Continuous extrusion temperature control method for high-fluidity PC plastic particles

Abstract

The invention relates to a continuous extrusion temperature control method for high-fluidity PC plastic cement particles in the field of PC plastic cement particle production temperature control, which comprises the following steps: establishing a nonlinear mathematical model of plastic cement particle temperature, screw rotating speed, back pressure and other multiple process parameters through a response surface method, solving an optimal parameter combination of the model by adopting a particle swarm optimization algorithm, and calculating the temperature of the plastic cement particles according to the optimal parameter combination. Optimal temperature control strategies under different working conditions are obtained, and parameter self-adaptive optimization control in the extrusion production process is achieved; a correlation model of plastic particle quality, temperature and rheological properties is established, a support vector machine algorithm is adopted to classify and identify quality defects of extruded products, reasons of abnormal temperature and rheological properties are reversely deduced through defect types, and a corresponding knowledge base of the quality defects and process parameter deviation is formed and used for guiding optimization and improvement of a temperature control strategy.

Description

Technical Field

[0001] The present invention relates to the field of information technology, in particular to the field of temperature control of PC plastic pellet production, and specifically to a method for controlling the continuous extrusion temperature of high-fluidity PC plastic pellets. Background Art

[0002] In the continuous extrusion production process of high-fluidity PC plastic pellets, there is a technical problem of insufficient temperature control accuracy. Because the flow performance of this type of plastic pellets is extremely excellent, it is very sensitive to fluctuations in processing temperature. During the extrusion process, if there is a slight deviation in the temperature of the plastic pellets, the melt fluidity will change significantly, which will affect the stability of extrusion molding and the consistency of product quality. When the temperature is too high, the plastic pellets are prone to thermal degradation, molecular chain breakage, and decreased fluidity; when the temperature is too low, the plastic pellets are not fully melted and the fluidity is insufficient, which will cause poor extrusion. In order to achieve precise control of the temperature of the plastic pellets during the production process, the existing single constant temperature control mode can no longer meet the requirements. It is urgent to develop a more refined zone temperature control technology, implement differentiated temperature control strategies according to the melting state of the plastic pellets in different areas of the extruder screw, and ensure the dynamic balance of the temperature of the plastic pellets. At the same time, it is also necessary to establish an accurate mathematical model of the temperature of the plastic pellets and process parameters such as screw speed and back pressure, and realize closed-loop feedback control through real-time calculation to improve the intelligent level of continuous extrusion production. Summary of the invention

[0003] The present invention provides a method for controlling the continuous extrusion temperature of high-fluidity PC plastic particles, which mainly comprises:

[0004] In view of the problem of insufficient temperature control accuracy of high-fluidity PC plastic pellets in the continuous extrusion production process, the zone temperature control technology is adopted to obtain the melting state parameters of the plastic pellets in different areas of the extruder screw. The target control temperature of each area is determined through the fuzzy control algorithm to achieve fine control of the temperature of the plastic pellets.

[0005] An online near-infrared spectrometer is used to detect the temperature and melt fluidity changes of plastic pellets in the extrusion process in real time. According to the preset temperature and fluidity thresholds, it is judged whether the melting state of the plastic pellets deviates from the optimal process window. If it deviates, the corresponding temperature adjustment measures are triggered to dynamically compensate for temperature fluctuations, ensure uniform melting of plastic pellets, and improve the consistency of product quality.

[0006] Through the response surface method, a nonlinear mathematical model of multiple process parameters such as plastic pellet temperature, screw speed, back pressure, etc. is established. The particle swarm optimization algorithm is used to solve the optimal parameter combination of the model, obtain the best temperature control strategy under different working conditions, and realize the adaptive optimization control of parameters in the extrusion production process.

[0007] Multiple independent temperature control devices are installed in the feeding area, compression area and metering area of ​​the extruder. A modular heating and cooling system is used. Differentiated temperature control modes are implemented according to the melting state of the plastic pellets in each area. Low-temperature preheating is used in the feeding area to prevent the plastic pellets from melting in advance. Gradual heating is used in the compression area to ensure that the plastic pellets are fully melted. Precise constant temperature is used in the metering area to prevent temperature fluctuations of the plastic pellets.

[0008] Multiple temperature sensors are set on the extruder screw, and the Kalman filter algorithm is used to denoise and fuse the temperature signal to obtain the real-time temperature distribution map of different areas of the plastic pellets. According to the color change trend of the image, it is judged whether the melting state of the plastic pellets is uniform. If the temperature distribution is uneven, the zone compensation heating is started to ensure that the plastic pellets are heated uniformly.

[0009] Establish a correlation model between plastic pellet quality, temperature and rheological properties, use support vector machine algorithm to classify and identify quality defects of extruded products, infer the causes of abnormal temperature and rheological properties through defect types, and form a corresponding knowledge base of quality defects and process parameter deviations to guide the optimization and improvement of temperature control strategies;

[0010] An adaptive neural fuzzy inference system is used to input multi-source process parameters such as plastic pellet temperature, melt fluidity, screw speed, back pressure, etc. into the fuzzy controller. Through reasoning and decision-making based on the fuzzy rule base, optimized temperature and speed control instructions are output to achieve multi-parameter collaborative optimization control of the extrusion production process and improve the accuracy and response speed of plastic pellet temperature regulation.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] In order to solve the problem of insufficient temperature control accuracy during the extrusion process, the present invention adopts zone temperature control technology, combined with online near-infrared spectrum detection and multi-parameter nonlinear modeling, to achieve dynamic and precise control of the temperature of plastic pellets. Specifically, the present invention installs independent temperature control devices in different areas of the extruder, determines the target temperature through a fuzzy control algorithm; uses near-infrared spectroscopy to monitor the melting state of plastic pellets in real time and triggers temperature compensation; uses response surface method and particle swarm optimization algorithm to solve the optimal temperature control strategy; and combines support vector machine and neural fuzzy inference system to achieve multi-parameter collaborative optimization control. The present invention significantly improves the temperature control accuracy and response speed of plastic pellets, ensures melting uniformity, and effectively improves the consistency of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flow chart of a method for controlling the temperature of continuous extrusion of high-fluidity PC plastic particles. DETAILED DESCRIPTION

[0014] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for the convenience of description, only the parts related to the invention are shown in the accompanying drawings.

[0015] like Figure 1 In this embodiment, a method for controlling the continuous extrusion temperature of high-fluidity PC plastic pellets may specifically include:

[0016] S101. Based on the problem of insufficient temperature control accuracy of high-fluidity PC plastic pellets during continuous extrusion production, zone temperature control technology is used to obtain the melting state parameters of plastic pellets in different areas of the extruder screw. The target control temperature of each area is determined through the fuzzy control algorithm to achieve fine control of the temperature of the plastic pellets.

[0017] According to the material properties of high-fluidity PC plastic pellets, the appropriate extrusion temperature range and temperature gradient distribution are determined as the target temperature reference for zone temperature control. Multi-point temperature sensors are used to arrange temperature collection points in different areas of the extruder screw to obtain the temperature parameters of the plastic pellets in each area in real time. According to the melting state characteristics of plastic pellets at different temperatures, a mapping relationship model between temperature and melting state parameters is established. Through the fuzzy control algorithm, the temperature parameters and melting state parameters of each area are comprehensively analyzed, and the target control temperature of each area is dynamically adjusted. If the temperature of a certain area deviates from the target temperature range, the heating or cooling control strategy of the area is determined according to the degree of deviation and duration through fuzzy rule reasoning. According to the determined zone temperature control strategy, the heating and cooling devices of each area are controlled to finely adjust the temperature of different areas of the extruder screw. Continuously monitor the changes in the temperature and melting state parameters of the plastic pellets in each area, and continuously optimize and adjust the zone temperature control process through closed-loop feedback control to ensure that the temperature of the plastic pellets is always within the target range, so as to achieve stable and controllable continuous extrusion production.

[0018] S102. Use an online near-infrared spectrometer to detect the temperature and melt fluidity changes of plastic pellets during the extrusion process in real time. According to the preset temperature and fluidity thresholds, determine whether the melting state of the plastic pellets deviates from the optimal process window. If deviated, trigger the corresponding temperature adjustment measures to dynamically compensate for temperature fluctuations, ensure uniform melting of plastic pellets, and improve the consistency of product quality.

[0019] The near-infrared spectral data of the plastic pellets during the extrusion process is obtained, and the spectral data is transmitted to the data processing module. The data processing module preprocesses the near-infrared spectral data, including denoising and normalization, to obtain the preprocessed spectral data. According to the preprocessed spectral data, the partial least squares regression algorithm is used to establish a quantitative relationship model between the temperature and melt fluidity of the plastic pellets and the spectral data. The near-infrared spectral data of the plastic pellets collected in real time is input into the established quantitative relationship model to predict the real-time temperature and melt fluidity values ​​of the plastic pellets. It is determined whether the predicted temperature and melt fluidity values ​​of the plastic pellets exceed the preset threshold range. If so, the temperature adjustment control signal is triggered. The temperature adjustment module uses the PID control algorithm according to the received control signal to dynamically adjust the heating temperature of the extruder and compensate for the temperature fluctuation of the plastic pellets. The above steps are performed in a continuous cycle to monitor the changes in the temperature and melt fluidity of the plastic pellets in real time, and dynamically adjust the extrusion temperature to ensure uniform melting of the plastic pellets and improve the consistency of product quality.

[0020] Specifically, the application of near-infrared spectroscopy in the extrusion process of plastic pellets can realize real-time and non-destructive monitoring of temperature and melt fluidity. When obtaining near-infrared spectral data, a fiber optic probe can be directly inserted into the extruder barrel, or an online spectrometer can be installed at the extrusion port. Spectral data preprocessing is a key step to improve model accuracy. Common preprocessing methods include moving average filtering denoising, multivariate scattering correction normalization, etc. Taking PC plastic pellets as an example, its near-infrared spectrum has multiple characteristic absorption peaks in the range of 1100-2500nm, which are related to the molecular structure and physical state. By analyzing the position and intensity changes of these characteristic peaks, a relationship model between the spectrum and temperature and melt fluidity can be established. The partial least squares regression algorithm can effectively deal with the multicollinearity problem of spectral data and extract the most relevant potential variables. In the process of model establishment, PC plastic pellet samples at different temperatures (such as 260℃, 280℃, 300℃) and shear rates can be selected, and their near-infrared spectra, actual temperatures, and melt flow index can be measured simultaneously.

[0021] 70% of the data is used for modeling and 30% for verification. The model parameters are optimized through cross-validation, and finally a quantitative relationship model with a prediction accuracy of ±2℃ and ±5% is obtained. During real-time prediction, the spectral data is collected every 1 second and input into the model to obtain a continuous change curve of the temperature and melt fluidity of the plastic pellets. Set a suitable threshold range, such as temperature 280±5℃, melt flow index 20±2g / 10min, and trigger an alarm and control signal once the predicted value exceeds the range. Compared with traditional thermocouple temperature measurement, this method can better reflect the actual state of the plastic pellets and avoid quality problems caused by local overheating or underheating. The temperature regulation adopts the PID control algorithm, which can flexibly adjust the heating power according to the deviation size and change trend. For example, when it is detected that the temperature continues to rise and approaches the upper limit, the controller will reduce the heating power in advance to prevent temperature overshoot. Compared with simple switch control, PID control can achieve smoother and more accurate temperature regulation and effectively suppress temperature fluctuations. Through this closed-loop control system based on near-infrared spectroscopy, the temperature fluctuation of PC plastic pellets can be controlled within the range of ±3℃, and the melt fluidity fluctuation can be controlled within the range of ±8%. This is significantly better than traditional control methods and can effectively improve the dimensional stability and mechanical property consistency of products. At the same time, real-time monitoring can also promptly detect abnormal situations, such as impurities mixed into raw materials or equipment failures, thus improving production efficiency and product quality.

[0022] S103. Through the response surface method, a nonlinear mathematical model of multiple process parameters such as plastic pellet temperature and screw speed, back pressure, etc. is established. The particle swarm optimization algorithm is used to solve the optimal parameter combination of the model, obtain the best temperature control strategy under different working conditions, and realize the adaptive optimization control of parameters in the extrusion production process.

[0023] According to multiple process parameters such as plastic pellet temperature, screw speed, back pressure, etc., the response surface method is used to establish a nonlinear mathematical model, and a nonlinear function expression describing the relationship between the plastic pellet temperature and each process parameter is obtained. The particle swarm optimization algorithm is used to solve the established nonlinear mathematical model. The optimal parameter combination of the model is obtained through iterative optimization search, and the values ​​of each process parameter that achieves the best temperature control effect under different working conditions are determined. According to the optimal parameter combination obtained by the particle swarm optimization algorithm, it is determined which preset working condition type the current working condition belongs to, and the optimal value of each process parameter is obtained from the corresponding parameter combination as the control target of the extrusion production process. During the extrusion production process, the current values ​​of parameters such as plastic pellet temperature, screw speed, back pressure, etc. are collected in real time, compared with the target value in the optimal parameter combination, the deviation value is calculated, and the corresponding adjustment instruction is generated. According to the generated adjustment instruction, the screw speed, back pressure and other actuators of the extruder are controlled, and each process parameter is dynamically adjusted to make it close to the target value in the optimal parameter combination, so as to achieve precise control of the plastic pellet temperature. During the adjustment process, the temperature change of the plastic pellets is continuously monitored to determine whether it has reached the target temperature range of the optimal temperature control strategy. If not, return to the previous step to continue parameter adjustment. When the temperature of the plastic pellets is stable within the target range of the optimal temperature control strategy, the current process parameter settings are maintained, the parameter adaptive optimization control of the extrusion production process is completed, and plastic pellet products with qualified temperatures are continuously produced.

[0024] Specifically, the response surface method is a commonly used experimental design and data analysis method, which can be used to establish a nonlinear relationship model between the temperature of plastic pellets and process parameters. For example, the screw speed, back pressure and barrel temperature can be selected as independent variables, and the temperature of plastic pellets can be used as the dependent variable. The experiment is carried out through a central composite design to obtain a series of experimental data points. These data points are used to fit the quadratic polynomial regression equation to obtain a nonlinear function expression describing the relationship between the temperature of plastic pellets and each process parameter. The particle swarm optimization algorithm is an intelligent optimization method that can be used to solve the optimal parameter combination of the above nonlinear model. The algorithm searches for the optimal solution in the parameter space by simulating the foraging behavior of bird flocks. For example, each particle can be represented as a set of process parameter values, and the particle position and speed are iteratively updated to gradually approach the optimal solution. During the search process, the objective function can be set to make the temperature of the plastic pellets closest to the target value, while considering other constraints such as production efficiency. According to the optimization results, several typical working conditions can be pre-set, such as high speed and low pressure, low speed and high pressure, etc., and the optimal parameter combination can be determined for each working condition. In actual production, by judging which preset type the current working condition belongs to, the corresponding optimal parameter combination can be quickly selected as the control target. This method can improve the system response speed and adapt to different production needs. In the extrusion production process, the current value of each process parameter needs to be collected in real time. For example, a thermocouple can be used to measure the temperature of the plastic pellets, the screw speed can be obtained through an encoder, and the back pressure can be measured using a pressure sensor. These measured values ​​are compared with the target values ​​in the optimal parameter combination, the deviation is calculated, and the corresponding adjustment instructions are generated according to the size and direction of the deviation.

[0025] According to the generated adjustment instructions, the screw speed can be controlled by adjusting the motor speed through the frequency converter, the back pressure can be adjusted by adjusting the back pressure valve opening, and the barrel temperature can be controlled by adjusting the heater power through the PID controller. The purpose of these adjustment measures is to make each process parameter approach the target value in the optimal combination, so as to achieve accurate control of the temperature of the plastic pellets. During the adjustment process, it is necessary to continuously monitor the changes in the temperature of the plastic pellets. A temperature tolerance range can be set, such as the target temperature ±2°C. If the actual temperature exceeds this range, it is necessary to continue to adjust the parameters. This closed-loop control method can effectively deal with external interference and system fluctuations to ensure the temperature control effect. When the temperature of the plastic pellets is stable within the target range, it means that the best temperature control effect has been achieved. At this time, the system will maintain the current process parameter settings and continue stable production. This adaptive optimization control method can automatically adjust the process parameters according to different production conditions and raw material characteristics to improve product quality stability and production efficiency.

[0026] S104. Install multiple sets of independent temperature control devices in the feeding area, compression area and metering area of ​​the extruder, and adopt a modular heating and cooling system. Implement differentiated temperature control modes according to the melting state of the plastic pellets in each area. Use low-temperature preheating for the feeding area to avoid premature melting of the plastic pellets. Use gradual heating for the compression area to ensure full melting of the plastic pellets. Use precise constant temperature for the metering area to prevent temperature fluctuations of the plastic pellets.

[0027] The real-time temperature data of the feeding zone, compression zone and metering zone of the extruder are obtained, and the temperature of each zone is judged to be within a reasonable range according to the preset temperature threshold range. If the temperature of the feeding zone is higher than the preset low-temperature preheating threshold, the cooling system of the feeding zone is controlled to start, and the temperature of the feeding zone is reduced until the temperature reaches the preset low-temperature preheating threshold range to avoid premature melting of the plastic pellets. If the temperature of the compression zone is lower than the preset gradual heating threshold, the heating system of the compression zone is controlled to start, and the temperature of the compression zone is increased. The gradual heating mode is adopted to ensure that the plastic pellets are fully melted in the compression zone. If the temperature of the metering zone exceeds the preset precise constant temperature threshold range, the heating system or cooling system of the metering zone is controlled to start according to the temperature deviation direction to achieve precise constant temperature control and prevent the temperature fluctuation of the plastic pellets in the metering zone. A mapping model between temperature and the melting state of plastic pellets is established through a machine learning algorithm. According to the real-time collected temperature data of each zone, the melting state of the plastic pellets is predicted, and the temperature control strategy of each zone is dynamically adjusted. During the production process, the temperature data of each area and the corresponding melting state of the plastic pellets are continuously recorded, and the temperature-melting state mapping model is updated and optimized in real time using the incremental learning algorithm to improve the accuracy of temperature control. According to the optimized temperature-melting state mapping model, the temperature threshold range of the feeding area, compression area and metering area is adaptively adjusted to realize the intelligent temperature management of the extruder, stabilize the melting state of the plastic pellets, and improve the extrusion quality.

[0028] Specifically, extruder temperature control is a key link in the plastic processing process and directly affects product quality. Temperature management in the feeding zone, compression zone and metering zone is particularly important. Taking polyethylene (PE) extrusion as an example, the temperature in the feeding zone is usually controlled within the range of 120-140°C. If the temperature is too high, PE may melt prematurely, causing difficulty in feeding. Therefore, the low-temperature preheating threshold is set to 130°C. When the temperature exceeds this value, the cooling system is started to cool down. The compression zone temperature is crucial to the melting of PE and is generally controlled at 160-180°C. The gradual heating mode can ensure uniform melting of PE and avoid local overheating. For example, the gradual heating threshold is set to 165°C. When the temperature is lower than this value, the heating power is gradually increased to slowly increase the temperature. This method can reduce the thermal degradation of PE and improve product quality. The temperature in the metering zone directly affects the uniformity and stability of the discharge and is usually controlled at 180-200°C.

[0029] The precise constant temperature threshold range is set to 190±5℃. When the temperature deviates from this range, the heating or cooling system is started according to the deviation direction. For example, when the temperature reaches 196℃, the cooling system is started; when the temperature drops to 184℃, the heating system is started. This precise control can ensure the fluidity and uniformity of the PE melt. The application of machine learning algorithms in temperature control can greatly improve the control accuracy. Taking the support vector machine (SVM) as an example, a mapping model between temperature and PE melting state can be established. The input variables include the temperature of each region, the screw speed, etc., and the output variable is the melt index (MI) of PE. By training the model with a large amount of historical data, real-time prediction of the PE melting state can be achieved. Incremental learning algorithms such as online stochastic gradient descent (OnlineSGD) can be used for real-time updating of the model.

[0030] During each production process, the system records the temperature data and the corresponding PE melting state, and uses these new data to fine-tune the model. For example, if there is a deviation between the predicted MI value and the actual measured value, the system will automatically adjust the model parameters to improve the prediction accuracy. Based on the optimized model, the system can adaptively adjust the temperature threshold range. For example, if the model predicts that the MI value of PE is too high under the current temperature setting, the system will automatically lower the temperature threshold of each area. This intelligent temperature management method can dynamically adjust the process parameters according to the changes in factors such as raw material batches and ambient temperature to maintain the stability of product quality. By implementing the above temperature control strategy, the stability of the PE extrusion process and product quality can be significantly improved. For example, in a continuous 24-hour production, when the traditional fixed temperature control method is used, the fluctuation range of the product MI value is ±10%; after adopting the intelligent temperature management system, the fluctuation range of the MI value is reduced to ±3%, which greatly improves the product consistency. At the same time, due to more precise temperature control, the degree of thermal degradation of the raw materials is reduced, the mechanical properties of the product are improved, and the tensile strength is increased by 5-8%. In addition, intelligent temperature management can also optimize energy utilization, while ensuring product quality, while reducing energy consumption by about 10-15%.

[0031] S105. Multiple temperature sensors are set on the extruder screw. The Kalman filter algorithm is used to denoise and fuse the temperature signal to obtain a real-time temperature distribution map of different areas of the plastic pellets. According to the color change trend of the image, it is judged whether the melting state of the plastic pellets is uniform. If the temperature distribution is uneven, the zone compensation heating is started to ensure that the plastic pellets are heated uniformly.

[0032] Install multiple high-precision temperature sensors in different areas of the extruder screw to collect temperature data of plastic pellets at different positions of the screw in real time. For the collected temperature data, the Kalman filter algorithm is used for denoising to eliminate high-frequency noise interference in the temperature signal and improve the accuracy of temperature measurement. The temperature data collected by multiple temperature sensors are fused and processed, and the temperature distribution of plastic pellets in different areas of the screw is comprehensively analyzed to generate a real-time temperature distribution map. Perform image analysis on the generated temperature distribution map, extract the color features of the image, and judge whether the melting state of the plastic pellets is uniform according to the color change trend. If the color analysis of the temperature distribution map finds that the melting state of the plastic pellets is uneven, the partition compensation heating control mechanism is triggered to focus on heating the areas with lower temperatures. In the partition compensation heating process, the heating power and heating time are dynamically adjusted, and the uniformity of heating of the plastic pellets is achieved through closed-loop control to eliminate the inconsistency of temperature distribution. Continuously monitor the changes in the temperature distribution map. When the color distribution tends to be consistent and the temperature distribution is uniform, it is judged that the plastic pellets have reached the optimal melting state and the temperature optimization control of the extruder screw is completed.

[0033] Specifically, installing high-precision temperature sensors in different areas of the extruder screw is the basis for achieving precise temperature control. For example, thermocouples or infrared temperature sensors can be installed in the feeding area, compression area, and metering area respectively. These sensors can respond quickly to temperature changes with an accuracy of ±0.1°C. The temperature data collected in real time may be affected by factors such as environmental noise and electromagnetic interference, so it is very necessary to use the Kalman filter algorithm for denoising. Kalman filtering can effectively eliminate high-frequency noise and improve the accuracy of temperature measurement through two steps of prediction and correction. Temperature data fusion processing is the key to comprehensive analysis of the melting state of plastic pellets. The weighted average method or Bayesian fusion algorithm can be used to integrate the data of multiple sensors to generate a more reliable temperature distribution map. For example, assuming that 10 temperature sensors are installed on the screw, a continuous temperature curve can be obtained through data fusion, which intuitively reflects the entire temperature change process of the plastic pellets from feeding to discharging. Computer vision technologies such as color segmentation and edge detection can be used for image analysis of the temperature distribution map. For example, temperature values ​​can be mapped to different colors, such as blue for low temperature and red for high temperature. By analyzing the gradient change of color, it is possible to determine whether the melting of plastic pellets is uniform. If it is found that the color of some areas changes suddenly or obvious color blocks appear, it means that there may be problems with the melting state of this area. Zoning compensation heating control is an effective means to solve the problem of uneven melting. For example, if the temperature of the compression zone is found to be low, the heating power of this area can be increased in a targeted manner. This compensatory heating can use the PID control algorithm to dynamically adjust the heating power according to the deviation between the target temperature and the actual temperature. At the same time, it can be combined with fuzzy control theory to formulate a more flexible heating strategy according to the size and change trend of the temperature deviation. Closed-loop control is the key to achieving uniform heating of plastic pellets. By continuously monitoring the changes in temperature distribution and adjusting the heating parameters in time, the inconsistency of temperature distribution can be effectively eliminated. For example, a temperature uniformity index can be set. When the temperature difference between each area is less than ±2℃, it is considered that the optimal melting state has been achieved. This closed-loop control can not only improve product quality, but also optimize energy utilization efficiency. The ultimate goal of temperature optimization control is to make plastic pellets reach the optimal melting state. By continuously monitoring the changes in the temperature distribution graph, when the color distribution tends to be consistent and the temperature distribution is uniform, it can be judged that the optimal state has been reached. This method can not only ensure product quality, but also improve production efficiency. For example, in actual production, the optimal melting temperature range can be pre-set according to the characteristics of different plastic materials. When the temperature distribution is detected to fall into this range and remain stable, it will automatically enter the next process to achieve intelligent production.

[0034] S106. Establish a correlation model between the quality of plastic pellets and temperature and rheological properties. Use the support vector machine algorithm to classify and identify the quality defects of extruded products. Infer the causes of abnormal temperature and rheological properties through the defect types, and form a corresponding knowledge base of quality defects and process parameter deviations to guide the optimization and improvement of temperature control strategies.

[0035] The temperature and rheological properties data of plastic pellets and the quality defect data of extruded products are obtained, and the association model between the quality of plastic pellets and temperature and rheological properties is established. The support vector machine algorithm is used to classify and identify the quality defects of extruded products, and sample data of different defect types are obtained. According to the quality defect type, combined with the association model between the quality of plastic pellets and temperature and rheological properties, the abnormal temperature and rheological properties that cause the defect type are determined. The quality defect type is associated with the corresponding abnormal temperature and rheological properties to form a corresponding knowledge base of quality defects and process parameter deviations. When a new quality defect of an extruded product occurs, the defect type is identified by the support vector machine algorithm, and the abnormal temperature and rheological properties corresponding to the defect type are searched in the corresponding knowledge base of quality defects and process parameter deviations. According to the abnormal temperature and rheological properties found, the temperature control strategy and process parameters that need to be optimized and adjusted are determined. The optimized temperature control strategy is applied to the plastic pellet extrusion production process. By real-time monitoring of the temperature and rheological properties data of the plastic pellets, the temperature control parameters are dynamically adjusted to ensure the quality stability of the extruded products.

[0036] Specifically, the correlation model between the quality of plastic pellets and temperature and rheological properties is the basis for the quality control of extruded products. By collecting the rheological property data of plastic pellets at different temperatures, such as melt flow rate, shear viscosity, etc., a temperature-rheological property curve can be established. For example, the melt flow rate of a polyethylene plastic pellet at 180°C is 2g / 10min, and it increases to 5g / 10min at 220°C, indicating that the increase in temperature will reduce the viscosity of the plastic pellets and improve the fluidity. The support vector machine algorithm plays an important role in the classification of quality defects of extruded products. The support vector machine model is trained by extracting the feature vectors of the defect image, such as texture, shape, color, etc. For example, for surface bubble defects, the roundness and size distribution of the bubbles can be extracted; for weld line defects, the continuity and width of the lines can be extracted. In this way, different types of defects can be accurately distinguished. The corresponding knowledge base of quality defects and process parameter deviations is the key to solving the problem. For example, surface bubble defects may be caused by excessive temperature or insufficient drying of the plastic pellets. If a batch of products is found to have a large number of bubbles, the possible causes can be quickly located by querying the knowledge base, such as the melt temperature exceeding 230°C or the water content of the plastic pellets exceeding 0.1%. In this way, measures such as lowering the temperature or strengthening drying can be quickly taken.

[0037] Optimization of temperature control strategy is an effective means to improve product quality. For example, for easily degradable plastics, a step-by-step temperature increase strategy can be adopted to maintain a low temperature such as 160°C in the feeding section and gradually increase it to 200°C in the metering section, which not only ensures that the plastic is fully melted, but also avoids excessive degradation. At the same time, the screw speed and back pressure can be adjusted according to the characteristics of different plastics to optimize the plasticizing effect. Real-time monitoring and dynamic adjustment are the key to ensuring the stability of the quality of extruded products. The melt state can be monitored in real time through online viscometers and temperature sensors. If a sudden decrease in viscosity is detected, it may be due to excessive temperature or shear degradation. At this time, the system will automatically reduce the screw speed or lower the temperature setting value. This closed-loop control method can quickly respond to process fluctuations and maintain product quality consistency. By establishing a correlation model between plastic pellet quality, processing parameters and product quality, combined with machine learning algorithms and expert knowledge bases, intelligent control of the extrusion process can be achieved. This not only improves product quality, but also reduces energy consumption and raw material waste, providing technical support for the sustainable development of the plastic processing industry.

[0038] S107. Adopting an adaptive neural fuzzy inference system, the multi-source process parameters such as plastic pellet temperature, melt fluidity, screw speed, back pressure, etc. are input into the fuzzy controller. Through the reasoning decision of the fuzzy rule base, the optimized temperature and speed control instructions are output to realize the multi-parameter coordinated optimization control of the extrusion production process and improve the accuracy and response speed of the temperature control of the plastic pellets.

[0039] The temperature value, melt fluidity, screw speed and back pressure data of the plastic pellets under the operation state of the extruder are collected, and an original database is constructed through these data. According to the data in the original database, data preprocessing is performed to eliminate abnormal values ​​and obtain a valid data set. The adaptive neural fuzzy inference system is trained through the effective data set to determine the rules in the fuzzy rule base and obtain a trained inference engine. The temperature value, melt fluidity, screw speed and back pressure data of the plastic pellets in real time operation of the extruder are obtained, and the real-time data are input into the trained inference engine. According to the output of the trained inference engine, the preliminary temperature control and speed control instructions are combined with the current state of the extruder to judge the rationality of the preliminary instructions. If it is unreasonable, the inference engine parameters are adjusted. By adjusting the inference engine parameters, the optimized temperature control and speed control instructions are obtained, and the optimized instructions are output to the extruder actuator. According to the operating state of the extruder actuator, it is judged whether the operating state of the extruder is consistent with the set value. If it is consistent, the real-time data of the extruder is continued to be collected.

[0040] Specifically, the data collection of the operating status of the extruder is the basis of the control of the plastic processing process. Taking polyethylene extrusion as an example, key parameters such as melt temperature, melt flow index (MFI), screw speed and back pressure can be collected. The temperature can be measured by a thermocouple, with a typical value of 180-220°C; the MFI can be measured by a capillary rheometer, with a common value of 2-10g / 10min; the screw speed can be measured by a motor tachometer, generally 30-100rpm; the back pressure can be measured by a pressure sensor, usually 5-15MPa. Data preprocessing is crucial to ensure the quality of model training. The 3σ criterion can be used to eliminate outliers, such as data points with temperatures exceeding 250°C or below 150°C. In addition, data standardization can be performed to normalize each parameter to the 0-1 interval to eliminate dimensional effects. The adaptive neuro-fuzzy inference system (ANFIS) combines the learning ability of neural networks with the reasoning ability of fuzzy logic.

[0041] During the training process, the membership function parameters can be optimized by the back propagation algorithm, such as using the Gaussian membership function to adjust its center value and width. At the same time, the consequent parameters are determined by the least squares method. In this way, a fuzzy rule base that can accurately describe the input-output relationship can be obtained. Real-time control is the core of the extrusion process. When the melt temperature is detected to increase by 2°C, ANF IS may output an instruction to reduce the screw speed by 5rpm. However, if the back pressure is already at a low level at this time, a direct speed reduction may lead to a decrease in product quality. At this time, it is necessary to combine expert experience to adjust the output weight of ANF IS, which may be changed to reduce the heating zone temperature setting value by 3°C. The execution of control instructions involves the coordination of multiple actuators. Temperature regulation is achieved through the PID controller, which may take 0.5-2 minutes to reach a new steady state; screw speed regulation can be quickly achieved through the frequency converter, and the response time is usually in seconds. Therefore, in actual control, it is necessary to consider the dynamic characteristics of different actuators and set the control cycle reasonably. Closed-loop control is the key to ensuring the stability of the extrusion process. By comparing the deviation between the actual operating parameters and the set values, abnormalities can be discovered and adjusted in time. For example, if the actual temperature is found to be continuously higher than the set value by more than 2°C, it may be due to excessive shear heat, and it is necessary to re-evaluate whether the screw speed setting is reasonable. Through this continuous monitoring and adjustment, the extrusion process can always be kept within the optimal process window.

[0042] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for controlling the continuous extrusion temperature of high-fluidity PC plastic particles, characterized in that: The method comprises: Step S101, based on the problem of insufficient temperature control accuracy of high-fluidity PC plastic pellets during continuous extrusion production, a zone temperature control technology is used to obtain the melting state parameters of the plastic pellets in different zones of the extruder screw, and a fuzzy control algorithm is used to determine the target control temperature of each zone to achieve fine control of the temperature of the plastic pellets; Step S102, using an online near-infrared spectrometer to detect in real time the temperature and melt fluidity changes of the plastic pellets during the extrusion process, and judging whether the melting state of the plastic pellets deviates from the optimal process window according to the preset temperature and fluidity thresholds, and if deviating, triggering corresponding temperature adjustment measures to dynamically compensate for temperature fluctuations, ensure uniform melting of the plastic pellets, and improve the consistency of product quality; Step S103, establishing a nonlinear mathematical model of the process parameters of the plastic pellet temperature, the screw speed and the back pressure by using the response surface method, solving the optimal parameter combination of the model by using the particle swarm optimization algorithm, obtaining the optimal temperature control strategy under different working conditions, and realizing the adaptive optimization control of the parameters of the extrusion production process; Step S104, multiple sets of independent temperature control devices are installed in the feeding area, compression area and metering area of ​​the extruder, and a modular heating and cooling system is used. Differentiated temperature control modes are implemented according to the melting state of the plastic pellets in each area. Low-temperature preheating is used for the feeding area to prevent the plastic pellets from melting in advance, gradual heating is used for the compression area to ensure that the plastic pellets are fully melted, and precise constant temperature is used for the metering area to prevent temperature fluctuations of the plastic pellets; Step S105, multiple temperature sensors are set on the extruder screw, and the Kalman filter algorithm is used to perform denoising and fusion processing on the temperature signal to obtain a real-time temperature distribution map of different areas of the plastic pellets. According to the color change trend of the image, it is judged whether the melting state of the plastic pellets is uniform. If the temperature distribution is uneven, the zone compensation heating is started to ensure that the plastic pellets are heated uniformly; Step S106, establishing a correlation model between the quality of plastic pellets and temperature and rheological properties, using a support vector machine algorithm to classify and identify quality defects of extruded products, inferring temperature and rheological property anomalies based on defect types, and forming a corresponding knowledge base of quality defects and process parameter deviations to guide the optimization and improvement of temperature control strategies; Step S107, using an adaptive neural fuzzy inference system, inputs the source process parameters of plastic pellet temperature, melt fluidity, screw speed and back pressure into the fuzzy controller, outputs optimized temperature and speed control instructions through reasoning and decision-making of the fuzzy rule base, realizes multi-parameter collaborative optimization control of the extrusion production process, and improves the accuracy and response speed of plastic pellet temperature control.

2. The method according to claim 1, characterized in that: The step S101 comprises: According to the material properties of high-fluidity PC plastic pellets, the extrusion temperature range and temperature gradient distribution are determined as the target temperature reference for zone temperature control; Use multi-point temperature sensors to arrange temperature collection points in different areas of the extruder screw to obtain the temperature parameters of plastic pellets in each area in real time; According to the melting state characteristics of plastic particles at different temperatures, a mapping relationship model between temperature and melting state parameters is established; Through fuzzy control algorithm, the temperature parameters and melting state parameters of each area are comprehensively analyzed to dynamically adjust the target control temperature of each area; If the temperature of a certain area deviates from the target temperature range, the heating or cooling control strategy of the area is determined based on the degree and duration of deviation through fuzzy rule reasoning; According to the determined zone temperature control strategy, the heating and cooling devices in each zone are controlled to finely adjust the temperature of different zones of the extruder screw; Continuously monitor the changes in the temperature and melting state parameters of the plastic pellets in each area, and continuously optimize and adjust the zone temperature control process through closed-loop feedback control to ensure that the temperature of the plastic pellets is always within the target range, thus achieving stable and controllable continuous extrusion production.

3. The method according to claim 1, characterized in that The step S102 comprises: Acquire near infrared spectral data of plastic pellets during the extrusion process, and transmit the spectral data to the data processing module; The data processing module preprocesses the near-infrared spectrum data, including denoising and normalization, to obtain preprocessed spectrum data; Based on the pre-processed spectral data, the partial least squares regression algorithm is used to establish a quantitative relationship model between the temperature of plastic pellets, melt fluidity and spectral data; Input the real-time collected near-infrared spectrum data of plastic pellets into the established quantitative relationship model to predict the real-time temperature and melt fluidity value of the plastic pellets; Determine whether the predicted plastic pellet temperature and melt fluidity values ​​exceed a preset threshold range, and if so, trigger a temperature adjustment control signal; The temperature regulation module uses the PID control algorithm according to the received control signal to dynamically adjust the heating temperature of the extruder and compensate for the temperature fluctuation of the plastic pellets; Continuous cycle execution, real-time monitoring of plastic pellet temperature and melt fluidity changes, dynamic adjustment of extrusion temperature, to ensure uniform melting of plastic pellets and improve product quality consistency.

4. The method according to claim 1, characterized in that The step S103 comprises: According to the process parameters of plastic pellet temperature, screw speed and back pressure, a nonlinear mathematical model was established using the response surface method to obtain a nonlinear function expression describing the relationship between the plastic pellet temperature and each process parameter. The particle swarm optimization algorithm is used to solve the established nonlinear mathematical model. The optimal parameter combination of the model is obtained through iterative optimization search, and the values ​​of each process parameter that achieves the best temperature control effect under different working conditions are determined; According to the optimal parameter combination obtained by the particle swarm optimization algorithm, it is determined which preset working condition type the current working condition belongs to, and the optimal value of each process parameter is obtained from the corresponding parameter combination as the control target of the extrusion production process; During the extrusion production process, the current values ​​of the plastic pellet temperature, screw speed and back pressure parameters are collected in real time, compared with the target values ​​in the optimal parameter combination, the deviation value is calculated, and the corresponding adjustment instructions are generated; According to the generated adjustment instructions, the screw speed and back pressure actuator of the extruder are controlled, and each process parameter is dynamically adjusted to approach the target value in the optimal parameter combination, so as to achieve precise control of the temperature of the plastic pellets; During the adjustment process, the temperature change of the plastic pellets is continuously monitored to determine whether it reaches the target temperature range in the optimal temperature control strategy. If not, the process returns to repeat the execution and continues to adjust the parameters. When the temperature of the plastic pellets is stable within the target range of the optimal temperature control strategy, the current process parameter settings are maintained, the parameter adaptive optimization control of the extrusion production process is completed, and plastic pellet products with qualified temperatures are continuously produced.

5. The method according to claim 1, characterized in that The step S104 comprises: Obtain real-time temperature data of the feeding zone, compression zone and metering zone of the extruder, and determine whether the temperature of each zone is within a reasonable range based on the preset temperature threshold range; If the temperature of the feeding zone is higher than the preset low-temperature preheating threshold, the cooling system of the feeding zone is controlled to start and reduce the temperature of the feeding zone until the temperature reaches the preset low-temperature preheating threshold range to avoid premature melting of the plastic pellets; If the temperature of the compression zone is lower than the preset gradual temperature rise threshold, the heating system of the compression zone is controlled to start, the temperature of the compression zone is increased, and a gradual temperature rise mode is adopted to ensure that the plastic particles are fully melted in the compression zone; If the temperature in the metering area exceeds the preset precise constant temperature threshold range, the heating system or cooling system in the metering area is controlled to start according to the temperature deviation direction to achieve precise constant temperature control and prevent the temperature fluctuation of the plastic pellets in the metering area; A mapping model between temperature and the melting state of plastic pellets is established through machine learning algorithms. Based on the real-time collected temperature data of each area, the melting state of plastic pellets is predicted and the temperature control strategy of each area is dynamically adjusted. During the production process, the temperature data of each area and the corresponding melting state of the plastic pellets are continuously recorded, and the temperature-melting state mapping model is updated and optimized in real time using the incremental learning algorithm to improve the accuracy of temperature control; According to the optimized temperature-melting state mapping model, the temperature threshold range of the feeding zone, compression zone and metering zone is adaptively adjusted to realize intelligent temperature management of the extruder, stabilize the melting state of the plastic pellets, and improve the extrusion quality.

6. The method according to claim 1, characterized in that The step S105 comprises: Install multiple high-precision temperature sensors in different areas of the extruder screw to collect real-time temperature data of plastic pellets at different positions of the screw; For the collected temperature data, the Kalman filter algorithm is used for denoising to eliminate the high-frequency noise interference in the temperature signal and improve the accuracy of temperature measurement; The temperature data collected by multiple temperature sensors are integrated and processed to comprehensively analyze the temperature distribution of plastic particles in different areas of the screw and generate a real-time temperature distribution map; Perform image analysis on the generated temperature distribution map, extract the color features of the image, and judge whether the melting state of the plastic particles is uniform based on the color change trend; If the color analysis of the temperature distribution graph shows that the melting state of the plastic pellets is uneven, the zone compensation heating control mechanism is triggered to focus on heating the area where the temperature is lower than the preset value; In the process of zone compensation heating, the heating power and heating time are adjusted dynamically, and the heating uniformity of the plastic pellets is achieved through closed-loop control to eliminate the inconsistency of temperature distribution; Continuously monitor the changes in the temperature distribution diagram. When the color distribution tends to be consistent and the temperature distribution is uniform, it is judged that the plastic particles have reached the optimal melting state and the temperature optimization control of the extruder screw is completed.

7. The method according to claim 1, characterized in that The step S106 comprises: Obtain the temperature and rheological properties data of plastic pellets, as well as the quality defect data of extruded products, and establish a correlation model between the quality of plastic pellets and temperature and rheological properties; The support vector machine algorithm is used to classify and identify the quality defects of extruded products, and sample data of different defect types are obtained; According to the quality defect type, combined with the correlation model between the quality of plastic pellets and temperature and rheological properties, the abnormal temperature and rheological properties that cause the defect type are determined; Associating the quality defect types with the corresponding temperature and rheological property anomalies to form a corresponding knowledge base of quality defects and process parameter deviations; When a new quality defect of an extruded product occurs, the defect type is identified through the support vector machine algorithm, and the temperature and rheological property anomalies corresponding to the defect type are searched in the corresponding knowledge base of quality defects and process parameter deviations; Determine the temperature control strategy and process parameters that need to be optimized and adjusted based on the temperature and rheological property anomalies found; The optimized temperature control strategy is applied to the plastic pellet extrusion production process. By real-time monitoring of the temperature and rheological properties of the plastic pellets, the temperature control parameters are dynamically adjusted to ensure the quality stability of the extruded products.

8. The method according to claim 1, characterized in that: The step S107 comprises: Collect the temperature value, melt fluidity, screw speed and back pressure data of plastic pellets under the operation state of the extruder, and build an original database with these data; According to the data in the original database, data preprocessing is performed to remove outliers and obtain a valid data set; By training the adaptive neuro-fuzzy inference system through valid data sets, the rules in the fuzzy rule base are determined to obtain a trained inference engine; Obtain the real-time data of plastic pellet temperature, melt fluidity, screw speed and back pressure of the extruder, and input the real-time data into the trained inference engine; According to the output of the trained inference engine, the preliminary temperature and speed control instructions are combined with the current extruder status to judge the rationality of the preliminary instructions. If they are unreasonable, the inference engine parameters are adjusted; By adjusting the inference engine parameters, the optimized temperature and speed control instructions are obtained, and the optimized instructions are output to the extruder actuator; According to the operating status of the extruder actuator, it is judged whether the operating status of the extruder is consistent with the set value. If it is consistent, the real-time data of the extruder continues to be collected.

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