Battery pole piece non-dry recovery system method and device based on air pollution detection

Abstract

The invention discloses a battery pole piece non-dry recovery system method and device based on air pollution detection, and particularly relates to the field of waste recycling and cyclic utilization. Which comprises a pollution sensing and regulating module, an intelligent pole piece preprocessing module, a self-adaptive non-dry reaction module, a pollutant in-situ control and recycling module and a data driving and cooperative control center, and is characterized in that characteristic gas in a recovery environment is monitored in real time through a high-sensitivity gas sensor array; according to the method, parameters and a circulation strategy of a non-dry treatment medium are dynamically regulated and controlled, a multispectral imaging and impedance spectroscopy analysis technology is combined to carry out nondestructive diagnosis and classification on the pole piece, a differentiated spraying-ultrasonic coupling dissociation scheme is implemented, efficient and low-pollution separation of an active material is realized, in-situ control and recycling of pollutants in the recovery process are realized, and the recovery efficiency of the pole piece is improved. And the whole process is optimized through a data driving model, so that the environmental friendliness and resource recovery efficiency of battery pole piece recovery are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of waste resource utilization and recycling, more specifically, the present application relates to a battery pole piece non-dry recovery system method and device based on air pollution detection. BACKGROUND

[0002] The battery pole piece is the core component of the power battery, rich in high-value metals such as lithium, cobalt and nickel, and its recovery and treatment is the key to resource regeneration. However, in the process of pole piece recovery, especially in the separation link of active material and current collector, organic matter such as electrolyte and binder is easy to volatilize, producing hydrogen fluoride (HF), volatile organic compounds (VOCs) and other toxic and harmful gases, causing secondary environmental pollution and safety risks. Therefore, it is urgent to develop a technology and equipment that can realize real-time sensing and control of pollution and precise clean recovery.

[0003] The existing technology adopts the battery pole piece recovery scheme of hydrometallurgy principle, which usually first mechanically crushes and sorts the battery pole piece to obtain fragments rich in active material, then places these fragments in a specific acid or alkali solution for soaking and stirring, and through chemical dissolution effect makes the active material separate from the current collector, and then recovers the valuable metals through subsequent steps such as precipitation and extraction.

[0004] However, in actual use, it still has some disadvantages, such as pollution control belongs to passive and end-of-pipe treatment mode, which cannot realize real-time and accurate source sensing and rapid regulation of the instantaneous generated characteristic pollutants (such as HF and VOCs) in the recovery process, and there is a risk of environmental pollution. Secondly, the pole pieces are usually treated in a "one-size-fits-all" manner, lacking fine diagnosis of the pole piece's own state (such as aging degree, interface bonding strength and residual electric quantity), resulting in extensive reaction conditions, high energy consumption and material consumption, and easy to cause valuable metal recovery rate to decrease or secondary reaction to produce more pollutants due to violent reaction process or mismatched conditions. Finally, the coordination between each process unit is poor, the system operation relies on fixed parameters, and lacks dynamic optimization ability based on whole process data feedback, and the overall resource recovery efficiency and environmental benefit need to be improved. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a battery pole piece non-dry recovery system method and device based on air pollution detection, which solves the problems raised in the above background art through the following scheme.

[0006] To achieve the above object, the application provides the following technical solutions: A battery pole piece non-dry recovery system based on air pollution detection, comprising a pollution perception and control module: deploying a high-sensitivity gas sensor array to monitor the concentration and type of characteristic gas in the recovery environment in real time, and adjusting the injection parameters and circulation strategy of the non-dry processing medium based on dynamic data;

[0007] An intelligent pole piece pretreatment module: using multispectral imaging and impedance spectrum analysis technology, the battery pole pieces to be processed are subjected to non-destructive state diagnosis and accurate physical stripping, and the pole pieces are classified and path planned according to the diagnosis results;

[0008] An adaptive non-dry reaction module: configured with a multi-stage spraying and ultrasonic coupling unit linked with the pollution perception and control module, applying differentiated non-dry processing schemes according to the pole piece type and pollution level to achieve selective and low-pollution dissociation of active materials;

[0009] A pollution in-situ control and resourceization module: capturing and processing volatile organic compounds and dust escaping during the reaction process, and recycling, enriching or converting valuable components therein;

[0010] A data-driven and collaborative control hub: integrating data from various modules, and optimizing material and energy flow in the entire recovery process through algorithm models.

[0011] A battery pole piece non-dry recovery method based on air pollution detection, comprising S1: a pollution real-time perception and control step, real-time monitoring of the concentration and type of characteristic gas in the recovery environment, and dynamically adjusting the injection parameters and circulation strategy of the subsequent non-dry processing medium based on the monitoring results;

[0012] S2: an intelligent diagnosis and sorting step of pole pieces, non-destructive state diagnosis of the battery pole pieces to be processed, classification of the pole pieces according to the diagnosis results, and planning of differentiated subsequent processing paths for different types of pole pieces;

[0013] S3: an adaptive non-dry dissociation step, calling a matching non-dry processing scheme according to the type and processing path of the pole piece, and adaptively adjusting the composition, spraying intensity and ultrasonic energy of the processing medium in combination with the concentration change of the characteristic gas to achieve selective and low-pollution dissociation of active materials;

[0014] S4: an in-situ control and resourceization step of escaping pollutants, capturing and processing escaping gas and aerosol generated during the dissociation process, and condensing, adsorbing and enriching or directionally converting volatile organic compounds and fluorine-containing components therein;

[0015] S5: a data-driven and whole-process collaborative optimization step, integrating real-time data from monitoring, diagnosis, dissociation and control steps, and dynamically optimizing material and energy configuration in the entire recovery process through algorithm models.

[0016] The battery pole piece non-dry recycling device based on air pollution detection comprises a pollution sensing unit, a pretreatment unit, a reaction unit, a pollution control unit and a central control unit, can monitor and transmit the concentration and type of characteristic gas in the recycling environment in real time, perform pole piece state diagnosis and physical stripping, perform differentiated non-dry processing according to the monitoring data, in-situ treatment and resource recovery of the fugitive pollutants generated in the reaction process, coordinate the operation of each unit and optimize the overall process flow.

[0017] The technical effects and advantages of the present application are as follows:

[0018] 1. The present application uses multi-spectral imaging and impedance spectrum analysis technology to non-destructively diagnose and finely classify battery pole pieces, and plans differentiated processing paths and parameters for different types of pole pieces accordingly. The "non-dry" of the present application specifically refers to a closed-loop controlled, low liquid-solid ratio, precise spraying-ultrasonic coupled mild wet process based on real-time air pollution detection. The present application overcomes the drawbacks of the traditional "one-size-fits-all" approach and realizes efficient and selective dissociation of active materials.

[0019] 2. The present application integrates a high-sensitivity gas sensor array into the recycling system to realize real-time and in-situ monitoring of characteristic pollutants such as HF and VOCs. The system dynamically adjusts the injection parameters and reaction intensity of the processing medium based on the monitored pollution concentration data, thereby suppressing the generation and diffusion of pollutants from the source. Combined with the subsequent "adsorption-catalytic oxidation" and "cyclone-electrostatic" secondary purification processes, the present application forms a full-cycle closed-loop pollution control from sensing, regulation to processing, effectively eliminating secondary pollution.

[0020] 3. The present application uses data-driven and collaborative control hubs to integrate all process data and use predictive, optimization and regulation three-layer algorithm models to dynamically optimize the material and energy configuration of the entire system, forming an intelligent whole that learns, collaboratively decides and self-adapts, and minimizing the overall operating cost of the system. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The present application is a system overall structure schematic diagram;

[0022] Figure 2 The present application is a system pole piece intelligent diagnosis and sorting schematic diagram;

[0023] Figure 3 The present application is a system pollution sensing and adaptive regulation process schematic diagram. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0025] As shown in FIGS. 1 to 3, the battery pole piece non-dry recovery system based on air pollution detection includes a pollution sensing and control module: a high-sensitivity gas sensor array is deployed to monitor the concentration and type of characteristic gases in the recovery environment in real time, and the injection parameters and circulation strategies of the non-dry processing medium are controlled based on dynamic data. Figure 1 Figure 2 As shown in FIGS. 1 to 3, the battery pole piece non-dry recovery system based on air pollution detection includes a pollution sensing and control module: a high-sensitivity gas sensor array is deployed to monitor the concentration and type of characteristic gases in the recovery environment in real time, and the injection parameters and circulation strategies of the non-dry processing medium are controlled based on dynamic data. Figure 3

[0026] It needs to be specifically pointed out that the pollution sensing and control module adopts a high-sensitivity gas sensor array based on MEMS technology, which is deployed at key positions of the reaction chamber, the pretreatment area and the exhaust port according to a "3m x 3m" grid spacing, and the sampling frequency is set to 1Hz.

[0027] It needs to be further pointed out that in terms of sensor array deployment, it is composed of 12 sensor units of different sensitive materials, including tin oxide-based sensors, polyimide sensors, graphene sensors, electrochemical CO sensors, infrared CO2 sensors, photoionization benzene series sensors, zinc oxide nanorod sensors, thermal conductivity nitrogen sensors, cadmium sulfide thick film sensors, strontium titanate sensors, ceramic-based humidity sensors and platinum resistance temperature sensors.

[0028] The detected air pollution gases include volatile organic compounds VOCs (N-methyl pyrrolidone NMP, ethanol, dimethyl carbonate DMC, methyl acetate, benzene series), acidic gases (mainly hydrogen fluoride HF, a small amount of hydrogen sulfide H2S), and other characteristic gases (carbon monoxide CO, carbon dioxide CO2, lithium hexafluorophosphate LiPF6 volatiles, nitrogen N2).

[0029] The reason for choosing the above air pollution gases is that in the non-dry recovery of battery pole pieces, the volatilization of the spraying medium, the dissolution or decomposition of the organic binder, and the residual volatilization of the electrolyte will release VOCs, most of which are toxic (such as carcinogenic benzene series) and irritating, and some of which are flammable and explosive substances (such as ethanol and DMC), which directly threaten the health of operating personnel and production safety.

[0030] LiPF6 in the battery electrolyte will decompose when it comes into contact with water or high temperature, and HF will be generated. HF has strong corrosive properties, which can damage equipment and have a serious irritating effect on the respiratory tract and skin mucosa of the human body, and it is a highly toxic gas. ​​

[0031] If the local temperature is too high during the recovery process, the organic medium may not be completely oxidized, producing CO, which is a colorless and odorless toxic gas that can bind to hemoglobin, causing hypoxia, and is a hidden safety hazard.

[0032] The products of VOCs catalytic oxidation treatment (such as NMP oxidation decomposition to generate CO2 and H2O), or the degradation products of the organic components of the electrode sheet, the CO2 concentration can indirectly reflect the efficiency of the VOCs treatment process, and at the same time assist in judging the sealing of the reaction chamber (such as abnormal accumulation of CO2, which may indicate a ventilation failure).

[0033] The volatilization of LiPF6 in the residual electrolyte of the battery electrode sheet, especially during ultrasonic vibration or temperature fluctuation, is more likely to release. LiPF6 volatiles are corrosive and can further decompose to produce HF when they come into contact with water, exacerbating acid pollution.

[0034] Nitrogen is usually used as a protective gas to prevent oxidation of active materials or explosion of VOCs during the recovery process. Monitoring the nitrogen concentration can determine the sealing of the reaction chamber, such as a sudden drop in nitrogen concentration, which may indicate a leak, and ensure the stability of the inert atmosphere.

[0035] In terms of data collection and control logic, two types of core data are collected, specifically:

[0036] Characteristic gas concentration data, focusing on monitoring the concentration values of HF (detection limit 0.01 ppm), DMC (detection limit 0.1 ppm), methyl acetate (detection limit 0.1 ppm), and total VOCs (detection limit 0.05 ppm).

[0037] Gas type identification data, through the cross-response mode of the sensor array, using principal component analysis (PCA) algorithm to distinguish different gas components, solving the problem of single sensor cross-interference.

[0038] Based on the above data, a dynamic control model is established: when the HF concentration exceeds 0.05 ppm, automatically increase the injection pressure of the non-dry treatment medium (composed of 5% citric acid solution and 0.2% surfactant) to 0.3 MPa, and shorten the medium circulation period to 5 minutes, to inhibit the escape of hydrogen fluoride by increasing the liquid film coverage. When the VOCs concentration is below 0.1 ppm and the gas type is mainly harmless water vapor, reduce the medium injection volume to 1.5 L / min to reduce water consumption. When CO exceeds 50 ppm, reduce the ultrasonic power and increase the nitrogen flow. When CO2 exceeds 2000 ppm and VOCs are low, reduce the reactor power. When CO is below 300 ppm, check the sealing. When LiPF6 volatiles exceed 5 ppm, add complexing agents. When N2 is below 95%, supplement nitrogen, to achieve dynamic control of the gas.

[0039] The pole piece intelligent pretreatment module: adopts multispectral imaging and impedance spectrum analysis technology to perform non-destructive state diagnosis and accurate physical stripping on the battery pole piece to be processed, and classifies and plans the path of the pole piece according to the diagnosis result.

[0040] Need to be specified, the multispectral imaging, the data obtained includes active material thickness data, surface contaminant distribution data, current collector damage data, and the functions realized include judging the aging degree of the pole piece, identifying the damage and pollution on the surface of the pole piece, and detecting the thickness uniformity of the pole piece.

[0041] It needs to be further explained that the multispectral imaging technology is based on the difference in absorption and reflection characteristics of different substances at different wavelengths, realizes non-destructive detection of the internal state of the pole piece, and the system selects a high-resolution multispectral camera with a wavelength range of 400-1700 nm, cooperates with a ring light source to realize uniform illumination of the surface of the pole piece, and obtains three types of key data by capturing the difference in reflected spectrum at different wavelengths, including:

[0042] Active material thickness data: using the gray value distribution of the 950nm waveband (corresponding to the characteristic absorption peak difference between active material and current collector), combined with the Lambert-Beer law to calculate the active material thickness, the resolution reaches 5μm, and the active material shedding area caused by cycle aging is identified.

[0043] Surface contaminant distribution data: through the spectrum comparison of 450nm (characteristic waveband of organic contaminants) and 1300nm (water feature waveband), the residual electrolyte salt (such as LiPF6) and oil stains on the surface of the pole piece are located.

[0044] Current collector damage data: using 1700nm waveband (infrared reflection difference of aluminum / copper current collector) image analysis to judge whether the current collector has scratches, corrosion and other defects.

[0045] Through processing of the multispectral image, the system realizes three core diagnosis functions, including:

[0046] Judging the aging degree of the pole piece: based on the reflectivity change of the active material in the pole piece at near-infrared wavelength (the higher the aging degree, the lower the reflectivity at 1200nm wavelength), the aging degree of the pole piece is divided into three levels: “mild (reflectivity > 60%), moderate (reflectivity 40%-60%), severe (reflectivity < 40%)”.

[0047] Identify the damage and pollution on the surface of the pole piece: by comparing the image gray value difference of normal pole piece and detected pole piece at 550nm wavelength (gray value deviation of damaged area exceeds ±15), the damage position and area are located.

[0048] The thickness uniformity of the electrode sheet is detected, the thickness deviation of different regions of the electrode sheet is calculated based on the image brightness distribution at 650 nm wavelength (the brightness value decreases by 5% for each 10 μm increase in thickness), and the accurate setting of the pressure parameter in the subsequent stripping process is ensured. The specific analysis formula is:

[0049]

[0050] In the formula, is the thickness of the active material, is the incident light intensity (the standard brightness value at 650 nm wavelength, which is preset to 255), is the reflected light intensity of the electrode sheet (the actual collected brightness value), is the molar absorption coefficient of the active material at 650 nm wavelength (for ternary materials, it is taken as 1.1×104 L / mol·cm, and for lithium iron phosphate, it is taken as 1.3×104 L / mol·cm), , , is the bulk density of the active material (for ternary materials, it is taken as 4.8 g / cm³, and for lithium iron phosphate, it is taken as 3.6 g / cm³).

[0051] The impedance spectrum analysis technology measures the impedance spectrum characteristics of the electrode sheet by applying a small amplitude alternating current signal to the electrode sheet, and then analyzes the electrochemical reaction state and interface bonding strength inside the electrode sheet. The system uses a four-electrode method (working electrode, counter electrode, reference electrode, and auxiliary electrode) to measure the impedance of the electrode sheet. The contact between the electrode and the electrode sheet uses an "elastic probe array" (probe diameter 0.5 mm, spacing 2 mm) to ensure contact stability and measurement accuracy.

[0052] The impedance spectrum analysis data is mainly used for two key tasks, including:

[0053] Judging the interface bonding strength of the active material and the current collector of the electrode sheet, based on the change of "charge transfer resistance" (Rct) in the impedance spectrum (the higher the bonding strength, the larger the Rct), the interface bonding state is divided into "strong bonding (Rct>500Ω), medium bonding (Rct is 200-500Ω), and weak bonding (Rct<200Ω)".

[0054] Evaluating the residual capacity inside the electrode sheet by analyzing the slope of "Warburg impedance" in the impedance spectrum (the higher the residual capacity, the larger the slope), calculating the residual capacity of the electrode sheet (error ≤±3%), and providing a basis for subsequent processing scheme selection. The specific analysis formula is:

[0055]

[0056] In the formula, is the residual capacity of the electrode sheet (unit: Ah), is the correction coefficient (for ternary materials, it is taken as 0.9, and for lithium iron phosphate, it is taken as 0.8), . , Warburg impedance slope, angular frequency, , AC signal frequency, Base capacity correction value (0.05 Ah, used to offset measurement system error).

[0057] Based on the diagnosis results of multi-spectral imaging and impedance spectrum analysis, the control center classifies and path plans the pole pieces through a "decision tree-support vector machine" fusion algorithm. The classification dimensions include aging degree (light / medium / severe), interface bonding strength (strong / medium / weak), residual capacity (high / medium / low, residual capacity > 70% is high, residual capacity < 30% is low), and damage condition (no damage / mild damage / severe damage, mild damage refers to an area < 5%, and severe damage refers to an area > 15%).

[0058] According to the classification results, the pole pieces are assigned to three different processing paths.

[0059] Path 1 (high-quality pole piece): suitable for pole pieces with mild aging, weak bonding, low residual capacity, and no damage, directly performing dissociation in the adaptive non-dry reaction module, and using a low-intensity processing scheme (such as ultrasonic power 300W, spraying pressure 0.3MPa).

[0060] Path 2 (medium-quality pole piece): suitable for pole pieces with moderate aging, medium bonding, medium residual capacity, and mild damage, first performing pretreatment repair (removing surface impurities by low-temperature heating, heating temperature 60℃, time 5min), and then performing dissociation (ultrasonic power 500W, spraying pressure 0.5MPa).

[0061] Path 3 (poor-quality pole piece): suitable for pole pieces with severe aging, strong bonding, high residual capacity, and severe damage, first performing intensive pretreatment (removing stubborn surface impurities by plasma cleaning, plasma power 800W, processing time 10min), and then performing dissociation (ultrasonic power 800W, spraying pressure 0.8MPa).

[0062] Adaptive non-dry reaction module: configured with a multi-stage spraying and ultrasonic coupling unit linked with the pollution perception and control module, applying differentiated non-dry processing schemes according to the pole piece type and pollution level, realizing selective and low-pollution dissociation of active materials.

[0063] It should be specifically noted that the multi-stage spraying and ultrasonic coupling unit implements differentiated spraying strategies according to the pole piece type data and real-time pollution concentration data, sets different main medium injection amounts and spraying pressures, and selects different auxiliary cleaning agents.

[0064] It needs to be further explained that the multi-stage spraying unit adopts a three-stage concentric nozzle design. The first-stage nozzle (diameter 1.5 mm) delivers the main treatment medium, the second-stage nozzle (diameter 0.8 mm) delivers the auxiliary cleaning agent (1% dilute sulfuric acid solution for high-quality polar plates and deionized water for medium polar plates), and the third-stage nozzle (diameter 0.5 mm) delivers the anti-fogging agent (0.1% polyethylene glycol solution). Two types of key data are collected: polar plate type data (from the pre-treatment module) and real-time pollution concentration data (from the pollution sensing module).

[0065] Based on the above data, a differentiated spraying strategy is implemented: when processing high-quality polar plates, the main medium injection amount is set to 2.5 L / min, the spraying pressure is 0.4 MPa, and the auxiliary cleaning agent is dilute sulfuric acid solution, which promotes the interface separation of active materials and current collectors in an acidic environment. When processing medium polar plates, the main medium injection amount is reduced to 2.0 L / min, the spraying pressure is 0.3 MPa, and the auxiliary cleaning agent is switched to deionized water to avoid excessive corrosion. When the pollution sensing module detects that the HF concentration exceeds 0.05 ppm, the anti-fogging agent injection amount is automatically increased to 0.5 L / min to form a protective film to inhibit gas emission.

[0066] The ultrasonic coupling unit is configured with 20 kHz and 40 kHz dual-frequency ultrasonic transducers. According to the type of polar plates, the ultrasonic parameters are adjusted. When processing high-hardness ternary material polar plates, 40 kHz high-frequency ultrasonic is used, and the ultrasonic power density is set to 30 W / cm² to destroy the crystal structure of active materials through high-frequency vibration. When processing flexible lithium iron phosphate polar plates, 20 kHz low-frequency ultrasonic is switched, and the power density is reduced to 20 W / cm² to reduce the deformation damage of the current collector.

[0067] When the spraying medium contacts the polar plate surface, the ultrasonic transducer is delayed for 0.5 seconds to start, avoiding medium splashing. According to the VOCs concentration feedback from the pollution sensing module, the ultrasonic working cycle is dynamically adjusted. When the VOCs concentration is too high, the single ultrasonic time is shortened to 10 seconds, and the interval time is extended to 30 seconds to control gas emission by reducing the reaction intensity.

[0068] Pollutant in-situ control and resource utilization module: captures and processes volatile organic compounds and dust emitted during the reaction process, and recycles, enriches or transforms valuable components.

[0069] It needs to be specifically explained that the pollutant in-situ control and resource utilization module uses the "adsorption-catalytic oxidation" combined process for in-situ control of pollutants, collects VOCs concentration and component data, and adsorption bed inlet and outlet concentration difference data. The resource utilization uses the "cyclone separation + electrostatic precipitation" two-stage purification process, and collects dust concentration data and dust component data.

[0070] Further, the module adopts a combination process of "adsorption-catalytic oxidation", and a honeycomb activated carbon adsorption bed (adsorption capacity > 800 mg / g) is arranged at the front end, and a low-temperature plasma catalytic reactor (working temperature 120°C) is arranged at the rear end. The system collects two types of data: VOCs concentration and component data (from the pollution perception module) and adsorption bed inlet and outlet concentration difference data.

[0071] When the adsorption bed inlet and outlet concentration difference is less than 5%, it is judged that the adsorption is saturated, the standby adsorption bed is automatically switched, and the desorption program (hot air blowing, temperature 80°C) is started. According to the VOCs component data, the plasma power is adjusted. When processing carbonate VOCs, the power is set to 500W, and when processing organic acid VOCs, the power is increased to 600W.

[0072] The high-concentration VOCs generated by desorption enter the catalytic reactor and are decomposed into CO2 and H2O under the action of nano-TiO2 catalyst. The heat energy generated at the same time is recovered through a heat exchanger for adsorption bed desorption heating, realizing energy recycling. The VOCs treatment effect is quantified by a specific analysis formula:

[0073]

[0074] In the formula, is the VOCs removal rate, is the VOCs concentration at the inlet of the catalytic reactor, is the VOCs concentration at the outlet of the catalytic reactor, and the design requirement .

[0075] The module adopts a two-stage purification process of "cyclone separation + electrostatic precipitation". The cyclone separator (separation efficiency > 95%) captures dust with a particle size greater than 10 μm, and the electrostatic precipitator (electric field strength 3 kV / cm) captures fine dust with a particle size of 1-10 μm. Dust concentration data (real-time monitoring by a laser dust sensor, detection range 0.01-100 mg / m³) and dust component data (offline analysis by an X-ray fluorescence spectrometer, once a week) are collected.

[0076] When the dust concentration exceeds 5 mg / m³, the voltage of the electrostatic precipitator is increased to 3.5 kV / cm, and the resource utilization path is adjusted according to the dust component data. If the lithium content in the dust exceeds 5%, it is leached by 0.5 mol / L hydrochloric acid solution, and lithium chloride is recovered by evaporation crystallization. If the cobalt and nickel content exceeds 10%, it is enriched by magnetic separation technology and used as smelting raw material. If it is inert dust (such as carbon powder), it is compressed into a shape and used as fuel.

[0077] Data-driven and collaborative control center: integrate data from all modules, and optimize material and energy flow in the entire recycling process through algorithm model.

[0078] It needs to be specifically pointed out that the module data is divided into environmental monitoring data, pole piece state data, reaction process data, and resource data, and the algorithm model has three layers, including a prediction model, an optimization model, and a control model.

[0079] It needs to be further pointed out that the cooperative control center establishes real-time communication with each module through industrial Ethernet and collects four types of core data: environmental monitoring data (characteristic gas concentration, dust concentration, temperature and humidity, sampling frequency 1 Hz), pole piece state data (active material thickness, Rct value, classification result, sampling frequency 0.5 Hz), reaction process data (medium injection amount, ultrasonic power, spraying pressure, sampling frequency 1 Hz), and resource data (VOCs removal rate, dust recovery rate, product purity, sampling frequency 0.1 Hz).

[0080] First, the original data is preprocessed, the sliding average method (window size 5) is used to remove random noise of environmental monitoring data, the Relyada criterion (3σ principle) is used to eliminate abnormal values in pole piece state data (such as negative values or out-of-range values caused by sensor failure), and the linear interpolation method is used to fill in missing values in reaction process data (due to communication delay). The preprocessed data is stored in the MySQL database, and the timing backup (every day at 2 am) and off-site disaster recovery (backup to cloud server) strategies are adopted to ensure data security.

[0081] A three-layer algorithm model of "prediction-optimization-control" is constructed to realize intelligent optimization of the recycling process.

[0082] The first layer is the prediction model, which uses the long short-term memory network (LSTM) to predict the trend of key parameters. Taking the active material recovery rate as an example, the model takes the pole piece Rct value, medium injection amount, and ultrasonic power as input features, and historical recovery rate data as labels. After training, it can predict the recovery rate change in the next 10 minutes. When the predicted value is lower than 98%, the optimization model is triggered. If the predicted value continues to be lower than 97%, a device failure warning is issued (such as ultrasonic transducer power attenuation).

[0083] The second layer is the optimization model, which is based on the particle swarm optimization algorithm (PSO) to optimize the operating parameters. The objective function is to "minimize energy consumption + maximize recovery rate", and the constraint conditions include: HF concentration ≤0.05 ppm, VOCs removal rate ≥99%, and medium consumption ≤2.5 L / min. The algorithm finds the optimal parameter combination (for example, when processing high-quality pole pieces, the optimal parameters are medium injection amount 2.3 L / min, ultrasonic power 28 W / cm², and spraying pressure 0.38 MPa. At this time, the energy consumption is reduced by 18% compared with the traditional parameters, and the recovery rate is increased to 99.5%).

[0084] The third layer is a regulation model that converts the optimized parameters into control instructions and issues them to each module for execution. For example, when the optimization model outputs "increase the ultrasonic power to 28 W / cm²", the central unit sends a control signal to the adaptive non-dry reaction module through the Modbus protocol. The ultrasonic transducer completes the power adjustment within 0.5 seconds. At the same time, the central unit collects real-time feedback data from the module (such as the actual ultrasonic power and the current recovery rate). If the deviation between the actual value and the optimized value exceeds 5%, the optimization model is restarted to achieve closed-loop control.

[0085] The battery pole piece non-dry recycling method based on air pollution detection comprises the following steps: S1: pollution real-time sensing and regulation, real-time monitoring of the concentration and type of characteristic gas in the recycling environment, and dynamically adjusting the injection parameters and circulation strategy of the subsequent non-dry processing medium based on the monitoring results.

[0086] S2: pole piece intelligent diagnosis and sorting, non-destructive state diagnosis of the battery pole piece to be processed, classification of the pole piece according to the diagnosis result, and planning of different subsequent processing paths for different categories of pole pieces.

[0087] S3: adaptive non-dry dissociation step, according to the category and processing path of the pole piece, calling the matching non-dry processing scheme, and adaptively adjusting the composition, spraying intensity and ultrasonic energy of the processing medium combined with the concentration change of the characteristic gas, to realize selective and low-pollution dissociation of the active material.

[0088] S4: in-situ control and resourceization of fugitive pollutants, capturing and processing the fugitive gas and aerosol generated in the dissociation process, and condensing and recovering, adsorbing and enriching, or directionally converting volatile organic compounds and fluorine-containing components.

[0089] S5: data-driven and whole-process collaborative optimization step, integrating real-time data of monitoring, diagnosis, dissociation and control steps, and dynamically optimizing material and energy configuration of the whole recycling process through algorithm model.

[0090] The battery pole piece non-dry recycling device based on air pollution detection comprises a pollution sensing unit, a pretreatment unit, a reaction unit, a pollutant control unit and a central control unit, which real-time monitors and transmits the concentration and type of characteristic gas in the recycling environment, performs pole piece state diagnosis and physical stripping, performs differential non-dry processing according to the monitoring data, in-situ processes and recycles the fugitive pollutants generated in the reaction process, coordinates the operation of each unit and optimizes the overall process flow.

[0091] Secondly, the drawings of the disclosed embodiments only involve the structures related to the disclosed embodiments, and other structures can be referred to the usual design. In the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other.

[0092] Finally: the above only for the preferred embodiments of the present application, and not for limiting the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the scope of protection of the present application.

Claims

1. A non-dry recycling system for battery electrodes based on air pollution detection, characterized in that, include: Pollution sensing and control module: Deploys a high-sensitivity gas sensor array to monitor the concentration and type of characteristic gases in the recovery environment in real time, and adjusts the injection parameters and circulation strategy of the non-dry treatment medium based on dynamic data; Intelligent pre-processing module for electrode sheets: It uses multispectral imaging and impedance spectroscopy analysis technology to perform non-destructive condition diagnosis and precise physical stripping of the battery electrode sheets to be processed, and classifies and plans the paths of the electrode sheets based on the diagnosis results; Adaptive non-dry reaction module: Equipped with a multi-stage spray and ultrasonic coupling unit linked with the pollution sensing and control module, it applies differentiated non-dry treatment schemes according to the electrode type and pollution level to achieve selective and low-pollution dissociation of active materials; In-situ control and resource utilization of pollutants module: captures and treats volatile organic compounds and dust released during the reaction process, and recovers, enriches or transforms the valuable components therein; Data-driven and collaborative control hub: Integrates data from various modules and optimizes the flow of materials and energy throughout the recycling process through algorithmic models.

2. The non-dry battery electrode recycling system based on air pollution detection according to claim 1, characterized in that: The pollution sensing and control module employs a high-sensitivity gas sensor array based on MEMS technology, deployed at key locations in the reaction chamber, pretreatment area, and exhaust port with a grid spacing of 3 meters × 3 meters, and the sampling frequency is set to 1 Hz.

3. The non-dry battery electrode recycling system based on air pollution detection according to claim 1, characterized in that: The multispectral imaging acquires data including active material thickness data, surface contaminant distribution data, and current collector damage data. Its functions include determining the aging degree of the electrode, identifying damage and contamination on the electrode surface, and detecting the thickness uniformity of the electrode.

4. The non-dry recycling system for battery electrodes based on air pollution detection according to claim 1, characterized in that: The multi-stage spraying and ultrasonic coupling unit implements differentiated spraying strategies based on electrode type data and real-time contamination concentration data, sets different main medium injection volumes and spraying pressures, and selects different auxiliary cleaning agents.

5. The non-dry battery electrode recycling system based on air pollution detection according to claim 1, characterized in that: The in-situ pollutant control and resource recovery module employs a combined "adsorption-catalytic oxidation" process for in-situ pollutant control, collecting VOCs concentration and component data, as well as concentration difference data between the inlet and outlet of the adsorption bed. Resource recovery utilizes a two-stage purification process of "cyclone separation + electrostatic dust removal," collecting dust concentration and dust composition data.

6. The non-dry battery electrode recycling system based on air pollution detection according to claim 1, characterized in that: The data in each module are divided into environmental monitoring data, electrode status data, reaction process data, and resource utilization data. The algorithm model has three layers, including a prediction model, an optimization model, and a control model.

7. A non-dry recycling method for battery electrodes based on air pollution detection, used to implement the non-dry recycling system for battery electrodes based on air pollution detection as described in any one of claims 1-6, characterized in that, include: S1: Real-time pollution sensing and control steps, real-time monitoring of the concentration and type of characteristic gases in the recovery environment, and dynamic adjustment of injection parameters and circulation strategies of subsequent non-dry treatment media based on monitoring results. S2: Intelligent Diagnosis and Sorting Steps for Electrodes. Non-destructive condition diagnosis of the battery electrodes to be processed is performed, the electrodes are classified according to the diagnosis results, and differentiated subsequent processing paths are planned for different categories of electrodes. S3: Adaptive non-dry dissociation step. Based on the type of electrode and the processing path, a matching non-dry processing scheme is invoked. Combined with the concentration changes of characteristic gases, the composition of the processing medium, the spray intensity and the ultrasonic energy are adaptively adjusted to achieve selective and low-pollution dissociation of active materials. S4: In-situ control and resource utilization of fugitive pollutants, capturing and treating fugitive gases and aerosols generated during dissociation, and condensing, adsorbing, enriching or directionally converting volatile organic compounds and fluorine-containing components therein. S5: Data-driven and whole-process collaborative optimization steps, integrating real-time data from monitoring, diagnosis, dissociation and control steps, and dynamically optimizing the material and energy configuration of the entire recycling process through algorithm models.

8. A non-dry battery electrode recycling device based on air pollution detection, used to implement the non-dry battery electrode recycling system based on air pollution detection as described in any one of claims 1-6, characterized in that, include: The system comprises a pollution sensing unit, a pretreatment unit, a reaction unit, a pollutant control unit, and a central control unit. It monitors and transmits the concentration and type of characteristic gases in the recovery environment in real time, performs electrode condition diagnosis and physical stripping, performs differentiated non-dry treatment based on monitoring data, treats and recovers the released pollutants generated during the reaction process in situ, coordinates the operation of each unit, and optimizes the overall process flow.

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