Efficient dissociation method and apparatus for electrode material in lithium battery waste residue, device and medium

Abstract

The present invention relates to an artificial intelligence technology. Disclosed are an efficient dissociation method and apparatus for an electrode material in lithium battery waste residues, a device and a medium. The method comprises: grouping preset experimental data, so as to obtain electrode material features of the experimental data, and extracting from the electrode material features a temperature data set, a dissociation efficiency data set and a dissociation purity data set; calculating the decomposition temperature of each electrode material feature; using the decomposition temperatures to calculate the metal vapor pressure of each electrode material feature (S3); and, on the basis of the metal vapor pressure, the decomposition temperatures, the dissociation efficiency data set and the dissociation purity data set, performing optimal temperature analysis on the material features, so as to obtain optimal dissociation temperature data (S4). The present invention further provides an efficient dissociation apparatus for an electrode material in lithium battery waste residues, an electronic device and a storage medium. The present invention can solve the problems of low dissociation efficiency and low dissociation purity of electrode materials during electrode material dissociation caused by failure to accurately determine pyrolysis temperatures.

Description

Efficient dissociation method, device, equipment and medium for electrode material in lithium battery waste residue TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, and in particular to an efficient dissociation method, device, equipment and computer readable storage medium for electrode material in lithium battery waste residue. BACKGROUND

[0002] The core value of lithium ion batteries lies in their rich content of high-value metal elements such as lithium (Li), cobalt (Co), nickel (Ni), and manganese (Mn). However, the reserves of these metals in nature are limited, the mining cost is high, and the supply often fluctuates. Therefore, effectively recycling these metals from waste batteries is crucial for reducing the pressure on raw material supply and demand and reducing dependence on newly mined mineral resources.

[0003] Currently, the main technology for recycling waste lithium ion batteries is wet metallurgy, which extracts metals from waste batteries using solvents. However, existing wet recycling processes for waste lithium ion batteries are often based on certain process conditions, focusing on one or a few key process parameters such as temperature and reaction time, treating each parameter as an independent variable and adjusting it separately to reduce the impact of process parameters on metal extraction efficiency. However, this method lacks systematic consideration of the synergistic effects of multiple factors. For example, increasing the temperature may accelerate metal dissolution, but it may also accelerate the evaporation of the solvent or the occurrence of side reactions, resulting in lower actual recovery efficiency than the theoretical optimum. Therefore, existing waste lithium ion battery recycling technologies have the problems of low metal recovery efficiency and high energy consumption. SUMMARY

[0004] The present application provides an efficient dissociation method, device, equipment and medium for electrode material in lithium battery waste residue, which mainly aims to solve the problems of low metal recovery efficiency and high energy consumption in waste lithium ion battery recycling technology.

[0005] To achieve the above-mentioned purpose, the present application provides an efficient dissociation method for electrode material in lithium battery waste residue, which comprises:

[0006] Obtaining dissociation experimental data of the lithium battery waste residue, grouping the experimental data, obtaining electrode material characteristics of the experimental data, and extracting temperature data set, dissociation efficiency data set and dissociation purity data set from the electrode material characteristics;

[0007] Calculating the decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set;

[0008] Calculating the metal vapor pressure of each electrode material characteristic using the decomposition temperature;

[0009] performing optimal temperature analysis on the material characteristics according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency dataset, and the dissociation purity dataset, to obtain dissociation optimal temperature data;

[0010] generating a high-efficiency dissociation strategy for electrode materials in lithium battery waste residues according to the dissociation optimal temperature data.

[0011] Optionally, the experimental data is grouped to obtain electrode material characteristics of the experimental data, including:

[0012] performing data correction on the experimental data to obtain corrected data;

[0013] grouping the corrected data according to experimental time in the experimental data to obtain grouped data;

[0014] extracting electrode material characteristics from the grouped data according to electrode material types in the experimental data.

[0015] Optionally, the decomposition temperature of each electrode material characteristic is calculated according to the temperature dataset, the dissociation purity dataset, and the dissociation efficiency dataset, including:

[0016] constructing a dissociation temperature change curve according to the temperature dataset, the dissociation purity dataset, and the dissociation efficiency dataset;

[0017] calculating a temperature extreme point of the dissociation temperature change curve;

[0018] selecting a decomposition temperature from the temperature extreme point.

[0019] Optionally, the metal vapor pressure of each electrode material characteristic is calculated using the decomposition temperature, including:

[0020] calculating the saturated partial pressure of different electrode material types in the electrode material characteristics at the decomposition temperature one by one;

[0021] performing data correction on the saturated partial pressure to obtain the metal vapor pressure.

[0022] Optionally, the dissociation optimal temperature data is obtained by performing optimal temperature analysis on the material characteristics according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency dataset, and the dissociation purity dataset, including:

[0023] selecting a temperature data with the largest decomposition temperature from the decomposition temperature as an initial temperature;

[0024] constructing an acceptance probability function according to the dissociation efficiency dataset, the dissociation purity dataset, and the metal vapor pressure.

[0025] performing a near neighbor search on the initial temperature to obtain a temperature candidate value;

[0026] calculating an acceptance probability of the temperature candidate value by using the acceptance probability function;

[0027] performing an acceptance judgment on the temperature candidate value by using the acceptance probability function, performing temperature updating on the initial temperature according to a judgment result, and obtaining a temperature variable;

[0028] returning to the step of performing a near neighbor search on the initial temperature to obtain a temperature candidate value with the temperature variable as the initial temperature until the updated temperature exceeds a preset temperature threshold value, and obtaining optimal temperature data of dissociation.

[0029] Optionally, the acceptance probability function is constructed according to the dissociation efficiency data set, the dissociation purity data set and the metal vapor pressure, and includes:

[0030] performing weighted summation on the dissociation efficiency data set and the dissociation purity data set to obtain a reward function;

[0031] constructing a penalty function according to the metal vapor pressure;

[0032] performing function integration according to the reward function and the target function to obtain a target function;

[0033] calculating a target value of the temperature candidate value and a target value of the initial temperature by using the target function, and constructing an acceptance probability function according to the target value of the initial temperature, the target value of the temperature candidate value, the initial temperature and the temperature candidate value.

[0034] Optionally, the high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue is generated according to the optimal temperature data of dissociation, and includes:

[0035] determining a temperature parameter in a pyrolysis operation process of the lithium battery waste residue according to the optimal temperature data of dissociation;

[0036] uploading the temperature parameter to a control system software of a preset pyrolysis device to obtain the high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue.

[0037] To solve the above problems, the application further provides a high-efficiency dissociation device of an electrode material in a lithium battery waste residue, which includes:

[0038] a data extraction module configured to acquire dissociation experimental data of the lithium battery waste residue, perform data grouping on the experimental data, obtain electrode material characteristics of the experimental data, and extract a temperature data set, a dissociation efficiency data set and a dissociation purity data set from the electrode material characteristics.

[0039] a decomposition temperature calculation module configured to calculate a decomposition temperature of each electrode material feature according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set;

[0040] a vapor pressure calculation module configured to calculate a metal vapor pressure of each electrode material feature using the decomposition temperature;

[0041] an optimal temperature calculation module configured to perform optimal temperature analysis on the material features according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set and the dissociation purity data set to obtain dissociation optimal temperature data;

[0042] a strategy generation module configured to generate an efficient dissociation strategy for the electrode material in the lithium battery waste residue according to the dissociation optimal temperature data.

[0043] To solve the above problems, the present application further provides an electronic device, which comprises:

[0044] at least one processor; and

[0045] a memory connected in communication with the at least one processor; wherein

[0046] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the efficient dissociation method for the electrode material in the lithium battery waste residue.

[0047] To solve the above problems, the present application further provides a computer readable storage medium, which stores at least one computer program, and the at least one computer program is executed by a processor in an electronic device to implement the efficient dissociation method for the electrode material in the lithium battery waste residue.

[0048] The embodiment of the present application can set an accurate basis for pyrolysis process by determining the temperature threshold at which the electrode material begins to significantly decompose and obtaining the decomposition temperature according to the temperature threshold, thereby avoiding insufficient dissociation or increased side reactions caused by improper temperature, and further improving the dissociation efficiency and dissociation purity of dissociation. By comprehensively considering multiple key factors such as metal vapor pressure, decomposition temperature, dissociation efficiency and dissociation purity, the optimal working temperature can be found, which can ensure effective dissociation of all electrode materials and also consider key performance indicators (such as efficiency and purity) for actual lithium battery waste dissociation process, reduce insufficient dissociation and side reactions, improve the recovery rate and purity of target metal elements, and improve the resource recycling efficiency. Therefore, the efficient dissociation method, device, equipment and medium for electrode materials in lithium battery waste provided by the present application can solve the problems of low metal recovery efficiency and high energy consumption in waste lithium ion battery recycling technology. BRIEF DESCRIPTION OF DRAWINGS

[0049] FIG. 1 is a flowchart of the efficient dissociation method for electrode materials in lithium battery waste provided by an embodiment of the present application;

[0050] FIG. 2 is a flowchart of metal vapor pressure calculation provided by an embodiment of the present application;

[0051] FIG. 3 is a flowchart of acceptance probability function construction provided by an embodiment of the present application;

[0052] FIG. 4 is a functional module diagram of the efficient dissociation device for electrode materials in lithium battery waste provided by an embodiment of the present application;

[0053] FIG. 5 is a structural diagram of an electronic device for implementing the efficient dissociation method for electrode materials in lithium battery waste provided by an embodiment of the present application.

[0054] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0056] Referring to FIG. 1, a flowchart of the efficient dissociation method for electrode materials in lithium battery waste provided by an embodiment of the present application is shown. In the embodiment, the efficient dissociation method for electrode materials in lithium battery waste includes:

[0057] S1, obtaining dissociation experimental data of the lithium battery waste, grouping the experimental data, obtaining electrode material characteristics of the experimental data, and extracting temperature data set, dissociation efficiency data set and dissociation purity data set from the electrode material characteristics.

[0058] The dissociation experiment data of the lithium battery waste residue in the embodiment of the application refer to the result data obtained when the lithium battery waste residue is dissociated by using the high-temperature pyrolysis method, including experimental time, experimental conditions (such as temperature, pressure, reaction time, etc.), electrode material type, dissociation product amount and composition, dissociation efficiency and dissociation purity, etc.

[0059] The electrode material feature in the embodiment of the application refers to the electrode material data set of different groups of electrode materials obtained by grouping the experimental data according to the electrode material attributes in the experimental data.

[0060] In the embodiment of the application, by extracting the electrode material feature from the experimental data and extracting the temperature data set, the dissociation efficiency data set and the dissociation purity data set from each electrode material feature, each performance index can be presented separately, the influence law of the temperature factor on the dissociation effect can be accurately obtained, and the inaccurate analysis result caused by the mixing of different types of electrode material data can be avoided.

[0061] In the embodiment of the application, the experimental data is grouped to obtain the electrode material feature of the experimental data, including:

[0062] The experimental data is data-corrected to obtain corrected data;

[0063] The corrected data is data-grouped by using the experimental time in the experimental data to obtain grouped data;

[0064] The grouped data is feature-extracted by using the electrode material type in the experimental data to obtain the electrode material feature.

[0065] In the embodiment of the application, the same type of data such as reaction time data in the experimental data is converted into a unified unit standard by unit conversion of the experimental data to obtain corrected data.

[0066] In the embodiment of the application, all experimental data in the same time interval is added into the same data set by using the experimental time in the experimental data to obtain the time data set, the time data set is sorted in time sequence and is assigned a unique group number to obtain grouped data.

[0067] In the embodiment of the application, the electrode material type data is screened from the grouped data, the grouped data is type-matched according to the electrode material type data, the group number, temperature, pressure, reaction time, dissociation product amount and composition, dissociation efficiency and dissociation purity corresponding to the electrode material type in each grouped data are extracted, the group number, temperature, pressure, reaction time, dissociation product amount and composition, dissociation efficiency and dissociation purity corresponding to the same electrode material type are added into the same data set to obtain the electrode material feature.

[0068] In the embodiment of the present application, the unique behavior patterns of different electrode material characteristics in the dissociation process can be integrated by data correction and material grouping on experimental data, and the influence of temperature factors on the dissociation process can be analyzed according to the formed temperature data set, dissociation efficiency data set and dissociation purity data set of different electrode material characteristics.

[0069] S2, calculate the decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set.

[0070] In the embodiment of the present application, the decomposition temperature refers to the inflection point of the dissociation efficiency or dissociation purity of different electrode materials corresponding to the electrode material characteristics with temperature change, and the temperature corresponding to the inflection point is the decomposition temperature.

[0071] In the embodiment of the present application, since different temperatures can affect the reaction efficiency and reaction purity of electrode materials, the calculated decomposition temperature can be used as a reference to determine the operating temperature of the dissociation process, and according to the reference, the insufficient dissociation or the increase of side reactions caused by inaccurate temperature can be avoided, so that different electrode materials can be fully dissociated.

[0072] In the embodiment of the present application, the calculation of the decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set comprises:

[0073] constructing a dissociation temperature change curve according to the temperature data set and the dissociation purity data set and the dissociation efficiency data set;

[0074] calculating the temperature extreme point of the dissociation temperature change curve;

[0075] selecting the decomposition temperature from the temperature extreme point.

[0076] In the embodiment of the present application, the dissociation temperature in the temperature data set is taken as the abscissa, and the dissociation purity in the dissociation purity data set is taken as the ordinate to construct a dissociation temperature-dissociation purity conversion curve, each point on the curve represents a dissociation experiment at a certain temperature (abscissa value), and the dissociation purity (ordinate value) reflects the purity of the product obtained after the dissociation of the electrode material at the temperature; the dissociation temperature is taken as the abscissa, but the dissociation efficiency is taken as the ordinate at this time, and another curve is drawn according to the aligned data set to obtain a dissociation temperature-dissociation efficiency conversion curve, each point on the curve represents the degree of successful separation of the electrode material when the dissociation experiment is carried out at the corresponding temperature, i.e. the dissociation efficiency.

[0077] In the embodiment of the present application, the first derivative value corresponding to each point in the dissociation temperature-dissociation purity conversion curve and the dissociation temperature-dissociation efficiency conversion curve is calculated, and the temperature value corresponding to the point with the first derivative value of 0 is taken as the temperature extreme point.

[0078] In the embodiment of the present application, the point corresponding to the temperature extreme point and having the positive left first derivative and the negative right first derivative is selected from the temperature extreme point, and the point is taken as the local maximum value. The temperature point with the dissociation purity and the dissociation efficiency higher than the preset value is selected from the local maximum value point, and the temperature point is taken as the decomposition temperature.

[0079] In the embodiment of the present application, the decomposition temperature in the dissociation purity data set and the dissociation efficiency data set is calculated through the constructed temperature data set, the temperature threshold at which the electrode material starts to significantly decompose under a specific dissociation condition can be determined, the probability of insufficient dissociation result is reduced, and the side reaction generated in the dissociation process is reduced.

[0080] S3, calculating the metal vapor pressure of each electrode material feature by using the decomposition temperature.

[0081] In the embodiment of the present application, the metal vapor pressure refers to the gas phase partial pressure of the electrode material in the electrode material feature under the decomposition temperature, when the electrode material component exists in a gaseous form in an equilibrium state with the solid or liquid form of the electrode material. The equilibrium state refers to that, under the given temperature (decomposition temperature) and total pressure condition, the conversion rate between the metal vapor of the electrode material and the solid or liquid metal is equal, that is, the speed of the metal atoms of the electrode material volatilizing from the solid or liquid phase into the gas phase is equal to the speed of the metal atoms condensing from the gas phase back to the solid or liquid phase.

[0082] In the embodiment of the present application, the metal vapor pressure is directly related to the volatility of the metal element, and a higher vapor pressure means that the metal is more volatile into the gas phase, which is conducive to recycling by the gas trapping mode. Calculating the metal vapor pressure helps to predict the recycling efficiency of the metal element at different temperatures, and accordingly the dissociation condition can be selected or adjusted to maximize the recycling efficiency of the target metal.

[0083] As shown in FIG. 2, the calculation of the metal vapor pressure of each electrode material feature by using the decomposition temperature includes:

[0084] S21, calculating the saturation partial pressure of different electrode material types in the electrode material feature under the decomposition temperature one by one;

[0085] S22, performing data correction on the saturation partial pressure to obtain the metal vapor pressure.

[0086] In the embodiment of the present application, the standard enthalpy of formation and the standard entropy of chemical reaction of each electrode material feature at the decomposition temperature are obtained, the product of the decomposition temperature and the standard entropy is calculated, the difference between the standard enthalpy of formation and the product is calculated, the difference is divided by the negative product of the preset gas constant and the decomposition temperature, the natural logarithm e is taken as the base, and the negative value is taken as the index to obtain the saturation partial pressure, wherein the standard enthalpy of formation refers to the heat change under standard conditions, and the standard entropy refers to the change of the degree of system disorder before and after the reaction in the electrolysis reaction.

[0087] In the embodiment of the present application, the ratio between the pressure data in the electrode material feature and the standard pressure is calculated, the ratio is taken as a correction coefficient, the product between the correction coefficient and the saturation partial pressure is calculated, and the actual partial pressure is obtained.

[0088] In the embodiment of the present application, the calculated metal vapor pressure can reflect the trend and strength of the conversion of metal elements in the electrode material into a gaseous state in the dissociation process, thereby improving the recovery rate and purity of the target metal elements.

[0089] S4, performing optimal temperature analysis on the material feature according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set, and the dissociation purity data set to obtain dissociation optimal temperature data.

[0090] In the embodiment of the present application, the optimal temperature analysis refers to finding an operating condition so that the efficiency, purity, etc. can be optimized in the entire electrode material dissociation process of the lithium battery waste residue.

[0091] In detail, the dissociation optimal temperature data refers to the best working temperature that can ensure effective dissociation of all electrode materials in the lithium battery waste residue and also take into account the key performance indicators (such as efficiency and purity) of all electrode materials in the actual lithium battery waste residue dissociation process, and the decomposition temperature refers to the best temperature of the key performance indicators of different electrode materials.

[0092] In the embodiment of the present application, the dissociation temperature that achieves the best balance of various indicators is found by comprehensively considering multiple key factors, the optimization of the overall process is realized, and the best process conditions for the electrode materials in the lithium battery waste residue are provided.

[0093] In the embodiment of the present application, the optimal temperature analysis on the material feature according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set, and the dissociation purity data set to obtain dissociation optimal temperature data comprises:

[0094] Selecting the temperature data with the maximum decomposition temperature from the decomposition temperature as an initial temperature;

[0095] Performing a near neighbor search on the initial temperature to obtain a temperature candidate value;

[0096] constructing an acceptance probability function according to the dissociation efficiency data set, the dissociation purity data set and the metal vapor pressure;

[0097] calculating an acceptance probability of the temperature candidate value by using the acceptance probability function;

[0098] judging the temperature candidate value by using the acceptance probability function, and performing temperature updating on the initial temperature according to a judgment result to obtain a temperature variable;

[0099] taking the temperature variable as the initial temperature, returning to the step of performing neighbor searching on the initial temperature to obtain a temperature candidate value, until the updated temperature exceeds a preset temperature threshold to obtain dissociation optimal temperature data.

[0100] As shown in FIG. 3, the step of constructing an acceptance probability function according to the dissociation efficiency data set, the dissociation purity data set and the metal vapor pressure comprises the following steps.

[0101] S31, performing weighted summation on the dissociation efficiency data set and the dissociation purity data set to obtain a reward function;

[0102] S32, constructing a penalty function according to the metal vapor pressure;

[0103] S33, performing function integration on the reward function and the target function to obtain a target function;

[0104] S34, calculating a target value of a temperature candidate value and a target value of an initial temperature by using the target function, and constructing an acceptance probability function according to the target value of the initial temperature, the target value of the temperature candidate value, the initial temperature and the temperature candidate value.

[0105] In the embodiment of the present application, the penalty function is constructed by using the calculated metal vapor pressure to perform piecewise function construction, that is, when the calculated metal vapor pressure is greater than a preset maximum value, the value of the penalty function is negative, and when the calculated metal vapor pressure is less than the preset maximum value, the value of the penalty function is positive.

[0106] In the embodiment of the present application, the target function is obtained by adding the penalty function and the reward function.

[0107] In the embodiment of the present application, the acceptance probability function is as follows: , wherein A(T new , T current ) is an acceptance probability, T new is a temperature candidate value, T current represents an initial temperature, and F(T new) is the target value of the temperature candidate value, F(T curren ) is the target value of the initial temperature, a is a preset proportional coefficient, and min is a minimum value operator of two numbers.

[0108] In the embodiment of the present application, the initial temperature is taken as the starting point of the search, and the preset search step and boundary range are used to sequentially screen all dissociation temperatures within the boundary range to obtain the temperature candidate value.

[0109] In the embodiment of the present application, first, it is judged whether the reward function value corresponding to the temperature candidate value is greater than the reward function value of the initial temperature, if less than the reward function value of the initial temperature, the temperature candidate value is rejected, and the next temperature candidate value is judged, if greater than the reward function value of the initial temperature, the next step is judged, then, it is judged whether the acceptance probability of the temperature candidate value is less than 0, if the acceptance probability is less than 0, the temperature candidate value is rejected, and the next temperature candidate value is judged, if the acceptance probability of the temperature candidate value is within the range of (0, 1), the temperature of the temperature candidate value is updated to obtain the temperature variable.

[0110] In the embodiment of the present application, the temperature of the temperature candidate value is updated by using the following formula: Wherein, T(K) represents the temperature variable obtained by the first update, T0 is the temperature candidate value, A preset cooling rate. In the embodiment of the present application, the dissociation optimal temperature data is determined according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set and the dissociation purity data set, so that the temperature point at which the dissociation efficiency and purity reach the best balance, unnecessary side reactions and harmful substances are generated in the lithium battery waste slag dissociation process, and the overall optimization of the electrode material recovery process is realized.

[0111] S5, generating an efficient dissociation strategy of electrode materials in lithium battery waste slag according to the dissociation optimal temperature data.

[0112] In the embodiment of the present application, the dissociation process of electrode materials in lithium battery waste slag is guided by using the obtained optimal temperature, which can ensure the efficiency of electrode material recovery in lithium battery waste slag, maximize the recovery purity of electrode materials, and improve the resource recycling efficiency.

[0113] In the embodiment of the present application, the efficient dissociation strategy of electrode materials in lithium battery waste slag according to the dissociation optimal temperature data comprises:

[0114] According to the dissociation optimal temperature data, the temperature parameters in the pyrolysis operation process of lithium battery waste slag are determined.

[0115] The temperature parameter is uploaded into a preset control system software of a pyrolysis device, and a high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue is obtained.

[0116] In the embodiment of the present application, the dissociation optimal temperature data reveals that the electrode material can achieve the highest dissociation efficiency at a specific temperature, and the dissociation optimal temperature data is used as the temperature parameter to generate the high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue, so that the lithium, cobalt, nickel, manganese and other electrode material metals can be effectively separated from the waste residue under the optimal conditions, thereby greatly improving the metal recovery rate.

[0117] As shown in FIG. 4, it is a functional module diagram of the high-efficiency dissociation device of the electrode material in the lithium battery waste residue provided by an embodiment of the present application.

[0118] The high-efficiency dissociation device 100 of the electrode material in the lithium battery waste residue can be installed in an electronic device. According to the functions to be realized, the high-efficiency dissociation device 100 of the electrode material in the lithium battery waste residue can include a data extraction module 101, a decomposition temperature calculation module 102, a vapor pressure calculation module 103, an optimal temperature calculation module 104 and a strategy generation module 105. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, and are stored in the memory of the electronic device.

[0119] In the embodiment, the functions of each module / unit are as follows:

[0120] The data extraction module 101 is used to obtain the dissociation experimental data of the lithium battery waste residue, group the experimental data, obtain the electrode material characteristics of the experimental data, and extract the temperature data set, the dissociation efficiency data set and the dissociation purity data set from the electrode material characteristics;

[0121] The decomposition temperature calculation module 102 is used to calculate the decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set;

[0122] The vapor pressure calculation module 103 is used to calculate the metal vapor pressure of each electrode material characteristic by using the decomposition temperature;

[0123] The optimal temperature calculation module 104 is used to analyze the optimal temperature of the material characteristics according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set and the dissociation purity data set, and obtain the dissociation optimal temperature data;

[0124] The strategy generation module 105 is used to generate the high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue according to the dissociation optimal temperature data.

[0125] In detail, each module in the lithium battery waste residue electrode material high-efficiency dissociation device 100 in the embodiment of the present application adopts the same technical means as the lithium battery waste residue electrode material high-efficiency dissociation method described in the above FIG. 1 to FIG. 3 when in use, and can produce the same technical effects, which will not be described here.

[0126] As shown in FIG. 5, it is a structural schematic diagram of an electronic device for implementing the lithium battery waste residue electrode material high-efficiency dissociation method according to an embodiment of the present application.

[0127] The electronic device 1 can include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and can further include a computer program stored in the memory 11 and executable on the processor 10, such as a lithium battery waste residue electrode material high-efficiency dissociation program.

[0128] In some embodiments, the processor 10 can be composed of an integrated circuit, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more combinations of central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, which connects all components of the electronic device through various interfaces and lines, executes or runs programs or modules stored in the memory 11 (such as executing a lithium battery waste residue electrode material high-efficiency dissociation program, etc.), and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0129] The memory 11 includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 11 can include both an internal storage unit and an external storage device of the electronic device. The memory 11 can be used not only to store application software and various data installed in the electronic device, such as the code of the lithium battery waste residue electrode material high-efficiency dissociation program, etc., but also to temporarily store data that has been output or will be output.

[0130] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize the connection communication between the memory 11, the at least one processor 10, etc.

[0131] The communication interface 13 is configured to realize the communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface can include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is usually configured to establish a communication connection between the electronic device and other electronic devices. The user interface can be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface can also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch screen, etc. The display can also be appropriately referred to as a display screen or a display unit, and is configured to display the information processed in the electronic device and display a visualized user interface.

[0132] Only the electronic device with components is shown in the figure, and those skilled in the art can understand that the structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device can include fewer or more components than those shown in the figure, or combine some components, or have a different component arrangement.

[0133] For example, although not shown, the electronic device can further include a power supply (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, so as to realize the functions of charge management, discharge management, and power consumption management, etc. through the power management device. The power supply can also include one or more direct current or alternating current power supplies, a recharging device, a power supply fault detection circuit, a power supply converter or inverter, a power supply state indicator, etc. Any component. The electronic device can also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not described here.

[0134] It should be understood that the embodiments are only for illustration, and the scope of the patent application is not limited by the structure.

[0135] The high-efficiency dissociation procedure of electrode materials in lithium battery waste slag stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, which can realize the following functions when running in the processor 10:

[0136] A data extraction module is configured to obtain dissociation experimental data of the lithium battery waste slag, perform data grouping on the experimental data, obtain electrode material characteristics of the experimental data, and extract a temperature data set, a dissociation efficiency data set and a dissociation purity data set from the electrode material characteristics;

[0137] A decomposition temperature calculation module is configured to calculate a decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set;

[0138] A vapor pressure calculation module is configured to calculate a metal vapor pressure of each electrode material characteristic by using the decomposition temperature;

[0139] An optimal temperature calculation module is configured to perform optimal temperature analysis on the material characteristics according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set and the dissociation purity data set, and obtain dissociation optimal temperature data;

[0140] A strategy generation module is configured to generate a high-efficiency dissociation strategy of electrode materials in lithium battery waste slag according to the dissociation optimal temperature data.

[0141] Specifically, the specific implementation method of the processor 10 on the above instructions can refer to the description of the related steps in the corresponding embodiment of the accompanying drawings, which will not be described here.

[0142] Further, the modules / units integrated in the electronic device 1 can be stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products. The computer readable storage medium can be volatile or non-volatile. For example, the computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).

[0143] The application also provides a computer readable storage medium, which stores a computer program, and the computer program can realize the following functions when executed by a processor of an electronic device:

[0144] A data extraction module is configured to obtain dissociation experimental data of the lithium battery waste slag, perform data grouping on the experimental data, obtain electrode material characteristics of the experimental data, and extract a temperature data set, a dissociation efficiency data set and a dissociation purity data set from the electrode material characteristics;

[0145] a decomposition temperature calculation module configured to calculate a decomposition temperature of each electrode material feature according to the temperature data set, the dissociation purity data set, and the dissociation efficiency data set;

[0146] a vapor pressure calculation module configured to calculate a metal vapor pressure of each electrode material feature using the decomposition temperature;

[0147] an optimal temperature calculation module configured to perform optimal temperature analysis on the material features according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set, and the dissociation purity data set to obtain dissociation optimal temperature data;

[0148] a strategy generation module configured to generate an efficient dissociation strategy of the electrode material in the lithium battery waste residue according to the dissociation optimal temperature data.

[0149] In several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other manners. For example, the above described device embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there can be another division manner in actual implementation.

[0150] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purposes of the embodiments.

[0151] In addition, each function module in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware, or in the form of hardware plus software function modules.

[0152] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0153] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is not limited only by the above description, and therefore all changes within the meaning and scope of equivalent elements falling within the scope of protection are intended to be included in the present application.

[0154] Embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is to use digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0155] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The plurality of units or devices stated in the system can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, not any specific order.

[0156] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for efficient dissociation of electrode materials in lithium battery waste residues, characterized in that, The method comprises: obtaining the dissociation experimental data of the lithium battery waste residue, grouping the experimental data, obtaining the electrode material characteristics of the experimental data, and extracting the temperature data set, the dissociation efficiency data set and the dissociation purity data set from the electrode material characteristics; calculating the decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set; calculating the metal vapor pressure of each electrode material characteristic by using the decomposition temperature; performing optimal temperature analysis on the material characteristics according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set and the dissociation purity data set to obtain the optimal dissociation temperature data; generating an efficient dissociation strategy for electrode materials in lithium battery waste residue according to the optimal dissociation temperature data.

2. The method of claim 1, wherein the lithium battery waste sludge is a cathode material. The grouping of the experimental data to obtain the electrode material characteristics of the experimental data comprises: performing data correction on the experimental data to obtain corrected data; grouping the corrected data by using the experimental time in the experimental data to obtain grouped data; extracting the electrode material characteristics from the grouped data by using the electrode material types in the experimental data.

3. The method of claim 1, wherein the lithium battery waste sludge is a cathode material. The calculation of the decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set comprises: constructing a dissociation temperature change curve according to the temperature data set, the dissociation purity data set and the dissociation efficiency data set; calculating the temperature extreme point of the dissociation temperature change curve; selecting the decomposition temperature from the temperature extreme point.

4. The method of claim 1, wherein the lithium battery waste sludge is a cathode material. The calculation of the metal vapor pressure of each electrode material characteristic by using the decomposition temperature comprises: calculating the saturation partial pressure of different electrode material types in the electrode material characteristics at the decomposition temperature one by one; performing data correction on the saturation partial pressure to obtain the metal vapor pressure.

5. The method of claim 1, wherein the lithium battery waste sludge is a cathode material. The optimal temperature analysis on the material characteristics according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set and the dissociation purity data set to obtain the optimal dissociation temperature data comprises: selecting the temperature data with the maximum decomposition temperature from the decomposition temperature as the initial temperature; performing a near neighbor search on the initial temperature to obtain a temperature candidate value; constructing an acceptance probability function according to the dissociation efficiency data set, the dissociation purity data set and the metal vapor pressure; calculating the acceptance probability of the temperature candidate value by using the acceptance probability function; performing acceptance judgment on the temperature candidate value by using the acceptance probability function, and updating the initial temperature according to the judgment result to obtain a temperature variable; taking the temperature variable as the initial temperature, returning to the step of performing a near neighbor search on the initial temperature to obtain a temperature candidate value, until the updated temperature exceeds a preset temperature threshold to obtain the optimal dissociation temperature data.

6. The method of efficiently separating electrode materials from lithium battery waste sludge according to claim 5, wherein the lithium battery waste sludge is a waste sludge of a lithium battery that has been used for more than 1 year. The construction of the acceptance probability function according to the dissociation efficiency data set, the dissociation purity data set and the metal vapor pressure comprises: performing weighted summation on the dissociation efficiency data set and the dissociation purity data set to obtain a reward function; constructing a penalty function according to the metal vapor pressure; According to the reward function and the target function, a target function is obtained by function integration; According to the initial temperature target value, the temperature candidate target value, the initial temperature, and the temperature candidate value, an acceptance probability function is constructed.

7. The method of claim 1, wherein the lithium battery waste sludge is a cathode material. The high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue is generated according to the dissociation optimal temperature data, and includes: According to the dissociation optimal temperature data, a temperature parameter in a pyrolysis operation process of the lithium battery waste residue is determined. The temperature parameter is uploaded to a control system software of a preset pyrolysis device to obtain the high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue.

8. A high-efficiency dissociation device for electrode materials in lithium battery waste residues, characterized in that, The device includes: A data extraction module is configured to obtain dissociation experiment data of the lithium battery waste residue, group the experiment data, obtain electrode material characteristics of the experiment data, and extract a temperature data set, a dissociation efficiency data set, and a dissociation purity data set from the electrode material characteristics; A decomposition temperature calculation module is configured to calculate a decomposition temperature of each electrode material characteristic according to the temperature data set, the dissociation purity data set, and the dissociation efficiency data set; A vapor pressure calculation module is configured to calculate a metal vapor pressure of each electrode material characteristic by using the decomposition temperature; An optimal temperature calculation module is configured to analyze the material characteristics according to the metal vapor pressure, the decomposition temperature, the dissociation efficiency data set, and the dissociation purity data set to obtain dissociation optimal temperature data; A strategy generation module is configured to generate a high-efficiency dissociation strategy of the electrode material in the lithium battery waste residue according to the dissociation optimal temperature data.

9. An electronic device, comprising: The electronic device includes: At least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the high-efficiency dissociation method of the electrode material in the lithium battery waste residue according to any one of claims 1 to 7.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the high-efficiency dissociation method of the electrode material in the lithium battery waste residue according to any one of claims 1 to 7.

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