A multi-stage identification plastic automatic separation and air separation electrostatic synergistic separation system and method

Abstract

The application provides a multi-stage identification plastic automatic separation and air separation electrostatic synergistic separation system and method, and relates to the technical field of solid waste resource treatment. The system comprises an intelligent bag breaking and feeding module, a paving and shaping light source enhancement module, an AI identification and stripping module, a wet material drying module, a negative pressure air separation module, a high-voltage electrostatic deflection module and a multi-path collection and output module. The system realizes automatic feeding and bag breaking of household garbage, paving of identifiable plastic materials to reduce the overlap rate, efficient classification by AI technology, and accurate separation through wet drying and electric field electrostatic deflection. The system effectively improves the plastic separation efficiency, reduces the equipment energy consumption and land occupation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of solid waste resource processing, in particular to a multi-level identification plastic automatic separation and air separation and electrostatic cooperative separation system and method. BACKGROUND

[0002] With the rapid increase in the amount of municipal solid waste, waste incineration for power generation has gradually become the mainstream processing method. However, the incineration process needs to be fully classified before the municipal solid waste is classified, so as to strip out the high-calorific-value plastics such as plastic bags and film, thereby reducing resource waste and environmental pressure. The existing technology generally uses a combination of shredding-air separation-magnetic separation to process mixed waste, but the separation effect of large plastic materials is still insufficient.

[0003] To solve the above problems, the current research trend mainly focuses on the integration of intelligent identification and processing methods, that is, by introducing advanced image recognition and deep learning technology, the accuracy and efficiency of material identification are improved. At the same time, with the increasing strictness of environmental regulations, the requirements for waste classification and resource recycling are also constantly improving, prompting various fields to explore more efficient and environmentally friendly waste treatment technologies.

[0004] Although the existing technology provides a variety of processing schemes in theory, it still faces many challenges in actual operation. The current equipment mostly relies on strict condition restrictions on material dryness, particle size and material, resulting in a decline in the ability to separate plastics in wet waste. In addition, air separation and electrostatic separation are usually independent devices, which have large floor space, high energy consumption, and lack of intelligent feedback optimization mechanism. SUMMARY

[0005] In order to overcome the deficiencies of the prior art, the purpose of the present application is to provide a multi-level identification plastic automatic separation and air separation and electrostatic cooperative separation system and method, which solves the problems of low precision separation efficiency of plastics in wet waste and lack of intelligent linkage control system in the prior art, resulting in large floor space and high energy consumption of the equipment.

[0006] To achieve the above purpose, the present application provides the following scheme:

[0007] A multi-level identification plastic automatic separation and air separation and electrostatic cooperative separation system, comprising:

[0008] An intelligent bag breaking and feeding module for realizing automatic feeding of municipal solid waste and breaking the bag in the municipal solid waste to obtain identifiable plastic materials;

[0009] A paving and shaping light source enhancement module for paving the identifiable plastic materials into a low-overlap material surface to obtain a material surface to be identified;

[0010] An AI recognition and stripping module is configured to recognize the types of the material surface to be recognized by using a deep learning algorithm and separate the recognized types to obtain plastic materials of multiple types.

[0011] A wet material drying module is configured to quickly and uniformly remove the moisture in the plastic materials of various types to obtain dried plastic materials.

[0012] A negative pressure air separation module is configured to preliminarily separate the dried plastic materials by using high-speed airflow and charge the preliminarily separated plastic materials by introducing an electric field to obtain charged preliminarily classified plastic materials.

[0013] A high-voltage electrostatic deflection module is configured to accurately guide the preliminarily classified plastic materials by deflection in the electric field to obtain finally classified plastic materials.

[0014] A multi-path collection and output module is configured to collect the separated plastic materials into multiple collection containers according to the finally classified plastic materials.

[0015] Preferably, the intelligent bag breaking and feeding module comprises:

[0016] a feeding hopper, a conveying belt, an automatic bag breaking module, and a primary screening module connected in sequence.

[0017] The feeding hopper is configured to receive and store household garbage.

[0018] The conveying belt is configured to continuously convey the stored household garbage in the feeding hopper to the automatic bag breaking module.

[0019] The automatic bag breaking module is configured to break the bags of the conveyed household garbage to obtain mixed materials.

[0020] The primary screening module is configured to screen the mixed materials to remove impurities from the mixed materials to obtain identifiable plastic materials.

[0021] Preferably, the paving and shaping light source enhancement module comprises:

[0022] A dual-axis paving module is configured to uniformly pave the identifiable plastic materials to obtain a material surface with a low overlap rate.

[0023] A vibrating and shaping platform is connected to the dual-axis paving module and located below the paving cutter head, and is configured to vibrate the material surface with a low overlap rate to obtain a material surface to be recognized.

[0024] An LED multi-waveband light supplementing module is installed above the paving area and cooperates with the vibrating and shaping platform to provide stable lighting.

[0025] The dual-axis paving module comprises:

[0026] A conveying device for uniformly conveying identifiable plastic materials to a paving area;

[0027] A paving head directly connected to the conveying device for uniformly paving the materials to form a low-overlap material surface through bi-axial movement along the width and length directions of the conveying belt.

[0028] Preferably, the AI identification and stripping module comprises:

[0029] An image acquisition sub-module for real-time monitoring and image capturing of the material surface to be identified to obtain an input image;

[0030] An AI identification sub-module connected to the image acquisition sub-module for analyzing the input image based on a deep learning algorithm to identify the type of material and obtain an identification result;

[0031] A stripping control sub-module connected to the AI identification sub-module for separating the material to be identified using an adsorption execution device according to the identification result to obtain multiple types of plastic materials;

[0032] The image acquisition sub-module comprises:

[0033] A high-resolution camera for obtaining images on the paved material surface to obtain a processed image;

[0034] An image preprocessing unit for denoising, enhancing, and cropping the processed image to obtain an input image;

[0035] The AI identification sub-module comprises:

[0036] A feature extraction unit for extracting specific features from the input image to obtain a multi-feature set;

[0037] A classification unit for classifying and identifying the multi-feature set through a trained deep learning model to obtain an identification result.

[0038] Preferably, the wet material drying module comprises:

[0039] A high-temperature airflow channel sub-module for guiding heated airflow to a drying area for drying;

[0040] A multi-angle turning and throwing sub-module connected to the high-temperature airflow channel sub-module for turning and throwing the wet plastic materials in different angles during the drying process using a turning and throwing mechanism;

[0041] A hot air heating sub-module connected to the high-temperature airflow channel sub-module for providing a stable hot air source;

[0042] A humidity monitoring sub-module connected with the hot air heating sub-module and the high-temperature airflow channel sub-module, for monitoring the moisture content of the wet plastic in the plastic materials in the drying area in real time, and adjusting the heating temperature of the hot air heating sub-module;

[0043] A material discharging sub-module for collecting the dried wet plastic to obtain dried plastic materials and conveying them to the negative pressure air separation module.

[0044] Preferably, the negative pressure air separation module comprises:

[0045] A negative pressure air blower sub-module for generating negative pressure airflow to introduce the dried plastic materials into the flight channel;

[0046] An airflow guide plate sub-module for optimizing the airflow path and guiding the movement path of the dried plastic materials in the flight channel for preliminary classification;

[0047] An ionization electrode sub-module for ionizing the dried plastic materials during flight to make them charged, obtaining charged preliminary classified plastic materials;

[0048] The airflow guide plate sub-module comprises:

[0049] A guide plate arranged in the flight channel for orienting the airflow and the dried plastic materials;

[0050] An airflow adjusting device for adjusting the negative pressure airflow of the negative pressure air blower sub-module.

[0051] Preferably, the high-voltage electrostatic deflection module comprises:

[0052] A high-voltage electrostatic deflection sub-module located at the end of the air separation flight channel, for generating an electric field according to a multi-pole electrostatic plate array to realize precise deflection guidance of the charged preliminary classified plastic materials;

[0053] A high-frequency signal control sub-module for adjusting the electrostatic strength and deflection angle of the electric field generated by the multi-pole electrostatic plate array to adjust the deflection guidance process of the preliminary classified plastic materials, obtaining final classified plastic materials.

[0054] Preferably, the multi-path collection output module comprises:

[0055] A multi-path collection channel connected with the high-voltage electrostatic deflection module, for corresponding different types of plastic materials in the final classified plastic materials;

[0056] A flow guide plate arranged inside the multi-path collection channel to optimize the flow direction and speed of the final classified plastic materials;

[0057] Buffering soft curtain is arranged at the outlet of the multi-path collecting channel, and is used for preventing the accumulation of different types of plastic materials in the final classified plastic materials and the rebound interference.

[0058] A multi-stage identification plastic automatic separation and air separation electrostatic synergistic separation method, the method comprises:

[0059] The life garbage is automatically fed, and the bag body in the life garbage is broken, so that the identifiable plastic material is obtained;

[0060] The identifiable plastic material is spread into a low-overlapping-rate material surface to obtain a to-be-identified material surface;

[0061] A deep learning algorithm is used to identify the to-be-identified material surface and separate the identified types, so that the multi-type plastic material is obtained;

[0062] The moisture in each type of plastic material is quickly and uniformly removed, so that the dried plastic material is obtained;

[0063] High-speed airflow is used to preliminarily separate the dried plastic material, and an electric field is introduced to charge the preliminarily separated plastic material, so that the charged preliminarily classified plastic material is obtained;

[0064] The preliminarily classified plastic material is precisely guided in the electric field through deflection, so that the final classified plastic material is obtained;

[0065] According to the final classified plastic material, the separated plastic material is collected into a plurality of collecting containers.

[0066] The present application discloses the following technical effects:

[0067] This invention provides a multi-level identification, automatic separation, and electrostatic-coordinated air-separation system and method for plastics. The system includes: an intelligent bag-breaking and feeding module for automatically feeding household waste and breaking down bags to obtain identifiable plastic materials; a spreading, shaping, and light-enhancing module for spreading the identifiable plastic materials into a low-overlap surface to obtain a surface to be identified; an AI identification and stripping module for using deep learning algorithms to identify the types of the surface to be identified and separating the identified types to obtain multiple types of plastic materials; a wet material drying module for quickly and uniformly removing moisture from various types of plastic materials to obtain dried plastic materials; a negative pressure air-separation module for using high-speed airflow to initially separate the dried plastic materials and introducing an electric field to charge the initially sorted plastic materials to obtain charged, pre-classified plastic materials; a high-voltage electrostatic deflection module for precisely guiding the pre-classified plastic materials through deflection in the electric field to obtain finally classified plastic materials; and a multi-channel collection and output module for collecting the separated plastic materials into multiple collection containers according to the finally classified plastic materials. This invention first utilizes an intelligent bag-breaking module to effectively peel off the bag, ensuring that the plastic material can be fully identified. The spreading and shaping light source enhancement module improves the uniformity of the material, enhancing the accuracy of subsequent identification. Secondly, the AI ​​identification and peeling module employs deep learning algorithms to improve recognition precision, quickly and accurately distinguishing various types of plastic materials. Furthermore, a wet material drying module achieves efficient moisture removal, thereby improving the effectiveness of air separation and electrostatic separation. Ultimately, this system offers advantages in energy consumption and space utilization compared to traditional technologies, effectively responding to increasingly stringent regulations on waste sorting and recycling. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is a schematic diagram of a multi-level identification plastic automatic separation and air-separation electrostatic collaborative sorting system provided in an embodiment of the present invention.

[0070] Explanation of reference numerals in the attached figures:

[0071] 1-Intelligent bag-breaking and feeding module; 2-Spreading and shaping light source enhancement module; 3-AI recognition and peeling module; 4-Wet material drying module; 5-Negative pressure air separation module; 6-High voltage electrostatic deflection module; 7-Multi-channel collection and output module. Detailed Implementation

[0072] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0073] In order to make the above objectives, characteristics and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0074] As shown in the accompanying drawings and specific embodiments, Figure 1 The present application provides a multi-stage identification plastic automatic separation and air separation electrostatic cooperative separation system, comprising:

[0075] The intelligent bag breaking and feeding module 1 is used to realize automatic feeding of household garbage and break the bag in the household garbage to obtain identifiable plastic materials;

[0076] The paving and shaping light source enhancement module 2 is used to pave the identifiable plastic materials into a low-overlapping material surface to obtain a to-be-identified material surface;

[0077] The AI identification and stripping module 3 is used to identify the to-be-identified material surface by using a deep learning algorithm and separate the identified types to obtain multi-type plastic materials;

[0078] The wet material drying module 4 is used to quickly and uniformly remove the moisture in the plastic materials of various types to obtain dried plastic materials;

[0079] The negative pressure air separation module 5 is used to preliminarily separate the dried plastic materials by using high-speed airflow and introduce an electric field to charge the preliminarily separated plastic materials to obtain charged preliminarily classified plastic materials;

[0080] The high-voltage electrostatic deflection module 6 is used to accurately guide the preliminarily classified plastic materials by deflection in the electric field to obtain final classified plastic materials;

[0081] The multi-path collection and output module 7 is used to collect the separated plastic materials into a plurality of collection containers according to the final classified plastic materials.

[0082] Specifically, the corresponding working process of the system is:

[0083] Firstly, the intelligent bag breaking and feeding module 1 receives household garbage, breaks the bag body through an automatic mechanism, and obtains identifiable plastic materials. Then, the paving and shaping light source enhancement module 2 spreads the plastic materials into a material surface with a low overlap rate to improve the effect of subsequent identification. Then, the AI identification and stripping module 3 uses a deep learning algorithm to monitor and analyze the materials to be identified in real time, identifies the types of the materials, and separates the materials to obtain plastic materials of multiple types. Subsequently, the wet material drying module 4 removes moisture in the plastic materials quickly and uniformly through a high-speed airflow and a turning mechanism to obtain dry materials. Next, the negative pressure air separation module 5 uses high-speed airflow to preliminarily separate the dry plastic materials, and makes the plastic materials charged through an electric field to generate charged preliminarily classified plastic materials. Then, the high-voltage electrostatic deflection module 6 precisely guides the charged materials in the electric field to achieve final classification. Finally, the multi-path collection and output module 7 collects the separated plastic materials into multiple collection containers according to different types of the plastic materials, and completes the entire separation process.

[0084] Further, the present application introduces an intelligent linkage feedback control system as a whole-process closed-loop "brain". The system is composed of a central control unit, a data acquisition unit and an execution control unit: the central control unit adopts an AI algorithm-based PLC controller or an industrial computer, comprehensively analyzes the plastic type signals output by the AI identification and stripping module, the humidity data of the wet material drying module, the airflow parameters of the negative pressure air separation module and the electric field intensity feedback of the high-voltage electrostatic deflection module; the data acquisition unit monitors the temperature, humidity, wind speed, voltage, current and material flow in real time through a sensor network, and connects the data channels of the paving and shaping light source enhancement module, the humidity monitoring sub-module, the airflow guide plate sub-module and the high-frequency signal control sub-module, realizes the time sequence alignment and quality check of multi-source data; the execution control unit faces the separation valves of the hot air heating sub-module, the negative pressure fan sub-module, the high-voltage electrostatic deflection sub-module and the multi-path collection and output module, dynamically adjusts the heating intensity, fan speed, electric field intensity and valve action time sequence. The system has a built-in self-learning algorithm and threshold self-correction mechanism, based on the feedback of the separation effect and the weighing sensor data, continuously optimizes the control parameters of the next cycle, forms a closed-loop control logic of "identification-regulation-optimization", and realizes adaptive optimization and energy minimization control in the whole process under the fluctuation of material composition and environment, improves the purity and continuous processing efficiency of the final classified plastic materials.

[0085] Further, the intelligent bag breaking and feeding module 1 includes:

[0086] a feeding hopper, a conveying belt, an automatic bag breaking sub-module and a primary screening sub-module connected in sequence;

[0087] the feeding hopper is used for receiving and storing household garbage;

[0088] A conveying belt is used to continuously convey the household garbage stored in the feeding hopper to the automatic bag breaking module;

[0089] The automatic bag breaking module is used to break the bags of the conveyed household garbage, obtaining a mixture;

[0090] The preliminary screening module is used to screen the mixture to remove impurities from the mixture to obtain identifiable plastic materials.

[0091] Specifically, the feeding hopper is located at the starting end of the system, which first receives and stores household garbage to ensure the continuity of subsequent processing. The conveying belt connected to the feeding hopper is responsible for conveying the stored household garbage forward. Through the conveying belt, the garbage continuously moves to the automatic bag breaking module. In this process, the design of the feeding hopper ensures the effective storage of the garbage, preventing overflow and waste, while maintaining smooth supply to the subsequent processing link, thereby providing stable support for the operation of the entire system; The automatic bag breaking module is located at the end of the conveying belt, which is internally provided with specially designed knives and movement mechanisms to break the bags of the conveyed household garbage using mechanical devices. This module can ensure efficient bag breaking through precise cutting and tearing actions, achieving the separation of the bag body and the contents. After such processing, the mixture will only contain identifiable plastic materials, thereby laying a good foundation for the subsequent sorting link; Finally, the preliminary screening module is connected to the automatic bag breaking module and is responsible for screening the mixture obtained after bag breaking to remove impurities and non-plastic components. The module is provided with a screen with a specific aperture and a device for adjusting the vibration frequency to achieve efficient screening of the mixture, thereby effectively separating the identifiable plastic materials from other impurities. This key step not only improves the identification accuracy of the subsequent sorting process, but also provides an important guarantee for the efficient operation of the entire system.

[0092] Further, the paving and shaping light source enhancement module 2 comprises:

[0093] A double-axis paving module is used to uniformly pave the identifiable plastic materials to obtain a low-overlap material surface;

[0094] A vibrating shaping platform is connected to the double-axis paving module and is located below the paving cutter head to vibrate the low-overlap material surface to obtain a to-be-identified material surface;

[0095] An LED multi-waveband light supplementing module is installed above the paving area and cooperates with the vibrating shaping platform to provide stable lighting;

[0096] The double-axis paving module comprises:

[0097] A conveying device is used to uniformly convey the identifiable plastic materials to the paving area;

[0098] The paving cutter head is directly connected to the conveying device and is used to spread materials evenly by moving along the width and length directions of the conveyor belt to form a material surface with low overlap.

[0099] Specifically, in this embodiment, the dual-axis paving submodule first receives the identifiable plastic material output from the automatic bag-breaking module and then evenly transports this material to the paving area via a conveyor. In the paving area, the paving cutter head is directly connected to the conveyor, employing dual-axis movement along the width and length of the conveyor belt to effectively and evenly spread the material, forming a stable material surface with low overlap. This uniform paving process provides a solid foundation for AI visual recognition. A vibration shaping platform, located below the paving cutter head and immediately following the dual-axis paving submodule, ensures further processing of the already paved material surface. This platform can meticulously shape the low-overlap material surface through vibration, further reducing material congestion and improving overall flatness, facilitating subsequent visual recognition and classification processes. Furthermore, the LED multi-band supplementary lighting submodule, located above the paving area, works in conjunction with the vibration shaping platform to ensure stable illumination under different ambient lighting conditions. Its light source can be adjusted according to actual needs, adapting to changes in lighting, thereby providing sufficient and uniform light for the AI ​​visual recognition unit, ensuring the quality and accuracy of image acquisition.

[0100] More specifically, the LED multi-band supplementary lighting sub-module consists of multiple LED light source units arranged in preset wavelengths to achieve optimal illumination of the material surface. Each LED light source unit can independently adjust its brightness and color temperature to ensure adaptability to different ambient lighting conditions. The module is fixed above the vibration shaping platform by a bracket, and its design allows light to illuminate the material surface at a specific angle, reducing shadows and reflections and improving image acquisition quality. Furthermore, the LED multi-band supplementary lighting sub-module is electrically connected to the vibration shaping platform for control, allowing real-time adjustment of the light source brightness during material shaping to ensure continuous, uniform, and stable lighting throughout the vibration process.

[0101] Furthermore, the AI ​​identification and stripping module 3 includes:

[0102] The image acquisition submodule is used to monitor and capture images of the surface of the material to be identified in real time to obtain the input image.

[0103] The AI ​​recognition submodule is connected to the image acquisition submodule and is used to analyze the input image based on deep learning algorithms to identify the type of material and obtain the recognition result.

[0104] The stripping control submodule is connected to the AI ​​recognition submodule and is used to separate the material to be identified by the adsorption actuator according to the recognition result, so as to obtain various types of plastic materials.

[0105] The image acquisition submodule includes:

[0106] A high-resolution camera is used to acquire images of the paved material surface to obtain the image to be processed;

[0107] The image preprocessing unit performs noise reduction, enhancement, and cropping on the image to be processed to obtain the input image;

[0108] The AI ​​recognition submodule includes:

[0109] The feature extraction unit is used to extract specific features from the input image to obtain a multi-feature set;

[0110] The classification unit uses a trained deep learning model to classify and identify multiple feature sets, thus obtaining the recognition result.

[0111] Specifically, the image acquisition submodule, located at the beginning of this module, consists of a high-resolution camera and an image preprocessing unit. The high-resolution camera acquires images of the paved material surface in real time, capturing the images to be processed. Then, the image preprocessing unit denoises, enhances, and crops the captured images to ensure the resulting input images are clear and suitable for subsequent analysis. This process is crucial for ensuring the accuracy of AI recognition. Next, the AI ​​recognition submodule is directly connected to the image acquisition submodule and is responsible for in-depth analysis of the input images to identify the material types. This submodule consists of a feature extraction unit and a classification unit. The feature extraction unit extracts specific visual features from the input images, generating multiple feature sets to describe the different properties of the materials. The classification unit uses a trained deep learning model to classify and recognize the large number of extracted features to generate accurate recognition results. This process fully leverages the advantages of deep learning technology, improving the efficiency and accuracy of material type recognition. Finally, the stripping control submodule connects to the AI ​​recognition submodule and performs material separation tasks based on the recognition results. This module controls the adsorption actuator to effectively separate the identified different types of materials, thereby obtaining various types of plastic materials. This crucial step ensures that various types of plastic materials can be collected independently, providing strong support for subsequent processing and reuse.

[0112] Specifically, the separation control submodule plays a crucial role in controlling the material separation process via the adsorption actuator. First, based on the identification results provided by the AI ​​recognition submodule, the separation control submodule categorizes the identified materials into different types and sets corresponding separation strategies for each type. These strategies are based on material characteristics, such as material type, shape, and surface features, to ensure efficient and accurate separation. Through this classification process, the separation control submodule can assign unique operating parameters to each type of material, thereby optimizing the separation effect. Next, control signals are transmitted to the adsorption actuator via an electrical interface. This actuator typically includes multiple adsorbers or grippers, which can effectively grasp and separate materials according to their characteristics. During separation, the intelligent control system of the adsorption actuator receives feedback information in real time and adjusts the adsorption force and movement path to ensure that the material is not damaged during separation. For example, for lighter, thin-film plastics, the adsorption actuator may use a gentler gripping method, while for heavier, harder plastics, the adsorption force needs to be increased to ensure effective separation. Finally, after material separation is complete, the adsorption actuator places the different types of materials into their respective collection containers. This process not only needs to consider the stability and integrity of the materials during separation, but also ensures that the collection containers are clearly labeled for subsequent processing and reuse. Through an effective material separation strategy, this embodiment ensures the purity and recycling value of various plastic materials, laying a solid foundation for waste resource utilization and environmental protection.

[0113] More specifically, a well-trained deep learning model can be represented by the following formula:

[0114] ;

[0115] in, The output of the model represents the recognition result; The input image contains the material features to be identified. This is the weight matrix, representing the strength of the connections between nodes in the model; This is a feature extraction function that extracts key features by transforming the input image; This is an activation function used to introduce nonlinearity and enhance the expressive power of the model. This is the bias term, used to adjust the output and improve the model's fit. Through the calculation of this formula, the model can effectively output the corresponding recognition result based on the input image features.

[0116] Furthermore, the wet material drying module 4 includes:

[0117] The high-temperature airflow channel submodule is used to guide the heated airflow to the drying area for drying.

[0118] The multi-angle turning and polishing submodule is connected to the high-temperature airflow channel submodule and is used to turn the wet plastic in various plastic materials at different angles during the drying process using the turning and polishing mechanism.

[0119] The hot air heating submodule is connected to the high-temperature airflow channel submodule and is used to provide a stable hot air source;

[0120] A humidity monitoring submodule is connected to the hot air heating submodule and the high-temperature airflow channel submodule, respectively, and is used to monitor the moisture content of wet plastics in various plastic materials in the drying area in real time, so as to adjust the heating temperature of the hot air heating submodule.

[0121] The material discharge submodule is used to collect the dried, moist plastic to obtain dried plastic material and convey it to the negative pressure air separation module 5.

[0122] Specifically, the wet material drying module 4 plays a crucial role in the drying process of wet plastic materials. The high-temperature airflow channel submodule is responsible for guiding the heated airflow to the drying area to ensure a stable supply of hot air during the drying process. This airflow channel design optimizes the airflow distribution, allowing hot air to evenly cover the drying area and maximizing moisture evaporation efficiency. Simultaneously, the structure of the high-temperature airflow channel prevents airflow loss, ensuring a continuous and effective flow of hot air into the wet material drying area, creating favorable conditions for subsequent turning and hot air heating. During the drying process, the multi-angle turning submodule is closely connected to the high-temperature airflow channel submodule, utilizing a turning mechanism to achieve dynamic drying of various plastic materials. This module adjusts the angle of the turning mechanism to ensure that the wet plastic is continuously turned during the drying process, allowing each part of the material to be fully exposed to the hot airflow, thereby improving drying efficiency. The turning action not only increases the contact area between the material and the hot air but also promotes rapid moisture evaporation, reduces the adsorption of moisture by the physical structure, and ensures the quality of the material after drying.

[0123] In addition, the humidity monitoring submodule monitors the moisture content of the plastic material in the drying area in real time. It is connected to both the hot air heating submodule and the high-temperature airflow channel submodule to achieve intelligent control of the drying process. Based on the monitored humidity data, the control system can automatically adjust the heating temperature of the hot air heating submodule to adapt to the drying requirements of different types of plastics. For example, when the humidity is too high, the system increases the hot air temperature and airflow to accelerate drying; and when the humidity drops to the set standard, the system will appropriately reduce the temperature to prevent the material from overheating and changing its properties. After processing, the material discharge submodule collects the dried, moist plastic and conveys it to the negative pressure air separation module 5 for efficient processing and subsequent utilization.

[0124] More specifically, the turning and polishing mechanism in this embodiment employs a hydraulic or electric drive, allowing for flexible adjustment of the turning and polishing angle. It is specifically designed with multiple turning blades to ensure that the moist plastic material can be turned omnidirectionally during the drying process. By controlling the tilt angle of these blades, the movement of the material throughout the drying area is effectively promoted, ensuring that each part of the material receives the heat from the airflow evenly, thus avoiding localized overheating or uneven drying. During the turning and polishing process, the dynamic movement of the material significantly increases the contact area with the heat flow. This mechanism utilizes the effect of high-speed airflow, allowing hot air to quickly penetrate into the material, thereby accelerating the evaporation rate of moisture. In specific operation, by increasing the speed and temperature of the airflow, the control system of the turning and polishing submodule can precisely apply the required force to the moist plastic to ensure that the material maintains a suitable flow state during drying, preventing clumping and uneven moisture distribution caused by stillness.

[0125] Furthermore, the negative pressure air separation module 5 includes:

[0126] The negative pressure fan submodule is used to generate negative pressure airflow to introduce the dried plastic material into the flight channel;

[0127] The airflow guide plate module is used to optimize the airflow path and guide the movement of dried plastic materials in the flight channel for preliminary classification;

[0128] The ionization electrode submodule is used to ionize the dried plastic material during flight, so that the dried plastic material is charged and the preliminarily classified plastic material is obtained;

[0129] The airflow guide plate module includes:

[0130] Guide plates, installed within the flight channel, are used to orient the airflow and the dried plastic material;

[0131] An airflow regulating device is used to adjust the negative pressure airflow of the negative pressure fan submodule.

[0132] Specifically, the negative pressure fan submodule is the core of the entire module. It is directly connected to the flight channel via connecting pipes, generating negative pressure airflow to introduce the dried plastic material into the flight channel. This negative pressure airflow lays the foundation for rapid material transport and ensures that the material is not easily lost during transport. Thus, the dried plastic material is attracted into the flight channel under negative pressure and enters the next processing stage. The airflow guide plate submodule, fixed inside the flight channel, is closely related to the airflow generated by the negative pressure fan submodule. The guide plate design not only guides the airflow in the direction of movement of the plastic material but also optimizes the airflow path within the flight channel through its guiding effect. The cooperation between this module and the negative pressure fan submodule ensures efficient airflow guidance, minimizing turbulence and dispersion, thereby achieving stable movement and initial sorting of the dried plastic material in the channel. The ionization electrode submodule is installed at an appropriate position in the flight channel, closely connected to the airflow guide plate submodule. Under the action of the electric field generated by the ionization electrode, the dried plastic material is ionized and charged during flight. This process relies on the preceding airflow guide plate module to maintain a stable flow direction for the material. The charged material generated by the ionization electrode submodule is further combined with the airflow regulation device, which is connected to the negative pressure fan submodule to regulate the airflow intensity and ensure that various charged materials can be effectively separated during the sorting process.

[0133] More specifically, in the negative pressure air separation module 5, the airflow regulation device is a crucial component for regulating airflow intensity, and it is connected to the negative pressure fan submodule. This device precisely controls the airflow speed and flow rate by adjusting the fan speed and outlet opening to meet the processing requirements of different plastic materials. During material sorting, the airflow intensity directly affects the movement state and separation effect of charged materials. Therefore, the airflow regulation device is responsible for monitoring and adjusting airflow parameters to ensure reasonable allocation based on the characteristics of different plastic materials. In actual operation, the airflow regulation device dynamically adjusts the airflow intensity by combining humidity monitoring and material characteristic analysis. For example, when the system detects plastic materials with high moisture content, the airflow regulation device can increase the airflow speed to improve moisture evaporation efficiency and material conveying capacity. When processing lighter or more easily dispersed items, the airflow intensity is appropriately reduced to avoid excessive disturbance or damage to the material due to excessive airflow. This refined airflow management strategy is crucial for improving the accuracy of material sorting, effectively achieving rapid and accurate classification of various types of plastics; the module's intelligent control section can also be linked with the ionization electrode submodule. When an orderly airflow carrying charged plastic material passes through the ionization electrode, an environment with moderate airflow intensity promotes better charging of the material, thereby enhancing its separation effect in the subsequent electrostatic separation process. By ensuring stable and moderate airflow, the airflow regulation device fundamentally improves the ability of the negative pressure air separation module 5 to achieve efficient separation in material sorting, making the processing of plastic materials more refined and optimized, and meeting the requirements of industrial production.

[0134] Furthermore, the high-voltage electrostatic deflection module 6 includes:

[0135] The high-voltage electrostatic deflector module, located at the end of the air-separation flight channel, is used to generate an electric field based on the multi-electrode electrostatic electrode plate array to accurately deflect and guide the charged plastic materials that have undergone preliminary classification.

[0136] The high-frequency signal control submodule is used to adjust the electrostatic intensity and deflection angle of the electric field generated by the multi-electrode static electrode plate array, so as to adjust the offset guidance process of the preliminary sorted plastic material and obtain the final sorted plastic material.

[0137] Specifically, the high-voltage electrostatic deflector module is located at the end of the air-separation flight channel. Its main task is to generate an electric field through a multi-electrode electrostatic electrode array to precisely deflect and guide charged plastic materials that have undergone preliminary sorting. This module has a rational structure; the electrode plates are arranged according to the material type, size, and electro-optical characteristics to form a strong electric field that improves material separation efficiency. When charged plastic materials pass through these electrostatic electrode plates, a preset electric field is generated. This process utilizes electrostatic force to displace the material, thereby achieving the sorting of different types of plastics. The design considerations for the strength and distribution of these electric fields ensure the effective deflection of charged materials, subsequently providing a basis for final sorting. Meanwhile, the high-frequency signal control submodule is a key component of the high-voltage electrostatic deflection module 6, responsible for adjusting the electrostatic strength and deflection angle of the electric field generated by the multi-electrode electrostatic electrode array. By controlling the frequency and amplitude of the voltage, this submodule can adjust the characteristics of the electric field in real time, allowing charged plastic materials to deflect at an appropriate angle and distance when passing through the high-voltage electrostatic deflector module. Its control algorithm intelligently adjusts the electrostatic parameters based on real-time data feedback from the material to ensure that even when dealing with plastic materials of different properties... After being processed by the high-voltage electrostatic deflection module 6, the plastic materials are guided to their respective predetermined collection areas for final classification. Based on the preliminary classification results, the materials will be precisely separated under an appropriate electric field strength, ensuring that each type of plastic can be effectively classified. By utilizing the principle of high-voltage electrostatic deflection combined with intelligent high-frequency signal control, the high-voltage electrostatic deflection module 6 in this embodiment can classify various plastic materials in a precise and efficient manner, improving the recycling rate of materials and providing technical support and guarantee for the circular economy development of the plastic recycling industry.

[0138] Furthermore, the multiplexed collection and output module 7 includes:

[0139] Multiple collection channels are connected to the high-voltage electrostatic deflection module 6 and are used as collection containers for different categories of plastic materials in the final classification of plastic materials.

[0140] A flow deflector is installed inside the multi-channel collection system to optimize the flow direction and speed of the final sorted plastic material;

[0141] A buffer curtain is installed at the outlet of the multi-channel collection system to prevent the accumulation and rebound interference of different types of plastic materials in the final sorting process.

[0142] Specifically, in the multi-channel collection output module 7, the multi-channel collection system is connected to the high-voltage electrostatic deflection module 6, responsible for collecting final-sorted plastic materials from different categories. This channel design is optimized, employing a segmented structure with collection containers of varying sizes and shapes to accommodate the characteristics of different plastic categories. The distribution strategy of these containers ensures that the type and physical properties of the materials are fully considered during the collection process, thereby improving collection efficiency and accuracy. Through the rationally designed multi-channel collection system, the final-sorted plastic materials are rapidly transported to their respective containers, avoiding material mixing and cross-contamination.

[0143] To optimize the flow direction and speed of the final sorted plastic material, guide vanes are installed inside the multi-channel collection system. Through their specific geometry and placement, these vanes effectively guide the plastic material along a predetermined flow path. This design not only reduces the material's residence time in the channels but also improves the fluidity of the plastic material during collection by adjusting the interaction between airflow and material. Thanks to the optimized flow vanes, the material's trajectory becomes smoother, effectively reducing flow instability caused by turbulence, thereby improving the accuracy and efficiency of sorting.

[0144] Finally, buffer curtains are installed at the outlets of the multi-channel collection system to prevent the accumulation and rebound interference of different types of plastic materials. The use of buffer curtains effectively slows down the material's outlet speed, enhancing the stability of the material during collection and preventing collisions, scattering, or cross-mixing of plastic materials due to high-speed outflow. This design not only improves the material sorting effect but also provides a good foundation for subsequent processing. Through the rational configuration and careful design of the multi-channel collection output module 7, this embodiment further ensures the efficiency and reliability of the plastic recycling process, laying an important foundation for maximizing resource utilization.

[0145] Furthermore, in this embodiment, the linkage feedback control system, as the core intelligent control unit, possesses real-time monitoring and control capabilities. This system continuously receives key parameters from various modules, including AI recognition, drying status monitoring, electrostatic deflection adjustment, and wind power operation feedback, through a highly efficient data acquisition module. This data includes not only physical quantities such as material humidity, temperature, and flow rate, but also information such as the potential of charged materials and the sorting effect. Through comprehensive analysis of this data, this embodiment ensures information transparency and real-time response during the sorting process, providing accurate data for subsequent processing.

[0146] In terms of data processing, the linkage feedback control system incorporates an advanced rule engine and learning optimization algorithms. These algorithms can automatically identify operational deviations and make self-adjustments based on historical data and current feedback. For example, when the drying state is detected to be substandard, the control system can automatically increase the energy output of the hot air heating submodule and optimize the airflow speed through the airflow adjustment device to ensure that the plastic material achieves the best effect during the drying stage. Simultaneously, during the electrostatic deflection process, the system dynamically adjusts according to the material characteristics and deflection parameters to improve the accuracy of classification, thereby achieving efficient material sorting.

[0147] Furthermore, the control system in this embodiment supports remote cloud monitoring and management. Managers can use mobile terminals or computers to view the system's operating status in real time and promptly obtain equipment alarm information. In the event of an anomaly, the system will automatically trigger an alarm, reminding operators to inspect and handle the situation. Through the historical data backtracking function, managers can easily query past operation records and performance data, thereby assessing the overall system efficiency and providing precise guidance for subsequent improvements. The implementation of these functions gives the sorting system in this embodiment a high level of intelligence, greatly improving production efficiency and safety.

[0148] This embodiment also provides a multi-level identification method for automatic separation of plastics and a combined air-separation and electrostatic sorting method, the method comprising:

[0149] The system automatically feeds household waste and breaks down the bags inside to obtain identifiable plastic materials.

[0150] The identifiable plastic material is spread into a low-overlap material surface to obtain the material surface to be identified;

[0151] The material to be identified is classified using a deep learning algorithm, and the identified types are then separated to obtain various types of plastic materials.

[0152] The moisture in various plastic materials is removed quickly and evenly to obtain dried plastic materials;

[0153] High-speed airflow is used to perform preliminary sorting of dried plastic materials and an electric field is introduced to charge the pre-sorted plastic materials, resulting in pre-sorted plastic materials with charges.

[0154] Precisely guided pre-sorted plastic materials in an electric field through deflection, resulting in final-sorted plastic materials;

[0155] Based on the final classification of plastic materials, the separated plastic materials are collected into multiple collection containers.

[0156] To verify the synergistic sorting effect of the system of the present invention, a comparative test was set up in the embodiments of the specification. Mixed municipal solid waste plastics from the same source were selected as samples, and continuous operation evaluations were conducted under consistent working conditions for the traditional single air separation system, the traditional electrostatic separation system, and the multi-stage identification plus air separation electrostatic synergistic system of the present invention. Comparisons were carried out from dimensions such as plastic purity, unit processing energy consumption, increase in processing capacity, and operational stability. The test results show that the present invention, through the synergistic effect of multi-stage identification for precise stripping, negative pressure air separation for initial screening, ionization, and high-voltage electrostatic deflection for fine guidance, significantly improves the plastic sorting purity, reduces unit processing energy consumption, and achieves a significant increase in processing capacity under the same equipment footprint and operating time conditions. Simultaneously, due to the introduction of intelligent linkage feedback control, adaptive adjustment of heating intensity, fan speed, and electric field strength is achieved, enabling the system to maintain higher continuous stability and consistency under complex material fluctuations. Comprehensive comparison shows that the present invention has achieved substantial progress in sorting accuracy, energy utilization efficiency, and large-scale processing capacity, strongly supporting the technical effectiveness and inventiveness.

[0157] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0158] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-stage identification automatic plastic separation and air-separation electrostatic coordinated sorting system, characterized in that, include: The intelligent bag-breaking and feeding module is used to automatically feed household waste and break open the bags in the waste to obtain identifiable plastic materials. The paving and shaping light source enhancement module is used to pave identifiable plastic materials into a low-overlap material surface to obtain the material surface to be identified; The AI ​​identification and stripping module is used to identify the types of the material surface to be identified using deep learning algorithms and to separate the identified types to obtain various types of plastic materials. The wet material drying module is used to quickly and evenly remove moisture from various plastic materials to obtain dried plastic materials. The negative pressure air separation module is used to perform preliminary sorting of dried plastic materials using high-speed airflow and introduce an electric field to charge the pre-sorted plastic materials, thus obtaining pre-sorted plastic materials with charge. The high-voltage electrostatic deflection module is used to precisely guide the initially classified plastic materials in an electric field through deflection, so as to obtain the final classified plastic materials. The multi-channel collection output module is used to collect the separated plastic materials into multiple collection containers according to the final classification of the plastic materials; The AI ​​identification and stripping module includes: The image acquisition submodule is used to monitor and capture images of the surface of the material to be identified in real time to obtain the input image. The AI ​​recognition submodule is connected to the image acquisition submodule and is used to analyze the input image based on deep learning algorithms to identify the type of material and obtain the recognition result. The stripping control submodule is connected to the AI ​​recognition submodule and is used to separate the material to be identified by the adsorption actuator according to the recognition result, so as to obtain various types of plastic materials. The image acquisition submodule includes: A high-resolution camera is used to acquire images of the paved material surface to obtain the image to be processed; The image preprocessing unit performs noise reduction, enhancement, and cropping on the image to be processed to obtain the input image; The AI ​​recognition submodule includes: The feature extraction unit is used to extract specific features from the input image to obtain a multi-feature set; The classification unit uses a trained deep learning model to classify and identify multiple feature sets, and obtains the recognition results. The wet material drying module includes: The high-temperature airflow channel submodule is used to guide the heated airflow to the drying area for drying. The multi-angle turning and polishing submodule is connected to the high-temperature airflow channel submodule and is used to turn the wet plastic in various plastic materials at different angles during the drying process using the turning and polishing mechanism. The hot air heating submodule is connected to the high-temperature airflow channel submodule and is used to provide a stable hot air source; A humidity monitoring submodule is connected to the hot air heating submodule and the high-temperature airflow channel submodule, respectively, and is used to monitor the moisture content of wet plastics in various plastic materials in the drying area in real time, so as to adjust the heating temperature of the hot air heating submodule. The material discharge submodule is used to collect the dried plastic to obtain dried plastic material and convey it to the negative pressure air separation module.

2. The multi-stage identification plastic automatic separation and air-separation electrostatic coordinated sorting system according to claim 1, characterized in that, The intelligent bag-breaking and feeding module includes: The feed hopper, conveyor belt, automatic bag breaking module, and primary screening module are connected in sequence. The feed hopper is used to receive and store household waste; The conveyor belt is used to continuously transport the domestic waste stored in the feed hopper to the automatic bag-breaking module; The automatic bag-breaking module is used to break open the bags of conveyed domestic waste to obtain a mixed material; The primary screening module is used to screen the mixture to remove impurities and obtain identifiable plastic material.

3. The multi-stage identification plastic automatic separation and air-separation electrostatic coordinated sorting system according to claim 1, characterized in that, The paving and shaping light source enhancement module includes: The dual-axis paving sub-module is used to evenly spread identifiable plastic materials to obtain a material surface with low overlap. The vibration shaping platform, connected to the dual-axis paving sub-module, is located below the paving cutter head and is used to vibrate the low-overlap material surface to obtain the material surface to be identified. LED multi-band supplementary lighting modules are installed above the paving area and work in conjunction with the vibration shaping platform to provide stable lighting; The dual-axis paving submodule includes: A conveying device is used to uniformly transport identifiable plastic material to the paving area; The paving cutter head is directly connected to the conveying device and is used to spread materials evenly by moving along the width and length directions of the conveyor belt to form a material surface with low overlap.

4. The multi-stage identification plastic automatic separation and air-separation electrostatic coordinated sorting system according to claim 1, characterized in that, The negative pressure air separation module includes: The negative pressure fan submodule is used to generate negative pressure airflow to introduce the dried plastic material into the flight channel; The airflow guide plate module is used to optimize the airflow path and guide the movement of dried plastic materials in the flight channel for preliminary classification; The ionization electrode submodule is used to ionize the dried plastic material during flight, so that the dried plastic material is charged and the preliminarily classified plastic material is obtained; The airflow guide plate module includes: Guide plates, installed within the flight channel, are used to orient the airflow and the dried plastic material; An airflow regulating device is used to adjust the negative pressure airflow of the negative pressure fan submodule.

5. The multi-stage identification plastic automatic separation and air-separation electrostatic coordinated sorting system according to claim 4, characterized in that, The high-voltage electrostatic deflection module includes: The high-voltage electrostatic deflector module, located at the end of the air-separation flight channel, is used to generate an electric field based on the multi-electrode electrostatic electrode plate array to accurately deflect and guide the charged plastic materials that have undergone preliminary classification. The high-frequency signal control submodule is used to adjust the electrostatic intensity and deflection angle of the electric field generated by the multi-electrode static electrode plate array, so as to adjust the offset guidance process of the preliminary sorted plastic material and obtain the final sorted plastic material.

6. The multi-stage identification plastic automatic separation and air-separation electrostatic coordinated sorting system according to claim 1, characterized in that, The multi-channel collection and output module includes: Multiple collection channels are connected to the high-voltage electrostatic deflection module and are used as collection containers for different categories of plastic materials in the final classification of plastic materials; A flow deflector is installed inside the multi-channel collection system to optimize the flow direction and speed of the final sorted plastic material; A buffer curtain is installed at the outlet of the multi-channel collection system to prevent the accumulation and rebound interference of different types of plastic materials in the final sorting process.

7. A multi-stage identification method for automatic separation of plastics and a combined air-separation and electrostatic sorting method, applied to the system according to any one of claims 1-6, characterized in that, The method includes: The system automatically feeds household waste and breaks down the bags inside to obtain identifiable plastic materials. The identifiable plastic material is spread into a low-overlap material surface to obtain the material surface to be identified; The material to be identified is classified using a deep learning algorithm, and the identified types are then separated to obtain various types of plastic materials. The moisture in various plastic materials is removed quickly and evenly to obtain dried plastic materials; High-speed airflow is used to perform preliminary sorting of dried plastic materials and an electric field is introduced to charge the pre-sorted plastic materials, resulting in pre-sorted plastic materials with charges. Precisely guided pre-sorted plastic materials in an electric field through deflection, resulting in final-sorted plastic materials; Based on the final classification of plastic materials, the separated plastic materials are collected into multiple collection containers.

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