentration of impurity ions and the crystal growth rate are continuously collected and adjusted using the phosphate recovery process data monitoring platform; S6, the separated phosphate product is purified through multi-stage filtration, washing and drying processes, and the process parameters are adjusted based on the purity data fed back from the monitoring platform to achieve precise recovery of phosphate.

[0006] Furthermore, the expression for the waste lithium battery resource potential assessment model is as follows: ,in, This represents the recyclability potential value of iron phosphate in spent lithium batteries. This is a parameter representing the proportion of active sites of iron phosphate. The electrode thickness influence coefficient is... This is a parameter for crystal structure integrity. The disturbance coefficient of impurity elements, This is a factor affecting the degree of battery aging. Let be the mass fraction of iron phosphate at the i-th sampling point. Let be the reactivity coefficient of the i-th sampling point. Let i be the morphological factor assigned to the i-th sampling point. This is a potential correction factor. The porosity parameter of the electrode. This is the dissolution rate constant of iron phosphate. This represents the total number of sampling points.

[0007] Furthermore, the expression for the thermodynamic control model of phosphorus-iron separation is: ,in, This represents the Gibbs free energy change of the phosphorus-iron separation reaction. It is the thermodynamic temperature coefficient. The temperature parameter of the reaction system This is the iron ion concentration parameter. This is a parameter representing the phosphate ion concentration. The acidity / alkalinity influence coefficient. The pH parameter of the reaction system. The interfacial tension parameter is the reaction interface tension parameter. The mass transfer resistance coefficient is... Where is the diffusion coefficient. For reaction time parameters, The synergistic effect coefficient, The interference factor for the j-th impurity ion. Let be the concentration parameter of the j-th impurity ion. This represents the number of impurity ion types.

[0008] Furthermore, the expression for the multi-objective optimization algorithm for leaching efficiency is: ,in, This represents the leaching efficiency value for ferric phosphate. The activity coefficient of the leaching agent. This is the liquid-to-solid ratio parameter. For stirring rate parameters, The viscosity coefficient of the reaction system, For the density parameter of the leachate, This refers to the volume parameter of the leaching agent. For solid material quality parameters, The kinetic rate coefficient, For reaction pressure parameters, This is the leaching time parameter. This refers to the interface mass transfer rate parameter. To optimize the weighting coefficients, Let be the priority factor for the k-th optimization objective. For the actual value of the k-th optimization objective, To optimize the number of targets.

[0009] Furthermore, the comprehensive evaluation expression for process monitoring of the iron phosphate recovery process data monitoring platform is as follows: ,in, This is a comprehensive evaluation value for process monitoring. This is the concentration change monitoring coefficient. The rate of change of iron phosphate concentration. For temperature change monitoring coefficient, The rate of temperature change of the system. This is the pH / alkalinity change monitoring coefficient. The rate of change of pH in the system. This is the impurity monitoring coefficient. Let be the monitoring weight of the q-th impurity ion. Let be the change in concentration of the q-th impurity ion. To monitor the number of impurity ion types.

[0010] Furthermore, the comprehensive parameter optimization model expression for recycling iron phosphate from waste lithium batteries is as follows: ,in, This is a comprehensive evaluation value for the recycling effect. This represents the potential value for recycling. This represents the change in Gibbs free energy. This is the leaching efficiency value. This is a comprehensive evaluation value for process monitoring. This is a parameter representing the proportion of active sites. The electrode thickness influence coefficient is... This is a parameter for crystal structure integrity. This is a potential correction factor. The porosity parameter of the electrode. The dissolution rate constant is For the reaction temperature parameter, For pH parameters, Let be the stability factor for the s-th process step. Let be the efficiency factor of the s-th process step. Let be the purity factor of the s-th process step. This represents the total number of process steps.

[0011] Further, S3 includes the following steps: S31, importing the thermodynamic equilibrium interval parameters obtained in S2 into the input module of the multi-objective optimization algorithm for leaching efficiency, and standardizing and transforming the temperature gradient, pH gradient, and reaction interfacial tension parameters through the algorithm's built-in parameter mapping rules to form a numerical matrix recognizable by the algorithm; S32, based on the transformed numerical matrix, constructing a multi-dimensional optimization objective function for leaching agent concentration, liquid-solid ratio, stirring rate, and reaction time, setting constraint boundary conditions for each parameter, and clarifying the coupling relationship between parameters; S33, using the algorithm's iterative solution mechanism to solve the optimization objective function, gradually approaching the optimal solution by dynamically adjusting the iteration step size and convergence threshold, and simultaneously recording the change trajectory of each parameter during the iteration process; S34, validating the optimal parameter combination obtained by the solution, and determining the final leaching process parameter scheme by comparing the theoretical leaching efficiency corresponding to the parameter combination with the preset threshold.

[0012] Further, S4 includes the following sub-steps: S41, according to the leaching process parameters determined in S3, the leaching agent and waste lithium battery electrode powder are added to the reaction device in a set ratio, the stirring device is started, and the stirring rate is controlled to reach the preset value to ensure that the materials are fully mixed and in contact; S42, the temperature change of the reaction system is monitored in real time, and the system temperature is maintained within the thermodynamic equilibrium range by the temperature control device, and the heating power or cooling rate is dynamically adjusted according to the temperature fluctuation; S43, the pH value change of the leachate during the reaction is collected by the online detection device, and the pH of the system is adjusted by adding acid-base regulators based on the feedback results of the phosphorus-iron separation thermodynamic control model to maintain the stability of the phase interface tension; S44, during the reaction, the mass transfer coefficient and diffusion rate are optimized by adjusting the internal pressure and ventilation rate of the reaction device, the phase separation process of iron phosphate with iron ions and impurity ions is strengthened, and the selective precipitation of iron phosphate from the leachate is promoted.

[0013] Further, S5 includes the following sub-steps: S51, activating the sensor array of the ferric phosphate recovery process data monitoring platform, deploying it at the calibration nodes of the reaction unit, separation unit, and purification unit, setting parameter acquisition frequency and data accuracy standards to ensure continuous parameter acquisition; S52, transmitting the collected data on leachate pH, ferric phosphate precipitation rate, impurity ion concentration, and crystal growth rate to the data analysis center of the monitoring platform in real time via the data transmission module for data preprocessing and noise reduction; S53, based on preset parameter threshold ranges, the data analysis center identifies and judges anomalies in the preprocessed data, generating feedback adjustment signals for parameters exceeding the thresholds, clarifying the adjustment direction and magnitude; S54, transmitting the feedback adjustment signals to each process execution unit, correcting process deviations in real time by adjusting parameters such as leachate addition amount, stirring rate, reaction temperature, or washing times, ensuring the stability and continuity of the ferric phosphate recovery process.

[0014] A method for efficiently recycling iron phosphate from spent lithium batteries is disclosed. This method is implemented through several units, including: a precise characterization unit for the resource potential of iron phosphate from spent lithium batteries; a dynamic control unit for the thermodynamic parameters of iron phosphate separation; a multi-objective synergistic optimization unit for leaching efficiency; a selective separation enhancement unit for iron phosphate; a real-time monitoring and feedback unit for recycling process data; and a purification and refining unit for iron phosphate products. Each unit is sequentially connected to a control module via a data bus for bidirectional communication. The precise characterization unit quantitatively analyzes the parameters of iron phosphate in spent lithium batteries and transmits the data to the dynamic control unit for the thermodynamic parameters of iron phosphate separation. The dynamic control unit for the thermodynamic parameters of iron phosphate separation constructs a thermodynamic separation system based on the characterization data. The mechanical equilibrium system transmits parameters to the leaching efficiency multi-objective collaborative optimization unit. The leaching efficiency multi-objective collaborative optimization unit outputs the optimal process parameters to the iron phosphate selective separation enhancement unit. The iron phosphate selective separation enhancement unit performs the separation operation and feeds back the process data to the recycling process data real-time monitoring and feedback unit. The recycling process data real-time monitoring and feedback unit analyzes and processes the data and generates adjustment commands that act in reverse on the first three units. The iron phosphate product purification and refining unit receives the separated material and performs purification operations based on the purity data provided by the monitoring unit, finally obtaining high-purity iron phosphate product. Each unit achieves efficient recovery of iron phosphate from waste lithium batteries through parameter interaction and collaborative control.

[0015] Beneficial Effects: This invention proposes a method for efficiently recycling iron phosphate from waste lithium batteries. By comprehensively and accurately analyzing the core characteristics of iron phosphate in waste lithium batteries, it closely links resource characteristic parameters with the control parameters of subsequent separation processes. Process parameters are dynamically adjusted based on the actual state of the resources, completely solving the problems of blindly setting process parameters and poor adaptability in traditional technologies, significantly improving the stability and consistency of the recycling process. By establishing a dynamic feedback mechanism for parameter optimization and process monitoring, changes in process status are captured in real time throughout the recycling process, and execution deviations are corrected promptly. This effectively compensates for the shortcomings of the disconnect between optimization and monitoring in traditional technologies, significantly enhancing leaching and separation effects, and improving product purity and recycling efficiency. Simultaneously, through the coordinated linkage and precise control of each process link, it reduces resource waste and environmental impact while ensuring efficient resource recycling, balancing the needs of resource recycling and environmental protection. Furthermore, the entire technical process requires no complex operations, adapts to the characteristic differences of different batches of waste lithium batteries, and has a wide range of applications, providing an efficient, stable, and reliable solution for the recycling of iron phosphate from waste lithium batteries. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a flowchart of method step S3 of the present invention; Figure 3This is a flowchart of method step S4 of the present invention; Figure 4 This is a flowchart of step S5 of the method of the present invention; Figure 5 This is a diagram showing the unit composition for implementing the method of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, a method for efficiently recycling iron phosphate from waste lithium batteries includes the following steps: S1, quantitatively characterizing the content distribution, active state, and occurrence morphology of iron phosphate in waste lithium batteries using a waste lithium battery resource potential assessment model, and simultaneously collecting parameters such as battery electrode thickness, iron phosphate crystal particle size, elemental molar ratio, and reactive site density; S2, constructing the initial input dataset for a phosphorus-iron separation thermodynamic control model based on the parameters obtained in S1, and determining the thermodynamic equilibrium range by controlling the system temperature gradient, pH gradient, and reaction interfacial tension parameters; S3, employing a multi-objective optimization algorithm for leaching efficiency to optimize the leaching agent concentration, liquid-solid ratio, stirring rate, and... The reaction time is synergistically controlled in multiple dimensions to form a dynamic optimization scheme; S4, the phosphate-iron separation process is started based on the optimized parameters output from S3, and the separation effect of phosphate and impurities is enhanced by adjusting the mass transfer coefficient, diffusion rate and phase interface contact area of ​​the reaction system in real time; S5, the pH value of the leachate, the precipitation rate of phosphate, the concentration of impurity ions and the crystal growth rate are continuously collected and adjusted using the phosphate recovery process data monitoring platform; S6, the separated phosphate product is purified through multi-stage filtration, washing and drying processes, and the process parameters are adjusted based on the purity data fed back from the monitoring platform to achieve precise recovery of phosphate.

[0019] Step S1, serving as the foundational data support for the entire recycling process, employs a specialized resource assessment system to conduct a comprehensive quantitative analysis of the core characteristics of iron phosphate in spent lithium batteries. During this process, stratified sampling of the battery electrodes is required, covering key areas such as the positive electrode, negative electrode, and the vicinity of the separator. The number of sampling points is controlled between 30 and 40 to ensure the representativeness and comprehensiveness of the data. The analysis focuses on detecting the distribution of iron phosphate content, accurately determining its proportion in different locations on the electrodes. Simultaneously, the active state of iron phosphate is assessed by detecting the number and distribution density of active sites to clarify its reaction participation capacity. Furthermore, the occurrence form of iron phosphate needs to be analyzed to determine whether it is crystalline, amorphous, or in a complex form with other substances. The core parameters collected synchronously include the thickness of the battery electrode, with the detection accuracy controlled at the 0.01 mm level; the measurement range of iron phosphate crystal particle size covers 10 nanometers to 100 micrometers; the elemental molar ratio focuses on detecting the proportion of core elements such as phosphorus, iron, and oxygen; and the density of reactive sites is quantified and statistically analyzed using dedicated detection equipment. The accurate acquisition of these parameters provides a reliable data foundation for the subsequent process optimization design, ensuring the targeting and effectiveness of the entire recycling process.

[0020] Step S2, based on all the core parameters obtained in Step S1, constructs the initial input dataset for thermodynamic control of phosphorus-iron separation. The dataset must include key information such as parameter names, detection values, error ranges, and weighting coefficients to ensure data integrity and usability. During implementation, the input data is first categorized and processed, separating qualitative parameters such as content distribution, active state, and occurrence morphology from quantitative parameters such as electrode thickness and crystal size. The data is then imported into a dedicated thermodynamic analysis system. This system is used to control the system's temperature gradient, with a control range of 20 to 100 degrees Celsius and a temperature change rate controlled at 5 degrees Celsius per hour. Simultaneously, the pH gradient is adjusted, with a pH range of 1 to 14 and an adjustment increment of 0.5 units. The interfacial tension parameter is controlled within a range of 10 to 50 millinewtons per meter. By gradually adjusting these key parameters, the changes in the system's thermodynamic state are observed. During this process, thermodynamic data under different parameter combinations need to be recorded in real time, including reaction enthalpy change, entropy change, and free energy change. The thermodynamic equilibrium range is determined through comparative analysis. This range needs to meet the separation conditions of iron phosphate with iron ions and other impurity ions, providing a thermodynamic theoretical basis for the parameter setting of subsequent leaching processes and ensuring the scientific and efficient separation of phosphorus and iron.

[0021] Step S3 employs a multi-objective optimization mechanism for leaching efficiency, coordinating and controlling key process parameters affecting leaching performance across multiple dimensions. The implementation process first clarifies optimization objectives, including leaching rate, selectivity, energy consumption, and cost, with each objective having a corresponding priority and evaluation standard. For the leachate concentration parameter, the control range is set to 5% to 30%, adjusted incrementally in 5% increments. The liquid-solid ratio is adjusted from 5:1 to 20:1, with increments of 2:1. The stirring rate is adjusted from 100 to 500 rpm, with adjustments of 50 rpm each time. The reaction time is adjusted from 1 to 8 hours, with increments of 1 hour. Multiple parallel experiments are conducted by systematically changing the combination of these parameters, with each experiment repeated three times to ensure data reliability. Changes in key indicators such as leaching rate and impurity removal rate are recorded in real time during the experiments. By using optimization mechanisms to comprehensively analyze experimental data, a correlation model between parameters and indicators is established, the primary and secondary influence relationships and interactions of each parameter are identified, and a dynamic optimization scheme is finally formed. This scheme can automatically adjust the values ​​of each process parameter according to the characteristic differences of different batches of waste lithium batteries, ensuring that the leaching process is always in the optimal state, and providing high-quality leachate for subsequent separation and purification.

[0022] Step S4 initiates the phosphorus-iron separation process based on the optimized parameters output from step S3. Before implementation, a comprehensive inspection of the reaction apparatus is required to ensure the normal operation of equipment such as the stirring system, temperature control system, and pH adjustment system. Then, the initial operating parameters of each device are set according to the optimized scheme. The pretreated waste lithium battery electrode powder and the prepared leaching agent are added to the reactor at the set liquid-to-solid ratio. The stirring device is started at the set speed, and the temperature of the reaction system is stabilized within the optimized range through the heating or cooling system. During the separation process, the mass transfer coefficient of the reaction system is adjusted to maintain it within the range of 0.001 to 0.01 square meters per second. The diffusion rate is optimized by changing the stirring rate and the internal structure of the reactor to ensure that the diffusion rate is between 1×10^-6 and 1×10^-5 square meters per second. At the same time, the contact area of ​​the phase interface is expanded by increasing the packing or using a spraying method, and the contact area is controlled between 10 and 50 square meters per cubic meter. The changes in the concentration of ferric phosphate and impurity ions in the leachate are monitored in real time. The above parameters are dynamically adjusted according to the monitoring results. When the concentration of ferric phosphate reaches the peak and the concentration of impurity ions is lower than the set threshold, the current parameters are maintained and the reaction continues for a period of time to enhance the separation effect of ferric phosphate and impurities. This ensures that the purity of ferric phosphate in the leachate meets the requirements for subsequent purification and lays the foundation for high-quality recovery of ferric phosphate.

[0023] Step S5 utilizes a dedicated recycling process data monitoring platform to achieve full control over the recycling process. This platform integrates a sensor array, data transmission module, data analysis center, and execution control module, enabling closed-loop operation of parameter acquisition, analysis, feedback, and regulation. During implementation, the sensor array is first deployed at key nodes of the reaction device, such as the feed inlet, reaction chamber, and discharge outlet. Sensor types include pH sensors, concentration sensors, particle size sensors, and temperature sensors. The parameter acquisition frequency is set to once every 10 seconds, with a data accuracy of 0.01%. The data transmission module transmits real-time data such as the pH value of the leachate, the ferric phosphate precipitation rate, the impurity ion concentration, and the crystal growth rate to the data analysis center. The center filters, reduces noise, and normalizes the data to remove outliers and interference signals. Based on preset parameter threshold ranges, the processed data is monitored and judged in real time. When a parameter exceeds the normal range, a feedback adjustment signal is immediately generated, specifying the direction and magnitude of adjustment. For example, when the pH value is too high, a signal to increase the amount of acidic leachate is issued; when the impurity ion concentration exceeds the standard, a signal to adjust the stirring rate or reaction temperature is issued. Feedback adjustment signals are transmitted to each process execution unit in a timely manner to realize real-time correction of process parameters, ensure the stability and continuity of the iron phosphate recovery process, and ensure that the final product quality meets the standards.

[0024] Step S6, as the final stage of the recycling process, purifies the separated ferric phosphate product through multi-stage filtration, washing, and drying. The process begins with primary filtration using a filter membrane with a pore size of 1 to 5 micrometers. The leachate is filtered under a pressure of 0.1 to 0.3 MPa to remove large particulate impurities and unreacted solid powder. The filtration rate is controlled at 10 to 20 liters per hour to ensure a balance between filtration effectiveness and efficiency. After primary filtration, the product enters the washing stage using deionized water as the washing solution. The washing cycle is set to 3 to 5 times, with a washing solution volume to filter cake volume ratio of 3:1 to 5:1 each time. The washing process employs a stirring method at a stirring rate of 50 to 100 rpm, with each washing session lasting 10 to 20 minutes. These multiple washes remove soluble impurities and residual leachate adhering to the surface of the ferric phosphate. After washing, a secondary filtration process is performed using a precision filter membrane with a pore size of 0.1 to 0.5 micrometers, filtered under a pressure of 0.05 to 0.1 MPa to further remove fine impurities and obtain a higher purity ferric phosphate filter cake. The filter cake is then sent to a drying device with a drying temperature set at 80 to 120 degrees Celsius and a drying time of 2 to 4 hours. During the drying process, the vacuum level inside the device is controlled at 0.05 to 0.09 MPa to ensure complete evaporation of moisture from the filter cake. Throughout the purification process, process parameters such as filtration pressure, washing frequency, and drying temperature are adjusted in real time based on purity data fed back from the monitoring platform. If the product purity does not meet the set standard, the washing frequency is increased or the drying time is extended until a high-purity ferric phosphate product is obtained, completing the entire recovery process.

[0025] Preferably, the expression for the waste lithium battery resource potential assessment model is: ,in, This represents the recyclability potential value of iron phosphate in spent lithium batteries. This is a parameter representing the proportion of active sites of iron phosphate. The electrode thickness influence coefficient is... This is a parameter for crystal structure integrity. The disturbance coefficient of impurity elements, This is a factor affecting the degree of battery aging. Let be the mass fraction of iron phosphate at the i-th sampling point. Let be the reactivity coefficient of the i-th sampling point. The morphological factor assigned to the i-th sampling point This is a potential correction factor. The porosity parameter of the electrode. This is the dissolution rate constant of iron phosphate. This represents the total number of sampling points.

[0026] Specifically, the waste lithium battery resource potential assessment model is a prerequisite for the accurate recycling of iron phosphate from waste lithium batteries. Its implementation process involves collecting multi-dimensional parameters in step S1, and then completing the potential assessment through hierarchical quantification and collaborative computation. During implementation, the mass fraction of iron phosphate at 30 to 40 sampling points is statistically analyzed to determine its distribution range in the electrode. Simultaneously, the reactivity coefficient of each sampling point is detected using specialized equipment, and the recycling difficulty of different forms of iron phosphate is quantified by combining the presence of a morphology factor. The electrode thickness influence coefficient is divided into gradients from 0.1 to 0.9, and the corresponding coefficient is matched based on the actual detected electrode thickness value. The crystal structure integrity parameter is determined through XRD detection results. The impurity element interference coefficient is set to 0.05 to 0.3 based on the content ratio of impurities such as iron, copper, and aluminum. The battery aging degree factor is assigned a value in the range of 0.2 to 0.8, referencing the battery cycle count and storage time. The electrode porosity parameter is detected by gas adsorption method, with a value range between 0.1 and 0.5. The iron phosphate dissolution rate constant is determined to be 0.001 to 0.01 through preliminary experiments. Through comprehensive calculations by the model, a recyclable potential value is output. This value directly reflects the actual recyclable proportion and recycling difficulty of iron phosphate in waste lithium batteries, providing a core basis for the targeted setting of subsequent process parameters. This avoids blind process design caused by inaccurate resource assessment, ensuring that the recycling process not only conforms to the actual resource situation, but also maximizes recycling efficiency and product quality.

[0027] Specifically, the thermodynamic control model for phosphorus-iron separation is the core technology ensuring efficient phosphorus-iron separation. Its implementation is based on the parameter data from step S1 and the initial dataset from step S2, determining the thermodynamic equilibrium range of the reaction through multi-parameter collaborative calculation. During implementation, the temperature parameter of the reaction system is initially set to a range of 20 to 100 degrees Celsius, gradually adjusted in 5-degree Celsius increments. Simultaneously, the concentration changes of iron ions and phosphate ions at different temperatures are detected, and the dynamic fluctuations of the concentration ratio are recorded. The pH influence coefficient is set to 0.1 to 1.0 according to the pH range; when the pH is between 1 and 4, the coefficient is 0.8 to 1.0, and when it is between 7 and 10, it is 0.3 to 0.6. The interfacial tension is stabilized at 10 to 50 millinewtons by precisely adjusting the pH parameter. The mass transfer resistance coefficient is set to 0.01 to 0.1 based on the viscosity of the reaction system and the degree of material mixing. The diffusion coefficient is determined by detecting the ion migration rate and is set to 1 × 10⁻⁶ to 1 × 10⁻⁵ square meters per second. The reaction time parameter is gradually increased in ranges from 1 to 8 hours. Interference factors and concentration parameters were set for various impurity ions such as iron, copper, and aluminum. The interference factor for iron ions was set to 0.2 to 0.5, while that for copper and aluminum ions was set to 0.1 to 0.3. The Gibbs free energy change value calculated by the model directly reflects the spontaneous trend and progress of the phosphorus-iron separation reaction. When this value reaches the set range, the corresponding parameters such as temperature, pH value, and interfacial tension constitute a thermodynamic equilibrium range. Phosphorus-iron separation within this range ensures that the separation effect of iron phosphate and impurity ions is optimal, providing a stable thermodynamic environment for subsequent leaching and separation processes and avoiding incomplete separation caused by imbalance of reaction conditions.

[0028] Preferably, the expression for the thermodynamic control model of phosphorus-iron separation is: ,in, This represents the Gibbs free energy change of the phosphorus-iron separation reaction. It is the thermodynamic temperature coefficient. The temperature parameter of the reaction system This is the iron ion concentration parameter. This is a parameter representing the phosphate ion concentration. The acidity / alkalinity influence coefficient. The pH parameter of the reaction system. The interfacial tension parameter is the reaction interface tension parameter. The mass transfer resistance coefficient is... Where is the diffusion coefficient. For reaction time parameters, The synergistic effect coefficient, The interference factor for the j-th impurity ion. Let be the concentration parameter of the j-th impurity ion. This represents the number of impurity ion types.

[0029] Specifically, the thermodynamic control model for phosphorus-iron separation is the core technology ensuring efficient phosphorus-iron separation. Its implementation is based on the parameter data from step S1 and the initial dataset from step S2, determining the thermodynamic equilibrium range of the reaction through multi-parameter collaborative calculation. During implementation, the temperature parameter of the reaction system is initially set to a range of 20 to 100 degrees Celsius, gradually adjusted in 5-degree Celsius increments. Simultaneously, the concentration changes of iron ions and phosphate ions at different temperatures are detected, and the dynamic fluctuations of the concentration ratio are recorded. The pH influence coefficient is set to 0.1 to 1.0 according to the pH range; when the pH is between 1 and 4, the coefficient is 0.8 to 1.0, and when it is between 7 and 10, it is 0.3 to 0.6. The interfacial tension is stabilized at 10 to 50 millinewtons by precisely adjusting the pH parameter. The mass transfer resistance coefficient is set to 0.01 to 0.1 based on the viscosity of the reaction system and the degree of material mixing. The diffusion coefficient is determined by detecting the ion migration rate and is set to 1 × 10⁻⁶ to 1 × 10⁻⁵ square meters per second. The reaction time parameter is gradually increased in ranges from 1 to 8 hours. Interference factors and concentration parameters were set for various impurity ions such as iron, copper, and aluminum. The interference factor for iron ions was set to 0.2 to 0.5, while that for copper and aluminum ions was set to 0.1 to 0.3. The Gibbs free energy change value calculated by the model directly reflects the spontaneous trend and progress of the phosphorus-iron separation reaction. When this value reaches the set range, the corresponding parameters such as temperature, pH value, and interfacial tension constitute a thermodynamic equilibrium range. Phosphorus-iron separation within this range ensures that the separation effect of iron phosphate and impurity ions is optimal, providing a stable thermodynamic environment for subsequent leaching and separation processes and avoiding incomplete separation caused by imbalance of reaction conditions.

[0030] Preferably, the expression for the multi-objective optimization algorithm for leaching efficiency is: ,in, This represents the leaching efficiency value for ferric phosphate. The activity coefficient of the leaching agent. This is the liquid-to-solid ratio parameter. For stirring rate parameters, The viscosity coefficient of the reaction system, For the density parameter of the leachate, This refers to the volume parameter of the leaching agent. For solid material quality parameters, The kinetic rate coefficient, For reaction pressure parameters, This is the leaching time parameter. This refers to the interface mass transfer rate parameter. To optimize the weighting coefficients, Let be the priority factor for the k-th optimization objective. For the actual value of the k-th optimization objective, To optimize the number of targets.

[0031] Specifically, the multi-objective optimization algorithm for leaching efficiency achieves precise and coordinated control of leaching process parameters. Its implementation revolves around four main objectives: leaching rate, selectivity, energy consumption, and cost. The optimal parameter combination is determined through multiple rounds of iterative calculations. During implementation, the leaching agent activity coefficient is first set, ranging from 0.6 to 0.95 depending on the type and purity of the leaching agent. The liquid-solid ratio parameter is divided into gradients from 5:1 to 20:1, with each gradient corresponding to a different material mixing ratio. The stirring rate parameter is set within the range of 100 to 500 revolutions per minute, with each 50 revolutions per minute adjustment level. The viscosity coefficient of the reaction system is determined by detecting the flow characteristics of the leachate, ranging from 0.001 to 0.01 Pa·s. The leachate density parameter is assigned a value from 1.0 to 1.3 g / cm³. The reaction pressure parameter is controlled between 0.1 and 0.3 MPa. The interfacial mass transfer rate parameter is monitored in real-time using online detection equipment, with a value ranging from 0.0001 to 0.001 m² / s. The optimization weighting coefficients are set according to the recovery requirements, with leaching rate weighting at 0.4 to 0.6, selectivity weighting at 0.2 to 0.3, and energy consumption and cost weighting at 0.1 to 0.2 each. The actual values ​​of each optimization objective are updated in real time using experimental data. The algorithm adjusts the values ​​of each parameter through multiple iterations, with the iteration step size set according to the parameter type. The iteration step sizes for liquid-to-solid ratio and stirring rate are 2:1 and 50 rpm, respectively, until the calculated leaching efficiency value reaches a stable range. The implementation of this algorithm enables the coordinated optimization of multiple leaching parameters, avoiding the problem of neglecting one aspect while optimizing a single parameter. It ensures that while improving the leaching rate, selectivity, energy consumption, and cost are also considered, providing optimal process parameter support for the phosphorus-iron separation process in step S4, and guaranteeing the quality of the leachate and the subsequent separation effect.

[0032] Preferably, the comprehensive evaluation expression for process monitoring of the iron phosphate recovery process data monitoring platform is as follows: ,in, This is a comprehensive evaluation value for process monitoring. This is the concentration change monitoring coefficient. The rate of change of iron phosphate concentration. For temperature change monitoring coefficient, The rate of temperature change of the system. This is the pH / alkalinity change monitoring coefficient. The rate of change of pH in the system. This is the impurity monitoring coefficient. Let be the monitoring weight of the q-th impurity ion. Let be the change in concentration of the q-th impurity ion. To monitor the number of impurity ion types.

[0033] Specifically, the comprehensive evaluation model for process monitoring in the ferric phosphate recovery process data monitoring platform is key to achieving precise control throughout the recovery process. Its implementation involves deploying sensor arrays and data analysis systems at each process stage, using real-time data acquisition and dynamic calculations to complete process evaluation. During implementation, the sensor array is first activated to collect the rate of change in leachate concentration every 10 seconds, with a detection accuracy of 0.001 per hour. The system temperature rate is continuously monitored by temperature sensors, ranging from 0.1 to 5 degrees Celsius per hour, and the pH rate is detected with an accuracy of 0.01 to 0.5 per hour. The concentration change monitoring coefficient is set to 0.3 to 0.8 based on the target recovery purity of ferric phosphate, the temperature change monitoring coefficient is set to 0.2 to 0.7 according to the requirements of the thermodynamic equilibrium range, and the pH change monitoring coefficient is set to 0.4 to 0.9 based on the optimal pH range for ferric phosphate separation. Impurity monitoring coefficients are set for different impurity ions. The monitoring weight for phosphate ions is 0.5 to 0.7, for iron ions it is 0.2 to 0.3, and for other impurity ions combined it is 0.1 to 0.2. The concentration change of each impurity ion is recorded in real time by a concentration sensor, with a value ranging from 0.001 to 0.1. The model outputs a comprehensive process monitoring evaluation value by comprehensively calculating the rate of change of each parameter and the monitoring coefficient. This value is quantified in the range of 0 to 1. A value above 0.8 indicates a stable process, while a value below 0.5 is considered abnormal. The implementation of this model can capture parameter fluctuations in the recovery process in real time, promptly identify process deviations, provide accurate basis for feedback adjustments, ensure that each process link is always in optimal operating condition, avoid decreased recovery efficiency and substandard product purity due to parameter drift, and guarantee the stability and continuity of the entire recovery process.

[0034] The preferred expression for the comprehensive parameter optimization model of recycled lithium iron phosphate is: ,in, This is a comprehensive evaluation value for the recycling effect. This represents the potential value for recycling. This represents the change in Gibbs free energy. This is the leaching efficiency value. This is a comprehensive evaluation value for process monitoring. This is a parameter representing the proportion of active sites. The electrode thickness influence coefficient is... This is a parameter for crystal structure integrity. This is a potential correction factor. The porosity parameter of the electrode. The dissolution rate constant is For the reaction temperature parameter, For pH parameters, Let be the stability factor for the s-th process step. Let be the efficiency factor of the s-th process step. Let be the purity factor of the s-th process step. This represents the total number of process steps.

[0035] Specifically, the comprehensive parameter optimization model for recycling iron phosphate from waste lithium batteries is the core of achieving accurate evaluation of the entire recycling process and synergistic optimization of parameters. Its implementation integrates the output results of previous models with the process parameters throughout the entire process, completing a comprehensive evaluation through multi-dimensional calculations. During implementation, the specific value of the recyclable potential is first obtained, and then combined with the Gibbs free energy change value, leaching efficiency value, and process monitoring comprehensive evaluation value, and calculated in the first round with equal weights. The active site ratio parameter is set to 0.3 to 0.7 based on the detection results of step S1. The electrode thickness influence coefficient and crystal structure integrity parameter follow the values ​​set in weight 2, and the potential correction coefficient is adjusted to 0.8 to 1.2 according to the process stability during actual recycling. The electrode porosity and dissolution rate constants were determined using values ​​from prior detection and preliminary experiments. Reaction temperature and pH parameters were taken from actual operating values ​​within the thermodynamic equilibrium range. The process stability factor was set to 0.6 to 0.95 based on the parameter fluctuation range of each step. The efficiency factor was assigned a value of 0.5 to 0.9 based on the actual operating efficiency of each step. The purity factor was set to 0.7 to 0.99 based on the real-time product purity. The total number of process steps was set as six steps, S1 to S6, with the three factors for each step updated through real-time data. The model outputs a comprehensive recovery effect evaluation value through integrated calculation. This value directly reflects the quality of the entire recovery process. When the value falls below a set threshold, the model automatically adjusts various process parameters, including leaching agent concentration, reaction temperature, and stirring rate, to achieve dynamic optimization of all process parameters. The implementation of this model can break down the information barriers between various process links and the model, achieve global collaborative optimization of parameters, ensure that the recycling process not only meets the resource potential requirements, but also conforms to thermodynamic laws and efficiency standards, and ultimately maximize the recycling effect, providing comprehensive technical support for the efficient and stable recycling of iron phosphate in waste lithium batteries.

[0036] Preferred, such as Figure 2As shown, step S3 includes the following steps: S31, importing the thermodynamic equilibrium interval parameters obtained in S2 into the input module of the multi-objective optimization algorithm for leaching efficiency, and standardizing the temperature gradient, pH gradient, and reaction interfacial tension parameters through the algorithm's built-in parameter mapping rules to form a numerical matrix recognizable by the algorithm; S32, based on the transformed numerical matrix, constructing a multi-dimensional optimization objective function for leaching agent concentration, liquid-solid ratio, stirring rate, and reaction time, setting constraint boundary conditions for each parameter, and clarifying the coupling relationship between parameters; S33, using the algorithm's iterative solution mechanism to solve the optimization objective function, gradually approaching the optimal solution by dynamically adjusting the iteration step size and convergence threshold, and synchronously recording the change trajectory of each parameter during the iteration process; S34, validating the optimal parameter combination obtained by the solution, and determining the final leaching process parameter scheme by comparing the theoretical leaching efficiency corresponding to the parameter combination with the preset threshold.

[0037] Specifically, the multi-objective optimization algorithm for leaching efficiency in step S3 is implemented through steps S31 to S34 to achieve precise parameter optimization. In step S31, the thermodynamic equilibrium parameters determined in step S2, including temperature gradient ranges of 20 to 100 degrees Celsius, pH gradients of 1 to 14, and interfacial tensions of 10 to 50 millinewtons, are imported into the algorithm input module. These parameters are categorized and coded according to their type, and the physical parameters are converted into a numerical matrix recognizable by the algorithm using built-in mapping rules. The matrix dimensions are set to 3 rows and 30 columns to ensure data transmission integrity. In step S32, based on the converted numerical matrix, a multi-dimensional optimization objective function is constructed, with variables including leaching agent concentration of 5% to 30%, liquid-to-solid ratio of 5:1 to 20:1, stirring speed of 100 to 500 rpm, and reaction time of 1 to 8 hours. Constraint boundaries are set for each parameter, clarifying the coupling coefficient between concentration and liquid-to-solid ratio as 0.6 to 0.8, and the correlation weight between stirring speed and reaction time as 0.3 to 0.5, clearly defining the interaction relationships between the parameters. Step S33 initiates the iterative solution mechanism of the algorithm. The initial iteration step size is set to a concentration gradient of 5%, a liquid-to-solid ratio gradient of 2:1, a stirring rate gradient of 50 rpm, and a reaction time gradient of 1 hour. The convergence threshold is set to 0.001. Through continuous iterative calculations, the algorithm gradually approaches the optimal solution. Simultaneously, the parameter change trajectory is recorded every 10 iterations, forming a complete optimization process data chain. Step S34 verifies the effectiveness of the obtained optimal parameter combination. By comparing the theoretical leaching rate under this combination with the preset 90% benchmark value, if the deviation is within 3%, it is determined as the final process parameter scheme. If the deviation exceeds the range, the algorithm returns to S32 to readjust the objective function weights, ensuring the reliability and applicability of the optimized parameters and providing accurate guidance for subsequent leaching processes.

[0038] Preferred, such as Figure 3As shown, S4 includes the following sub-steps: S41, according to the leaching process parameters determined in S3, the leaching agent and waste lithium battery electrode powder are added to the reaction device in a set ratio, the stirring device is started, and the stirring rate is controlled to reach the preset value to ensure that the materials are fully mixed and in contact; S42, the temperature change of the reaction system is monitored in real time, and the system temperature is maintained within the thermodynamic equilibrium range by the temperature control device, and the heating power or cooling rate is dynamically adjusted according to the temperature fluctuation; S43, the pH value change of the leachate during the reaction is collected by the online detection device, and the pH of the system is adjusted by adding acid-base regulators based on the feedback results of the phosphorus-iron separation thermodynamic control model to maintain the stability of the phase interface tension; S44, during the reaction, the mass transfer coefficient and diffusion rate are optimized by adjusting the internal pressure and ventilation rate of the reaction device, the phase separation process of iron phosphate with iron ions and impurity ions is strengthened, and the selective precipitation of iron phosphate from the leachate is promoted.

[0039] Specifically, in step S4, the phosphorus-iron separation process is enhanced through steps S41 to S44 to ensure efficient separation of ferric phosphate from impurities. In step S41, according to the optimal process parameters determined in step S3, the leaching agent and waste lithium battery electrode powder are added to a reactor equipped with a stirring device at a set liquid-to-solid ratio. The particle size of the electrode powder is controlled between 100 and 200 mesh. After starting the stirring device, the stirring rate is stabilized at the optimized value, and continuous stirring is maintained to promote thorough mixing of the materials. The mixing uniformity is controlled to be above 95% through sampling and testing, ensuring full contact between the leaching agent and the electrode powder. In step S42, the temperature change of the system is monitored in real time by a temperature sensor built into the reactor. The temperature detection accuracy is controlled within ±0.5 degrees Celsius. When the temperature deviates from the thermodynamic equilibrium range of ±2 degrees Celsius, the heating or cooling system is activated for adjustment. The heating power is adjusted in gradients from 50 to 200 watts, and the cooling rate is controlled at 2 to 5 degrees Celsius per hour, ensuring that the system temperature remains stable within the optimal range, providing a stable thermodynamic environment for phosphorus-iron separation. In step S43, the pH of the leachate is collected every 5 minutes using an online pH meter with a detection accuracy of ±0.01. Based on the feedback from the thermodynamic control model for phosphorus-iron separation, when the pH value exceeds the optimal range by 0.2 units, an acid-base regulator is precisely added via an automatic dripping device at a rate controlled between 1 and 5 ml per minute. Simultaneously, changes in interfacial tension are monitored to ensure it remains stable within a range of 10 to 50 mN / m, guaranteeing the smooth progress of the separation reaction. In step S44, the internal pressure is controlled by adjusting the gas inlet flow rate of the reactor, with the pressure range set between 0.1 and 0.3 MPa. The aeration rate is adjusted to 1 to 5 L per minute to optimize the mass transfer coefficient and diffusion rate. The mass transfer coefficient is maintained between 0.001 and 0.01 m² / s, and the diffusion rate is controlled between 1 × 10⁻⁶ and 1 × 10⁻⁵ m² / s. By enhancing interfacial contact and mass transfer, the selective precipitation of iron phosphate from the leachate is promoted, reducing the probability of co-precipitation of impurity ions and improving the separation effect.

[0040] Preferred, such as Figure 4As shown, S5 includes the following sub-steps: S51, activating the sensor array of the ferric phosphate recovery process data monitoring platform, deploying it at the calibration nodes of the reaction device, separation device, and purification device, setting the parameter acquisition frequency and data accuracy standards to ensure continuous parameter acquisition; S52, transmitting the collected data on leachate pH, ferric phosphate precipitation rate, impurity ion concentration, and crystal growth rate to the data analysis center of the monitoring platform in real time through the data transmission module for data preprocessing and noise reduction; S53, based on the preset parameter threshold range, the data analysis center identifies and judges anomalies in the preprocessed data, generating feedback adjustment signals for parameters exceeding the threshold, clarifying the adjustment direction and amplitude; S54, transmitting the feedback adjustment signals to each process execution unit, correcting process deviations in real time by adjusting parameters such as leachate addition amount, stirring rate, reaction temperature, or washing times, ensuring the stability and continuity of the ferric phosphate recovery process.

[0041] Specifically, the process data monitoring process in step S5 is achieved through a closed-loop feedback control system constructed in steps S51 to S54 to ensure the stability of the recovery process. In step S51, the sensor array of the iron phosphate recovery process data monitoring platform is activated. A total of 15 to 20 sensors are deployed at the feed inlet, middle of the reaction chamber, discharge outlet, inlet and outlet of the separation unit, and key nodes of the purification unit. These sensors include pH sensors, concentration sensors, particle size sensors, and temperature sensors. The parameter acquisition frequency is set to once every 10 seconds, with a data accuracy of ±0.01%, ensuring continuous and accurate acquisition of core parameters and providing a reliable data source for subsequent analysis. In step S52, the collected parameters are transmitted in real time to the data analysis center of the monitoring platform via a wireless transmission module. The transmission delay is controlled within 1 second. The data analysis center first filters the raw data to remove random interference signals, then performs noise reduction and normalization transformation, mapping the data uniformly to the 0-1 range to ensure data comparability and accuracy and avoid the impact of abnormal data on the analysis results. In step S53, the data analysis center monitors and identifies anomalies in pre-processed data in real time based on preset parameter threshold ranges. Thresholds are set for the leachate pH value (1-6), ferric phosphate precipitation rate (0.01-0.1 rpm), impurity ion concentration (0.001-0.01 ppm), and crystal growth rate (0.1-1 micrometer per hour). When a parameter exceeds the threshold range by ±10%, a feedback adjustment signal is automatically generated, specifying the adjustment direction and magnitude to ensure accuracy. In step S54, the feedback adjustment signal is transmitted to each process execution unit via a data bus, with a transmission response time controlled within 2 seconds. The execution units adjust parameters such as leachate addition, stirring rate, reaction temperature, and washing frequency based on the adjustment signal. The leachate addition adjustment gradient is 5%-10%, the stirring rate adjustment range is 50-100 rpm, the reaction temperature adjustment range is ±2 degrees Celsius, and the washing frequency can be increased by 1-2 times. By correcting process deviations in real time, the stability and continuity of the ferric phosphate recovery process are ensured, guaranteeing that the product quality meets requirements.

[0042] like Figure 5As shown, a method for efficiently recycling iron phosphate from waste lithium batteries is implemented through different units, including: a precise characterization unit for the resource potential of iron phosphate from waste lithium batteries, a dynamic control unit for the thermodynamic parameters of iron phosphate separation, a multi-objective synergistic optimization unit for leaching efficiency, a selective separation enhancement unit for iron phosphate, a real-time monitoring and feedback unit for recycling process data, and a purification and refining unit for iron phosphate products. Each unit is sequentially connected to a control module via a data bus for bidirectional communication. The precise characterization unit for the resource potential of iron phosphate from waste lithium batteries quantitatively analyzes the parameters of iron phosphate in waste lithium batteries and transmits them to the dynamic control unit for the thermodynamic parameters of iron phosphate separation. The dynamic control unit for the thermodynamic parameters of iron phosphate separation constructs a system based on the characterization data. The thermodynamic equilibrium system transmits parameters to the multi-objective collaborative optimization unit for leaching efficiency. The multi-objective collaborative optimization unit for leaching efficiency outputs the optimal process parameters to the selective separation and enhancement unit for iron phosphate. The selective separation and enhancement unit for iron phosphate executes the separation operation and feeds back the process data to the real-time monitoring and feedback unit for recycling process data. The real-time monitoring and feedback unit for recycling process data analyzes and processes the data and generates adjustment commands that act in reverse on the first three units. The iron phosphate product purification and refining unit receives the separated material and performs purification operations based on the purity data provided by the monitoring unit, ultimately obtaining high-purity iron phosphate product. Each unit achieves efficient recovery of iron phosphate from waste lithium batteries through parameter interaction and collaborative control.

[0043] A method for efficiently recycling iron phosphate from spent lithium batteries is proposed. This method involves a comprehensive and systematic analysis of the core characteristics of iron phosphate, establishing a parameter linkage system throughout the entire recycling process to achieve precise adaptation and coordinated operation of each process step. A dynamic control mechanism throughout the process flexibly addresses the characteristic differences between different batches of spent lithium batteries, ensuring the stability and continuity of the recycling process. By strengthening the synergy of key process steps, the method improves the selectivity of iron phosphate separation while simultaneously optimizing product quality and recycling efficiency. The technical process is scientifically and rationally designed, with clearly defined and closely connected functions for each unit. It achieves efficient recycling without additional complex operations and is applicable to a wide range of scenarios. The method emphasizes the full utilization of resources during recycling, reducing unnecessary consumption and emissions, and balancing resource recycling and environmental protection requirements, demonstrating significant practical advantages.

[0044] This method addresses the problems of blindly setting process parameters and poor adaptability in traditional technologies. It comprehensively and accurately captures the characteristic parameters of iron phosphate in waste lithium batteries and deeply integrates them with the control parameters of processes such as separation and leaching. It dynamically adjusts process execution parameters based on the actual state of the resources, making process settings more targeted and effectively adapting to the characteristic differences of different batches of waste batteries, significantly improving the stability and consistency of the recycling process. Addressing the shortcoming of the disconnect between parameter optimization and process monitoring in traditional technologies, this method establishes a real-time dynamic feedback mechanism. It continuously captures changes in process status throughout the recycling process, promptly correcting process execution deviations, enhancing leaching and separation effects, significantly improving product purity and recycling efficiency, and completely changing the problem of accumulated process deviations caused by the lack of effective feedback in traditional technologies.

[0045] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for efficiently recycling iron phosphate from waste lithium batteries, characterized in that, Includes the following steps: S1, through the waste lithium battery resource potential assessment model, quantitatively characterizes the content distribution, active state and occurrence form of iron phosphate in waste lithium batteries, and simultaneously collects parameters such as battery electrode thickness, iron phosphate crystal particle size, element molar ratio and reactive site density. S2 is the initial input dataset for constructing a thermodynamic control model for phosphorus-iron separation based on the parameters obtained in S1. The thermodynamic equilibrium range is determined by controlling the temperature gradient, acid-base gradient, and interfacial tension parameters of the system. S3 employs a multi-objective optimization algorithm for leaching efficiency to coordinate and regulate leaching agent concentration, liquid-solid ratio, stirring rate, and reaction time in multiple dimensions, forming a dynamic optimization scheme. S4 starts the phosphorus-iron separation process based on the optimized parameters output by S3. By adjusting the mass transfer coefficient, diffusion rate and phase interface contact area of ​​the reaction system in real time, the separation effect of phosphorus-iron and impurities is enhanced. S5 utilizes the ferric phosphate recovery process data monitoring platform to continuously collect and adjust parameters such as pH value of leachate, ferric phosphate precipitation rate, impurity ion concentration and crystal growth rate. S6 purifies the separated ferric phosphate product through multi-stage filtration, washing and drying processes. Based on the purity data fed back from the monitoring platform, the process parameters are adjusted to achieve precise recovery of ferric phosphate.

2. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, The expression for the waste lithium battery resource potential assessment model is as follows: ， in, This represents the recyclability potential value of iron phosphate in spent lithium batteries. This is a parameter representing the proportion of active sites of iron phosphate. The electrode thickness influence coefficient is... This is a parameter for crystal structure integrity. The disturbance coefficient of impurity elements, This is a factor affecting the degree of battery aging. Let be the mass fraction of iron phosphate at the i-th sampling point. Let be the reactivity coefficient of the i-th sampling point. Let i be the morphological factor assigned to the i-th sampling point. This is a potential correction factor. The porosity parameter of the electrode. This is the dissolution rate constant of iron phosphate. This represents the total number of sampling points.

3. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, The expression for the thermodynamic control model of phosphorus-iron separation is: ， in, This represents the Gibbs free energy change of the phosphorus-iron separation reaction. It is the thermodynamic temperature coefficient. The temperature parameter of the reaction system This is the iron ion concentration parameter. This is a parameter representing the phosphate ion concentration. The acidity / alkalinity influence coefficient. The pH parameter of the reaction system. The interfacial tension parameter is the reaction interface tension parameter. The mass transfer resistance coefficient is... The diffusion coefficient is... For reaction time parameters, The synergistic effect coefficient, The interference factor for the j-th impurity ion. Let be the concentration parameter of the j-th impurity ion. This represents the number of impurity ion types.

4. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, The expression for the multi-objective optimization algorithm for leaching efficiency is: ， in, This represents the leaching efficiency value for ferric phosphate. The activity coefficient of the leaching agent. This is the liquid-to-solid ratio parameter. For stirring rate parameters, The viscosity coefficient of the reaction system is... For the density parameter of the leachate, This refers to the volume parameter of the leaching agent. For solid material quality parameters, The kinetic rate coefficient, For reaction pressure parameters, This is the leaching time parameter. This refers to the interface mass transfer rate parameter. To optimize the weighting coefficients, Let be the priority factor for the k-th optimization objective. For the actual value of the k-th optimization objective, To optimize the number of targets.

5. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, The comprehensive evaluation expression for process monitoring of the iron phosphate recovery process data monitoring platform is as follows: ， in, This is a comprehensive evaluation value for process monitoring. This is the concentration change monitoring coefficient. The rate of change of iron phosphate concentration. For temperature change monitoring coefficient, The rate of temperature change of the system. This is the pH / alkalinity change monitoring coefficient. The rate of change of pH in the system. This is the impurity monitoring coefficient. Let be the monitoring weight of the q-th impurity ion. Let be the change in concentration of the q-th impurity ion. To monitor the number of impurity ion types.

6. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, The comprehensive parameter optimization model expression for recycling iron phosphate from waste lithium batteries is as follows: ， in, This is a comprehensive evaluation value for the recycling effect. This represents the potential value for recycling. This represents the change in Gibbs free energy. This is the leaching efficiency value. This is a comprehensive evaluation value for process monitoring. This is a parameter representing the proportion of active sites. The electrode thickness influence coefficient is... This is a parameter for crystal structure integrity. This is a potential correction factor. The porosity parameter of the electrode. The dissolution rate constant is For the reaction temperature parameter, For pH parameters, Let be the stability factor for the s-th process step. Let be the efficiency factor of the s-th process step. Let be the purity factor of the s-th process step. This represents the total number of process steps.

7. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, S3 includes the following steps: S31. The thermodynamic equilibrium interval parameters obtained in S2 are imported into the input module of the leaching efficiency multi-objective optimization algorithm. The temperature gradient, acid-base gradient and reaction interfacial tension parameters are standardized and transformed by the parameter mapping rules built into the algorithm to form a numerical matrix that the algorithm can recognize. S32, based on the transformed numerical matrix, construct a multi-dimensional optimization objective function for leachate concentration, liquid-solid ratio, stirring rate and reaction time, set the constraint boundary conditions for each parameter, and clarify the coupling relationship between parameters; S33 uses an iterative solution mechanism to solve the objective function. By dynamically adjusting the iteration step size and convergence threshold, it gradually approaches the optimal solution and records the change trajectory of each parameter during the iteration process. S34. The effectiveness of the optimal parameter combination obtained by the solution is verified. By comparing the theoretical leaching efficiency corresponding to the parameter combination with the preset threshold, the final leaching process parameter scheme is determined.

8. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, S4 includes the following steps: S41, according to the leaching process parameters determined in S3, the leaching agent and waste lithium battery electrode powder are added to the reaction device in a set ratio, the stirring device is started, and the stirring rate is controlled to reach the preset value to ensure that the materials are fully mixed and in contact. S42 monitors the temperature change of the reaction system in real time, maintains the system temperature within the thermodynamic equilibrium range through a temperature control device, and dynamically adjusts the heating power or cooling rate according to temperature fluctuations. S43, by collecting the pH value change of the leachate during the reaction process through an online detection device, and based on the feedback results of the phosphorus-iron separation thermodynamic control model, the pH of the system is adjusted by adding acid-base regulators to maintain the stability of the phase interface tension; S44, during the reaction process, by adjusting the internal pressure and ventilation rate of the reaction device, the mass transfer coefficient and diffusion rate are optimized, the phase separation process of iron phosphate with iron ions and impurity ions is enhanced, and the selective precipitation of iron phosphate from the leachate is promoted.

9. The method for efficiently recycling iron phosphate from waste lithium batteries as described in claim 1, characterized in that, S5 includes the following steps: S51, start the sensor array of the iron phosphate recovery process data monitoring platform, deploy it at the calibration nodes of the reaction unit, separation unit and purification unit, set the parameter acquisition frequency and data accuracy standards to ensure continuous parameter acquisition; S52 transmits the collected data on leachate pH, ferric phosphate precipitation rate, impurity ion concentration, and crystal growth rate to the data analysis center of the monitoring platform in real time via the data transmission module for data preprocessing and noise reduction. S53, the data analysis center identifies and judges anomalies in the preprocessed data based on the preset parameter threshold range, and generates feedback adjustment signals for parameters that exceed the threshold, clarifying the adjustment direction and magnitude; S54 transmits feedback adjustment signals to each process execution unit, and corrects process deviations in real time by adjusting parameters such as the amount of leaching agent added, stirring rate, reaction temperature, or number of washing cycles, ensuring the stability and continuity of the iron phosphate recovery process.

10. A method for efficiently recycling iron phosphate from waste lithium batteries as described in any one of claims 1-9, characterized in that, The method is implemented through different units, including: a precise characterization unit for the resource potential of iron phosphate from waste lithium batteries, a dynamic control unit for thermodynamic parameters of iron phosphate separation, a multi-objective synergistic optimization unit for leaching efficiency, a selective separation enhancement unit for iron phosphate, a real-time monitoring and feedback unit for recycling process data, and a purification and refining unit for iron phosphate products. Each unit is connected to the control module bidirectionally via a data bus. The precise characterization unit for the resource potential of iron phosphate from waste lithium batteries quantitatively analyzes the parameters of iron phosphate in waste lithium batteries and transmits them to the dynamic control unit for thermodynamic parameters of iron phosphate separation. Based on the characterization data, the dynamic control unit for thermodynamic parameters of iron phosphate separation constructs a thermodynamic equilibrium system and transmits the parameters to the multi-objective collaborative optimization unit for leaching efficiency. The multi-objective collaborative optimization unit for leaching efficiency outputs the optimal process parameters to the selective separation enhancement unit for iron phosphate. The selective separation enhancement unit for iron phosphate performs the separation operation and feeds back the process data to the real-time monitoring and feedback unit for recycling process data. The real-time monitoring and feedback unit for recycling process data analyzes and processes the data and generates adjustment commands that act in reverse on the first three units. The iron phosphate product purification and refining unit receives the separated material and performs purification operations based on the purity data provided by the monitoring unit, ultimately obtaining high-purity iron phosphate product. Through parameter interaction and collaborative control, each unit achieves efficient recovery of iron phosphate from waste lithium batteries.

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