An application digital model suitable for full-chain integrated manufacturing

Abstract

The application relates to the technical field of battery recycling, and particularly provides an application digital model suitable for full-chain integrated manufacturing, which comprises the following modules: an index acquisition module, which acquires a hidden resistance index according to an actual temperature rising curve and a standard reference temperature rising curve; a parameter determination module, which determines a roasting temperature curve, a rotation speed upper limit value and a reagent adding upper limit set of a roasting kiln according to initial state information, the hidden resistance index, a target lithium recovery rate and a target graphite retention rate; a rotation speed adjustment module, which adjusts the rotation speed of the roasting kiln according to the deviation between the hidden resistance index and a preset resistance index under the condition that the rotation speed of the roasting kiln is less than or equal to the rotation speed upper limit value; and a reagent adjustment module, which adjusts the reagent dosage of each section according to the deviation between the section where a carbon reaction peak is located and a preset carbon reaction peak section under the condition that the dosages of all reagents are less than or equal to the corresponding reagent adding upper limit; and the model can effectively improve the overall efficiency and economic benefits of battery recycling.

Description

Technical Field

[0001] This application relates to the field of battery recycling technology, and more specifically, to an application digital model adapted to integrated manufacturing across the entire supply chain. Background Technology

[0002] Integrated manufacturing technologies for the entire battery recycling chain are rapidly developing, aiming to efficiently process retired batteries and convert them into high-purity products such as lithium carbonate, graphite, and iron phosphate. As the industry progresses, handling raw materials with complex origins and significant batch variations has become a key trend, driving the need to optimize continuous physical and chemical processes to adapt to uncertainties at the raw material level and improve overall recycling efficiency.

[0003] However, in the core aerobic roasting stage of battery recycling, the disturbance caused by batch-to-batch variations in raw materials makes parameter control difficult. This variation evolves into cross-process, variable-interacting interference with non-linear behavior. When the organic content in the raw materials fluctuates or impurities remain, blindly increasing the roasting intensity to pursue a high lithium leaching rate can trigger peroxidation. Peroxidation promotes lithium conversion but damages the graphite surface lattice structure, causing irreversible changes in surface energy, leading to increased hydrophilicity and sedimentation of graphite in subsequent flotation processes. Lithium recovery rate and graphite recovery rate exhibit conflicting relationships under specific process windows, accompanied by significant time delays and concealment. When a decline in downstream flotation yield is detected, a large amount of material already processed in the upstream roasting furnace may be under incorrect parameters, resulting in economic losses. Existing technologies struggle to maintain graphite structural integrity while ensuring lithium recovery rates and lack the ability to monitor and dynamically adjust material state changes during roasting, making it difficult to balance recovery efficiency and product quality.

[0004] There is currently no effective technical solution to the above problems. Summary of the Invention

[0005] The purpose of this application is to provide an application digital model that is compatible with integrated manufacturing across the entire supply chain, which can effectively improve the overall efficiency and economic benefits of battery recycling.

[0006] This application provides an application digital model adapted to integrated manufacturing across the entire supply chain, which includes: The information acquisition module is used to acquire the initial state parameters of the material to be roasted and map the initial state parameters to the initial state information of virtual particles. The curve acquisition module is used to acquire the actual temperature rise curve of the material to be roasted after it enters the preheating section of the roasting kiln. The index acquisition module is used to acquire latent resistance indexes based on the actual temperature rise curve and the preset standard reference temperature rise curve. The parameter determination module is used to determine the calcination temperature curve, rotation speed limit, and reagent dosage limit set of the calcination kiln based on the initial state information, hidden resistance index, preset target lithium recovery rate, and preset target graphite retention ratio. Then, it controls the calcination kiln to calcinate according to the calcination temperature curve. The reagent dosage limit set includes the reagent dosage limit corresponding to different reagents. The speed adjustment module is used to adjust the speed of the roasting kiln according to the deviation between the hidden resistance index and the preset resistance index, so as to adjust the residence time of the material to be roasted in the roasting kiln, provided that the speed of the roasting kiln is less than or equal to the upper limit of the speed. The reagent adjustment module is used to monitor the changes in carbon dioxide generation rate in segments along the length of the roasting kiln, locate the segment where the carbon reaction peak is located based on the changes in the carbon oxidation rate, and then adjust the reagent dosage of each segment according to the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment, provided that the dosage of all reagents is less than or equal to the corresponding reagent dosage limit.

[0007] As can be seen from the above, the application digital model adapted to the whole-chain integrated manufacturing provided by this application, by introducing a virtual particle model, quantification of hidden resistance indicators, multi-parameter predictive optimization, and dynamic real-time control mechanism, can more accurately perceive material characteristics and more intelligently set and adjust calcination parameters. Thus, while ensuring a high lithium recovery rate, it can maximize the protection of graphite integrity. Therefore, this application can effectively solve the conflict between lithium recovery rate and graphite retention ratio, thereby effectively improving the overall efficiency and economic benefits of battery recycling. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the structure of an application digital model adapted to integrated manufacturing across the entire supply chain, provided as an embodiment of this application.

[0009] Attached reference numerals: 1. Information acquisition module; 2. Curve acquisition module; 3. Index acquisition module; 4. Parameter determination module; 5. Speed ​​adjustment module; 6. Reagent adjustment module. Detailed Implementation

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0011] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0012] like Figure 1 As shown, this application provides an application digital model adapted to integrated manufacturing across the entire supply chain, which includes: Information acquisition module 1 is used to acquire the initial state parameters of the material to be roasted and map the initial state parameters to the initial state information of virtual particles; Curve acquisition module 2 is used to acquire the actual temperature rise curve of the material to be roasted after it enters the preheating section of the roasting kiln. The index acquisition module 3 is used to acquire hidden resistance indexes based on the actual temperature rise curve and the preset standard reference temperature rise curve. The parameter determination module 4 is used to determine the roasting temperature curve, rotation speed limit, and reagent addition limit set of the roasting kiln based on the initial state information, hidden resistance index, preset target lithium recovery rate, and preset target graphite retention ratio, and then control the roasting kiln to roast according to the roasting temperature curve; the reagent addition limit set includes the reagent addition limit corresponding to different reagents. The speed adjustment module 5 is used to adjust the speed of the roasting kiln according to the deviation between the hidden resistance index and the preset resistance index, so as to adjust the residence time of the material to be roasted in the roasting kiln, provided that the speed of the roasting kiln is less than or equal to the upper limit of the speed. The reagent adjustment module 6 is used to monitor the changes in carbon dioxide generation rate in segments along the length of the roasting kiln, locate the segment where the carbon reaction peak is located based on the changes in all carbon oxide generation rates, and then adjust the reagent dosage of each segment according to the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment, provided that the dosage of all reagents is less than or equal to the corresponding reagent addition limit.

[0013] Information acquisition module 1 is used to acquire the initial state parameters of the material to be roasted and map these parameters to the initial state information of virtual particles. This module performs online particle size measurement and component ratio determination on the output at the end of the centrifugal gravity separation process, obtaining quantifiable indicators such as particle size distribution, copper and aluminum residue rate, organic carbon content, and particle specific surface area. These detection results are mapped to the initial state information of each virtual particle, which includes particle diameter, estimated surface organic cover thickness, and estimated specific surface area and thermal conductivity. All mapping rules are calculated using explicit physical formulas and empirical constants, prohibiting the introduction of inductive inferences based on historical statistical patterns as the sole criterion for judgment.

[0014] The application digital model proposed in this application, adapted to integrated manufacturing across the entire supply chain, aims to optimize the aerobic roasting process in battery recycling through digital means. The material to be roasted in this embodiment typically refers to pre-treated waste battery materials, such as crushed and sorted black powder, which has a complex composition and significant batch-to-batch variations. The virtual particles in this embodiment are abstract concepts used in the digital model to simulate the behavior of real materials. Each virtual particle carries the physical and chemical properties of the material, such as particle diameter, surface organic coating thickness, specific surface area, and thermal conductivity, which together constitute the initial state information. The implicit resistance index in this embodiment is a key parameter that quantifies the material's inherent resistance to temperature increases during roasting, reflecting the material's reactivity and thermophysical properties—deep attributes that are difficult to obtain directly using traditional detection methods. The roasting kiln in this embodiment is the core equipment for aerobic roasting. Its roasting temperature curve, maximum rotation speed, and maximum reagent dosage set are key control parameters affecting the roasting effect. This model aims to optimize the roasting process while ensuring the target lithium recovery rate and target graphite retention ratio through intelligent control of these parameters.

[0015] The digital model applied in this application works collaboratively through multiple modules to achieve refined control of the battery recycling roasting process. First, the information acquisition module 1 is responsible for acquiring the initial state parameters of the material to be roasted. These parameters are the basic properties of the material before it enters the roasting kiln. For example, particle size distribution, copper and aluminum residue rates, organic carbon content, and particle specific surface area can be collected manually or automatically by sensors. After acquiring these parameters, the information acquisition module 1 maps them to the initial state information of virtual particles. For example, based on preset empirical formulas, particle size distribution can be converted into the particle diameter of virtual particles, organic carbon content into the thickness of the surface organic coating, and particle specific surface area into the specific surface area of ​​virtual particles. This information is then combined with a preset thermal conductivity coefficient to form the initial state information of virtual particles. Second, the curve acquisition module 2 is used to acquire the actual temperature rise curve of the material to be roasted. After the material enters the preheating section of the roasting kiln, temperature sensors can be deployed inside the kiln to monitor the temperature changes of the material in real time and record the temperature values ​​at different time points, thereby forming the actual temperature rise curve. Next, the index acquisition module 3 acquires the latent resistance index based on the actual heating curve and the preset standard reference heating curve. The preset standard reference heating curve is preferably an ideal heating curve derived from extensive experimental data or theoretical calculations; the measurement of the standard reference heating curve is existing technology. The index acquisition module 3 can quantify the latent resistance of the material by comparing the differences between the actual heating curve and the standard reference heating curve, such as calculating the area difference between the two curves or the temperature deviation at a specific point. Subsequently, the parameter determination module 4 determines the calcination temperature curve, the upper limit of the rotation speed, and the upper limit set of reagent dosage for the calcining kiln based on the initial state information, the latent resistance index, the preset target lithium recovery rate, and the preset target graphite retention ratio. After determining these parameters, the parameter determination module 4 controls the calcining kiln to calcinate according to the determined calcination temperature curve. Furthermore, the rotation speed adjustment module 5 is used to adjust the rotation speed of the calcining kiln based on the deviation between the latent resistance index and the preset resistance index, provided that the rotation speed of the calcining kiln is less than or equal to the upper limit of the rotation speed. For example, if the latent resistance index shows that the material's reactivity is lower than expected (the reactivity corresponding to the preset resistance index), it indicates that the material may need a longer residence time to react fully. The speed adjustment module 5 can appropriately reduce the speed of the roasting kiln to extend the material's residence time within the kiln. Conversely, if the material's reactivity is too high, the speed can be appropriately increased. Finally, the reagent adjustment module 6 monitors the changes in carbon dioxide generation rate segmentally along the length of the roasting kiln. For example, gas analyzers can be installed at different sections of the roasting kiln to monitor the carbon dioxide concentration in each section in real time and calculate its generation rate. Based on all the changes in carbon dioxide generation rate, the reagent adjustment module 6 can locate the section where the carbon reaction peak is located, i.e., the section in the roasting furnace where the carbon oxidation reaction is most intense.Then, provided that the dosage of all reagents is less than or equal to their corresponding upper limit, the dosage of reagents in each segment is adjusted according to the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment. For example, if the carbon reaction peak appears in a segment earlier or later than the preset position, the reagent adjustment module 6 can adjust the dosage of reagents in that segment or adjacent segments accordingly to bring the carbon reaction peak back to the ideal position, thereby optimizing the carbon removal efficiency and graphite retention.

[0016] The core innovation of this application lies in the introduction of a hidden resistance index and a multi-parameter synergistic control mechanism. Unlike existing technologies that primarily rely on experience or fixed parameters for roasting control, this application uses an information acquisition module 1 to acquire the initial state parameters of the material to be roasted and maps them to the initial state information of virtual particles, laying the foundation for subsequent refined control. More importantly, the curve acquisition module 2 acquires the actual heating curve, and the index acquisition module 3 acquires the hidden resistance index based on the actual heating curve and a preset standard reference heating curve. This hidden resistance index can quantify the intrinsic reaction characteristics of the material during roasting, effectively compensating for the shortcomings of existing technologies in understanding the deep properties of materials. Based on this, the parameter determination module 4 can intelligently determine the roasting temperature curve, the upper limit of the rotation speed, and the upper limit set of reagent dosage in the roasting kiln according to the initial state information, the hidden resistance index, the preset target lithium recovery rate, and the preset target graphite retention ratio. This predictive optimization capability allows roasting parameters to be customized according to the actual characteristics of the material, rather than using unchanging universal parameters, thus effectively avoiding problems such as over-oxidation or incomplete roasting caused by parameter mismatch. The speed control module 5 and reagent control module 6 of this application achieve dynamic real-time control of the roasting process. Specifically, the speed control module 5 adjusts the speed of the roasting kiln according to the deviation of the hidden resistance index to regulate the residence time of the material and ensure sufficient reaction. The reagent control module 6 monitors the carbon dioxide generation rate in segments, locates the carbon reaction peak, and adjusts the reagent dosage in each segment according to its deviation, thus achieving precise control of the carbon oxidation reaction. This dynamic feedback and control mechanism enables this application to respond promptly to minor changes in the roasting process, effectively avoiding the problem of discovering that a large amount of material processed in the front-end roasting furnace is under incorrect parameters only when the downstream flotation yield declines, significantly reducing economic losses.

[0017] As can be seen, by introducing a virtual particle model, quantifying the hidden resistance index, multi-parameter predictive optimization, and a dynamic real-time control mechanism, this application can more accurately perceive material characteristics and more intelligently set and adjust calcination parameters. This ensures a high lithium recovery rate while maximizing the protection of graphite integrity. Therefore, this application can effectively resolve the conflict between lithium recovery rate and graphite retention ratio, thereby effectively improving the overall efficiency and economic benefits of battery recycling.

[0018] In some preferred embodiments, the initial state parameters include particle size distribution, copper-aluminum residue ratio, organic carbon content, and particle specific surface area. In this embodiment, particle size distribution refers to the distribution of particle size in the material to be roasted. This parameter helps assess the material's bulk density, porosity, heat and mass transfer efficiency, and reaction uniformity. In this embodiment, particle size distribution is obtained by measuring the percentage of particles within different particle size ranges using a laser diffraction particle size analyzer. In this embodiment, copper-aluminum residue ratio refers to the proportion of metallic impurities such as copper and aluminum in the material to be roasted. These metallic impurities may undergo side reactions with the target product during roasting, thereby affecting lithium recovery and graphite purity or altering the thermophysical properties of the material. In this embodiment, the copper-aluminum residue ratio can be obtained by quantitatively determining the copper and aluminum content through elemental analysis of the material to be roasted using X-ray fluorescence spectrometry (XRF) or inductively coupled plasma optical emission spectrometry (ICP-OES). In this embodiment, the organic carbon content refers to the proportion of carbon in the organic matter of the material to be roasted. Organic carbon undergoes a combustion reaction during roasting, releasing heat and consuming oxygen. Its content directly affects the thermal balance and redox atmosphere of the roasting process; excessively high levels may lead to peroxidation. In this embodiment, the organic carbon content can be obtained by performing combustion analysis on the material to be roasted using an elemental analyzer to determine the organic carbon content. The particle specific surface area in this embodiment refers to the total surface area per unit mass or unit volume of material. The specific surface area directly affects the contact area and reaction rate between the material and gases (such as oxygen and reagents) in the roasting atmosphere, significantly influencing heat conduction and chemical reaction kinetics. This parameter can be calculated by measuring the amount of nitrogen adsorbed by the material using the BET (Brunauer-Emmett-Teller) method, based on the principle of gas adsorption, and then calculating the specific surface area.

[0019] In some preferred embodiments, the initial state information includes particle diameter, surface organic coating thickness, specific surface area, and thermal conductivity. In this embodiment, particle diameter refers to the size of a single particle in the material to be roasted. Particle diameter is a key physical parameter affecting the material's thermal conductivity and chemical reaction rate. Specifically, smaller particles typically have a larger specific surface area, enabling them to absorb heat and react more quickly; while larger particles may have internal heat transfer resistance, affecting the overall reaction uniformity. In this embodiment, surface organic coating thickness refers to the thickness of the organic layer adhering to the surface of the material particles to be roasted. This organic coating affects the transfer of heat to the particle interior and may undergo decomposition or oxidation reactions during roasting, releasing heat or consuming oxidants, thus affecting the overall thermal balance and reaction kinetics of the roasting process. In this embodiment, specific surface area refers to the total surface area per unit mass of material. Specific surface area is an important indicator of the material's contact efficiency with its surrounding environment (such as heat sources and reactant gases). A larger specific surface area means more reaction sites, which is beneficial for heat transfer and chemical reactions, especially for redox reactions. In this embodiment, the thermal conductivity coefficient refers to the material's ability to transfer heat, that is, the amount of heat passing through a unit area per unit time under a unit temperature gradient. The thermal conductivity coefficient directly determines the efficiency of heat transfer within and between material particles. An accurate thermal conductivity coefficient helps in establishing precise heat transfer models and predicting the temperature distribution and heating rate of the material within the calcining kiln.

[0020] In some preferred embodiments, the process of mapping the initial state parameters to the initial state information of the virtual particles includes: A1. Use preset mapping rules to map the initial state parameters to the preliminary state information of virtual particles; A2. Based on the preliminary state information of the virtual particles, predict the temperature rise curve of the material to be roasted; A3. Correct the preset mapping rules based on the deviation between the actual heating curve and the predicted heating curve; A5. Use the modified preset mapping rules to map the initial state parameters to the initial state information of the virtual particles.

[0021] The preset mapping rule in this embodiment is a lookup table used to convert the physical or chemical properties (i.e., initial state parameters) of the material to be roasted into corresponding attributes (i.e., preliminary state information of the virtual particles) of the virtual particles in the simulated environment. This lookup table stores the state information corresponding to different state parameters. The preliminary state information of the virtual particles is the initial representation of the real material particles after abstraction and modeling in the digital model. It may include particle diameter, surface organic coverage thickness, specific surface area, and thermal conductivity, etc. This information is obtained after the initial state parameters are converted by the preset mapping rule and is used to simulate the behavior of the material in the virtual environment. This embodiment can use a pre-trained temperature change prediction model to predict the heating curve of the material to be roasted based on the preliminary state information of the virtual particles. Specifically, the pre-training process of the temperature change prediction model is as follows: pre-calibrate multiple sets of state information and their corresponding temperature change curves; divide the multiple sets of state information and their corresponding temperature change curves into training sets and test sets; train the existing machine learning model using the training set until the similarity rate between the temperature change curve predicted by the machine learning model based on the state information of the test set and the temperature change curve of the test set reaches a preset target similarity rate, and then use the machine learning model as the trained temperature change prediction model. The deviation between the actual and predicted heating curves refers to the difference between them. This difference can be quantified using various mathematical methods, such as calculating the root mean square error, mean absolute error, or integral absolute error of the temperature difference between the two curves at the same time point or location. This deviation is a key indicator for evaluating the accuracy of the preset mapping rule. Correcting the preset mapping rule involves adjusting and optimizing it based on the deviation between the actual and predicted heating curves to improve its accuracy and adaptability. The correction method may include: extracting the corresponding rule correction amount from a preset mapping relationship table of curve deviation and rule correction amount based on the deviation between the actual and predicted heating curves; then adjusting the empirical coefficients or weights used in the mapping rule using the rule correction amount to make the mapping rule more accurately reflect the characteristics of different batches of materials. After the preset mapping rule is corrected, this embodiment uses the rule to map the initial state parameters to the initial state information of virtual particles.

[0022] This application introduces a dynamic correction mechanism to ensure that the virtual particle state information more closely matches the actual roasting process. Specifically, this application first uses preset mapping rules to map the initial state parameters of the material to be roasted, such as particle size distribution, copper and aluminum residue rate, organic carbon content, and particle specific surface area, to preliminary state information of virtual particles, such as particle diameter, surface organic coverage thickness, specific surface area, and thermal conductivity. This preliminary mapping provides a basic model, but it may have inherent errors due to batch differences in raw materials. Subsequently, based on the preliminary state information of these virtual particles, the system predicts the temperature rise curve of the material to be roasted in the preheating section of the roasting kiln. This prediction process allows for the initial evaluation of the mapping rules in the virtual model, thereby exposing potential prediction biases, rather than directly relying on the original parameters. To eliminate such biases, this application further dynamically corrects the preset mapping rules based on the deviation between the actual and predicted temperature rise curves. By comparing the actual data with the prediction results, the system can directly optimize for the sources of bias and adjust the mapping rules to better suit the characteristics of the current batch of materials. Finally, using the modified preset mapping rules, the initial state parameters are remapped to the initial state information of virtual particles. Through the above dynamic correction mechanism, this application ensures that the information acquisition module 1 can provide highly accurate initial state information of virtual particles for subsequent roasting control. This precise initial state information, along with the actual temperature rise curve obtained by the curve acquisition module 2 and the hidden resistance index calculated by the index acquisition module 3, are input into the parameter determination module 4. Based on these reliable inputs, the parameter determination module 4 can more accurately determine the roasting temperature curve, the upper limit of the rotation speed, and the upper limit set of reagent addition for the roasting kiln, so that the rotation speed adjustment module 5 and the reagent adjustment module 6 can achieve refined and adaptive control of the roasting process, thereby effectively dealing with the disturbances caused by batch differences in raw materials, avoiding over-oxidation, and preserving the integrity of graphite to the greatest extent while ensuring lithium recovery rate. This dynamic feedback correction mechanism enables the application digital model of the entire integrated manufacturing process to continuously optimize its understanding of material characteristics, thereby significantly improving the stability of the roasting process and product recovery efficiency.

[0023] In some preferred embodiments, the information acquisition module 1 is further used to acquire the initial temperature of the material to be calcined, and the process of acquiring the latent resistance index based on the actual heating curve and the preset standard reference heating curve includes: B1. For each sampling point in the actual heating curve, obtain the first temperature value deviation based on the actual temperature value corresponding to the sampling point on the actual heating curve and the reference temperature value corresponding to the sampling point on the preset standard reference heating curve, and obtain the second temperature value deviation based on the reference temperature value corresponding to the sampling point on the standard reference heating curve and the initial temperature of the material to be roasted. B2. The summation of all deviations of the first temperature value is taken as the first cumulative temperature difference value; B3. The summation of all deviations of the second temperature value is taken as the cumulative value of the second temperature difference; B4. The ratio of the first cumulative temperature difference to the second cumulative temperature difference is used as an indicator of latent resistance.

[0024] This embodiment first uses information acquisition module 1 to obtain the initial temperature of the material to be roasted before it enters the preheating section of the roasting kiln. This initial temperature is the reference thermal state of the material at the start of the roasting process, which is crucial for accurately assessing the material's heating behavior in the preheating section. The initial temperature can be obtained in various ways. For example, a non-contact infrared thermometer can be installed on the conveyor belt before the material enters the roasting kiln, or the average temperature of the material pile can be directly measured using a contact thermocouple.

[0025] Subsequently, for each sampling point in the actual heating curve, the system performs two temperature deviation calculations. First, it obtains the first temperature deviation, which compares the difference between the actual temperature value measured at that sampling point on the actual heating curve and the corresponding ideal reference temperature value on the preset standard reference heating curve. Second, it obtains the second temperature deviation, which compares the difference between the reference temperature value at that sampling point on the preset standard reference heating curve and the initial temperature of the material to be roasted. This dual deviation calculation method can comprehensively capture the degree of deviation between the actual heating process and the ideal heating process of the material, and takes into account the initial thermal state of the material. Next, the first temperature deviations at all sampling points in the preheating section are accumulated to obtain the first cumulative temperature difference value. This cumulative value reflects the overall degree of deviation between the actual heating process and the standard reference heating process of the material to be roasted throughout the entire preheating section. Meanwhile, the deviations of the second temperature values ​​at all sampling points in the preheating section are summed to obtain the second cumulative temperature difference value. This cumulative value represents the total temperature rise achieved by the material from the initial temperature to the standard reference temperature rise curve under ideal conditions. This cumulative value provides a benchmark for the standardization of the hidden resistance index.

[0026] Ultimately, the hidden resistance index is quantified by calculating the ratio of the first cumulative temperature difference to the second cumulative temperature difference. The larger the ratio, the greater the deviation between the actual temperature rise and the ideal temperature rise of the material, and the stronger its resistance. This ratio calculation method can eliminate the influence of different measurement times or the number of sampling points, making the index more comparable and robust.

[0027] This embodiment cleverly combines the initial thermal state of the material, its actual heating behavior, and an ideal heating model, and standardizes and quantifies the material's heating resistance through a ratio. Because this embodiment not only considers the overall heating performance of the material in the preheating section but also introduces the initial temperature as a reference, this index can more accurately reflect the influence of the material's own physicochemical properties on the thermal response, thus providing a more objective assessment of the material's resistance rather than simply attributing it to external heating conditions. This effectively improves the accuracy and reliability of the latent resistance index, and consequently, effectively improves the accuracy and reliability of the calcination temperature curve, the upper limit of rotational speed, and the upper limit set of reagent dosage.

[0028] In some preferred embodiments, the process of determining the calcination temperature curve, rotation speed upper limit, and reagent dosage upper limit set of the calcination kiln based on initial state information, occult resistance index, preset target lithium recovery rate, and preset target graphite retention ratio includes: C1. Based on the initial state information and hidden resistance indicators, extract the corresponding parameter set, expected lithium recovery rate, and expected graphite retention ratio from the preset mapping table of combinations of state information and resistance indicators and their corresponding parameter sets, expected lithium recovery rate, and expected graphite retention ratio; the parameter set includes temperature curves, maximum rotation speed, and maximum reagent dosage set; C2. Analyze whether the expected lithium recovery rate and the expected graphite retention ratio are simultaneously met. If yes, proceed to step C3; otherwise, proceed to step C4. C3. The extracted temperature curve is used as the calcination temperature curve, the highest extraction speed is used as the upper limit of the speed, and the maximum amount of extracted reagent is used as the upper limit of reagent addition. C4. Obtain the current lithium price and graphite price; C5. When the price of lithium is greater than the price of graphite, extract the parameter set that the expected lithium recovery rate reaches the target lithium recovery rate from the mapping table as the first candidate parameter set, and take the first candidate parameter set with the largest expected graphite retention ratio as the first target parameter set. Then, take the temperature curve, maximum rotation speed and maximum reagent dosage set contained in the first target parameter set as the calcination temperature curve, rotation speed upper limit and reagent dosage upper limit set, respectively. C6. When the price of graphite is greater than the price of lithium, extract the parameter set from the mapping table that the expected graphite retention ratio reaches the target lithium recovery rate as the second candidate parameter set, and take the first candidate parameter set with the largest expected lithium recovery rate as the second target parameter set. Then, take the temperature curve, maximum rotation speed and maximum reagent dosage set contained in the second target parameter set as the calcination temperature curve, rotation speed upper limit and reagent dosage upper limit set, respectively. C7. When the price of lithium is equal to the price of graphite, extract the parameter set that maximizes the sum of the expected lithium recovery rate and the graphite retention ratio from the mapping table as the third target parameter set. Then, use the temperature curve, maximum rotation speed and maximum reagent dosage set contained in the third target parameter set as the calcination temperature curve, rotation speed upper limit and reagent dosage upper limit set, respectively.

[0029] Step C1 involves extracting corresponding parameters from a pre-constructed mapping table based on initial state information and latent resistance indicators. This mapping table is a pre-established database that can store or calculate the corresponding set of process parameters (including temperature curves, maximum rotation speed, and maximum reagent dosage) and the expected lithium recovery rate and expected graphite retention ratio under different combinations of state information and resistance indicators. This mapping table can be constructed based on a large amount of experimental data and / or simulation data. The data acquisition and construction processes of the mapping table are both existing technologies. In step C2, the system analyzes whether the expected lithium recovery rate and expected graphite retention ratio extracted from the mapping table can simultaneously meet the preset target lithium recovery rate and preset target graphite retention ratio. These target values ​​can be preset according to production needs or market strategies. If the expected results can simultaneously meet these two targets, then in step C3, the system directly uses the extracted temperature curve, maximum rotation speed, and maximum reagent dosage set as the control parameters and constraints of the calcining kiln, i.e., the calcining temperature curve, the upper limit of rotation speed, and the upper limit of reagent dosage. When the expected results fail to simultaneously meet the preset targets, the system proceeds to step C4 to obtain the current lithium and graphite prices. This can be achieved through real-time market data interfaces, financial information platforms, or manual input. Obtaining the current lithium and graphite prices aims to incorporate real-time market economic factors into decision-making. Steps C5, C6, and C7 are equivalent to dynamically adjusting the parameter selection strategy based on the relative relationship between lithium and graphite prices. In step C5, when the lithium price is higher than the graphite price, the system prioritizes the economic value of lithium. At this time, the system filters all parameter sets that can make the expected lithium recovery rate reach the preset target lithium recovery rate from the mapping table, forming a first candidate parameter set. Based on this, the system further selects the parameter set that maximizes the expected graphite retention ratio from the first candidate parameter set as the first target parameter set. Finally, the temperature curve, maximum rotation speed, and maximum reagent dosage set contained in the first target parameter set are used as the roasting temperature curve, rotation speed upper limit, and reagent dosage upper limit set of the roasting kiln, respectively. In step C6, when the price of graphite is higher than the price of lithium, the system will prioritize the economic value of graphite. At this time, the system will select all parameter sets that can make the expected graphite retention ratio reach the preset target graphite retention ratio from the mapping relationship table to form a second candidate parameter set. On this basis, the system will further select the parameter set that maximizes the expected lithium recovery rate from the second candidate parameter set as the second target parameter set. Finally, the temperature curve, maximum rotation speed and maximum reagent dosage set contained in the second target parameter set will be used as the roasting temperature curve, rotation speed upper limit and reagent dosage upper limit set of the roasting kiln, respectively.In step C7, when the price of lithium is equal to the price of graphite, the system seeks to maximize the combined economic value of the two. At this time, the system selects the parameter set that maximizes the sum of the expected lithium recovery rate and the expected graphite retention ratio from the mapping table as the third target parameter set. Finally, the temperature curve, maximum rotation speed and maximum reagent dosage set contained in the third target parameter set are used as the roasting temperature curve, rotation speed upper limit and reagent dosage upper limit set of the roasting kiln, respectively.

[0030] This embodiment addresses the challenge of maximizing economic benefits when lithium recovery and graphite retention conflict by incorporating real-time market prices as a decision-making basis. First, the system extracts a preliminary set of process parameters, along with corresponding expected lithium recovery and graphite retention rates, from a pre-defined mapping table based on the initial state information and implicit resistance indicators of the material to be calcined. This process ensures that parameter selection is based on the actual characteristics of the material. Subsequently, the system determines whether these preliminary parameters can simultaneously meet the preset targets for lithium recovery and graphite retention. If they do, these parameters are directly used for calcination, avoiding unnecessary complex adjustments. When the preliminary parameters cannot simultaneously meet the preset targets, the advantages of this solution become fully apparent. In this case, the system obtains the current lithium and graphite prices, incorporating real-time market value into the decision-making process. Based on the relative prices of lithium and graphite, the system dynamically adjusts its optimization strategy: when lithium prices are high, priority is given to ensuring the lithium recovery rate reaches the target, and under this condition, the parameter set that maximizes the graphite retention ratio is selected to maximize the value of lithium while balancing graphite revenue as much as possible; when graphite prices are high, priority is given to ensuring the graphite retention ratio reaches the target, and under this condition, the parameter set that maximizes the lithium recovery rate is selected to maximize the value of graphite while balancing lithium revenue as much as possible; when the prices of lithium and graphite are equal, the parameter set that maximizes the sum of the lithium recovery rate and the graphite retention ratio is selected to achieve a balance in overall revenue. This market-dynamic parameter selection mechanism allows the roasting process to flexibly adapt to raw material fluctuations and market changes, thereby maximizing economic benefits even when lithium recovery rate and graphite retention ratio conflict.

[0031] In some preferred embodiments, the target lithium recovery rate and graphite retention ratio are determined based on the current lithium and graphite prices. This embodiment links the setting of the target lithium recovery rate and graphite retention ratio to real-time market prices, enabling the parameter determination module 4 of the entire battery recycling process to dynamically optimize based on economic benefits. Since the parameter determination module 4 determines the roasting temperature curve, rotation speed limit, and reagent dosage limit set of the roasting kiln based on initial state information, latent resistance indicators, and the target lithium recovery rate and target graphite retention ratio, this embodiment effectively makes the target lithium recovery rate and graphite retention ratio no longer static preset values, but dynamically adjusted according to the current lithium and graphite prices. This means that when the market value of lithium is high, the system will automatically adjust the target, prioritizing increasing the lithium recovery rate, even if this may have some impact on the graphite retention ratio; conversely, when the market value of graphite is higher, the system will prioritize protecting the integrity of the graphite. Therefore, this embodiment enables the roasting parameters output by the parameter determination module 4 to maximize the economic benefits of the recycling process, thereby effectively solving the technical problem of economic suboptimality caused by ignoring market factors in preset target values.

[0032] As a specific implementation method, the application digital model adapted to integrated manufacturing across the entire supply chain can integrate a market data interface. This interface can obtain real-time spot prices of lithium (e.g., lithium carbonate or lithium hydroxide) and graphite (e.g., high-purity graphite) globally or in specific regions. For example, the system can automatically query and update this price data at regular intervals (e.g., hourly or daily). Upon obtaining the current lithium and graphite prices, the system's internal decision-making logic, based on this price information and a pre-defined mapping table of price combinations and their corresponding target combinations, selects the corresponding target lithium recovery rate and target graphite retention ratio. For instance, if the lithium price is 500,000 yuan per ton and the graphite price is 50,000 yuan per ton, the mapping table simply sets the target lithium recovery rate to 98% and the target graphite retention ratio to 85%. When the lithium price is 300,000 yuan per ton and the graphite price is 80,000 yuan per ton, the mapping table indicates that the target graphite retention ratio be adjusted to 90% and the target lithium recovery rate to 95%. These dynamically adjusted target values ​​are then passed to parameter determination module 4 as input for determining the operating parameters of the roasting kiln, ensuring that the entire roasting process is always guided by maximizing economic benefits.

[0033] In some preferred embodiments, when the rotational speed of the calcining kiln is less than or equal to the upper limit of the rotational speed, the process of adjusting the rotational speed of the calcining kiln according to the deviation between the latent resistance index and the preset resistance index includes: D1. Determine the initial speed adjustment amount based on the deviation between the hidden resistance index and the preset resistance index; D2. Obtain the real-time axial movement speed of the material to be roasted in the roasting kiln; D3. Correct the initial speed adjustment amount based on the deviation between the real-time axial movement speed and the preset target axial movement speed to obtain the corrected speed adjustment amount; D4. If the rotational speed of the calcining kiln is less than or equal to the upper limit of the rotational speed, adjust the rotational speed of the calcining kiln according to the correction adjustment amount.

[0034] The initial rotational speed adjustment is determined based on the deviation between the latent resistance index and the preset resistance index. This aims to provide an initial direction and magnitude for rotational speed adjustment based on the inherent thermochemical properties of the material to be roasted. This initial adjustment can be determined using a lookup table method (by consulting a preset mapping table of index deviations and rotational speed adjustments based on the deviation between the latent and preset resistance indices. This mapping table maps specific index deviations to corresponding rotational speed adjustment values; for example, when the latent resistance index is significantly higher than the preset resistance index, the rotational speed adjustment simply requires reducing the kiln speed to prolong the material residence time and ensure sufficient reaction). Obtaining the real-time axial velocity of the material within the kiln is crucial for monitoring its actual physical transport state. The axial velocity is influenced by various factors, including the kiln's rotational speed, inclination angle, material bulk density, particle shape, and internal kiln friction. Real-time axial movement speed can be obtained in various ways. For example, a machine vision system can be used, where an industrial camera installed on the kiln wall continuously captures images of the material surface, and image processing algorithms (such as feature point tracking and optical flow methods) are used to calculate the average movement speed of the material. Alternatively, non-contact sensors installed inside the kiln (such as laser Doppler velocimeters) can be used to directly measure the speed of the material surface. The initial rotational speed adjustment is corrected based on the deviation between the real-time axial movement speed and the preset target axial movement speed. This aims to incorporate the actual physical transport behavior of the material into the rotational speed adjustment. The preset target axial movement speed is determined based on ideal roasting process conditions and material residence time requirements. When there is a deviation between the real-time axial movement speed and the preset target axial movement speed, it indicates that the actual residence time of the material in the kiln may deviate from expectations. For example, if the material moves too fast, the rotational speed needs to be further reduced to extend the residence time; if the material moves too slowly, the rotational speed may need to be appropriately increased. This correction can be achieved using a proportional-integral-derivative (PID) control algorithm. The axial movement speed deviation is used as input to calculate a correction term, which is then superimposed on the initial speed adjustment to obtain a more accurate and comprehensive corrected speed adjustment. Assuming the calcining kiln's speed is less than or equal to the upper speed limit, the kiln's speed is adjusted according to the corrected speed adjustment, ensuring the kiln operates within safe and process-permissible limits. The upper speed limit is determined by parameter determination module 4 based on initial state information, latent resistance indicators, target lithium recovery rate, and target graphite retention ratio. During speed adjustment, the control system first checks whether the corrected speed exceeds this upper limit. If the corrected speed exceeds the upper limit, it is limited to that limit to avoid equipment overload or excessive material tumbling.

[0035] This embodiment optimizes the rotational speed adjustment process by combining the deviation of the latent resistance index and the axial movement speed information of the material to more accurately control the material residence time. First, an initial rotational speed adjustment is determined based on the deviation between the latent resistance index and the preset resistance index. Second, the real-time axial movement speed of the material to be roasted in the kiln is acquired, introducing dynamic data on the actual movement of the material within the kiln and capturing changes in movement speed caused by raw material differences. Then, the initial rotational speed adjustment is corrected based on the deviation between the real-time axial movement speed and the preset target axial movement speed. This integrates resistance deviation and movement speed deviation, making the adjustment more comprehensive and avoiding errors caused by ignoring the dynamic movement of the material. Finally, while ensuring that the rotational speed does not exceed the upper limit, the rotational speed is adjusted according to the corrected adjustment, ensuring that the operation is performed within a safe range and preventing the risk of overspeed. Through this step-by-step correction mechanism, this scheme enhances the real-time performance and accuracy of rotational speed adjustment, effectively responding to raw material disturbances. This scheme, in conjunction with the upper limit of rotational speed, ensures that the roasting kiln can finely control the real-time behavior of the material under established safety and process constraints, thereby achieving more precise residence time control when dealing with batch differences in raw materials.

[0036] In some preferred embodiments, provided that the dosage of all reagents is less than or equal to their corresponding upper limit, the process of adjusting the dosage of reagents in each segment based on the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment includes: E1. Determine the initial reagent adjustment amount for each segment based on the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment. E2. At the outlet of the roasting kiln, obtain the real-time surface wettability of the roasted product. E3. When the real-time surface wettability is greater than the preset surface wettability, the first preliminary reagent adjustment amount corresponding to each segment is corrected according to the deviation between the real-time surface wettability and the preset surface wettability, so as to obtain the corrected reagent adjustment amount corresponding to each segment. E4. Provided that the dosage of all agents is less than or equal to the corresponding upper limit of agent dosage, the dosage of agents in each segment is adjusted according to the correction agent adjustment amount corresponding to each segment.

[0037] The segment where the carbon reaction peak is located refers to the kiln segment where the carbon dioxide generation rate reaches its peak when monitoring changes in the carbon dioxide generation rate along the length of the kiln. The preset carbon reaction peak segment is the kiln segment where the carbon reaction peak should appear, determined based on ideal roasting conditions or historical data. The deviation between the two reflects the degree of deviation between the actual and expected progress of the carbon oxidation reaction during the current roasting process. For example, if the carbon reaction peak moves towards the kiln tail, it means that the reaction in the front section is insufficient; if the carbon reaction peak appears too early, it means that the reaction is too vigorous. The first preliminary reagent adjustment amount is based on the above deviation and is extracted from the preset mapping relationship table of peak segment deviation and reagent adjustment amount set. It is used to initially adjust the reagent dosage of each segment. This adjustment amount aims to guide the progress of the carbon oxidation reaction to the preset ideal state, for example, by increasing the amount of oxidant to accelerate the reaction or by decreasing the amount of oxidant to slow down the reaction. Real-time surface wettability of roasted products refers to the affinity of the roasted material (especially graphite) surface for water. Since graphite needs to maintain good hydrophobicity in subsequent flotation processes for effective separation, its surface wettability is a key indicator for evaluating the roasting effect, particularly the graphite protection effect. Real-time acquisition of this indicator can promptly reflect the impact of the roasting process on the graphite surface structure. Surface wettability can be obtained through online optical detection equipment, such as sensors based on the contact angle measurement principle, or indirectly by assessing the surface chemical state through spectral analysis techniques. The deviation between real-time surface wettability and preset surface wettability reflects the degree of deviation between the surface state of the roasted graphite and the ideal state. Specifically, the preset surface wettability is an ideal wettability threshold determined based on the target graphite retention ratio and subsequent flotation process requirements. When the real-time surface wettability is greater than the preset surface wettability, it indicates that the roasting process has caused excessive oxidation of the graphite. Therefore, it is necessary to correct the initial reagent adjustment amount for each segment based on the deviation between the real-time and preset surface wettability to ensure that the graphite surface structure is effectively protected, avoid excessive oxidation, and thus maintain its hydrophobicity. The correction process can employ a feedback control algorithm (e.g., a PID controller) to adjust the reagent dosage according to the magnitude and direction of the deviation.

[0038] This scheme addresses the errors that can occur when adjusting reagent dosage solely based on the position deviation of the carbon reaction peak by introducing real-time monitoring of the surface wettability of the calcined product and using this as a feedback signal to correct the reagent dosage. This makes reagent adjustment more comprehensive and accurate. Specifically, firstly, the segment of the carbon reaction peak is located based on the change in the carbon dioxide generation rate, and the reagent adjustment amount for each segment is initially determined by combining the deviation of the preset carbon reaction peak segment. This initial adjustment can quickly respond to the macroscopic process of carbon oxidation reaction during calcination and make a rough correction to the reagent dosage. However, due to the hidden and delayed nature of excessive graphite oxidation, it is difficult to accurately determine the damage to the graphite surface lattice structure based solely on the position of the carbon reaction peak. Therefore, this scheme further acquires the surface wettability of the calcined product in real time at the outlet of the calcination kiln. This real-time wettability data directly reflects the change in the hydrophilicity of the graphite surface and is direct evidence of whether the graphite has suffered excessive oxidation or structural damage. Subsequently, the real-time surface wettability is compared with the preset surface wettability. When the real-time surface wettability is greater than the preset surface wettability, the previously determined initial reagent adjustment amount is corrected based on the deviation between the two. This correction mechanism ensures that the reagent dosage adjustment is no longer based solely on reaction kinetic parameters, but also incorporates key indicators of the final product quality. This allows for more precise control of the calcination process, avoiding irreversible damage to graphite. Finally, while ensuring that the dosage of all reagents does not exceed their upper limit, the corrected reagent adjustment amount is applied to precisely control the reagent dosage in each segment.

[0039] In some preferred embodiments, step E3 includes: E31. When the real-time surface wettability is greater than the preset surface wettability, the first preliminary reagent adjustment amount corresponding to each segment is corrected according to the deviation between the real-time surface wettability and the preset surface wettability to obtain the second preliminary reagent adjustment amount corresponding to each segment. E32. Obtain the real-time temperature distribution and real-time oxygen concentration distribution of each section in the calcining kiln; E33. Based on the real-time temperature distribution and real-time oxygen concentration distribution, analyze whether the deviation between the real-time surface wettability and the preset surface wettability is caused by excessive graphite oxidation. If yes, proceed to step E34; otherwise, proceed to step E35. E34. Multiply the initial reagent adjustment amount corresponding to each segment by the preset ratio to obtain the corrected reagent adjustment amount corresponding to each segment; the preset ratio is less than 1 and greater than 0. E35. Use the second preliminary reagent adjustment amount corresponding to each segment as the correction reagent adjustment amount corresponding to each segment.

[0040] Step E32 is used to monitor the temperature and oxygen concentration in different sections inside the calcining kiln in real time. Temperature and oxygen concentration are key environmental parameters affecting the calcination reaction, especially the graphite oxidation reaction. Real-time acquisition of this distribution data provides important environmental information for determining the root cause of surface wettability deviations, thus supporting more accurate decision-making. This embodiment can obtain real-time temperature and oxygen concentration distribution maps by deploying multiple thermocouples or infrared temperature sensors, as well as electrochemical oxygen sensors or zirconium oxide oxygen sensors in each section of the calcining kiln.

[0041] Step E33 aims to intelligently determine whether the specific cause of the surface wettability deviation is excessive graphite oxidation by utilizing real-time acquired temperature and oxygen concentration distribution data. The principle of this step is as follows: temperature is the driving force of chemical reactions. The oxidation reaction of graphite has a certain activation energy. Only when the temperature reaches a certain level can the oxidation reaction proceed significantly. The real-time temperature distribution can reflect the actual thermal environment of the materials in each section of the calcining kiln. If the temperature of a certain section is continuously too high, exceeding the temperature threshold for safe calcination of graphite, it provides thermodynamic conditions for excessive graphite oxidation. Oxygen concentration is a necessary reactant for the oxidation reaction. In an aerobic calcination environment, oxygen is the main oxidant that causes graphite oxidation. The real-time oxygen concentration distribution can reflect the supply of oxidant in each section of the calcining kiln. If the oxygen concentration in a certain section is too high, especially under high temperature conditions, it will significantly accelerate the oxidation reaction of graphite, providing kinetic conditions for its excessive oxidation. Therefore, when the real-time surface wettability is greater than the preset surface wettability, and the calcining kiln simultaneously has sections with high temperature and high oxygen concentration, this embodiment considers the change in surface wettability to be caused by excessive graphite oxidation.

[0042] When the diagnostic results indicate that the surface wettability deviation is caused by excessive graphite oxidation, step E34 conservatively corrects the reagent adjustment amount by multiplying the initial reagent adjustment amount by a preset ratio less than 1 and greater than 0, thereby effectively reducing the actual amount of reagent added, thus slowing down or inhibiting the excessive oxidation reaction of graphite, protecting the lattice structure of graphite, and preventing its hydrophilicity from further increasing.

[0043] In step E35, if the analysis results indicate that the deviation is not caused by excessive graphite oxidation, the second preliminary reagent adjustment amount corresponding to each segment is used as the correction reagent adjustment amount corresponding to each segment. When the diagnostic results indicate that the surface wettability deviation is not caused by excessive graphite oxidation, this step directly uses the previously calculated second preliminary reagent adjustment amount as the final correction reagent adjustment amount. This means that the surface wettability deviation at this time may be caused by other factors, such as fluctuations in the properties of the material itself, the influence of other impurities, etc., and the correction of these factors does not require reducing the reagent dosage to suppress oxidation. Therefore, the preliminary correction based on the surface wettability deviation can be applied directly. This step is a direct assignment operation, that is, the control system directly transmits the second preliminary reagent adjustment amount to the reagent dosing actuator as the final reagent dosage adjustment instruction.

[0044] This embodiment not only considers the position of the carbon reaction peak and surface wettability, but also introduces real-time monitoring and intelligent diagnosis of the temperature and oxygen concentration distribution inside the calcining kiln. This multi-dimensional, hierarchical correction strategy makes the adjustment of reagent dosage more precise and intelligent. Combined with the mechanism in reagent adjustment module 6 that arranges segmented gas sampling points along the kiln body to measure changes in the carbon dioxide generation rate, and adjusts the reagent dosage based on the offset of the carbon reaction peak position, it constitutes a refined and adaptive reagent dosing control system. This synergy ensures that while optimizing lithium recovery rate, the integrity of graphite is protected to the maximum extent, thereby improving the overall economic benefits and product quality of the integrated manufacturing process across the entire battery recycling chain.

[0045] As can be seen from the above, the application digital model adapted to the whole-chain integrated manufacturing provided by this application, by introducing a virtual particle model, quantification of hidden resistance indicators, multi-parameter predictive optimization, and dynamic real-time control mechanism, can more accurately perceive material characteristics and more intelligently set and adjust calcination parameters. Thus, while ensuring a high lithium recovery rate, it can maximize the protection of graphite integrity. Therefore, this application can effectively solve the conflict between lithium recovery rate and graphite retention ratio, thereby effectively improving the overall efficiency and economic benefits of battery recycling.

[0046] In the embodiments provided in this application, it should be understood that the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0047] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0048] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An application digital model adapted to integrated manufacturing across the entire supply chain, characterized in that, The application digital model for adapting to integrated manufacturing across the entire supply chain includes: The information acquisition module is used to acquire the initial state parameters of the material to be roasted and map the initial state parameters to the initial state information of virtual particles. The curve acquisition module is used to acquire the actual temperature rise curve of the material to be roasted after it enters the preheating section of the roasting kiln. The index acquisition module is used to acquire the hidden resistance index based on the actual heating curve and the preset standard reference heating curve. The parameter determination module is used to determine the calcination temperature curve, rotation speed limit, and reagent dosage limit set of the calcination kiln based on the initial state information, the occult resistance index, the preset target lithium recovery rate, and the preset target graphite retention ratio, and then control the calcination kiln to calcinate according to the calcination temperature curve; the reagent dosage limit set includes the reagent dosage limit corresponding to different reagents. The speed adjustment module is used to adjust the speed of the calcining kiln according to the deviation between the hidden resistance index and the preset resistance index, so as to adjust the residence time of the material to be calcined in the calcining kiln, provided that the speed of the calcining kiln is less than or equal to the upper limit of the speed. The reagent adjustment module is used to monitor the changes in carbon dioxide generation rate in segments along the length of the calcining kiln, locate the segment where the carbon reaction peak is located based on the changes in all the carbon oxide generation rates, and then adjust the reagent dosage of each segment according to the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment, provided that the dosage of all reagents is less than or equal to the corresponding reagent dosage limit.

2. The application digital model for integrated manufacturing across the entire supply chain as described in claim 1, characterized in that, The initial state parameters include particle size distribution, copper and aluminum residue rate, organic carbon content, and particle specific surface area.

3. The application digital model for integrated manufacturing across the entire supply chain as described in claim 1, characterized in that, The initial state information includes particle diameter, surface organic cover thickness, specific surface area, and thermal conductivity.

4. The application digital model for integrated manufacturing across the entire supply chain as described in claim 1, characterized in that, The process of mapping the initial state parameters to the initial state information of the virtual particles includes: A1. Using a preset mapping rule, the initial state parameters are mapped to the preliminary state information of the virtual particles; A2. Based on the preliminary state information of the virtual particles, predict the temperature rise curve of the material to be roasted; A3. Correct the preset mapping rule based on the deviation between the actual heating curve and the predicted heating curve; A5. Using the modified preset mapping rules, the initial state parameters are mapped to the initial state information of the virtual particles.

5. The application digital model for integrated manufacturing across the entire supply chain as described in claim 1, characterized in that, The information acquisition module is also used to acquire the initial temperature of the material to be roasted, and the process of acquiring the latent resistance index based on the actual heating curve and the preset standard reference heating curve includes: B1. For each sampling point in the actual heating curve, a first temperature value deviation is obtained based on the actual temperature value corresponding to the sampling point on the actual heating curve and the reference temperature value corresponding to the sampling point on the preset standard reference heating curve, and a second temperature value deviation is obtained based on the reference temperature value corresponding to the sampling point on the standard reference heating curve and the initial temperature of the material to be roasted. B2. The summation of all deviations of the first temperature value is taken as the first cumulative temperature difference value; B3. The summation of all deviations of the second temperature value is taken as the second cumulative temperature difference value; B4. The ratio of the first cumulative temperature difference value to the second cumulative temperature difference value is used as the indicator of hidden resistance.

6. The application digital model for integrated manufacturing across the entire supply chain as described in claim 1, characterized in that, The process of determining the calcination temperature curve, rotation speed limit, and reagent dosage limit set of the calcination kiln based on the initial state information, the cloaking resistance index, the preset target lithium recovery rate, and the preset target graphite retention ratio includes: C1. Based on the initial state information and the hidden resistance index, extract the corresponding parameter set, expected lithium recovery rate, and expected graphite retention ratio from a preset mapping table of combinations of state information and resistance index and their corresponding parameter sets, expected lithium recovery rate, and expected graphite retention ratio; the parameter set includes temperature curves, maximum rotation speed, and maximum reagent dosage set; C2. Analyze whether the expected lithium recovery rate reaches the preset target lithium recovery rate and the expected graphite retention ratio reaches the preset target graphite retention ratio at the same time. If yes, proceed to step C3; otherwise, proceed to step C4. C3. The extracted temperature curve is used as the calcination temperature curve, the highest extraction speed is used as the upper limit of the speed, and the maximum amount of extracted reagent is used as the upper limit of reagent addition. C4. Obtain the current lithium price and graphite price; C5. When the lithium price is greater than the graphite price, extract the parameter set from the mapping table that the expected lithium recovery rate reaches the target lithium recovery rate as the first candidate parameter set, and take the first candidate parameter set with the largest expected graphite retention ratio as the first target parameter set. Then, take the temperature curve, maximum rotation speed and maximum reagent dosage set contained in the first target parameter set as the calcination temperature curve, the upper limit of rotation speed and the upper limit of reagent dosage set, respectively. C6. When the price of graphite is greater than the price of lithium, extract the parameter set from the mapping table that the expected graphite retention ratio reaches the target lithium recovery rate as the second candidate parameter set, and take the first candidate parameter set with the largest expected lithium recovery rate as the second target parameter set. Then, take the temperature curve, the maximum rotation speed and the maximum reagent dosage set contained in the second target parameter set as the calcination temperature curve, the upper limit of rotation speed and the upper limit of reagent dosage set, respectively. C7. When the price of lithium is equal to the price of graphite, extract the parameter set from the mapping table at which the sum of the expected lithium recovery rate and the graphite retention ratio is maximized as the third target parameter set. Then, use the temperature curve, maximum rotation speed and maximum reagent dosage set contained in the third target parameter set as the calcination temperature curve, the upper limit of rotation speed and the upper limit of reagent dosage set, respectively.

7. The application digital model for integrated manufacturing across the entire supply chain as described in claim 6, characterized in that, The target lithium recovery rate and the graphite retention ratio are determined based on the current lithium price and graphite price.

8. The application digital model for integrated manufacturing across the entire supply chain as described in claim 1, characterized in that, The process of adjusting the rotational speed of the calcining kiln according to the deviation between the latent resistance index and the preset resistance index, when the rotational speed of the calcining kiln is less than or equal to the upper limit of the rotational speed, includes: D1. Determine the initial rotational speed adjustment amount based on the deviation between the hidden resistance index and the preset resistance index; D2. Obtain the real-time axial movement speed of the material to be roasted in the roasting kiln; D3. Correct the initial rotational speed adjustment amount based on the deviation between the real-time axial movement speed and the preset target axial movement speed to obtain the corrected rotational speed adjustment amount; D4. If the rotational speed of the calcining kiln is less than or equal to the upper limit of the rotational speed, the rotational speed of the calcining kiln shall be adjusted according to the corrected rotational speed adjustment amount.

9. The application digital model for integrated manufacturing across the entire supply chain as described in claim 1, characterized in that, The process of adjusting the dosage of each reagent segment based on the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment, provided that the dosage of all reagents is less than or equal to their corresponding upper limit, includes: E1. Determine the first preliminary reagent adjustment amount for each segment based on the deviation between the segment where the carbon reaction peak is located and the preset carbon reaction peak segment; E2. At the outlet of the roasting kiln, obtain the real-time surface wettability of the roasted product. E3. When the real-time surface wettability is greater than the preset surface wettability, the first preliminary reagent adjustment amount corresponding to each segment is corrected according to the deviation between the real-time surface wettability and the preset surface wettability, so as to obtain the corrected reagent adjustment amount corresponding to each segment. E4. Provided that the dosage of all agents is less than or equal to the corresponding upper limit of agent dosage, the dosage of agents in each segment is adjusted according to the correction agent adjustment amount corresponding to each segment.

10. The application digital model for integrated manufacturing across the entire supply chain as described in claim 9, characterized in that, Step E3 includes: E31. When the real-time surface wettability is greater than the preset surface wettability, the first preliminary reagent adjustment amount corresponding to each segment is corrected according to the deviation between the real-time surface wettability and the preset surface wettability to obtain the second preliminary reagent adjustment amount corresponding to each segment. E32. Obtain the real-time temperature distribution and real-time oxygen concentration distribution of each section in the calcining kiln; E33. Based on the real-time temperature distribution and the real-time oxygen concentration distribution, analyze whether the deviation between the real-time surface wettability and the preset surface wettability is caused by excessive graphite oxidation. If yes, proceed to step E34; otherwise, proceed to step E35. E34. Multiply the initial drug adjustment amount corresponding to each segment by a preset ratio to obtain the corrected drug adjustment amount corresponding to each segment; the preset ratio is less than 1 and greater than 0. E35. Use the second preliminary reagent adjustment amount corresponding to each segment as the correction reagent adjustment amount corresponding to each segment.

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