Temperature gradient control method for melting and plasticizing food packaging PET bottle blank

Abstract

The application relates to the technical field of polymer material processing and molding, and discloses a temperature gradient control method for melting and plasticizing of food packaging PET bottle blanks. The method aims to solve the problems of static temperature control, poor melt homogeneity and weak process adaptability in the traditional plasticizing process. The method comprises the following steps: constructing an axial partition temperature setting model, dividing a solid conveying section, a melting transition section and a melt homogenization section and setting a reference temperature interval; deploying a distributed temperature sensing array to collect temperatures at key positions in real time; establishing a melt thermal state prediction model based on a non-Newtonian fluid heat conduction equation; dynamically regulating and controlling heating and cooling through a PID compound algorithm to realize closed-loop control of axial gradient and radial uniformity; and combining a process database to perform adaptive parameter correction. The application can significantly improve temperature control precision and melt homogeneity, enhance adaptability to raw material changes and high-speed production, effectively reduce bottle blank internal stress, wall thickness deviation and acetaldehyde content, and guarantee product quality and food safety.

Description

Technical Field

[0001] This invention relates to the field of polymer material processing and molding technology, specifically to a method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging. Background Technology

[0002] With the increasing demand for lightweight, high transparency, and good barrier properties in the food packaging industry, the melt plasticizing process of polyethylene terephthalate (PET) preforms before blow molding has become a crucial step determining the quality of the final product. PET material exhibits significant thermosensitivity and non-Newtonian fluid characteristics during heating; its melt uniformity, molecular orientation control, and thermal history directly affect the mechanical strength, optical properties, and food safety of the bottle. Therefore, achieving precise control of the melt temperature field during the plasticizing stage has become a core technical challenge for improving the molding quality of PET preforms.

[0003] Temperature gradient control, as a key parameter in the melt plasticizing process, directly affects the plasticizing efficiency and thermal stability of the material within the screw extrusion system. An ideal temperature distribution should form a reasonably increasing or zoned steady-state gradient along the material conveying direction to accommodate the needs of multiple stages of physical changes, including solid bed breakup, melt film formation, and melt homogenization. However, existing plasticizing equipment often employs a segmented constant-temperature heating strategy, with each temperature zone independently controlled and lacking a dynamic coupling mechanism. This makes it difficult to adapt to differences in thermal response caused by variations in the crystallinity, moisture content, and screw speed of different batches of raw materials.

[0004] In existing technologies, temperature settings typically rely on empirical formulas or static process curves, failing to capture the actual temperature distribution within the melt in real time. This leads to frequent occurrences of localized overheating and degradation or uneven plasticization. Furthermore, the lag in the response of heating and cooling systems makes it difficult to maintain stable axial and radial temperature gradients under high-speed production conditions. In addition, the lack of closed-loop feedback on the thermal state of the melt flow front causes a disconnect between the temperature field and shear history, affecting the consistency of molecular chain orientation and ultimately resulting in large deviations in preform wall thickness, decreased transparency, and an increased risk of bottle explosion. Therefore, a control method is needed that can dynamically construct and precisely maintain the optimal temperature gradient during the melting and plasticizing process of PET preforms. Summary of the Invention

[0005] The purpose of this invention is to provide a method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging includes the following specific steps: Step 1: Constructing an axial zone temperature setting model: Based on the crystallinity, moisture content, and target preform specifications of the PET raw material, combined with the screw geometry and rotational speed range, a multi-temperature zone dynamic temperature setting model distributed along the screw axis is established. This model divides the plasticizing process into a solid conveying section, a melting transition section, and a melt homogenization section, and assigns a corresponding reference temperature range to each section; Step 2: Deploying a distributed temperature sensing array: High-response-rate thermocouple sensor arrays are embedded in key locations on the outer wall of the screw barrel and in the internal melt channel to collect surface temperature and melt front temperature data of each temperature zone in real time. The sampling frequency is not lower than a preset frequency, and the data is transmitted to the central controller through an anti-interference signal conditioning circuit; Step 3: Establishing a melt thermal state prediction model: Based on the real-time collected data... Temperature data, screw speed, back pressure, and material residence time are used to identify the axial and radial temperature distribution inside the melt online using the recursive least squares method. The non-Newtonian fluid heat conduction equation is then combined to predict the melt thermal state evolution trend for the next cycle. Step 4 involves dynamic temperature gradient control: based on the deviation between the predicted melt thermal state and the preset temperature gradient target curve, a proportional-integral-derivative composite control algorithm is used to synchronously adjust the power output of each heating zone and the opening of the cooling air valves, achieving closed-loop dynamic control of the axial temperature gradient slope and radial temperature uniformity. Step 5 involves adaptive correction of process parameters: when a change in raw material batch or adjustment of production speed is detected, the optimal temperature gradient template under similar conditions in the historical process database is automatically called, and online fine-tuning is performed based on the current melt thermal response characteristics to ensure that the temperature field always matches the material plasticization kinetics requirements.

[0008] Preferably, in step 1, the reference temperature range of the solid conveying section is set to a first preset temperature range, the melting transition section is set to a second preset temperature range, the melt homogenization section is set to a third preset temperature range, and a smooth transition zone is set between adjacent temperature zones to avoid stress concentration caused by sudden temperature changes.

[0009] Preferably, in step 2, the thermocouple sensor array is arranged with no less than a predetermined number of measuring points along the screw axis, wherein multiple radially symmetrical measuring points are densely arranged at the melting initiation position and the melt outlet position, respectively, to monitor the temperature distribution difference between the initial stage of melt film formation and the end of melt homogenization. The sensor response time is less than a preset response threshold, and the measurement accuracy meets the preset accuracy requirements.

[0010] Preferably, in step 3, the non-Newtonian fluid heat conduction equation considers the shear-thinning characteristics of the PET melt, its apparent viscosity is dynamically adjusted with the shear rate, the heat conduction coefficient is corrected online based on the measured melt temperature and pressure, the prediction model update cycle is a preset time period, and the prediction error is controlled within a preset error range.

[0011] Preferably, in step 4, the integral time constant of the proportional-integral-derivative composite control algorithm is set to a preset time interval, the derivative gain coefficient is dynamically adjusted according to the screw speed, and when the speed is greater than or equal to the preset speed threshold, the derivative action is enhanced to suppress temperature overshoot, the cooling air valve is controlled by pulse width modulation, and the minimum adjustment resolution reaches the predetermined resolution.

[0012] Preferably, in step 5, the historical process database stores no less than a predetermined number of temperature gradient templates corresponding to different raw material grades, moisture content ranges, and preform specifications. Each template includes an axial temperature distribution curve, screw speed matching relationship, and cooling intensity parameters. The system uses an Euclidean distance algorithm to match the similarity between the current operating condition and the historical templates, and the similarity threshold is set to a preset similarity threshold.

[0013] Preferably, it also includes a melt temperature uniformity assessment module: calculates the standard deviation of melt cross-section temperature based on the temperature data of each radial measuring point, and automatically triggers a local heating compensation mechanism when the standard deviation is greater than the preset temperature uniformity threshold, increasing the heating power by a predetermined proportion in the corresponding azimuth angle region until the standard deviation drops below the preset uniformity target value.

[0014] Preferably, it also includes a temperature gradient stability monitoring unit: continuously recording the rate of change of the axial temperature gradient slope; if the slope fluctuation exceeds the preset slope fluctuation threshold within the preset monitoring time period, it is determined that the temperature field is unstable, and an emergency cooling program is immediately started and the screw speed is reduced by a predetermined proportion, while issuing a process abnormality warning signal.

[0015] Preferably, the method is integrated into a fully automated PET preform injection molding production line, achieving data interconnection with the drying system, metering device, and mold temperature control unit. The overall machine temperature control response delay is less than a preset delay threshold, and the single-cycle temperature control accuracy reaches the preset control accuracy, making it suitable for high-speed production scenarios.

[0016] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0017] 1. Achieve high-precision dynamic temperature gradient control, breaking through the static limitations of traditional segmented constant temperature control: By constructing a collaborative mechanism of axial partition temperature setting model and melt thermal state prediction model, dynamic reconstruction and precise tracking of the temperature field throughout the PET melting and plasticizing process are realized. The axial temperature gradient control error is less than the preset gradient error threshold, which is significantly better than the fluctuation level in the existing technology. 2. Improve melt homogeneity: The combination of distributed temperature sensing array and radial temperature uniformity assessment module effectively suppresses the temperature difference of melt cross section, making the melt temperature standard deviation stable within the preset uniformity target value, and solving the problem of reduced preform transparency and discrete mechanical properties caused by uneven plasticization.

[0018] 2. Enhanced process robustness and adaptability to varying raw material conditions: The adaptive correction mechanism based on the historical process database can quickly match the thermal response characteristics of raw materials with different crystallinity and moisture content, maintaining the optimal temperature gradient without manual readjustment, and significantly shortening the material changeover and debugging time; ensuring high-speed production stability: The combination of proportional-integral-derivative composite control algorithm and cooling air valve pulse width modulation technology greatly improves the dynamic response speed of the system, maintaining a stable temperature field even when the screw speed is high, meeting the stringent requirements of modern high-speed production lines for process consistency.

[0019] 3. Improve product quality and production safety, and optimize molecular orientation consistency: Closed-loop control tightly couples the temperature field with the shear history, ensuring that the PET molecular chain obtains a uniform thermo-mechanical history during the plasticization stage. The final preform wall thickness deviation is controlled within the preset dimensional tolerance range, significantly reducing the bottle explosion rate. Prevent thermal degradation risks: The temperature gradient stability monitoring unit can identify signs of temperature runaway in advance and activate protective measures, effectively avoiding excessive acetaldehyde generation caused by local overheating, ensuring the safety and compliance of food contact materials, and stably controlling the acetaldehyde content below the preset safety limit. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical solution architecture of the method of the present invention;

[0021] Figure 2 This is a schematic diagram of the core principle framework of the collaborative mechanism for melt thermal state prediction and dynamic temperature gradient control in this invention.

[0022] Figure 3 This is the logical flow of the axial partition temperature setting model and the distributed temperature sensing array working together in this invention;

[0023] Figure 4 This is a schematic diagram of the multi-level interactive process of adaptive correction of process parameters and monitoring of temperature field stability in this invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1

[0026] In the above-mentioned temperature gradient control method for melting and plasticizing PET preforms for food packaging, step 1, which constructs an axial zone temperature setting model, specifically includes the following operational procedures: First, acquire the crystallinity parameters and moisture content data of the current batch of PET raw materials. The crystallinity is determined by differential scanning calorimetry (DSC), with a typical range of 35% to 45%. The moisture content is detected in real time by an online infrared moisture analyzer, with an accuracy of no less than ±0.005% and a value range controlled below 0.02%. Second, read the target preform specification parameters, including preform quality, wall thickness distribution requirements, and final blown bottle volume. These parameters are automatically sent to the central controller by the production order management system. Simultaneously, collect the screw geometry parameters, covering screw diameter, length-to-diameter ratio (L / D), compression ratio, length ratio of each functional section, and the currently set screw speed range, which is typically set between 50 rpm and 180 rpm. Based on the aforementioned multi-source input data, the system invokes its built-in axial partitioning modeling engine to divide the entire plasticizing process along the screw axis into three continuous functional segments: a solid conveying segment, a melt transition segment, and a melt homogenization segment. The solid conveying segment begins at the feed inlet and ends at the critical point where the material begins to soften significantly; its reference temperature range is set to the first preset temperature range, i.e., 160℃ to 180℃. The melt transition segment covers the area from partial melting to complete melting of the material; its reference temperature range is set to the second preset temperature range, i.e., 240℃ to 260℃. The melt homogenization segment, located from the end of the metering segment to the nozzle inlet, ensures uniform melt temperature and composition; its reference temperature range is set to the third preset temperature range, i.e., 270℃ to 285℃. At the boundary between adjacent temperature zones, the system automatically generates a smooth transition zone with a width of 3% to 5% of the screw axial length. A cubic spline interpolation function is used to achieve continuous differentiability of the temperature curve, avoiding stress concentration in the polymer caused by abrupt temperature changes. The dynamic temperature setting model is stored in the non-volatile memory of the central controller in the form of a discrete point sequence. Each axial position corresponds to a target temperature value. The update cycle is synchronized with the production cycle to ensure that the model always matches the current process conditions.

[0027] In the above method, step 2, deploying a distributed temperature sensing array, is specifically implemented as follows: At least 12 high-response-rate K-type thermocouple sensors are uniformly embedded axially along the outer wall of the screw cylinder. The sensors are encapsulated in a stainless steel armor structure with an outer diameter of 1.5 mm. The installation depth penetrates the cylinder wall thickness and is tightly attached to the inner lining surface to minimize heat conduction delay. Four sets of radially symmetrical measuring points are densely arranged at the melting initiation position (corresponding to the end of the solid conveying section) and the melt outlet position (corresponding to the end of the melt homogenization section). Each set contains three miniature thermocouples distributed in a 120° circle to accurately capture the asymmetric temperature field in the early stage of melt film formation and the cross-sectional temperature difference at the end of melt homogenization. The response time of all sensors is less than 50 ms, and the measurement accuracy meets the requirement of ±0.5℃. The temperature signal is transmitted via shielded twisted-pair cable to an anti-interference signal conditioning circuit. This circuit integrates a low-pass filter (cutoff frequency of 100 Hz), a common-mode rejection module (CMRR ≥ 100 dB), and a 16-bit analog-to-digital converter (ADC), with a sampling frequency of no less than 20 Hz to ensure that high-frequency temperature fluctuations are not missed. The conditioned digital signal is uploaded to the central controller in real time via industrial Ethernet (EtherCAT protocol), with a transmission delay controlled within 1 ms. The sensor array data structure adopts a three-dimensional tensor format of timestamp-position-temperature, where the axial position coordinates are based on the screw feed end as the origin and the unit is millimeters; the radial azimuth angle is based on 0° and increases clockwise. The system performs a sensor health self-check every 100 ms, including open circuit, short circuit, and drift anomaly diagnosis. Once a fault is detected, a redundant channel switching mechanism is immediately triggered, and backup measurement point data is used for compensation.

[0028] In the above method, step 3 establishes a melt thermal state prediction model, the core of which lies in integrating real-time sensor data with fluid dynamics equations for online state estimation. Specifically, the central controller receives temperature data from step 2, screw speed signals from the drive system (resolution 0.1 rpm), back pressure values ​​from the pressure sensor (range 0 to 200 bar, accuracy ±0.5 bar), and material residence time calculated from the material volumetric flow rate and the effective screw volume. Based on the above inputs, the system uses Recursive Least Squares (RLS) to identify the axial and radial temperature distribution inside the melt online. The state vector of the RLS algorithm includes the center temperature of each axial partition and the radial temperature gradient coefficient. The forgetting factor λ is set to 0.98 to balance the weight of historical data and the influence of new observations. The identification results are used as initial conditions and substituted into the non-Newtonian fluid heat conduction equation considering the shear thinning characteristics of PET melt to predict the thermal state evolution of the next cycle. The equation is expressed as follows:

[0029]

[0030] in, The melt density is (kg / m³). Specific heat capacity (J / ), Temperature (K) For time (s), This is the velocity vector (m / s). Thermal conductivity ( Its value is corrected online based on the measured melt temperature and pressure using a lookup table method. For viscous heat dissipation, Shear rate Apparent viscosity Described using a Cross model:

[0031]

[0032] in It has zero shear viscosity and decreases exponentially with increasing temperature. For infinite shear viscosity, For relaxation time, The power-law exponent is used. The prediction model iterates with an update cycle of 50ms, and is solved discretized on an axial-radial two-dimensional grid using the finite volume method. The number of grid cells is 20 axial cells × 8 radial cells. The prediction error is corrected by comparing it with the latest measured temperature data to ensure that the root mean square error between the predicted and measured values ​​is controlled within ±1.5℃.

[0033] In the above method, step 4 performs dynamic temperature gradient control, specifically implemented as follows: the central controller compares the melt thermal state predicted in step 3 with the preset temperature gradient target curve generated in step 1 point by point, calculating the temperature deviation sequence at each axial position. Based on this deviation sequence, the system activates a proportional-integral-derivative (PID) composite control algorithm, outputting power commands for each heating zone and opening commands for the cooling air valves. The integral time constant of the PID algorithm is set to the range of 15 s to 30 s, and the derivative gain coefficient is... The adjustment is dynamically based on the real-time screw speed, and the adjustment rule is as follows: when the screw speed... At rpm, Increased to 1.5 times the base value to enhance the system's ability to suppress temperature overshoot under high-speed operating conditions; when hour, Maintain baseline values. Each heating zone uses a solid-state relay (SSR) to drive the resistance heating coil, with a power adjustment resolution of 1 W. The cooling system is configured with multiple independent air ducts, each equipped with a stepper motor-driven damper. Pulse width modulation (PWM) is used for opening control, with a PWM carrier frequency of 10 Hz and a minimum adjustment resolution of 0.5% duty cycle. Control commands are synchronously sent to all execution units to ensure that the axial temperature gradient slope (defined as the ratio of the temperature difference between adjacent temperature zones to the axial distance) remains stable within the target value of ±0.8℃ / cm. Simultaneously, a radially symmetrical heating strategy maintains the uniformity of the melt cross-section temperature. The control process completes a closed-loop cycle every 200 ms, with an overall temperature control response delay of less than 300 ms.

[0034] In the above method, step 5 implements adaptive correction of process parameters, and its execution logic is as follows: The system continuously monitors the raw material batch identification code and the production line speed setpoint. When a change in raw material batch is detected (confirmed by RFID tag or barcode scanning) or the screw speed adjustment exceeds ±10 rpm, the adaptive correction process is immediately triggered. First, the central controller accesses the historical process database, which stores no less than 500 sets of temperature gradient templates corresponding to different raw material grades (such as EastmanTritan™, Toray PET, etc.), moisture content ranges (0.005% to 0.02%), and preform specifications (mass 15 g to 45 g). Each template contains a complete axial temperature distribution curve (sampling point interval 5 mm), a recommended screw speed matching table, and a cooling intensity parameter set (including the basic opening degree of each air valve and PWM frequency). The system extracts the feature vector of the current operating condition. ,in For raw material grade codes, To measure the moisture content, To achieve the target preform quality, This includes the current screw speed, etc. Then, this vector is compared with all template feature vectors in the database. Euclidean distance between Select the one with the smallest distance and Templates with a similarity threshold less than the preset threshold (set to 0.8) are used as initial references. If no template meets the criteria, a new template is synthesized using the nearest neighbor interpolation method. After selecting a template, the system performs online fine-tuning based on the current melt thermal response characteristics (output from the prediction model in step 3): a Gaussian perturbation is applied to a local region of the temperature curve, with the perturbation amplitude proportional to the predicted temperature deviation and the standard deviation a function of the axial position. The fine-tuned temperature setpoint is immediately loaded into the model in step 1 to ensure that the temperature field dynamically matches the material plasticizing kinetics requirements. The entire correction process is completed within 2 seconds without affecting continuous production.

[0035] Furthermore, the above method also includes a melt temperature uniformity assessment module. This module receives temperature data from each radially symmetrical measuring point in step 2 in real time and constructs a temperature distribution matrix at the end cross-section of the melt homogenization section. For each sampling time, the standard deviation of the temperature at that cross-section is calculated. The formula is Where $N$ is the number of radial measurement points, For the first Temperature at each measuring point The average temperature of the cross section. When When the temperature uniformity exceeds the preset threshold (set to 2.0℃), the system automatically triggers a local heating compensation mechanism. The compensation strategy is as follows: identify the azimuth region with the lowest temperature (in 120° sectors), and increase the heating power of the corresponding heating coil in that region by 10% to 15% for 500 ms. The system is then re-evaluated after compensation. If the level still exceeds the limit, the compensation process will be repeated until... The temperature drops below the preset uniformity target value (1.2℃). This mechanism triggers a maximum of 3 times per second to prevent overcompensation from causing temperature oscillations.

[0036] Furthermore, the above method also includes a temperature gradient stability monitoring unit. This unit continuously records the slope of the axial temperature gradient. rate of change ,in The axis is used as the coordinate. The system calculates the slope sequence at 100 ms intervals and uses a sliding window (window length of 5 s) to statistically analyze the slope fluctuation amplitude, defined as the difference between the maximum and minimum slope within the window. If the fluctuation amplitude exceeds the preset slope fluctuation threshold (set to 1.5℃ / cm) within the preset monitoring period (set to 10 s), it is determined to be a temperature field instability. At this time, the system immediately initiates a three-level emergency response: Level 1, opening all cooling ducts to 80% of their maximum opening for 3 s; Level 2, sending a command to the screw drive system to reduce the speed by 20%; Level 3, issuing a process abnormality warning signal through the human-machine interface and recording the event log for subsequent analysis. After the emergency response ends, the system enters recovery mode, gradually restoring parameters to the normal range while increasing the correction frequency of the prediction model.

[0037] The above method is integrated into a fully automated PET preform injection molding production line, achieving data interconnection with the upstream drying system, metering device, and downstream mold temperature control unit via the OPC UA protocol. The drying system provides real-time data on raw material moisture content, the metering device provides feedback on the actual injection volume, and the mold temperature control unit shares cooling water temperature information. The overall temperature control response delay is less than 300 ms, and the single-cycle temperature control accuracy reaches ±1.0℃, suitable for high-speed production scenarios with screw speeds up to 180 rpm. In a typical application, 500 ml mineral water preforms (28 g in weight) are produced using PET chips with 40% crystallinity and 0.012% moisture content. The system first loads the corresponding historical template, initially setting the solid conveying section temperature to 170℃, the melt transition section to 250℃, and the melt homogenization section to 278℃. During operation, the distributed sensor array detects a radial temperature difference of 2.8℃ at the melt initiation position, triggering local heating compensation, increasing power by 12% in the 240° azimuth region, and reducing the temperature difference to 1.0℃ after 1.2 seconds. Meanwhile, when the screw speed increases from 100 rpm to 150 rpm, the adaptive correction module automatically calls the high-speed operating mode template, fine-tunes the melt homogenization section temperature to 280℃, and enhances the differential control effect. Throughout the entire 8-hour continuous production run, the axial temperature gradient slope remained stable at 2.1±0.7℃ / cm, the average standard deviation of the melt cross-section temperature was 1.1℃, the final preform wall thickness deviation was controlled within ±0.05 mm, and the acetaldehyde content was measured at 2.8 ppb, below the safety limit of 3.0 ppb.

[0038] Example 2

[0039] In another specific implementation, the melt thermal state prediction model in step 3 uses an extended Kalman filter (EKF) instead of the recursive least squares method for state estimation. The EKF state vector includes axial temperature distribution, radial temperature gradient, and viscous heat dissipation term. The process noise covariance matrix is ​​dynamically adjusted according to the screw speed, and the observation noise covariance is determined by the sensor accuracy parameters. The prediction equation still uses the aforementioned non-Newtonian fluid heat conduction model, but the discretization method is changed to the Crank-Nicolson scheme to improve numerical stability. This scheme shows stronger robustness under conditions with large fluctuations in raw material moisture content (>0.015%), and the prediction error can be controlled within ±1.2℃. Correspondingly, the PID composite control algorithm in step 4 introduces a feedforward compensation term. The feedforward signal is directly generated by the viscous heat dissipation predicted by the EKF and is used to offset the temperature disturbance caused by shear heat generation. The cooling air valve control strategy is also adjusted, adopting a fuzzy PID controller. The input variables are temperature deviation and its rate of change, and the output is the PWM duty cycle increment. The rule base contains 9 fuzzy rules, and the membership function adopts a Gaussian type. This embodiment is applicable to the production of high-end drinking water preforms that are extremely sensitive to acetaldehyde formation, and the acetaldehyde content can be stably controlled below 2.5 ppb.

[0040] Example 3

[0041] In another specific embodiment, the distributed temperature sensing array in step 2 uses fiber Bragg grating (FBG) sensors instead of thermocouples. The FBG sensors are spirally arranged along the inner wall of the screw cylinder with an axial spacing of 20 mm. Each turn contains 6 radially uniformly distributed grating points, for a total of 72 measurement points. A broadband ASE light source is used, with a wavelength range of 1520 nm to 1570 nm. The demodulator sampling frequency is 50 Hz, the temperature resolution is 0.1℃, and the response time is 20 ms. Because FBGs inherently possess electromagnetic interference resistance, the signal conditioning circuit is simplified to an optical isolation and amplification module, eliminating the need for complex filtering. The prediction model in step 3 correspondingly adds grating strain data as auxiliary input to invert the melt pressure distribution, thereby correcting the thermal conductivity coefficient $k(T, P)$. The historical process database in step 5 is expanded to include FBG feature data templates, and the similarity matching algorithm is upgraded to a weighted Euclidean distance algorithm, assigning different weights to temperature and strain features. This embodiment is particularly suitable for high-speed production lines (speed > 160 rpm) in environments with strong electromagnetic interference, improving the temperature field reconstruction accuracy by 15% and the axial gradient control error by less than ±0.6℃ / cm.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the temperature gradient during the melt plasticizing of PET preforms for food packaging, characterized in that, Includes the following steps: An axial partition temperature setting model was constructed. Based on the crystallinity, moisture content and target preform specifications of PET raw materials, combined with screw geometry and speed range, the plasticizing process was divided into a solid conveying section, a melt transition section and a melt homogenization section, and a corresponding reference temperature range was assigned to each section. Deploy a distributed temperature sensing array, embedding a high-response-rate thermocouple sensor array at key locations on the outer wall of the screw barrel and the internal melt channel, to collect surface temperature and melt front temperature data of each temperature zone in real time, with a sampling frequency not lower than the preset frequency, and transmit the data to the central controller through an anti-interference signal conditioning circuit. A melt thermal state prediction model was established. Based on real-time collected temperature data, screw speed, back pressure value and material residence time, the recursive least squares method was used to identify the axial and radial temperature distribution inside the melt online, and the non-Newtonian fluid heat conduction equation was combined to predict the melt thermal state evolution trend in the next cycle. Dynamic temperature gradient control is implemented. Based on the deviation between the predicted melt thermal state and the preset temperature gradient target curve, the power output of each heating zone and the opening of the cooling air valve are adjusted synchronously through a proportional-integral-derivative composite control algorithm to achieve closed-loop dynamic control of the axial temperature gradient slope and radial temperature uniformity. The system implements adaptive correction of process parameters. When a change in raw material batch or adjustment of production speed is detected, it automatically calls the optimal temperature gradient template under similar conditions in the historical process database and performs online fine-tuning based on the current melt thermal response characteristics to ensure that the temperature field always matches the material plasticization kinetics requirements.

2. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, The reference temperature range of the solid conveying section is 160℃ to 180℃, the reference temperature range of the melting transition section is 240℃ to 260℃, and the reference temperature range of the melt homogenization section is 270℃ to 285℃. A smooth transition zone with a width of 3% to 5% of the screw axial length is set between adjacent temperature zones. The temperature curve is continuously differentiable by using a cubic spline interpolation function.

3. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, The thermocouple sensor array has no fewer than 12 measuring points arranged along the screw axis. Multiple sets of radially symmetrical measuring points are densely arranged at the melting initiation position and the melt outlet position. Each set contains 3 miniature thermocouples distributed in a 120° circle. The sensor response time is less than 50 milliseconds, and the measurement accuracy meets ±0.5℃. The temperature signal is transmitted via shielded twisted pair to the signal conditioning circuit of the integrated low-pass filter, common-mode rejection module and 16-bit analog-to-digital converter.

4. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, The non-Newtonian fluid heat conduction equation includes a viscous heat dissipation term. The apparent viscosity is described by the Cross model. The heat transfer coefficient is corrected online by looking up a table based on the measured melt temperature and pressure. The prediction model is discretized and solved on an axial-radial two-dimensional grid with an update cycle of 50 milliseconds. The number of grid cells is 20 axial cells × 8 radial cells.

5. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, The integral time constant of the proportional-integral-derivative composite control algorithm is set to 15 to 30 seconds. The derivative gain coefficient is dynamically adjusted according to the screw speed. When the screw speed is greater than or equal to 120 revolutions per minute, the derivative gain coefficient is increased to 1.5 times the base value. The cooling air valve is controlled by pulse width modulation with a carrier frequency of 10 Hz and a minimum adjustment resolution of 0.5% duty cycle.

6. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, The historical process database stores no less than 500 sets of temperature gradient templates corresponding to different raw material grades, moisture content ranges and preform specifications. Each set of templates includes axial temperature distribution curves, screw speed matching relationships and cooling intensity parameters. The system performs similarity matching by calculating the Euclidean distance between the current operating condition feature vector and the template feature vector, with the similarity threshold set to 0.

8.

7. The method for controlling the temperature gradient during the melt plasticizing of PET preforms for food packaging according to claim 1, characterized in that, It also includes a melt temperature uniformity assessment step: calculate the standard deviation of the cross-sectional temperature based on the temperature data of each radial measuring point at the end of the melt homogenization section. When the standard deviation is greater than 2.0℃, identify the 120° azimuth angle region with the lowest temperature, increase the heating power of the corresponding heating coil by 10% to 15%, and continue for 500 milliseconds until the standard deviation drops below 1.2℃.

8. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, It also includes a temperature gradient stability monitoring step: continuously record the rate of change of the axial temperature gradient slope, use a 5-second sliding window to count the slope fluctuation amplitude, and if the fluctuation amplitude exceeds 1.5℃ / cm within a 10-second monitoring period, then open all cooling air ducts to 80% of their maximum opening, reduce the screw speed by 20%, and issue a process abnormality warning signal.

9. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, The melt thermal state prediction model uses extended Kalman filtering for state estimation. The state vector includes axial temperature distribution, radial temperature gradient, and viscous heat dissipation term. The process noise covariance matrix is ​​dynamically adjusted according to the screw speed. The prediction equation is discretized using the Crank-Nicolson scheme. The proportional-integral-derivative composite control algorithm introduces a feedforward compensation term generated by viscous heat dissipation.

10. The method for controlling the temperature gradient during the melting and plasticizing of PET preforms for food packaging according to claim 1, characterized in that, The distributed temperature sensing array uses fiber Bragg grating sensors spirally laid along the inner wall of the screw cylinder with an axial spacing of 20 mm. Each turn contains 6 radially uniformly distributed grating points. The demodulator has a sampling frequency of 50 Hz, a temperature resolution of 0.1℃, and a response time of 20 milliseconds. The prediction model adds grating strain data as an auxiliary input for inverting the melt pressure distribution.

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