Method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries by cascade pyrolysis

Abstract

The invention discloses a method for recycling lithium cobalt oxide positive electrode materials in waste lithium batteries by a cascade pyrolysis method, and relates to the technical field of battery positive electrode recycling. The invention comprises the following steps: the waste lithium batteries are regulated in real time according to a first-level temperature regulation formula within a first-level pyrolysis temperature range to obtain a first-level product; the second-level formula temperature of the first-level product is calculated according to the second-level temperature formula within a second-level pyrolysis temperature range, a second-level temperature control model is constructed, and the second-level real-time predicted temperature is predicted; the second-level product is obtained according to the second-level formula temperature and the second-level real-time predicted temperature; the third-level formula temperature of the second-level product is calculated according to the third-level temperature formula within a third-level pyrolysis temperature range, a third-level temperature control model is constructed, and the third-level real-time predicted temperature is predicted; the lithium cobalt oxide product is obtained according to the third-level formula temperature and the third-level real-time predicted temperature, thereby improving the purity and electrochemical performance of the lithium cobalt oxide positive electrode material and reducing energy consumption.

Description

Technical Field

[0001] The invention relates to the technical field of battery positive electrode recovery, in particular to a method for recovering lithium cobalt oxide positive electrode materials in waste lithium batteries using a cascade pyrolysis method. Background Art

[0002] With the rapid development of new energy vehicles and portable electronic devices, the use of lithium-ion batteries has increased dramatically. Among them, lithium cobalt oxide (LiCoO2), as a common positive electrode material, accounts for a considerable proportion of waste lithium batteries. Traditional recycling methods mainly include pyrometallurgy and hydrometallurgy:

[0003] Pyrometallurgy: Advantages: Simple process, can process large quantities of batteries. Disadvantages: High energy consumption, produces harmful gases, and has low metal recovery rate.

[0004] Hydrometallurgy: Advantages: High metal recovery rate and high product purity. Disadvantages: Uses a large amount of chemical reagents, generates secondary pollution, and is costly.

[0005] However, these traditional methods have problems such as high energy consumption, environmental pollution and high cost, which make it difficult to meet the needs of sustainable development. Therefore, the currently recommended method is the cascade pyrolysis method for recycling;

[0006] In the process of recycling lithium cobalt oxide positive electrode materials from waste lithium batteries by the cascade pyrolysis method, precise control of temperature is crucial. The chemical reactions in each pyrolysis stage are extremely sensitive to temperature. A slight deviation may lead to incomplete removal of binders and organic matter, insufficient cobalt reduction, or uneven recrystallization, which in turn affects the purity and electrochemical properties of the final material. In existing pyrolysis processes, traditional temperature control methods are usually relied upon, such as preset temperature curves and simple feedback control. However, these methods are often difficult to adapt to the changing characteristics of raw materials and complex reaction processes, resulting in the system's response to gas concentration, oxygen changes and product quality feedback not being timely enough. Due to the strong nonlinear characteristics of the reactions in each stage and the rapid changes in reaction rates, simple preset and real-time control often lead to problems such as frequent temperature fluctuations, over-adjustment or lagging adjustment, which ultimately affect the quality stability of the product and process energy consumption.

[0007] A Chinese patent with authorization announcement number CN112607787B discloses a method for recycling lithium cobalt oxide high-iron material, comprising the following steps: (1) sintering the lithium cobalt oxide high-iron material at 900°C to 1100°C for 5h to 30h, crushing and classifying the sintered lithium cobalt oxide high-iron material to obtain a lithium cobalt oxide product; (2) uniformly mixing the lithium cobalt oxide product with a cobalt compound to obtain a uniformly mixed lithium cobalt oxide material; (3) sintering the uniformly mixed lithium cobalt oxide material at 800°C to 1100°C for 5h to 30h, crushing and classifying the sintered uniformly mixed lithium cobalt oxide material to obtain a coated lithium cobalt oxide product; however, this method fails to achieve precise temperature control.

[0008] To this end, the present invention proposes a method for recycling lithium cobalt oxide positive electrode materials in waste lithium batteries by a stepwise pyrolysis method. Summary of the Invention

[0009] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method for recovering lithium cobalt oxide cathode material from waste lithium batteries using a cascade pyrolysis process, which improves the purity and electrochemical performance of the potassium cobalt oxide cathode material while reducing energy consumption.

[0010] To achieve the above object, a method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries by a cascade pyrolysis method is proposed, comprising the following steps:

[0011] Step 1: performing primary pyrolysis on the waste lithium battery within a primary pyrolysis temperature range, wherein the real-time temperature of the primary pyrolysis is adjusted in real time according to a primary temperature adjustment formula to obtain a primary product;

[0012] Step 2: performing secondary pyrolysis on the primary product within the secondary pyrolysis temperature range; calculating the secondary formula temperature according to the secondary temperature formula for the secondary pyrolysis process, and constructing a secondary temperature control model to predict the secondary real-time predicted temperature; performing real-time adjustment according to the secondary temperature adjustment formula based on the secondary formula temperature and the secondary real-time predicted temperature to obtain the secondary product;

[0013] Step 3: performing a three-stage pyrolysis on the secondary product within a three-stage pyrolysis temperature range, calculating the three-stage formula temperature of the three-stage pyrolysis process according to the three-stage temperature formula, and constructing a three-stage temperature control model to predict the three-stage real-time predicted temperature. Based on the three-stage formula temperature and the three-stage real-time predicted temperature, real-time adjustment is performed according to the three-stage temperature adjustment formula to obtain a lithium cobalt oxide product;

[0014] The specific temperature range of the primary pyrolysis temperature range is [200°C, 300°C], the equipment used for the primary pyrolysis is a rotary kiln equipped with a temperature gradient control system, and the atmosphere of the primary pyrolysis adopts an inert gas;

[0015] The method of performing real-time adjustment according to the first-level temperature adjustment formula is as follows:

[0016] Construct a first-stage temperature adjustment formula based on the preset first-stage initial temperature, real-time gas concentration, and process product quality;

[0017] The temperature range of the secondary pyrolysis is [500°C, 700°C], the pyrolysis equipment used in the secondary pyrolysis is a vertical reactor equipped with a precise temperature control system, and the pyrolysis atmosphere used is a reducing gas;

[0018] The method for calculating the secondary formula temperature according to the secondary temperature formula for the secondary pyrolysis process is:

[0019] Construct a secondary temperature formula based on the preset secondary initial temperature, primary product quality index, and secondary gas concentration ratio;

[0020] The method of constructing the secondary temperature control model and predicting the secondary real-time predicted temperature is as follows:

[0021] The secondary temperature control model selects the LSTM model;

[0022] The prediction of the secondary real-time predicted temperature includes the following steps:

[0023] Step 11: Collect secondary training data; the secondary training data includes secondary input sample data and secondary label sample data;

[0024] The secondary input sample data includes, at fixed time intervals, a gas concentration time series consisting of the concentrations of CO and CO2 collected in chronological order, a volatile concentration time series consisting of the concentrations of volatile organic compounds collected in chronological order, and a temperature time series consisting of the actual temperature in the secondary pyrolysis equipment changing with time, in the historical optimal recovery experience of the secondary pyrolysis process. The gas concentration time series, volatile concentration time series, and temperature time series within each time length are regarded as a set of input samples;

[0025] The secondary label sample data includes a temperature label corresponding to each set of input samples in the historical optimal recovery experience of the secondary pyrolysis process, and the temperature label is a temperature value per unit time after the corresponding input sample;

[0026] Step 12: Model the secondary temperature control model;

[0027] Step 13: Use the secondary input sample as the input of the secondary temperature control model and the temperature label as the actual temperature of the secondary temperature control model. Use the Adam optimizer to perform backpropagation training on the secondary temperature control model and adjust the model weights.

[0028] Step 14: Collect actual input data from the secondary pyrolysis process in the form of secondary input samples, input the actual input data into the secondary temperature control model, and obtain the secondary real-time predicted temperature output by the secondary temperature control model;

[0029] The temperature range of the three-stage pyrolysis temperature is [800°C, 1000°C], the equipment used for the three-stage pyrolysis is a tubular furnace equipped with a rapid temperature rise and fall system, and the atmosphere used is oxygen;

[0030] The method for calculating the three-stage temperature according to the three-stage temperature formula for the three-stage pyrolysis process is as follows:

[0031] Construct a three-stage temperature formula based on the preset three-stage initial temperature, the first-stage product quality index, the second-stage product quality index, and the three-stage gas concentration ratio;

[0032] The method of constructing the three-level temperature control model and predicting the three-level real-time predicted temperature is as follows:

[0033] The three-level real-time temperature prediction selection LSTM model;

[0034] The prediction of the three-level real-time prediction temperature includes the following steps:

[0035] Step 21: Collect three-level training data; the three-level training data includes three-level input sample data and three-level label sample data;

[0036] The three-level input sample data includes a corresponding oxygen concentration time series composed of oxygen concentrations collected at fixed time intervals in the historical optimal recovery experience of the three-level pyrolysis process, and a temperature time series of actual temperature changes over time in the three-level pyrolysis equipment. The oxygen concentration time series and temperature time series within each time interval are regarded as a set of three-level input samples;

[0037] The three-level label sample data includes a three-level temperature label corresponding to each set of three-level input samples in the historical optimal recovery experience of the three-level pyrolysis process, and the three-level temperature label is a temperature value of one unit time after the corresponding three-level input sample;

[0038] Step 22: Model the three-stage temperature control model;

[0039] Step 23: Use the three-level input sample as the input of the three-level temperature control model, and the three-level temperature label as the actual temperature of the three-level temperature control model. Use the Adam optimizer to perform backpropagation training on the three-level temperature control model and adjust the model weights.

[0040] Step 24: According to the data form of the three-level input sample, collect the three-level actual input data in the three-level pyrolysis process, input the three-level actual input data into the three-level temperature control model, and obtain the three-level real-time predicted temperature output by the three-level temperature control model.

[0041] An electronic device is proposed, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0042] The processor executes the above-mentioned method of recycling lithium cobalt oxide positive electrode materials in waste lithium batteries by using the cascade pyrolysis method by calling the computer program stored in the memory.

[0043] A computer-readable storage medium is provided, on which a rewritable computer program is stored.

[0044] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries by the cascade pyrolysis method.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention realizes multi-level intelligent temperature control by combining formula-based real-time feedback control with a neural network model. Formula control is used to respond to changes in current gas concentration and product quality in real time to ensure precise regulation in the short term; the neural network model predicts the optimal temperature at the next moment by learning from historical data to avoid excessive or delayed regulation of the system. The control systems at different stages can work together based on real-time data and long-term trends to overcome the problems of delayed response and frequent fluctuations in traditional temperature control systems. Through this solution, the temperature regulation during the pyrolysis process is more precise, can adapt to different batches of raw materials and complex reaction conditions, improve the purity and electrochemical properties of potassium cobalt oxide positive electrode materials, and reduce energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of the method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries using a cascade pyrolysis method in Example 1 of the present invention;

[0048] Figure 2 Flowchart of the prediction process of the secondary real-time temperature prediction in Example 1 of the present invention;

[0049] Figure 3 Flowchart of the prediction process of the three-level real-time temperature prediction in Example 1 of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] Example 1

[0052] like Figure 1As shown, the method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries by cascade pyrolysis method comprises the following steps:

[0053] Step 1: performing primary pyrolysis on the waste lithium battery within a primary pyrolysis temperature range, wherein the real-time temperature of the primary pyrolysis is adjusted in real time according to a primary temperature adjustment formula to obtain a primary product;

[0054] Step 2: performing secondary pyrolysis on the primary product within the secondary pyrolysis temperature range; calculating the secondary formula temperature according to the secondary temperature formula for the secondary pyrolysis process, and constructing a secondary temperature control model to predict the secondary real-time predicted temperature; performing real-time adjustment according to the secondary temperature adjustment formula based on the secondary formula temperature and the secondary real-time predicted temperature to obtain the secondary product;

[0055] Step 3: performing a three-stage pyrolysis on the secondary product within a three-stage pyrolysis temperature range, calculating the three-stage formula temperature of the three-stage pyrolysis process according to the three-stage temperature formula, and constructing a three-stage temperature control model to predict the three-stage real-time predicted temperature. Based on the three-stage formula temperature and the three-stage real-time predicted temperature, real-time adjustment is performed according to the three-stage temperature adjustment formula to obtain a lithium cobalt oxide product;

[0056] The specific temperature range of the primary pyrolysis temperature range is [200°C, 300°C]. The purpose of the primary pyrolysis is to remove the organic binder (such as PVDF) in the electrode material. The equipment generally used is a rotary kiln equipped with a temperature gradient control system. The atmosphere of the primary pyrolysis is protected by an inert gas (such as nitrogen) to prevent oxidation of the material.

[0057] The method of performing real-time adjustment according to the first-level temperature adjustment formula is as follows:

[0058] Construct a first-stage temperature adjustment formula based on the preset first-stage initial temperature, real-time gas concentration, and process product quality;

[0059] The main goal of the primary pyrolysis is to remove binders and organic matter from lithium batteries, so the quality assessment focuses on the removal efficiency of residues. Therefore, the process product quality is the real-time removal efficiency of residues during the primary pyrolysis process.

[0060] It is understandable that in the process of removing organic matter and binders, gases such as carbon dioxide and carbon monoxide will be generated, and some residues will also be generated. Therefore, by evaluating the real-time generation rate of gas and the change rate of residues, the removal efficiency of residues can be evaluated;

[0061] Preferably, the calculation formula for the real-time removal efficiency is:

[0062]

[0063] Where X is the real-time removal efficiency, Vco2 is the generation rate of carbon dioxide, Vco is the generation rate of carbon monoxide, Mc is the residual organic matter mass, and Mz is the total organic matter mass; α1 and α2 are both preset proportional coefficients;

[0064] Specifically, gas sensors for carbon dioxide and carbon monoxide are used to monitor the gas generation rate in real time;

[0065] A thermogravimetric analyzer (TGA) or online spectrometer is used to detect the real-time changes in the residual organic matter mass of waste lithium batteries during the primary pyrolysis process;

[0066] Further preferably, the first-level temperature adjustment formula may be:

[0067]

[0068] Wherein, T_1 is the real-time temperature during the first-stage pyrolysis process, T0 is the preset first-stage initial temperature. Generally, T0 is the lower limit of the first-stage pyrolysis temperature range, i.e., 200°C. Cco2 is the real-time concentration of carbon dioxide, Cco is the real-time concentration of carbon monoxide, and k1 and k2 are preset proportional coefficients. The ratio of carbon dioxide concentration to carbon monoxide concentration can be used to determine the reaction degree of the first-stage pyrolysis. When the ratio tends to be stable, it indicates that the binder removal is relatively thorough and the temperature can also be kept stable.

[0069] It can be understood that the primary temperature regulation formula is based on real-time changes in gas concentrations (such as CO and CO2), which can reflect the decomposition state of organic matter. When the binder removal rate changes, the system can quickly adjust the temperature to maintain the range that is most conducive to organic matter removal. At the same time, it monitors the deviation of the residual material quality to control the temperature. This can avoid excessive temperature that may lead to material degradation or excessive decomposition, ensuring the purity and structural integrity of the product.

[0070] Furthermore, the secondary pyrolysis temperature range is [500° C., 700° C.], the purpose of the secondary pyrolysis is to reduce the trivalent cobalt in the lithium cobalt oxide and improve the subsequent extraction efficiency, the pyrolysis equipment used is a vertical reactor equipped with a precise temperature control system, and the pyrolysis atmosphere used is a reducing gas (such as a H2 / N2 mixture);

[0071] The method for calculating the secondary temperature of the secondary pyrolysis process according to the secondary temperature formula is as follows:

[0072] Construct a secondary temperature formula based on the preset secondary initial temperature, primary product quality index, and secondary gas concentration ratio;

[0073] Preferably, the quality index of the primary product is the purity of the primary product obtained after the primary pyrolysis process is completed;

[0074] It can be understood that the purpose of the primary pyrolysis process is to remove binders and organic matter, so the purity of the primary product can be expressed as the remaining amount of binders and organic matter;

[0075] As another preferred embodiment, the quality indicator of the primary product may also be the integrity of the crystal structure of the primary product, that is, evaluating the degree of damage to the crystal structure of lithium cobalt oxide caused by long-term pyrolysis; specifically, the integrity of the crystal structure can be analyzed by X-ray diffraction (XRD);

[0076] The specific secondary temperature formula can be:

[0077]

[0078] Wherein, T_2 is the secondary formula temperature during the secondary pyrolysis process, T1 is the preset secondary initial temperature, which is generally taken as the lower limit of the secondary pyrolysis temperature range, i.e. 500°C; Q1 is the value of the primary product quality index, Qy1 is the primary target quality index value of the primary product; CH2 is the real-time concentration of hydrogen; α3 and α4 are both preset proportional coefficients;

[0079] Specifically, the first-level target quality index value can be obtained by conducting several experiments in advance. Specifically, for the first-level pyrolysis, the goal of the first-level pyrolysis is to remove the binder. Through experiments, it can be determined that the removal rate of the binder and organic matter is the highest or the integrity of the crystal structure is the highest at a specific temperature and time, and thus the quality index of the corresponding first-level product is used as the first-level target quality index value.

[0080] In other preferred embodiments, the primary target quality index value can also be predicted by using a big data model through data analysis of multiple production processes to obtain the optimal target quality index value for each stage. For example, a machine learning method can be used to analyze a large amount of production data to find the optimal target quality value under different process conditions.

[0081] It can be understood that the secondary temperature formula combines the quality feedback of the previous stage product and the gas concentration control, not only based on the current gas concentration (such as H2 and CO2), but also combined with the reduction degree information of the previous stage product, so that the temperature control is more accurately targeted at the actual progress of the reduction process, to ensure that the reduction reaction proceeds as expected;

[0082] Furthermore, the method of constructing the secondary temperature control model and predicting the secondary real-time predicted temperature is as follows:

[0083] The secondary temperature control model selects the LSTM model;

[0084] like Figure 2 As shown, the prediction of the secondary real-time predicted temperature includes the following steps:

[0085] Step 11: Collect secondary training data;

[0086] Specifically, the secondary training data includes secondary input sample data and secondary label sample data;

[0087] The secondary input sample data includes, in the historical optimal recovery experience of the secondary pyrolysis process, a corresponding gas concentration time series consisting of the concentrations of CO and CO2 collected at fixed time intervals, a volatile concentration time series consisting of the concentrations of volatile organic compounds collected at fixed time intervals, and a temperature time series consisting of the actual temperature in the secondary pyrolysis equipment changing with time, the gas concentration time series, volatile concentration time series, and temperature time series within each time interval serving as a set of input samples; the historical optimal recovery experience of the secondary pyrolysis process refers to recovery experience data selected from past recycling processes of waste lithium batteries using a cascade pyrolysis method, in which the quality indicators of secondary products achieved the expected secondary target quality indicator values;

[0088] The secondary target quality index value may be the reduction rate of the reduced cobalt element; because the secondary pyrolysis process requires setting a target value for the completion of the reduction reaction, such as if the cobalt reduction rate reaches above 95%, the corresponding quality can be used as the secondary target quality index value;

[0089] The secondary label sample data includes a temperature label corresponding to each set of input samples in the historical optimal recovery experience of the secondary pyrolysis process, and the temperature label is a temperature value per unit time after the corresponding input sample;

[0090] Step 12: Model the secondary temperature control model;

[0091] The input layer of the two-level temperature control model receives multidimensional time series input (gas concentration time series, temperature time series), the hidden layer is one or more layers of LSTM units, which are used to capture long-term dependencies in the time series, and the output layer is a single node, which is used to predict the temperature at the next moment. The mean square error is used to calculate the error between the model-predicted temperature and the actual temperature.

[0092] Step 13: Use the secondary input sample as the input of the secondary temperature control model and the temperature label as the actual temperature of the secondary temperature control model. Use the Adam optimizer to perform backpropagation training on the secondary temperature control model and adjust the model weights.

[0093] Step 14: Collect actual input data from the secondary pyrolysis process in the form of secondary input samples, input the actual input data into the secondary temperature control model, and obtain the secondary real-time predicted temperature output by the secondary temperature control model;

[0094] Furthermore, the specific form of the secondary temperature adjustment formula may be:

[0095] Tr_2=β×T_2+(1-β)×Tp2;

[0096] Among them, Tr_2 is the real-time temperature of secondary pyrolysis, Tp2 is the secondary real-time predicted temperature predicted by the secondary temperature control model, and β is the preset control parameter;

[0097] It is understandable that the LSTM model can reduce system temperature fluctuations through smooth predictions, while formula adjustment can quickly correct deviations. Their combined use can minimize the adverse effects of temperature changes and ensure the stability of product quality.

[0098] Furthermore, the temperature range of the three-stage pyrolysis is [800°C, 1000°C]. The purpose of the three-stage pyrolysis is to recrystallize lithium cobalt oxide to improve purity and electrochemical properties. The equipment used is a tubular furnace equipped with a rapid temperature rise and fall system, and the atmosphere used is oxygen to promote the lithiation reaction.

[0099] The method for calculating the three-stage temperature of the three-stage pyrolysis process according to the three-stage temperature formula is as follows:

[0100] Construct a three-stage temperature formula based on the preset three-stage initial temperature, the first-stage product quality index, the second-stage product quality index, and the three-stage gas concentration ratio;

[0101] The secondary product quality index is the cobalt reduction rate of the secondary product obtained after the secondary pyrolysis process is completed;

[0102] Specifically, the three-level temperature formula may be:

[0103]

[0104] Wherein, T_3 is the third-stage formula temperature during the three-stage pyrolysis process, T2 is the preset third-stage initial temperature, which is generally taken as the lower limit of the three-stage pyrolysis temperature range, i.e. 800°C; Q2 is the secondary product quality index, Qy2 is the secondary target quality index value, ΔCo2 is the real-time change rate of oxygen concentration, and Co2 is the real-time oxygen concentration; α5, α6, and α7 are all preset proportional coefficients;

[0105] It is understandable that the three-stage temperature formula ensures the optimized reorganization of the crystal structure through multi-level feedback control. Specifically, the temperature setting combines the product quality feedback of the first two stages, the current oxygen concentration change and the real-time monitoring of the crystal structure, which can better adjust the temperature to adapt to the process of crystal reorganization and improve the purity of lithium cobalt oxide. Adjusting the temperature through oxygen concentration feedback can optimize the use of oxygen at different stages of the oxidation reaction, prevent insufficient or excessive oxidation, and improve the electrochemical performance of the final product.

[0106] Furthermore, the three-level temperature control model is constructed to predict the three-level real-time predicted temperature in the following manner:

[0107] The three-level real-time temperature prediction selection LSTM model;

[0108] like Figure 3 As shown, the three-level real-time temperature prediction includes the following steps:

[0109] Step 21: Collect three-level training data;

[0110] Specifically, the three-level training data includes three-level input sample data and three-level label sample data;

[0111] The three-level input sample data includes a corresponding oxygen concentration time series composed of oxygen concentrations collected at fixed time intervals in the historical optimal recovery experience of the three-level pyrolysis process, and a temperature time series of the actual temperature in the three-level pyrolysis equipment changing with time, with the oxygen concentration time series and temperature time series within each time interval serving as a set of three-level input samples; the historical optimal recovery experience of the three-level pyrolysis process refers to recovery experience data that screens out the quality indicators of the final product in the past recycling process of waste lithium batteries using the cascade pyrolysis method, and the final target quality can be a target purity of crystallized lithium cobalt oxide set based on experience;

[0112] The three-level label sample data includes a three-level temperature label corresponding to each set of three-level input samples in the historical optimal recovery experience of the three-level pyrolysis process, and the three-level temperature label is a temperature value of one unit time after the corresponding three-level input sample;

[0113] Step 22: Model the three-stage temperature control model;

[0114] The input layer of the three-level temperature control model receives multidimensional time series input (oxygen concentration time series, temperature time series), the hidden layer is one or more layers of LSTM units, which are used to capture long-term dependencies in the time series, and the output layer is a single node, which is used to predict the temperature at the next moment. The mean square error is used to calculate the error between the model-predicted temperature and the actual temperature.

[0115] Step 23: Use the three-level input sample as the input of the three-level temperature control model, and the three-level temperature label as the actual temperature of the three-level temperature control model. Use the Adam optimizer to perform backpropagation training on the three-level temperature control model and adjust the model weights.

[0116] Step 24: Collect the three-level actual input data during the three-level pyrolysis process in the data format of the three-level input sample, input the three-level actual input data into the three-level temperature control model, and obtain the three-level real-time predicted temperature output by the three-level temperature control model;

[0117] Furthermore, the specific form of the three-stage temperature adjustment formula may be:

[0118] Tr_3=γ×T_3+(1-γ)×Tp3;

[0119] Among them, Tr_3 is the real-time temperature of the secondary pyrolysis, Tp3 is the third-level real-time predicted temperature predicted by the three-level temperature control model, and γ is a pre-set control parameter.

[0120] Example 2

[0121] According to another aspect of the present application, an electronic device is provided. The electronic device may include one or more processors and one or more memories. The memories may store computer-readable code that, when executed by the one or more processors, may execute the aforementioned method for recovering lithium cobalt oxide cathode materials from waste lithium batteries using the cascade pyrolysis method.

[0122] The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output components, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, may store the method provided in this application for recycling lithium cobalt oxide positive electrode materials from waste lithium batteries by a cascade pyrolysis method.

[0123] Furthermore, the electronic device may further include a user interface. Of course, when implementing different devices, one or more components in the above-mentioned electronic device may be omitted according to actual needs.

[0124] Example 3

[0125] A computer-readable storage medium according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor, the method for recovering lithium cobalt oxide positive electrode materials in waste lithium batteries by the cascade pyrolysis method according to the embodiment of the present application described with reference to the above drawings can be executed. The computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may, for example, include random access memory (RAM) and cache memory (cache). Non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0126] In addition, according to embodiments of the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions capable of being executed by a processor to execute instructions corresponding to the steps of the method provided in the present application. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are performed.

[0127] The methods, apparatuses, and devices of the present application may be implemented in many ways. For example, the methods, apparatuses, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing the methods according to the present application.

[0128] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0129] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0130] The above preset parameters or preset thresholds are all set by those skilled in the art according to actual conditions or obtained through large amounts of data simulation.

[0131] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries by a stepwise pyrolysis method, characterized in that: The following steps are involved: Step 1: performing primary pyrolysis on the waste lithium battery within a primary pyrolysis temperature range, wherein the real-time temperature of the primary pyrolysis is adjusted in real time according to a primary temperature adjustment formula to obtain a primary product; Step 2: performing secondary pyrolysis on the primary product within the secondary pyrolysis temperature range; For the secondary pyrolysis process, the secondary formula temperature is calculated according to the secondary temperature formula, and a secondary temperature control model is constructed to predict the secondary real-time predicted temperature; According to the secondary formula temperature and the secondary real-time predicted temperature, real-time adjustment is performed according to the secondary temperature adjustment formula to obtain the secondary product; Step 3: performing a three-stage pyrolysis on the secondary product within a three-stage pyrolysis temperature range, calculating the three-stage formula temperature of the three-stage pyrolysis process according to the three-stage temperature formula, and constructing a three-stage temperature control model to predict the three-stage real-time predicted temperature. Based on the three-stage formula temperature and the three-stage real-time predicted temperature, real-time adjustment is performed according to the three-stage temperature adjustment formula to obtain a lithium cobalt oxide product; The method of constructing the three-level temperature control model and predicting the three-level real-time predicted temperature is as follows: The three-level real-time temperature prediction selection LSTM model; The prediction of the three-level real-time prediction temperature includes the following steps: Step 21: Collect three-level training data; the three-level training data includes three-level input sample data and three-level label sample data; Step 22: Model the three-stage temperature control model; Step 23: Use the three-level input sample as the input of the three-level temperature control model, and the three-level temperature label as the actual temperature of the three-level temperature control model. Use the Adam optimizer to perform backpropagation training on the three-level temperature control model and adjust the model weights. Step 24: Collect the three-level actual input data during the three-level pyrolysis process in the data format of the three-level input sample, input the three-level actual input data into the three-level temperature control model, and obtain the three-level real-time predicted temperature output by the three-level temperature control model; The method of performing real-time adjustment according to the first-level temperature adjustment formula is: Construct a first-stage temperature adjustment formula based on the preset first-stage initial temperature, real-time gas concentration, and process product quality; The method for calculating the secondary formula temperature according to the secondary temperature formula for the secondary pyrolysis process is: Construct a secondary temperature formula based on the preset secondary initial temperature, primary product quality index, and secondary gas concentration ratio; The method for calculating the three-stage formula temperature according to the three-stage temperature formula for the three-stage pyrolysis process is: Construct a three-stage temperature formula based on the preset three-stage initial temperature, the first-stage product quality index, the second-stage product quality index, and the three-stage gas concentration ratio; The method of constructing the secondary temperature control model and predicting the secondary real-time predicted temperature is as follows: The secondary temperature control model selects the LSTM model; The prediction of the secondary real-time predicted temperature includes the following steps: Step 11: Collect secondary training data; the secondary training data includes secondary input sample data and secondary label sample data; the secondary input sample data includes the historical optimal recovery experience of the secondary pyrolysis process, at fixed time intervals, the corresponding gas concentration time series composed of the concentrations of CO and CO2, the volatile concentration time series composed of the concentrations of volatile organic compounds, and the temperature time series of the actual temperature in the secondary pyrolysis equipment changing with time, the gas concentration time series, the volatile concentration time series, and the temperature time series within each time length are used as a set of input samples; the historical optimal recovery experience of the secondary pyrolysis process refers to the recovery experience data that screens out the quality indicators of the secondary products in the past recycling process of waste lithium batteries using the cascade pyrolysis method, and achieves the expected secondary target quality indicator values; The secondary target quality index value is the reduction rate of the reduced cobalt element; The secondary label sample data includes a temperature label corresponding to each set of input samples in the historical optimal recovery experience of the secondary pyrolysis process, and the temperature label is a temperature value per unit time after the corresponding input sample; Step 12: Model the secondary temperature control model; Step 13: Use the secondary input sample as the input of the secondary temperature control model and the temperature label as the actual temperature of the secondary temperature control model. Use the Adam optimizer to perform backpropagation training on the secondary temperature control model and adjust the model weights. Step 14: Collect actual input data from the secondary pyrolysis process in the form of secondary input samples, input the actual input data into the secondary temperature control model, and obtain the secondary real-time predicted temperature output by the secondary temperature control model; The three-level input sample data includes a corresponding oxygen concentration time series composed of oxygen concentrations collected at fixed time intervals in the historical optimal recovery experience of the three-level pyrolysis process, and a temperature time series of the actual temperature in the three-level pyrolysis equipment changing with time, with the oxygen concentration time series and temperature time series within each time interval serving as a set of three-level input samples; the historical optimal recovery experience of the three-level pyrolysis process refers to recovery experience data that screens out the quality indicators of the final product in the past recycling process of waste lithium batteries using the cascade pyrolysis method, and the final target quality is the target purity of crystallized lithium cobalt oxide set based on experience; The three-level label sample data includes a three-level temperature label corresponding to each set of three-level input samples in the historical optimal recovery experience of the three-level pyrolysis process, and the three-level temperature label is a temperature value of one unit time after the corresponding three-level input sample; The specific temperature range of the primary pyrolysis temperature range is [200°C, 300°C], The secondary pyrolysis temperature range is [500°C, 700°C], The temperature range of the three-stage pyrolysis temperature range is [800°C, 1000°C].

2. The method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries by cascade pyrolysis according to claim 1, characterized in that: The equipment used for the primary pyrolysis is a rotary kiln equipped with a temperature gradient control system, and the atmosphere of the primary pyrolysis is an inert gas; The pyrolysis equipment used in the secondary pyrolysis is a vertical reactor equipped with a precise temperature control system, and the pyrolysis atmosphere used is a reducing gas; The equipment used for the three-stage pyrolysis is a tubular furnace equipped with a rapid temperature rise and fall system, and the atmosphere used is oxygen.

3. An electronic device, characterized in that: include: A processor and a memory, wherein: The memory stores a computer program that can be called by the processor; The processor executes the method for recovering lithium cobalt oxide positive electrode materials from waste lithium batteries by the cascade pyrolysis method as described in any one of claims 1-2 in the background by calling the computer program stored in the memory.

4. A computer-readable storage medium, characterized in that A rewritable computer program is stored thereon; When the computer program is run on a computer device, the computer device executes the method for recovering lithium cobalt oxide positive electrode materials in waste lithium batteries by the cascade pyrolysis method as described in any one of claims 1 to 2 in the background.

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