Waste circuit board wet gravity separation method capable of improving separation efficiency

Abstract

The invention discloses a waste circuit board wet gravity separation method capable of improving separation efficiency, and relates to the technical field of waste circuit board recycling, the method comprises the following steps: S1, carrying out crushing treatment on a waste circuit board, and removing large impurities in the waste circuit board to obtain a waste circuit board crushed material to be separated; according to the method, key data of particle size distribution, component density and surface moisture content of the waste circuit board crushed materials are collected in real time by arranging multiple types of sensors, a self-adaptive regulation and control model is constructed in combination with a fuzzy PID algorithm, full-link coupling linkage regulation and control of material characteristics, medium parameters and equipment operation parameters are achieved, frequent manual intervention is not needed, and the working efficiency is improved. Component fluctuation of different batches of waste circuit boards is effectively coped with, the problems of poor sorting stability and low efficiency caused by single parameter adjustment in the prior art are solved, precise and automatic control of the sorting process is achieved, the sorting stability and efficiency are improved, and reliable technical support is provided for efficient recycling of waste circuit board resources.

Description

Technical Field

[0001] This invention relates to the field of waste circuit board recycling technology, specifically to a wet gravity sorting method for waste circuit boards that improves sorting efficiency. Background Technology

[0002] Waste circuit boards are core components generated after electronic devices are upgraded or scrapped. They are widely found in electronic waste such as discarded computers, mobile phones, and home appliances. They integrate various metallic materials such as copper, aluminum, gold, and silver, as well as non-metallic materials such as resin and fiberglass. Effective sorting of waste circuit boards not only enables the recycling of metallic resources, saving on the extraction of primary mineral resources, but also avoids environmental pollution caused by the indiscriminate disposal of non-metallic materials. This aligns with the principles of resource recycling and sustainable environmental development. Wet gravity separation is a commonly used technique in the sorting of waste circuit boards. Its core principle is to utilize the density difference between metallic and non-metallic components in the waste circuit board. In a gravity field environment formed by water or a composite heavy medium, components of different densities are separated into layers. Compared to dry sorting technology, wet gravity separation effectively reduces dust pollution generated during the sorting process, is more adaptable to the sorting of fine-particle materials, and has higher sorting accuracy. Therefore, it is widely used in the recycling of waste circuit boards.

[0003] However, existing wet gravity separation technology still has certain shortcomings. The sources of waste circuit boards are complex, and different batches of crushed material exhibit significant fluctuations in key parameters such as particle size distribution, component density, and surface moisture content. Existing separation methods mostly adopt a single parameter adjustment mode, which can only adjust a single index such as medium density or separation bed frequency. It is impossible to achieve coordinated matching of material characteristics, medium parameters, and equipment operating parameters. Due to the limitations of the control method, existing technologies require frequent manual intervention to adjust the separation parameters, which not only increases labor costs but also leads to poor stability of the separation process and difficulty in adapting to the separation needs of different batches of materials. Ultimately, this results in low separation efficiency, which restricts the efficient recycling of waste circuit board resources. Therefore, developing a wet gravity separation method for waste circuit boards that improves separation efficiency is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a wet gravity sorting method for waste circuit boards that improves sorting efficiency. This method uses multiple types of sensors to collect key data in real time, such as particle size distribution, component density, and surface moisture content of the crushed waste circuit board material. Combined with a fuzzy PID algorithm, an adaptive control model is constructed to achieve full-link coupled and coordinated control of material characteristics, medium parameters, and equipment operating parameters. This eliminates the need for frequent manual intervention, effectively addressing component fluctuations in different batches of waste circuit boards. It solves the problems of poor sorting stability and low efficiency caused by single-parameter adjustment in existing technologies, achieving precise and automated control of the sorting process and improving sorting stability and efficiency.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wet gravity sorting method for waste circuit boards to improve sorting efficiency, the method comprising the following steps: S1. The waste circuit boards are crushed to remove large impurities and obtain crushed waste circuit board material to be sorted. S2. In the wet gravity separation system, a laser particle size analyzer, an online density meter and a moisture content sensor are configured to collect the particle size distribution data, component density data and surface moisture content data of the material to be separated and crushed. S3. Construct an adaptive control model based on fuzzy PID algorithm, and preset the sorting thresholds corresponding to different components according to the separation requirements of metal and non-metal components in waste circuit boards. S4. The collected particle size distribution data, component density data and surface moisture content data are input into the adaptive control model in real time. The model automatically adjusts the temperature of the composite heavy medium, the pulsation frequency of the sorting bed and the transverse slope of the sorting system through data calculation and analysis. The temperature adjustment of the composite heavy medium is related to the medium density. S5. The pre-treated waste circuit board crushed material is fed into the sorting bed. Under the composite heavy medium environment and sorting bed operating parameters adjusted by the model, the density difference of different components is used to carry out wet gravity separation of metal and non-metal components. S6. During the sorting process, the sensor continuously collects material sorting status data and feeds it back to the adaptive control model. The model dynamically adjusts the temperature of the composite heavy medium, the pulsation frequency of the sorting bed, and the transverse slope based on the feedback results, forming a closed-loop control of material detection, parameter calculation, execution adjustment, and effect feedback until the entire sorting process is completed.

[0006] Furthermore, step S1, when crushing the waste circuit boards, includes the following steps: Jaw crushers are used for primary crushing of waste circuit boards, crushing them into block materials with a particle size of 10-20mm. During the primary crushing process, the feeding speed of the crusher is controlled to keep it uniform. The initially crushed lumpy material is fed into a vibrating screen for screening. Material with a particle size of less than 3mm is screened out and enters the next step of processing. Material with a particle size greater than 3mm on the screen is returned to the jaw crusher for re-crushing. The material that meets the particle size requirements after screening is fed into an impact crusher for secondary crushing. During the crushing process, the discharge particle size is controlled by adjusting the rotor speed of the crusher. The maximum particle size of the material after secondary crushing does not exceed 1mm. The material after secondary crushing is fed into an air classifier to remove the light flocculent impurities mixed in. The air speed of the air classifier is set according to the density characteristics of the material to obtain the waste circuit board crushed material to be sorted.

[0007] Furthermore, step S2, when configuring the sensor and collecting data, includes the following steps: Determine the installation positions of the laser particle size analyzer, online density meter, and moisture content sensor in the sorting system. The laser particle size analyzer is installed at the front end of the feed inlet of the sorting bed, the online density meter is installed in the middle area of ​​the sorting bed, and the moisture content sensor is installed above the feed conveyor belt and close to the material surface without direct contact with the material. The three sensors were calibrated after installation. The laser particle size analyzer was calibrated using glass beads of standard particle size, the online density meter was calibrated by passing a standard solution of known density through it, and the moisture content sensor was calibrated at multiple points using a standard humidity block. Set the data acquisition timing sequence, and the three sensors will acquire data synchronously. The acquisition interval is determined according to the feeding speed. The collected raw data were initially processed to remove obvious outliers. The data was then smoothed using a moving average method to obtain continuous and stable particle size distribution data, component density data, and surface moisture content data.

[0008] Furthermore, step S4, when automatically adjusting relevant parameters of the model, includes the following steps: After receiving the transmitted material characteristic data, the adaptive control model compares it with the preset sorting threshold to determine the deviation between the current material characteristics and the ideal sorting conditions. The priority of parameter adjustment is determined based on the degree of deviation. The temperature adjustment of the composite heavy medium has the highest priority, followed by the pulsation frequency of the sorting bed, and finally the transverse slope. The priority is set based on the sensitivity of each parameter to the effect of sorting. The model calculates the adjustment amount of each parameter using a fuzzy PID algorithm. The adjustment amount is calculated using the following formula: ,in, For the first The current adjustment amount of each parameter, For the first The deviation response coefficient of each parameter, This represents the combined deviation between the current material characteristics and the ideal sorting conditions. For the first Historical adjustment weights of each parameter For the first The previous adjustment amount of each parameter, and All data were obtained by linear fitting of sorting test data from more than 100 different batches of waste circuit boards. The fitting process aimed at the consistency of material stratification after parameter adjustment. The adjustment signal is transmitted to the corresponding actuator through the control system. Temperature adjustment is completed by heating or cooling devices, pulse frequency adjustment is completed by adjusting the speed of the drive motor of the sorting bed, and transverse slope adjustment is completed by changing the tilt angle of the sorting bed through the hydraulic lifting mechanism. All actuators respond synchronously.

[0009] Furthermore, the adaptive control model based on the fuzzy PID algorithm constructed in step S3 includes an input layer, a fuzzy inference layer, a PID adjustment layer, and an output layer. The input layer receives material characteristic data and sorting status feedback data collected by sensors. The fuzzy inference layer fuzzifies the input data, establishes a fuzzy rule base, and performs inference calculations. The PID adjustment layer performs online self-tuning of the PID parameters based on the fuzzy inference results. The self-tuning process is achieved through the following formula: ,in, These are the self-tuned PID parameters. These are the initial baseline parameters for the PID controller. The influence coefficient of the fuzzy inference result. For fuzzy inference output values, This is a correction factor for historical adjustment effects. This is a historical evaluation value for the adjustment effect. Based on the sorting benchmark test of standard components of waste circuit boards, and By statistically analyzing the fuzzy inference results, historical adjustment effects, and PID parameter adaptation relationships under different material compositions, the output layer outputs adjustment signals for the composite heavy medium temperature, sorting bed pulsation frequency, and lateral slope. The model's fuzzy rule base is established based on a large amount of sorting test data from different batches of waste circuit boards, covering the optimal parameter adjustment schemes under different material characteristics. The model supports continuous updating of the fuzzy rule base based on actual sorting data.

[0010] Furthermore, the composite heavy medium involved in step S4 is made by mixing high-density inorganic particles with a dispersant. The high-density inorganic particles are modified magnetite powder or tungsten ore powder, and the dispersant is polyethylene glycol or silane coupling agent. The mass mixing ratio of high-density inorganic particles and dispersant is determined according to the density characteristics of the material to be sorted. In the preparation process of the composite heavy medium, the high-density inorganic particles are first added to water and mechanically stirred at a stirring speed of 300-500 r / min for 20-30 min. Then, the dispersant is added and stirring is continued for 15-20 min. Finally, the air bubbles in the medium are eliminated by ultrasonic dispersion treatment.

[0011] Furthermore, the preset sorting threshold in step S3 is determined in the following way: waste circuit boards from different sources and of different models are collected, their components are separated and tested, density data of each metal component and non-metal component are obtained, a material component density database is established, and the density critical value for effective separation of each component is determined in combination with the equipment performance of the sorting system. Based on this critical value, the upper and lower limits of the sorting threshold are set to form sorting threshold intervals corresponding to different components. The sorting threshold intervals are stored in the database of the adaptive control model, and the model automatically matches the corresponding sorting threshold intervals according to the input material characteristic data.

[0012] Furthermore, in step S2, the data transmission between the sensor and the adaptive control model adopts a combination of wireless transmission module and wired transmission. The laser particle size analyzer, online density meter and moisture content sensor are all equipped with wireless transmission units. At the same time, a backup communication link is established with the control system through shielded cables. During the data transmission process, the CRC cyclic redundancy check algorithm is used to verify the data. The control system timestamps each set of received data to form a complete material characteristic data traceability chain.

[0013] Furthermore, in step S5, the feeding of the waste circuit board shredded material is achieved by a screw feeder. The screw pitch and rotation speed of the screw feeder are matched according to the flow rate of the material to be sorted. The feeding speed is controlled within the range where the material thickness is uniform in the sorting bed. A flow guiding device is set at the feed inlet. The flow guiding device includes multiple parallel flow guiding plates. The flow guiding plates are at an angle of 30-45° with the feeding direction, guiding the material to be distributed in the width direction of the sorting bed.

[0014] Furthermore, the feedback period of the closed-loop control in step S6 can be dynamically adjusted, and the feedback period is calculated using the following formula: ,in, For the current feedback cycle, For the initial feedback period, The response coefficient is the magnitude of the data change. This represents the change in material sorting status data between two consecutive data collections. The effective sorting length of the sorting bed and the material moving speed are determined accordingly. By calibrating the parameter response sensitivity of the sorting system, the adaptive control model adjusts the feedback cycle according to the change range of material sorting status data during the sorting process. When the material characteristics fluctuate greatly, the feedback cycle is shortened; when the material characteristics tend to be stable, the feedback cycle is extended. The feedback data includes the stratification distance between metal and non-metal components, the moving speed of the material in the sorting bed, and the real-time density of the composite heavy medium. The model judges the sorting effect by comprehensively analyzing the above data and completes the dynamic adjustment of parameters.

[0015] Compared with existing technologies, this wet gravity sorting method for waste circuit boards, which improves sorting efficiency, has the following advantages: This invention uses multiple types of sensors to collect key data such as particle size distribution, component density, and surface moisture content of crushed waste circuit boards in real time. Combined with a fuzzy PID algorithm, an adaptive control model is constructed to achieve full-link coupled and coordinated control of material characteristics, medium parameters, and equipment operating parameters. This eliminates the need for frequent manual intervention, effectively addressing component fluctuations in different batches of waste circuit boards. It solves the problems of poor sorting stability and low efficiency caused by single-parameter adjustment in existing technologies, achieving precise and automated control of the sorting process, improving sorting stability and efficiency, and providing reliable technical support for the efficient recycling of waste circuit board resources, balancing resource recycling benefits and environmental value.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 A flowchart of a wet gravity sorting method for waste circuit boards to improve sorting efficiency; Figure 2 A flowchart illustrating the steps of a wet gravity sorting method for waste circuit boards to improve sorting efficiency; Figure 3 A flowchart for configuring sensors and collecting data. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] Example 1: A waste electronics recycling company has long processed waste circuit boards from various sources, including discarded computers, mobile phones, and home appliances. These circuit boards vary significantly in their internal metal and non-metal composition, particle size distribution, and surface moisture content due to differences in manufacturer, model, and age. Traditional wet gravity separation methods can only adjust the medium density or separation bed frequency, making it difficult to adapt to fluctuations in material characteristics. This necessitates frequent manual parameter adjustments during the separation process, increasing labor costs and resulting in unstable separation accuracy and low metal recovery rates. To address this challenge, the company adopted the waste circuit board wet gravity separation method proposed in this invention, which improves separation efficiency. Through multi-sensor data acquisition and fuzzy PID adaptive control, it achieves precise and automated control of the separation process, effectively addressing compositional fluctuations in different batches of materials. (See [link to relevant documentation]). Figure 1 and Figure 2 The specific implementation process of this embodiment is as follows: First, the recycled waste circuit boards undergo pre-crushing to remove large impurities and obtain crushed material that meets sorting requirements. A jaw crusher is used for primary crushing, ensuring a uniform feed rate and breaking the circuit boards into uniform block-shaped materials. The primary crushed material is then fed to a vibrating screen for screening. Material meeting the particle size requirements is screened for the next step, while material that does not meet the requirements is returned to the jaw crusher for re-crushing to ensure consistent particle size in subsequent processing. Subsequently, the qualified material is fed to an impact crusher for secondary crushing. The output particle size is precisely controlled by adjusting the crusher's rotor speed to ensure that the particle size of the crushed material meets the requirements for subsequent sorting. Finally, the secondary crushed material is fed to an air classifier. An appropriate air velocity is set according to the material's density characteristics to remove mixed light flocculent impurities, ultimately yielding pure waste circuit board crushed material for sorting.

[0021] To accurately obtain key characteristic data of the materials to be sorted, the configuration and data acquisition of a laser particle size analyzer, online density meter, and moisture content sensor were completed in the wet gravity separation system. (See [link to relevant documentation]). Figure 3 First, determine the installation locations of the three sensors. The laser particle size analyzer is installed at the front end of the feed inlet of the sorting bed to collect particle size distribution data before the material enters the sorting bed. The online density meter is installed in the middle area of ​​the sorting bed to monitor the changes in the component density of the material in real time during the sorting process. The moisture content sensor is installed above the feed conveyor belt, close to the material surface but not in direct contact with the material, to avoid interfering with the material while accurately collecting surface moisture content data.

[0022] After the sensors were installed, calibration was performed on each sensor. The laser particle size analyzer was calibrated using glass beads of standard particle size, the online density meter was calibrated by passing a standard solution of known density through it, and the moisture content sensor was calibrated at multiple points using a standard humidity block to ensure the accuracy of the sensor data. Subsequently, a data acquisition sequence was set to control the three sensors to acquire data synchronously, with the acquisition interval adjusted according to the feed rate. The raw data was initially processed by removing obvious outliers and then smoothing the data using a moving average method. Finally, continuous and stable particle size distribution data, component density data, and surface moisture content data were obtained, providing reliable data support for subsequent control models.

[0023] An adaptive control model based on the fuzzy PID algorithm is constructed, consisting of an input layer, a fuzzy inference layer, a PID control layer, and an output layer. The input layer receives material characteristic data collected by sensors and state feedback data from the subsequent sorting process. The fuzzy inference layer fuzzifies the precise input data and establishes a fuzzy rule base based on sorting test data from a large number of different batches of waste circuit boards. This rule base covers optimal parameter adjustment schemes under different material characteristics, and the model supports continuous updating of the rule base based on actual sorting data, completing data inference calculations through the fuzzy inference algorithm. The PID control layer performs online self-tuning of the PID parameters based on the fuzzy inference results. The output layer outputs adjustment signals for the composite heavy medium temperature, sorting bed pulsation frequency, and lateral slope.

[0024] In the specific implementation of this embodiment, PID parameter self-tuning is achieved through the following formula: ,in These are the self-tuned PID parameters. The initial reference parameters for the PID controller are determined based on the sorting benchmark test of standard-component waste circuit boards. The influence coefficient of the fuzzy inference result. For fuzzy inference output values; This is a correction factor for historical adjustment effects. This is a historical evaluation value for the adjustment effect; and The results were obtained by statistically analyzing the fuzzy inference results, historical adjustment effects, and the adaptation relationship between PID parameters under different material compositions. Simultaneously, sorting thresholds corresponding to different components were preset. By collecting waste circuit boards from different sources and of different models, component separation and detection were performed to obtain density data for each metallic and non-metallic component, establishing a material composition density database. Combined with the equipment performance of the sorting system, the density critical values ​​for effective separation of each component were determined. Based on these critical values, upper and lower limits for the sorting thresholds were set, forming sorting threshold ranges corresponding to different components. These ranges were stored in the adaptive control model's database for subsequent matching and retrieval by the model.

[0025] The processed particle size distribution data, component density data, and surface moisture content data are input into the adaptive control model in real time. The model first compares the received material characteristic data with the preset sorting threshold to accurately determine the comprehensive deviation between the current material characteristics and the ideal sorting conditions. The priority of parameter adjustment is determined based on the degree of deviation, with temperature adjustment of the composite heavy medium having the highest priority, followed by the pulsation frequency of the sorting bed, and finally the transverse slope. This priority setting is based on the sensitivity analysis of the influence of each parameter on the sorting effect.

[0026] In the specific implementation of this embodiment, the model calculates the adjustment amount of each parameter using a fuzzy PID algorithm. The adjustment amount is calculated using the following formula: ,in For the first The current adjustment amount of each parameter, For the first The deviation response coefficient of each parameter, This represents the combined deviation between the current material characteristics and the ideal sorting conditions. For the first Historical adjustment weights of each parameter For the first The previous adjustment amount of each parameter; and All results were obtained by linear fitting of sorting test data from more than 100 different batches of waste circuit boards. The fitting process took the consistency of material stratification after parameter adjustment as the core objective.

[0027] The adjustment signals are transmitted to the corresponding actuators through the control system. Temperature adjustment is achieved through heating or cooling devices, pulsation frequency adjustment is achieved by adjusting the speed of the drive motor of the sorting bed, and lateral slope adjustment is achieved by changing the tilt angle of the sorting bed through a hydraulic lifting mechanism. All actuators respond synchronously to ensure the timeliness and coordination of parameter adjustments. In addition, the composite heavy medium is made by mixing high-density inorganic particles with a dispersant. In the preparation process, the high-density inorganic particles are first added to water and mechanically stirred, then the dispersant is added and stirring is continued. Finally, ultrasonic dispersion treatment is used to eliminate air bubbles in the medium, ensuring the stability of the medium's performance.

[0028] The pre-treated shredded waste circuit boards are fed into the sorting bed via a screw feeder. The screw pitch and rotation speed of the screw feeder are matched according to the flow rate of the material to be sorted, ensuring that the feeding speed is controlled within a range where the material thickness is uniform within the sorting bed. The flow guiding device at the feed inlet includes multiple parallel guide plates. The guide plates are at a specific angle to the feeding direction, which can guide the material to be evenly distributed in the width direction of the sorting bed, avoiding local accumulation of material that would affect the sorting effect.

[0029] After the material enters the sorting bed, under the composite heavy medium environment and sorting bed operating parameters adjusted by the adaptive control model, the density difference between the metal components and non-metal components is used to achieve stratification and separation. The denser metal components settle to the bottom of the sorting bed, while the less dense non-metal components are suspended in the upper part of the composite heavy medium, laying the foundation for subsequent separation and collection.

[0030] During the sorting process, each sensor continuously collects material sorting status data and feeds it back to the adaptive control model in real time. The feedback data includes the separation distance between metallic and non-metallic components, the material's movement speed within the sorting bed, and the real-time density of the composite heavy medium. In this embodiment, the feedback cycle of the closed-loop control can be dynamically adjusted, and the feedback cycle is calculated using the following formula: ,in For the current feedback cycle This parameter, representing the initial feedback cycle, is determined based on the effective sorting length of the sorting bed and the material moving speed. The response coefficient for the magnitude of data change is obtained by calibrating the parameter response sensitivity of the sorting system. This represents the change in material sorting status data between two consecutive data collections.

[0031] The model dynamically adjusts the feedback cycle based on the changes in material sorting status data. When material characteristics fluctuate significantly, the feedback cycle is shortened for rapid response and adjustment; when material characteristics tend to stabilize, the feedback cycle is extended to reduce unnecessary adjustments. The model comprehensively analyzes the feedback data to determine whether the current sorting effect meets the preset requirements. If not, it dynamically adjusts the temperature of the composite heavy medium, the pulsation frequency of the sorting bed, and the transverse slope, forming a complete closed-loop control of "material detection - parameter calculation - execution adjustment - effect feedback" until the entire sorting process is completed, achieving efficient separation of metallic and non-metallic components.

[0032] In summary, this embodiment successfully solved the sorting problem caused by the fluctuation of component composition in different batches of waste circuit boards by adopting the complete wet gravity sorting method for waste circuit boards described above. Precise data acquisition and preprocessing through multiple types of sensors provided a reliable data foundation for the control model; the adaptive control model based on the fuzzy PID algorithm achieved full-link coupled and coordinated control of material characteristics, medium parameters, and equipment operating parameters, eliminating the need for frequent manual intervention and significantly reducing labor costs; the dynamically adjusted closed-loop control mechanism ensured the stability and accuracy of the sorting process, effectively improving the recovery rate and sorting efficiency of metal components.

[0033] Example 2: An electronic equipment repair and dismantling workshop has long processed scattered waste circuit boards such as mobile phone motherboards, computer graphics cards, and home appliance control boards generated during repairs. These circuit boards have diverse models and origins, with small batch sizes but highly variable composition, showing significant differences in the proportion of metals and non-metals and particle morphology. Previously, the workshop used simple wet sorting equipment, which could only manually adjust the sorting bed frequency, failing to adapt to the rapid changes in material characteristics. This resulted in insufficient metal purity and a high amount of non-metallic impurities after sorting. Furthermore, frequent manual operation increased operational difficulty and time costs, leading to low resource recycling efficiency. To solve this practical problem, the workshop adopted the waste circuit board wet gravity sorting method of this invention, which improves sorting efficiency. Utilizing an automated control system to adapt to the complex characteristics of scattered materials, it achieves efficient and accurate sorting. See [link to relevant documentation]. Figure 1 and Figure 2 The specific implementation process of this embodiment is as follows: Based on the aforementioned pre-treatment process, the processing details are optimized for small batches of scattered materials. First, a jaw crusher is used to initially crush various types of waste circuit boards. The feeding device is adjusted to ensure uniform feeding and prevent uneven particle size due to material accumulation. The initially crushed material is then fed to a vibrating screen for screening. Material meeting the particle size requirements enters the secondary crushing stage, while material that does not meet the requirements is returned to the initial crushing process for recycling. The secondary crushing uses an impact crusher. The rotor speed is adjusted according to the hardness of different types of circuit boards to ensure uniform particle size after crushing. Finally, the secondary crushed material is sent to an air classifier. The airflow is finely adjusted in real time based on the actual density characteristics of the material to efficiently remove light, flocculent impurities, resulting in a higher purity crushed material for subsequent precise sorting.

[0034] In the wet gravity separation system, sensor configuration and data acquisition are completed. (See [link to relevant documentation]) Figure 3 The laser particle size analyzer, online density meter, and moisture content sensor are fixed at the installation locations determined in the aforementioned embodiments to ensure the relevance and accuracy of data acquisition. After sensor installation, a strict calibration process is performed to guarantee the reliability of the collected data. Considering the complexity of the workshop sorting environment, data transmission between the sensors and the adaptive control model adopts a combination of wireless and wired transmission. The laser particle size analyzer, online density meter, and moisture content sensor are all equipped with wireless transmission units. Simultaneously, a backup communication link is established with the control system via shielded cables to avoid data loss due to failure of a single transmission method.

[0035] During data transmission, a CRC cyclic redundancy check algorithm is used to verify each set of data. The control system timestamps the received data, forming a complete material characteristic data traceability chain, which facilitates subsequent traceability of parameter matching during the sorting process. Three types of sensors collect data synchronously, with the collection interval flexibly adjusted according to the current feed rate. The raw data after collection undergoes outlier removal and moving average smoothing to obtain continuous and stable material characteristic data.

[0036] An adaptive control model based on the fuzzy PID algorithm is constructed, adopting the architecture of an input layer, fuzzy inference layer, PID control layer, and output layer. The input layer receives material characteristic data and sorting status feedback data collected by sensors. The fuzzy inference layer performs fuzzification processing on the input data and then performs inference calculations based on a preset fuzzy rule base. This rule base covers sorting test data of various scattered circuit boards and supports continuous updates based on actual sorting results. The PID control layer performs online self-tuning of PID parameters based on the fuzzy inference results. The self-tuning process is implemented using formulas. The sorting threshold is preset by calling the established material component density database and combining it with the source information of the materials being processed in the current period to automatically match the corresponding sorting threshold range, ensuring that the threshold setting is accurately adapted to the material characteristics.

[0037] The processed material characteristic data is input into the adaptive control model in real time. The model first compares the data with the preset sorting threshold to determine the comprehensive deviation between the current material characteristics and the ideal sorting conditions. Based on the degree of deviation, and according to the priority order of composite heavy medium temperature, sorting bed pulsation frequency, and transverse slope, the adjustment amount of each parameter is calculated using a fuzzy PID algorithm. The formula for calculating the adjustment amount is as follows: The adjustment signal is transmitted to the corresponding actuator, and each actuator responds synchronously to complete the parameter adjustment.

[0038] For small-batch, multi-batch sorting needs, the composite heavy media is prepared on demand. The mixing ratio of high-density inorganic particles and dispersant is determined based on the density characteristics of the material being processed. Modified magnetite powder or tungsten ore powder is selected as the high-density inorganic particles, and polyethylene glycol or silane coupling agents are selected as the dispersant. During preparation, the high-density inorganic particles are first added to water and mechanically stirred, then the dispersant is added and stirring continues. Finally, ultrasonic dispersion is used to eliminate air bubbles, ensuring the stability of the composite heavy media and precise matching with the material characteristics.

[0039] The crushed material to be sorted is fed into the sorting bed via a screw feeder. The screw pitch and rotation speed of the screw feeder are matched according to the current material flow rate to ensure a stable feeding speed and uniform material thickness within the sorting bed. A flow guide device at the feed inlet guides the material to be evenly distributed across the width of the sorting bed, preventing localized accumulation that could affect the sorting effect. Under the composite heavy medium environment and sorting bed operating parameters adjusted by the model, the material achieves stratification and separation based on the density difference between metals and non-metals. High-density metal components settle, while low-density non-metal components remain suspended.

[0040] During the sorting process, sensors continuously collect status data such as the separation distance between metallic and non-metallic components, material movement speed, and real-time density of the composite heavy medium, feeding this data back to the adaptive control model. The model dynamically adjusts the feedback cycle of the closed-loop control based on the feedback data. The feedback cycle is determined by the formula... The system performs calculations. When significant fluctuations in material properties are detected, the feedback cycle automatically shortens, enabling rapid parameter adjustments. When material properties stabilize, the feedback cycle lengthens accordingly, reducing unnecessary adjustments. The model comprehensively analyzes the feedback data to determine the sorting effect, dynamically adjusting the temperature of the composite heavy medium, the pulsation frequency of the sorting bed, and the transverse slope to continuously optimize sorting conditions until the entire sorting process is completed, achieving efficient separation of metallic and non-metallic components.

[0041] In summary, this embodiment addresses the processing needs of scattered waste circuit boards from electronic equipment repair and dismantling workshops. Building upon the technical solutions of the aforementioned embodiments, it optimizes the adaptation details for small-batch materials. Through reliable data transmission links, on-demand preparation of composite heavy media, and dynamically adjusted closed-loop control feedback cycles, it further enhances the sorting system's adaptability to complex and variable materials. Implementing this method eliminates the need for frequent manual intervention in sorting parameters, lowering the operational threshold and labor costs. It effectively solves the sorting instability problem caused by fluctuations in the composition of scattered circuit boards, significantly improves the purity and recovery rate of metallic components, and substantially reduces the residual impurities in non-metallic components.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A wet gravity sorting method for waste circuit boards to improve sorting efficiency, characterized in that, The method includes the following steps: S1. The waste circuit boards are crushed to remove large impurities and obtain crushed waste circuit board material to be sorted. S2. In the wet gravity separation system, a laser particle size analyzer, an online density meter and a moisture content sensor are configured to collect the particle size distribution data, component density data and surface moisture content data of the material to be separated and crushed. S3. Construct an adaptive control model based on fuzzy PID algorithm, and preset the sorting thresholds corresponding to different components according to the separation requirements of metal and non-metal components in waste circuit boards. S4. The collected particle size distribution data, component density data and surface moisture content data are input into the adaptive control model in real time. The model automatically adjusts the temperature of the composite heavy medium, the pulsation frequency of the sorting bed and the transverse slope of the sorting system through data calculation and analysis. The temperature adjustment of the composite heavy medium is related to the medium density. S5. The pre-treated waste circuit board crushed material is fed into the sorting bed. Under the composite heavy medium environment and sorting bed operating parameters adjusted by the model, the density difference of different components is used to carry out wet gravity separation of metal and non-metal components. S6. During the sorting process, the sensor continuously collects material sorting status data and feeds it back to the adaptive control model. The model dynamically adjusts the temperature of the composite heavy medium, the pulsation frequency of the sorting bed, and the transverse slope based on the feedback results, forming a closed-loop control of material detection, parameter calculation, execution adjustment, and effect feedback until the entire sorting process is completed.

2. The wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, Step S1, when crushing waste circuit boards, includes the following steps: Jaw crushers are used for primary crushing of waste circuit boards, crushing them into block materials with a particle size of 10-20mm. During the primary crushing process, the feeding speed of the crusher is controlled to keep it uniform. The initially crushed lumpy material is fed into a vibrating screen for screening. Material with a particle size of less than 3mm is screened out and enters the next step of processing. Material with a particle size greater than 3mm on the screen is returned to the jaw crusher for re-crushing. The material that meets the particle size requirements after screening is fed into an impact crusher for secondary crushing. During the crushing process, the discharge particle size is controlled by adjusting the rotor speed of the crusher. The maximum particle size of the material after secondary crushing does not exceed 1mm. The material after secondary crushing is fed into an air classifier to remove the light flocculent impurities mixed in. The air speed of the air classifier is set according to the density characteristics of the material to obtain the waste circuit board crushed material to be sorted.

3. The wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, Step S2, when configuring the sensor and collecting data, includes the following steps: Determine the installation positions of the laser particle size analyzer, online density meter, and moisture content sensor in the sorting system. The laser particle size analyzer is installed at the front end of the feed inlet of the sorting bed, the online density meter is installed in the middle area of ​​the sorting bed, and the moisture content sensor is installed above the feed conveyor belt and close to the material surface without direct contact with the material. The three sensors were calibrated after installation. The laser particle size analyzer was calibrated using glass beads of standard particle size, the online density meter was calibrated by passing a standard solution of known density through it, and the moisture content sensor was calibrated at multiple points using a standard humidity block. Set the data acquisition timing sequence, and the three sensors will acquire data synchronously. The acquisition interval is determined according to the feeding speed. The collected raw data were initially processed to remove obvious outliers. The data was then smoothed using a moving average method to obtain continuous and stable particle size distribution data, component density data, and surface moisture content data.

4. The wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, Step S4, when automatically adjusting relevant parameters of the model, includes the following steps: After receiving the transmitted material characteristic data, the adaptive control model compares it with the preset sorting threshold to determine the deviation between the current material characteristics and the ideal sorting conditions. The priority of parameter adjustment is determined based on the degree of deviation. The temperature adjustment of the composite heavy medium has the highest priority, followed by the pulsation frequency of the sorting bed, and finally the transverse slope. The priority is set based on the sensitivity of each parameter to the effect of sorting. The model calculates the adjustment amount of each parameter using a fuzzy PID algorithm. The adjustment amount is calculated using the following formula: ,in, For the first The current adjustment amount of each parameter. For the first The deviation response coefficient of each parameter, This represents the combined deviation between the current material characteristics and the ideal sorting conditions. For the first Historical adjustment weights of each parameter For the first The previous adjustment amount of each parameter; The adjustment signal is transmitted to the corresponding actuator through the control system. Temperature adjustment is completed by heating or cooling devices, pulse frequency adjustment is completed by adjusting the speed of the drive motor of the sorting bed, and transverse slope adjustment is completed by changing the tilt angle of the sorting bed through the hydraulic lifting mechanism. All actuators respond synchronously.

5. A wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, The adaptive control model based on the fuzzy PID algorithm constructed in step S3 includes an input layer, a fuzzy inference layer, a PID control layer, and an output layer. The input layer receives material characteristic data and sorting status feedback data collected by sensors. The fuzzy inference layer fuzzifies the input data, establishes a fuzzy rule base, and performs inference calculations. The PID control layer performs online self-tuning of the PID parameters based on the fuzzy inference results. The self-tuning process is achieved through the following formula: ,in, These are the self-tuned PID parameters. These are the initial baseline parameters for the PID controller. The influence coefficient of the fuzzy inference result. For fuzzy inference output values, This is a correction factor for historical adjustment effects. As historical adjustment effect evaluation values, the output layer outputs adjustment signals for composite heavy medium temperature, sorting bed pulsation frequency, and lateral slope. The model's fuzzy rule base is established based on a large amount of sorting test data from different batches of waste circuit boards, covering the optimal parameter adjustment schemes under different material characteristics. The model supports continuous updating of the fuzzy rule base according to actual sorting data.

6. The wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, The composite heavy medium involved in step S4 is made by mixing high-density inorganic particles and a dispersant. The high-density inorganic particles are modified magnetite powder or tungsten ore powder, and the dispersant is polyethylene glycol or silane coupling agent. The mass mixing ratio of high-density inorganic particles and dispersant is determined according to the density characteristics of the material to be sorted. In the preparation process of the composite heavy medium, the high-density inorganic particles are first added to water and mechanically stirred at a stirring speed of 300-500 r / min for 20-30 min. Then, the dispersant is added and stirring is continued for 15-20 min. Finally, the air bubbles in the medium are eliminated by ultrasonic dispersion treatment.

7. A wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, The preset sorting threshold in step S3 is determined as follows: Collect waste circuit boards from different sources and of different models, perform component separation and testing, obtain density data of each metal and non-metal component, establish a material component density database, and determine the density critical value for effective separation of each component based on the equipment performance of the sorting system. Based on this critical value, set the upper and lower limits of the sorting threshold to form sorting threshold intervals corresponding to different components. The sorting threshold intervals are stored in the database of the adaptive control model, and the model automatically matches the corresponding sorting threshold intervals according to the input material characteristic data.

8. A wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, In step S2, the data transmission between the sensor and the adaptive control model adopts a combination of wireless transmission module and wired transmission. The laser particle size analyzer, online density meter and moisture content sensor are all equipped with wireless transmission units. At the same time, a backup communication link is established with the control system through shielded cable. During the data transmission process, the CRC cyclic redundancy check algorithm is used to verify the data. The control system timestamps each set of received data to form a complete material characteristic data traceability chain.

9. A wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, In step S5, the feeding of the waste circuit board crushed material is achieved by a screw feeder. The screw pitch and rotation speed of the screw feeder are matched according to the flow rate of the material to be sorted. The feeding speed is controlled within the range where the material thickness is uniform in the sorting bed. A flow guiding device is set at the feed inlet. The flow guiding device includes multiple parallel flow guiding plates. The flow guiding plates are at an angle of 30-45° with the feeding direction, guiding the material to be distributed in the width direction of the sorting bed.

10. A wet gravity sorting method for waste circuit boards to improve sorting efficiency according to claim 1, characterized in that, The feedback period of the closed-loop control in step S6 can be dynamically adjusted, and the feedback period is calculated using the following formula: ,in, For the current feedback cycle, For the initial feedback period, The response coefficient is the magnitude of the data change. The adaptive control model adjusts the feedback cycle based on the variation range of material sorting status data collected between two consecutive collections during the sorting process. When the material characteristics fluctuate greatly, the feedback cycle is shortened; when the material characteristics tend to stabilize, the feedback cycle is extended. The feedback data includes the stratification distance between metallic and non-metallic components, the moving speed of the material in the sorting bed, and the real-time density of the composite heavy medium. The model judges the sorting effect by comprehensively analyzing the above data and completes the dynamic adjustment of parameters.

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