A waste and old textile poly-particle recovery method based on spectrum recognition and multi-stage impurity removal

Abstract

The present application relates to a kind of based on spectrum identification and multistage impurity removal waste and old textile polygranular recovery method, belong to waste and old resource recycling and automation control cross technical field.Wherein, the method includes: using infrared spectrum detection material quality combines photoelectric sorting machine to realize material quality accurate sorting;Adopt orthogonal double-stage cutting method to process qualified material into the square block material of uniform size;Based on electromagnetic induction and image recognition technology separates metal and zipper button impurities;Utilize cross shear system to carry out fine crushing, and improve flocculent material purity by airflow classification method;Finally, granular material is generated by polygranular forming integrated mechanism, realizes waste and old textile automation efficient recovery.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of waste resource recycling and automated control, specifically relating to a method for recycling waste textile pellets based on spectral recognition and multi-stage impurity removal. Background Technology

[0002] With the rapid development of the textile industry, the amount of waste textiles generated is increasing year by year, making their resource recycling a crucial issue for environmental protection and resource recycling. Currently, common waste textile recycling methods still rely mainly on manual sorting, mechanical crushing, and melt-regeneration, which suffer from problems such as low sorting accuracy, high energy consumption, and serious secondary pollution. Especially in the material sorting stage, traditional methods struggle to achieve efficient identification and separation of polyester fibers; during crushing, fiber entanglement and uneven crushing are common problems; and in the forming stage, the lack of end-to-end collaborative control leads to mismatched operating parameters between units, resulting in unstable product quality and high energy consumption. While some automated processing equipment has emerged in existing technologies, it primarily focuses on improving single processes and has not yet formed a complete solution for end-to-end collaborative control, failing to meet the demands of efficient, energy-saving, and high-quality industrial recycling. Therefore, there is an urgent need for a resource-based recycling method for waste textiles that can achieve precise sorting, efficient crushing, and intelligent collaborative processing. Summary of the Invention

[0003] To address the aforementioned problems in the existing technology, this invention provides a method for recycling waste textile pellets based on spectral identification and multi-stage impurity removal;

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] S1: The material is dispersed and transported by conveyor belt. The material is detected by infrared spectroscopy. The polyester fiber material is screened, and the sorting status and equipment operating parameters are monitored in real time to screen out qualified materials.

[0006] S2: The orthogonal double-stage cutting method is used to cut the qualified material horizontally and vertically. At the same time, the blade distance parameter is preset and the matching relationship between the blade roller and the conveying speed is locked to generate square blocks of uniform size.

[0007] S3: Receive the block-shaped material, separate the metal impurities through the electromagnetic induction mechanism, capture the surface features of the material through the hard impurity image recognition component after the initial impurity removal, and separate the hard impurities by comparing the exclusive morphological parameters of the zipper and button with the feature matching mechanism to generate clean material.

[0008] S4: Based on the waste textile crushing-air classification collaborative control mechanism, the clean material is finely crushed by locking the cutter head speed and feed rate through a high-intensity crushing mechanism and cooperating with the waste textile shredder of the cross shearing system. The material is then fed into the air classification device, where impurities are removed by airflow classification to generate flocculent material.

[0009] S5: Construct an integrated mechanism for waste textile pelletizing and molding. The flocculent material is conveyed into the material transition silo by air, and automatically and quantitatively fed to the integrated system with the assistance of high and low material level monitoring instruments to obtain the qualified granules.

[0010] As a preferred embodiment of the present invention, the specific process of dispersing and transferring the material via a conveyor belt is as follows:

[0011] When the materials are dispersed and transferred by the conveyor belt, infrared spectral data of each material is collected, and the time and real-time position information of the conveyor belt are recorded. The collected spectral data is processed to remove external interference and extract the characteristic spectrum of polyester fiber. The characteristic spectrum is compared with the standard spectral library, and the spectrum that meets the standard is judged as qualified material.

[0012] Specifically, the orthogonal dual-stage cutting method conveys the qualified material to the first waste textile slitting machine. The PLC presets and locks the slitting machine's blade distance, sets the current monitoring threshold based on the transverse cutting load requirements, and simultaneously displays the matching speed of the blade roller and the conveyor belt on the HMI touch screen, cutting the material into long strips of a preset width. The long strips are then transferred to the second waste textile slitting machine via a transfer conveyor belt. The blade rollers of the second waste textile slitting machine are perpendicular and orthogonal to the blade rollers of the first waste textile slitting machine, and the blade distance remains consistent, vertically cutting the long strips to form square blocks of uniform size.

[0013] Specifically, the electromagnetic induction mechanism includes:

[0014] The electromagnetic induction mechanism continuously generates an alternating electromagnetic field of a specific frequency to establish a stable detection area; by monitoring the disturbance changes of the electromagnetic field in real time, the eddy current effect generated by the metal conductor in the material is detected and converted into a corresponding electrical signal output.

[0015] The output electrical signal is analyzed in real time, and the phase difference and amplitude change data of the induced electromotive force are collected. The differential electromagnetic response characteristics of ferromagnetic metals and non-ferromagnetic metals are identified simultaneously. The classification and identification of metal impurities are completed by comparing with the preset metal feature threshold library.

[0016] The high-pressure pneumatic nozzle array of the sorting actuator is triggered by the identification result to blow the material containing metal impurities away from the main conveying channel. At the same time, the detected metal type, size and location data are uploaded in real time, and the real-time efficiency statistics curve of metal impurity sorting is dynamically updated on the HMI interface.

[0017] Specifically, the hard impurity image recognition component includes:

[0018] Uniform illumination is provided by a ring-shaped LED light source, and continuous image acquisition is performed on the material to obtain a clear image containing surface texture and geometric features. The image is processed to remove noise, enhance contrast, and extract the complete outline of the material. Hard impurity features in the outline are identified and compared with the feature library. If the match meets the criteria, a coordinate instruction is sent to the sorting mechanism to trigger the impurity rejection action.

[0019] Specifically, the feature matching mechanism includes:

[0020] The system receives geometric feature parameters transmitted by the image recognition component, extracts parameters of the zipper teeth and button holes, and combines them into a feature vector. It performs effective range verification on the parameters, calculates the deviation from the standard parameters in the feature library, and obtains a matching score. The score is compared with a threshold, and recognition results exceeding the threshold are used to generate impurity location information and sent to the sorting mechanism. At the same time, the matched feature vector is stored in the feature library, and the matching mechanism is dynamically optimized by updating the standard parameters.

[0021] Specifically, the waste textile shredding-air separation synergistic control mechanism includes:

[0022] A material state sensing network is constructed to monitor the material flow rate entering the crusher in real time, capture changes in the main motor load, and track the heat accumulation process in the cavity. The mechanism integrates the collected parameters into a material processing state index and establishes a correlation mechanism between feed rate, crushing intensity, and heat dissipation requirements.

[0023] Multi-parameter collaborative control is performed based on the state index, mapping the material handling state index to equipment control commands. The mechanism increases the cutter head speed to maintain shearing force based on the detected increase in material density; based on the temperature rise in the cavity, the feed rate is reduced and the fan speed is increased simultaneously; based on current fluctuations exceeding the threshold, the cutter head rotation direction is automatically switched to eliminate fiber entanglement.

[0024] A dynamic optimization mechanism is established to feed back the purity data of the material after air separation to the control loop. The mechanism recalibrates the mapping relationship between the state index and the control parameters based on the purity feedback, forming an intelligent control system based on material characteristics.

[0025] Specifically, the high-intensity fine crushing mechanism continuously collects the main motor current data of the crusher through a current transformer, calculates the average value of the continuously collected current data to obtain the instantaneous load rate, compares the load rate value with a preset speed control table, and outputs a cutter head speed adjustment signal; at the same time, it performs spectrum analysis on the current data, identifies abnormal harmonic components caused by fiber entanglement in the current spectrum, and starts a forward, pause, and reverse rotation control program based on the detected abnormal harmonics; and dynamically adjusts the step size of the speed change by real-time detection of material density value, and controls the shear force by gradually adjusting the speed in stages.

[0026] Specifically, the cross-cutting system includes:

[0027] Two sets of cutter shaft assemblies are arranged symmetrically in an X-shape. Multiple trapezoidal blades are installed on each cutter shaft in a spiral arrangement. The cutting edges of the two sets of blades form a continuous shearing grid in the intersection area.

[0028] The gearbox synchronously drives two sets of cutter shafts to rotate in opposite directions, transporting the sorted material to the shearing zone where it contacts the first set of blades rotating in the forward direction. The blades initially tear the material based on a preset working linear speed and bring the material into the intersection area of ​​the two sets of blades. The second set of blades rotating in the opposite direction performs reverse shearing on the material at a corresponding linear speed, forming a bidirectional shearing action.

[0029] The system collects bearing temperature data in real time and generates feed speed adjustment commands based on temperature change trends; it also continuously monitors blade rotation speed and feeds the rotation speed data back to the high-strength fine crushing mechanism.

[0030] Specifically, the airflow grading method includes:

[0031] Based on the rising airflow field with a stable flow rate, the crushed flocculent material is evenly dispersed from the feed inlet into the separation channel and undergoes stratification in the rising airflow based on the preset specific gravity difference. The heavy fibers fall along the wall to the main discharge port, while the light impurities rise with the airflow.

[0032] The airflow velocity is monitored by differential pressure sensors at multiple heights in the separation channel, and the fan speed is adjusted by the velocity deviation. At the same time, online component detection devices are set at the main discharge port and the impurity outlet to analyze the material composition in real time.

[0033] The fan parameters are dynamically adjusted based on the component detection results. When the impurity content at the main outlet increases, the fan speed is increased; when the fiber loss at the impurity outlet increases, the speed is decreased, forming a closed-loop control.

[0034] Specifically, the integrated mechanism for waste textile pelletizing includes:

[0035] The material level sensor monitors the material accumulation height in the transition hopper in real time, and controls the opening and closing of the feed valve based on the high and low material level thresholds to quantitatively feed the granulation system; at the same time, the mechanism collects the pressure data and turbine speed of the compaction system to establish the correspondence between material filling density and compaction strength.

[0036] The material temperature changes during the granulation process are monitored in real time. The molding temperature is controlled by adjusting the water flow of the cooling system. At the same time, the pressure parameters of the compaction system are adjusted in reverse according to the temperature data, forming a temperature-pressure coordinated control mechanism.

[0037] By integrating and analyzing the material level data of the transition silo, the operating parameters of the compaction system, and the working conditions of the cooling system, and by dynamically adjusting the matching relationship between the feeding speed, compaction strength, and cooling efficiency, the entire process of pelletizing is optimized and controlled.

[0038] Specifically, the high and low material level monitoring instrument continuously detects the material height in the transition hopper through a radio frequency admittance sensor and generates a control signal based on a preset material level threshold. The instrument closes the feed valve when the material reaches the upper limit position and opens the feed device when the material reaches the lower limit position. The real-time material level data is sent to the PLC controller through an analog transmission channel. The PLC dynamically adjusts the feeding rate of the granulation system according to the material level data to maintain the material level in the transition hopper within the preset working range.

[0039] The beneficial effects of this invention are as follows:

[0040] (1) By constructing a full process, the entire process from sorting, cutting, impurity removal to granulation is fully automated. Through data sharing and parameter linkage, a closed-loop optimization is formed, which significantly improves the continuity and stability of waste textile processing.

[0041] (2) By adopting a multi-level sorting strategy that combines spectral recognition, electromagnetic induction and image recognition, the limitations of the traditional single sorting method are overcome, and the precise identification and separation of polyester fibers, metal impurities and hard attachments are achieved, which greatly improves the purity and recycling value of raw materials.

[0042] (3) Based on the material state perception and dynamic parameter adjustment mechanism, the energy consumption and quality balance optimization are achieved in the key processes of crushing, air separation and granulation. Real-time monitoring and feedback control effectively avoid equipment overload, fiber entanglement and abnormal temperature problems, and improve system energy efficiency and product consistency. Attached Figure Description

[0043] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0044] Figure 1This is a schematic diagram of the process for recycling waste textile pellets based on spectral recognition and multi-stage impurity removal according to the present invention.

[0045] Figure 2 This is a time-series diagram of a waste textile pellet recycling method based on spectral recognition and multi-stage impurity removal, as described in this invention. Detailed Implementation

[0046] 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.

[0047] Please see Figure 1-2 A method for recycling waste textile pellets based on spectral recognition and multi-stage impurity removal, comprising:

[0048] S1: The material is dispersed and transported by conveyor belt. The material is detected by infrared spectroscopy. The polyester fiber material is screened, and the sorting status and equipment operating parameters are monitored in real time to screen out qualified materials.

[0049] S2: The orthogonal double-stage cutting method is used to cut the qualified material horizontally and vertically. At the same time, the blade distance parameter is preset and the matching relationship between the blade roller and the conveying speed is locked to generate square blocks of uniform size.

[0050] S3: Receive the block-shaped material, separate the metal impurities through the electromagnetic induction mechanism, capture the surface features of the material through the hard impurity image recognition component after the initial impurity removal, and separate the hard impurities by comparing the exclusive morphological parameters of the zipper and button with the feature matching mechanism to generate clean material.

[0051] S4: Based on the waste textile crushing-air classification collaborative control mechanism, the clean material is finely crushed by locking the cutter head speed and feed rate through a high-intensity crushing mechanism and cooperating with the waste textile shredder of the cross shearing system. The material is then fed into the air classification device, where impurities are removed by airflow classification to generate flocculent material.

[0052] S5: Construct an integrated mechanism for waste textile pelletizing and molding. The flocculent material is conveyed into the material transition silo by air, and automatically and quantitatively fed to the integrated system with the assistance of high and low material level monitoring instruments to obtain the qualified granules.

[0053] Specifically, the process of dispersing and transferring the materials via a conveyor belt includes:

[0054] When the materials are dispersed and transferred by the conveyor belt, infrared spectral data of each material is collected, and the time and real-time position information of the conveyor belt are recorded. The collected spectral data is processed to remove external interference and extract the characteristic spectrum of polyester fiber. The characteristic spectrum is compared with the standard spectral library, and the spectrum that meets the standard is judged as qualified material.

[0055] In this embodiment, in the scenario of an automated waste textile sorting production line with a processing capacity of 2000 kg / h (conveyor belt speed 1.5 m / s, single material size 50-100 mm), a start command is sent to the photoelectric sorting machine (equipped with an InGaAs detector, spectral range 900-1700 nm) through a spectral acquisition association mechanism. After the equipment responds, it continuously acquires raw infrared spectral data of the waste textiles on the conveyor belt (sampling interval 5 ms). Simultaneously, the encoder generates the acquisition timestamp (accurate to 1 ms) and the conveyor belt position coordinates (X-axis deviation ≤ ±2 mm) for each material. The spectral data is bound to the single material through hash mapping to ensure 100% association accuracy.

[0056] The collected spectral data were preprocessed by using a Butterworth low-pass filter with a cutoff frequency of 50Hz to filter out high-frequency noise of 100-200Hz generated by motor operation; a dynamic adaptive baseline fitting mechanism (sliding window size of 15 data points) was enabled to eliminate baseline drift caused by the superposition of ambient light (natural light + equipment light source) (baseline flatness after correction ≤0.02 absorbance); the 1100-1300nm band (the concentrated region of characteristic absorption peaks of polyester fiber) was truncated, and invalid bands of 900-1100nm and 1300-1700nm were removed to form a simplified characteristic spectrum. A standard feature spectrum library composed of 500 pure polyester fiber samples is pre-built. The cosine similarity between the simplified spectrum and the standard spectrum is calculated through a feature comparison judgment mechanism (calculation window 20nm). When the similarity of a certain material is 0.93 or higher than the preset threshold of 0.85, a qualified identification signal is generated and transmitted to the control system within 10ms, triggering the sorting pusher to move (stroke 30mm) and guide the material to the cutting unit. The measured polyester fiber identification accuracy is ≥99.2% and the false judgment rate is ≤0.5%.

[0057] Specifically, the orthogonal dual-stage cutting method conveys the qualified material to the first waste textile slitting machine. The PLC presets and locks the slitting machine's blade distance, sets the current monitoring threshold based on the transverse cutting load requirements, and simultaneously displays the matching speed of the blade roller and the conveyor belt on the HMI touch screen, cutting the material into long strips of a preset width. The long strips are then transferred to the second waste textile slitting machine via a transfer conveyor belt. The blade rollers of the second waste textile slitting machine are perpendicular and orthogonal to the blade rollers of the first waste textile slitting machine, and the blade distance remains consistent, vertically cutting the long strips to form square blocks of uniform size.

[0058] In this embodiment, the processing capacity of this recycling production line is 1500 kg / h. The qualified polyester fiber material output by the automatic sorting unit is in sheet form, with a size of 30×50cm-80×100cm and a weight of 40-180g / piece. The workshop operating temperature is 15-30℃ and the relative humidity is 30%-65%. The rated speed of the conveyor belt connecting to the slitting unit is 1.0m / s. After the operator selects the corresponding cutting mode through the HMI touch screen, the PLC controller first sends parameter instructions to the first waste textile slitting machine, presets and locks the blade distance to 55mm (within the standard range of 50-70mm), and sets the speed matching ratio between the blade roller and the conveyor belt to 1:1 (i.e., the blade roller speed is 30r / min). After startup, the blade roller distance is monitored in real time by the encoder at the blade roller shaft end (resolution 2000 pulses / r). Every 50ms, the preset value is compared. When the deviation is ≥0.2mm, the adjustment motor (step angle 0.9°) is immediately driven to make fine adjustments to ensure the blade distance accuracy is ±0.1mm.

[0059] The contactor triggers the main motor of the first slitting machine (rated power 4kW, rated current 8.5A) to start. The thermal relay uses staged current protection. During the start-up stage (0-3s), the monitoring threshold is set to 1.8 times the rated current (15.3A) to adapt to the initial large cutting load. During the stabilization stage (after 3s), it is reduced to 1.5 times the rated current (12.75A). If the current exceeds the threshold, the PLC immediately and temporarily reduces the speed of the cutter roller by 10% (to 27r / min) and simultaneously slows down the conveyor belt speed (to 0.9m / s). Once the current falls back below the threshold, the original speed is restored, ultimately cutting the material laterally into 55mm width pieces. The long strip of material is displayed in real time by the HMI, showing the speed of the cutter roller, the speed of the conveyor belt, and the motor current (approximately 10.2A during the stable phase). The long strip of material enters the transfer conveyor belt (with built-in dual diffuse reflection photoelectric sensors (model E3Z-LS63, spaced 10cm apart along the conveying direction) at a speed synchronized with the first slitting machine conveyor belt, both at 1.0m / s). The front sensor detects the position of the material head and records the initial coordinates, while the rear sensor detects the position of the material tail and calculates the deviation between the actual length and the theoretical length. If the deviation is ≥2cm (indicating material offset), the PLC immediately triggers the side push cylinder (stroke 20mm, response time ≤80ms) to push the material to fine-tune it to the center position, ensuring the alignment accuracy of the head and tail is ±1mm. If three consecutive pieces of material show an offset of ≥1.5cm, the PLC automatically adjusts the tension of the transfer conveyor belt (controlled within the range of 500-800N) to avoid drift caused by belt slack.

[0060] After the material enters the second waste textile slitting machine, the PLC controls the cutter roller to be perpendicular to the cutter roller of the first slitting machine at 90° (calibrated by gearbox rotation, orthogonal deviation ≤0.5°), and the cutter distance is kept at 55mm, consistent with the first slitting machine. At the same time, the rotation speed of the cutter rollers of the two slitting machines is collected in real time. If the rotation speed deviation is ≥1r / min, the frequency of the motor of the second slitting machine is immediately adjusted (frequency conversion range 45-55Hz) to ensure the synchronization of the two machines' rotation speeds within ±0.5r / min. A laser size detection sensor (detection accuracy ±0.1mm) is installed at the outlet of the second slitting machine to collect the size of the vertically cut square material in real time. If the detected size deviation is ≥±0.3mm, the sensor feeds back the deviation data to the PLC. If the size is "too large" (e.g., 55.3mm), the cutter roller distance is reduced by 0.05mm; if the size is "too small" (e.g., 54.7mm), the cutter roller distance is increased by 0.05mm. Finally, square materials with a uniform size of 55±0.3mm are generated, meeting the standardization requirements of the subsequent metal sorting unit for material shape.

[0061] Specifically, the electromagnetic induction mechanism includes:

[0062] The electromagnetic induction mechanism continuously generates an alternating electromagnetic field of a specific frequency to establish a stable detection area; by monitoring the disturbance changes of the electromagnetic field in real time, the eddy current effect generated by the metal conductor in the material is detected and converted into a corresponding electrical signal output.

[0063] The output electrical signal is analyzed in real time, and the phase difference and amplitude change data of the induced electromotive force are collected. The differential electromagnetic response characteristics of ferromagnetic metals and non-ferromagnetic metals are identified simultaneously. The classification and identification of metal impurities are completed by comparing with the preset metal feature threshold library.

[0064] The high-pressure pneumatic nozzle array of the sorting actuator is triggered by the identification result to blow the material containing metal impurities away from the main conveying channel. At the same time, the detected metal type, size and location data are uploaded in real time, and the real-time efficiency statistics curve of metal impurity sorting is dynamically updated on the HMI interface.

[0065] In this embodiment, after the block is started by the metal sorting unit, a high-frequency alternating electromagnetic field of 20kHz specific frequency is immediately generated by a high-frequency oscillator. A stable detection area is established with the help of a ring induction coil (covering the conveyor belt 1.2m wide and 5cm away from the belt surface). At the same time, through the built-in temperature compensation circuit, the oscillation frequency is finely adjusted (fluctuation ≤0.1kHz) when the ambient temperature fluctuates by ±5℃ to ensure the stability of the magnetic field. The material enters the detection area with the conveyor belt (speed 1.0m / s, matching the output speed of the slitting unit). If it contains metal impurities, the eddy current effect generated by the metal conductor will disturb the electromagnetic field. Three detection coils arranged at 15cm intervals along the conveyor belt capture the disturbance in real time and convert the eddy current effect into a 0-5V analog electrical signal. After receiving the electrical signal, the channel signal processing circuit collects the phase difference and amplitude change of the induced electromotive force. Employing a 15-25kHz (1kHz step) multi-band scanning technique, it compares the differential responses of ferromagnetic metals (e.g., iron nails, 18kHz band phase difference ≥15%, amplitude attenuation ≥30%) with non-ferromagnetic metals (e.g., aluminum sheets, 22kHz band phase difference ≤8%, amplitude attenuation ≥15%) against a preset metal characteristic threshold library (established based on 100 sets of metal samples measured in real-world tests). Classification and identification are completed within 10ms. Subsequently, the mechanism sends commands to eight high-pressure pneumatic nozzles (15cm interval along the bandwidth, working air pressure 0.6MPa), which activate within 0.1s, blowing the metal-containing material away from the main conveying channel (blowing distance 15cm, falling into two different waste bins). Simultaneously, the mechanism uploads data on metal type, size (amplitude estimation, accuracy ±0.5mm), and position (encoder positioning, accuracy ±1cm) to the PLC via RS485 communication (115200bps). The control system refreshes the real-time efficiency curve of metal sorting every 5 seconds on the HMI interface to prevent damage to subsequent crushing unit equipment.

[0066] Specifically, the hard impurity image recognition component includes:

[0067] Uniform illumination is provided by a ring-shaped LED light source, and continuous image acquisition is performed on the material to obtain a clear image containing surface texture and geometric features. The image is processed to remove noise, enhance contrast, and extract the complete outline of the material. Hard impurity features in the outline are identified and compared with the feature library. If the match meets the criteria, a coordinate instruction is sent to the sorting mechanism to trigger the impurity rejection action.

[0068] In this embodiment, during the recycling of waste textiles, the component continuously acquires images of the material on the conveyor belt (speed 1.0 m / s) using an array of four industrial cameras (resolution 2048×1536, frame rate 20fps). A ring-shaped LED light source (30W, 5000K) provides uniform illumination, ensuring that the standard deviation of the image grayscale value is ≤20. The original image is first subjected to Gaussian filtering (5×5 template, σ=1.2) to remove noise, and then histogram equalization (enhancement coefficient 1.5) is used to amplify the grayscale difference between impurities and materials, thus preprocessing the image.

[0069] The preprocessed image enters the edge detection stage. A designed edge enhancement extraction mechanism for hard impurities in waste textiles is used to replace the conventional edge detection operator. Valid edges are screened using a "grayscale difference-gradient dual threshold judgment," with the formula: P_edge = 1 if |G_imp - G_fab| ≥ ΔG_th and ∇G ≥ ∇G_th; otherwise, P_edge = 0. Where G_imp: grayscale value of the hard impurity (measured range 180-220, typical value 200); G_fab: grayscale value of the waste textile material (measured range 80-120, typical value 100); ΔG_th: grayscale difference threshold (calibrated to 40 after 100 sets of samples); ∇G: local grayscale gradient (threshold ∇G_th = 30). The setting of the threshold and range threshold is based on the process document records under actual production line conditions and 100... A set of typical sample measured data was obtained by continuously sampling common materials on the production line using an industrial camera (resolution 2048×1536). 100 sets of valid samples (including hard impurities and waste textile materials) were obtained. After statistical analysis of gray values, it was found that when pixel G_imp=200 and G_fab=100, |200-100|=100≥40. If the Sobel operator calculates ∇G=35≥30, then P_edge=1, which is determined to be a valid edge.

[0070] For the effective edges, perform parameter space cumulative calculation (angle step 0.5°, distance step 1px). For a certain straight line segment, the cumulative value is 18≥15, the length L_zip=8mm∈[5,10] mm, and the angle deviation θ_zip=2°≤3°, which is determined to be a zipper tooth. In another frame containing a resin button, the button hole edge has G_imp=210, G_fab=90, grayscale difference 120≥40, gradient 40≥30, P_edge=1, and the calculated radius R_btn=5mm (curvature K=0.2mm⁻). 1 ≥0.15mm⁻ 1 If the closure degree C=90%≥90%, it is determined to be a button hole; the similarity between the recognition result and the feature library is 0.92≥0.85, and a coordinate command (X=352px, Y=186px) is sent within 10ms to trigger the pneumatic push rod to remove the impurity.

[0071] Specifically, the feature matching mechanism includes:

[0072] The system receives geometric feature parameters transmitted by the image recognition component, extracts parameters of the zipper teeth and button holes, and combines them into a feature vector. It performs effective range verification on the parameters, calculates the deviation from the standard parameters in the feature library, and obtains a matching score. The score is compared with a threshold, and recognition results exceeding the threshold are used to generate impurity location information and sent to the sorting mechanism. At the same time, the matched feature vector is stored in the feature library, and the matching mechanism is dynamically optimized by updating the standard parameters.

[0073] Specifically, the waste textile shredding-air separation synergistic control mechanism includes:

[0074] A material state sensing network is constructed to monitor the material flow rate entering the crusher in real time, capture changes in the main motor load, and track the heat accumulation process in the cavity. The mechanism integrates the collected parameters into a material processing state index and establishes a correlation mechanism between feed rate, crushing intensity, and heat dissipation requirements.

[0075] Multi-parameter collaborative control is performed based on the state index, mapping the material handling state index to equipment control commands. The mechanism increases the cutter head speed to maintain shearing force based on the detected increase in material density; based on the temperature rise in the cavity, the feed rate is reduced and the fan speed is increased simultaneously; based on current fluctuations exceeding the threshold, the cutter head rotation direction is automatically switched to eliminate fiber entanglement.

[0076] A dynamic optimization mechanism is established to feed back the purity data of the material after air separation to the control loop. The mechanism recalibrates the mapping relationship between the state index and the control parameters based on the purity feedback, forming an intelligent control system based on material characteristics.

[0077] Specifically, the high-intensity fine crushing mechanism continuously collects the main motor current data of the crusher through a current transformer, calculates the average value of the continuously collected current data to obtain the instantaneous load rate, compares the load rate value with a preset speed control table, and outputs a cutter head speed adjustment signal; at the same time, it performs spectrum analysis on the current data, identifies abnormal harmonic components caused by fiber entanglement in the current spectrum, and starts a forward, pause, and reverse rotation control program based on the detected abnormal harmonics; and dynamically adjusts the step size of the speed change by real-time detection of material density value, and controls the shear force by gradually adjusting the speed in stages.

[0078] In this practical application, based on a waste textile shredding production line with a processing capacity of 1000 kg / h (equipped with a 30kW shredder main motor, 800mm cutter head diameter, and rated speed of 1500 rpm), after the production line starts, the current transformer continuously collects the main motor current data at a frequency of 1 kHz. Within a 50 ms sampling period, the current I(t) = 16A, and the currents in the previous two periods are I(t-1) = 15A and I(t-2) = 14A, respectively. The preset speed control table (70%-80% load rate corresponds to 1250 rpm) is consulted, and an adjustment signal is output, dynamically adjusting the cutter head speed from the current 1100 rpm.

[0079] Simultaneously, the algorithm performs FFT spectrum analysis (frequency resolution 1Hz) on the current data, detecting a 150Hz harmonic distortion rate THD_150 = 30% and a rate of change ΔTHD_150 / Δt = 6% / s. This is then substituted into the fiber entanglement determination formula:

[0080] F_wrap = 1 (if THD_150 ≥ 25% and ΔTHD_150 / Δt ≥ 5% / s; otherwise F_wrap = 0).

[0081] F_wrap = 1 indicates that fiber entanglement with the cutter head is detected (the unwinding procedure needs to be started); F_wrap = 0 indicates that no entanglement is detected (the equipment is operating normally). After comparison, if F_wrap = 1, the dedicated steering control program is immediately triggered. The cutter head rotates forward at 1250 rpm for 5 seconds (using inertia to pre-cut the entangled fiber) → pauses for 1 second (releasing instantaneous stress) → reverses at 800 rpm for 3 seconds, and the entanglement is unwound within 2 minutes.

[0082] Meanwhile, the laser density meter detects the material density ρ=80kg / m³ in real time. 3 (Base density ρ0 = 60 kg / m³) 3Substituting the parameters, we get Δn is approximately 42 rpm. The control of the cutter head is to complete the speed increase in three stages (1100→1142→1184→1250 rpm).

[0083] Specifically, the cross-cutting system includes:

[0084] Two sets of cutter shaft assemblies are arranged symmetrically in an X-shape. Multiple trapezoidal blades are installed on each cutter shaft in a spiral arrangement. The cutting edges of the two sets of blades form a continuous shearing grid in the intersection area.

[0085] The gearbox synchronously drives two sets of cutter shafts to rotate in opposite directions, transporting the sorted material to the shearing zone where it contacts the first set of blades rotating in the forward direction. The blades initially tear the material based on a preset working linear speed and bring the material into the intersection area of ​​the two sets of blades. The second set of blades rotating in the opposite direction performs reverse shearing on the material at a corresponding linear speed, forming a bidirectional shearing action.

[0086] The system collects bearing temperature data in real time and generates feed speed adjustment commands based on temperature change trends; it also continuously monitors blade rotation speed and feeds the rotation speed data back to the high-strength fine crushing mechanism.

[0087] Specifically, the airflow grading method includes:

[0088] Based on the rising airflow field with a stable flow rate, the crushed flocculent material is evenly dispersed from the feed inlet into the separation channel and undergoes stratification in the rising airflow based on the preset specific gravity difference. The heavy fibers fall along the wall to the main discharge port, while the light impurities rise with the airflow.

[0089] The airflow velocity is monitored by differential pressure sensors at multiple heights in the separation channel, and the fan speed is adjusted by the velocity deviation. At the same time, online component detection devices are set at the main discharge port and the impurity outlet to analyze the material composition in real time.

[0090] The fan parameters are dynamically adjusted based on the component detection results. When the impurity content at the main outlet increases, the fan speed is increased; when the fiber loss at the impurity outlet increases, the speed is decreased, forming a closed-loop control.

[0091] Specifically, the integrated mechanism for waste textile pelletizing includes:

[0092] The material level sensor monitors the material accumulation height in the transition hopper in real time, and controls the opening and closing of the feed valve based on the high and low material level thresholds to quantitatively feed the granulation system; at the same time, the mechanism collects the pressure data and turbine speed of the compaction system to establish the correspondence between material filling density and compaction strength.

[0093] The material temperature changes during the granulation process are monitored in real time. The molding temperature is controlled by adjusting the water flow of the cooling system. At the same time, the pressure parameters of the compaction system are adjusted in reverse according to the temperature data, forming a temperature-pressure coordinated control mechanism.

[0094] By integrating and analyzing the material level data of the transition silo, the operating parameters of the compaction system, and the working conditions of the cooling system, and by dynamically adjusting the matching relationship between the feeding speed, compaction strength, and cooling efficiency, the entire process of pelletizing is optimized and controlled.

[0095] Specifically, the high and low material level monitoring instrument continuously detects the material height in the transition hopper through a radio frequency admittance sensor and generates a control signal based on a preset material level threshold. The instrument closes the feed valve when the material reaches the upper limit position and opens the feed device when the material reaches the lower limit position. The real-time material level data is sent to the PLC controller through an analog transmission channel. The PLC dynamically adjusts the feeding rate of the granulation system according to the material level data to maintain the material level in the transition hopper within the preset working range.

[0096] 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 method for recycling waste textile pellets based on spectral recognition and multi-stage impurity removal, characterized in that, include: S1: The material is dispersed and transported by conveyor belt. The material is detected by infrared spectroscopy. The polyester fiber material is screened, and the sorting status and equipment operating parameters are monitored in real time to screen out qualified materials. S2: The orthogonal double-stage cutting method is used to cut the qualified material horizontally and vertically. At the same time, the blade distance parameter is preset and the matching relationship between the blade roller and the conveying speed is locked to generate square blocks of uniform size. S3: Receive the block-shaped material, separate the metal impurities through the electromagnetic induction mechanism, capture the surface features of the material through the hard impurity image recognition component after the initial impurity removal, and separate the hard impurities by comparing the exclusive morphological parameters of the zipper and button with the feature matching mechanism to generate clean material. S4: Based on the waste textile crushing-air classification collaborative control mechanism, the clean material is finely crushed by locking the cutter head speed and feed rate through a high-intensity crushing mechanism and cooperating with the waste textile shredder of the cross shearing system. The material is then fed into the air classification device, where impurities are removed by airflow classification to generate flocculent material. S5: Construct an integrated mechanism for waste textile pelletizing, wherein the flocculent material is pneumatically conveyed into a material transition silo, and automatically quantitatively fed to the integrated system with the assistance of high and low material level monitoring instruments to obtain qualified pellets. The integrated mechanism for waste textile pelletizing includes: The material level sensor monitors the material accumulation height in the transition hopper in real time, and controls the opening and closing of the feed valve based on the high and low material level thresholds to quantitatively feed the granulation system; at the same time, the mechanism collects the pressure data and turbine speed of the compaction system to establish the correspondence between material filling density and compaction strength. The material temperature changes during the granulation process are monitored in real time. The molding temperature is controlled by adjusting the water flow of the cooling system. At the same time, the pressure parameters of the compaction system are adjusted in reverse according to the temperature data, forming a temperature-pressure coordinated control mechanism. By integrating and analyzing the material level data of the transition silo, the operating parameters of the compaction system, and the working conditions of the cooling system, and by dynamically adjusting the matching relationship between the feeding speed, compaction strength, and cooling efficiency, the entire process of pelletizing is optimized and controlled.

2. The method according to claim 1, characterized in that, The specific process of dispersing and transferring materials via conveyor belt is as follows: When the materials are dispersed and transferred by the conveyor belt, infrared spectral data of each material is collected, and the time and real-time position information of the conveyor belt are recorded. The collected spectral data is processed to remove external interference and extract the characteristic spectrum of polyester fiber. The characteristic spectrum is compared with the standard spectral library, and the spectrum that meets the standard is judged as qualified material.

3. The method according to claim 1, characterized in that, The orthogonal double-stage cutting method transports the qualified material to the first waste textile slitting machine, presets and locks the slitting machine's blade distance, sets a current monitoring threshold based on the transverse cutting load requirements, and simultaneously displays the matching speed of the blade roller and the conveyor belt, cutting the material into long strips of preset width; the long strips are then transferred to the second waste textile slitting machine via a transfer conveyor belt, where the blade rollers of the second waste textile slitting machine are perpendicular and orthogonal to those of the first waste textile slitting machine, and the blade distance remains consistent, vertically cutting the long strips to form square blocks of uniform size.

4. The method according to claim 1, characterized in that, The electromagnetic induction mechanism includes: The electromagnetic induction mechanism continuously generates an alternating electromagnetic field of a specific frequency to establish a stable detection area; by monitoring the disturbance changes of the electromagnetic field in real time, the eddy current effect generated by the metal conductor in the material is detected and converted into a corresponding electrical signal output. The output electrical signal is analyzed in real time, and the phase difference and amplitude change data of the induced electromotive force are collected. The differential electromagnetic response characteristics of ferromagnetic metals and non-ferromagnetic metals are identified simultaneously. The classification and identification of metal impurities are completed by comparing with the preset metal feature threshold library. The high-pressure pneumatic nozzle array of the sorting actuator is triggered by the identification result to blow the material containing metal impurities away from the main conveying channel. At the same time, the detected metal type, size and location data are uploaded in real time, and the real-time efficiency statistics curve of metal impurity sorting is dynamically updated on the HMI interface.

5. The method according to claim 1, characterized in that, The hard impurity image recognition component includes: Uniform illumination is provided by a ring-shaped LED light source, and continuous image acquisition is performed on the material to obtain a clear image containing surface texture and geometric features. The image is processed to remove noise, enhance contrast, and extract the complete outline of the material. Hard impurity features in the outline are identified and compared with the feature library. If the match meets the criteria, a coordinate instruction is sent to the sorting mechanism to trigger the impurity rejection action.

6. The method according to claim 1, characterized in that, The feature matching mechanism includes: The system receives geometric feature parameters transmitted by the image recognition component, extracts parameters of the zipper teeth and button holes, and combines them into a feature vector. It performs effective range verification on the parameters, calculates the deviation from the standard parameters in the feature library, and obtains a matching score. The score is compared with a threshold, and recognition results exceeding the threshold are used to generate impurity location information and sent to the sorting mechanism. At the same time, the matched feature vector is stored in the feature library, and the matching mechanism is dynamically optimized by updating the standard parameters.

7. The method according to claim 1, characterized in that, The waste textile shredding-air separation synergistic control mechanism includes: A material state sensing network is constructed to monitor the material flow rate entering the crusher in real time, capture changes in the main motor load, and track the heat accumulation process in the cavity. The mechanism integrates the collected parameters into a material processing state index and establishes a correlation mechanism between feed rate, crushing intensity, and heat dissipation requirements. Multi-parameter collaborative control is performed based on the state index, mapping the material handling state index to equipment control commands. The mechanism increases the cutter head speed to maintain shearing force based on the detected increase in material density; based on the temperature rise in the cavity, the feed rate is reduced and the fan speed is increased simultaneously; based on current fluctuations exceeding the threshold, the cutter head rotation direction is automatically switched to eliminate fiber entanglement. A dynamic optimization mechanism is established to feed back the purity data of the material after air separation to the control loop. The mechanism recalibrates the mapping relationship between the state index and the control parameters based on the purity feedback, forming an intelligent control system based on material characteristics.

8. The method according to claim 1, characterized in that, The high-strength fine crushing mechanism continuously collects the main motor current data of the crusher through a current transformer, calculates the average value of the continuously collected current data to obtain the instantaneous load rate, compares the load rate value with the preset speed control table, and outputs the cutter head speed adjustment signal; at the same time, it performs spectrum analysis on the current data to identify abnormal harmonic components caused by fiber entanglement in the current spectrum, and starts the forward rotation, pause, and reverse rotation steering control program based on the detected abnormal harmonics; the step value of the speed change is dynamically adjusted by the real-time detected material density value, and the shear force is controlled by gradually adjusting the speed in stages.

9. The method according to claim 1, characterized in that, The cross-cutting system includes: Two sets of cutter shaft assemblies are arranged symmetrically in an X-shape. Multiple trapezoidal blades are installed on each cutter shaft in a spiral arrangement. The cutting edges of the two sets of blades form a continuous shearing grid in the intersection area. The gearbox synchronously drives two sets of cutter shafts to rotate in opposite directions, transporting the sorted material to the shearing zone where it contacts the first set of blades rotating in the forward direction. The blades initially tear the material based on a preset working linear speed and bring the material into the intersection area of ​​the two sets of blades. The second set of blades rotating in the opposite direction performs reverse shearing on the material at a corresponding linear speed, forming a bidirectional shearing action. The system collects bearing temperature data in real time and generates feed speed adjustment commands based on temperature change trends; it also continuously monitors blade rotation speed and feeds the rotation speed data back to the high-strength fine crushing mechanism.

10. The method according to claim 1, characterized in that, The airflow classification method includes: Based on the rising airflow field with a stable flow rate, the crushed flocculent material is evenly dispersed from the feed inlet into the separation channel and undergoes stratification in the rising airflow based on the preset specific gravity difference. The heavy fibers fall along the wall to the main discharge port, while the light impurities rise with the airflow. The airflow velocity is monitored by differential pressure sensors at multiple heights in the separation channel, and the fan speed is adjusted by the velocity deviation. At the same time, online component detection devices are set at the main discharge port and the impurity outlet to analyze the material composition in real time. The fan parameters are dynamically adjusted based on the component detection results. When the impurity content at the main outlet increases, the fan speed is increased; when the fiber loss at the impurity outlet increases, the speed is decreased, forming a closed-loop control.

11. The method according to claim 1, characterized in that, The high and low material level monitoring instrument continuously detects the material height in the transition hopper through a radio frequency admittance sensor and generates a control signal based on a preset material level threshold. The instrument closes the feed valve when the material reaches the upper limit position and opens the feed device when the material reaches the lower limit position. The real-time material level data is sent to the PLC controller through an analog transmission channel. The PLC dynamically adjusts the feeding rate of the granulation system according to the material level data to maintain the material level in the transition hopper within the preset working range.

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