Multi-area intelligent temperature regulation and control system of PET extruder

Abstract

The invention relates to the technical field of industrial control systems, in particular to a PET extruder multi-zone intelligent temperature regulation and control system, which comprises a temperature field parameter acquisition module for acquiring the input power, the real-time temperature value, the temperature change rate value and the temperature difference value between adjacent temperature zones of an extruder to obtain a temperature zone associated state set; and summarizing and mapping the temperature zone association state set, and establishing temperature field response characteristics. According to the method, through correlation analysis of the temperature states of the multiple areas, independent adjustment of a single area is upgraded to dynamic optimization based on the thermodynamic coupling relation, and the coordination of temperature adjustment of the multiple areas is improved. Dynamic parameters required by temperature regulation and control can be calculated in real time in combination with the heat transfer rate, the temperature response delay time and the temperature response gain coefficient of the temperature interval instead of depending on a fixed set value, different operation working conditions are adapted, and the regulation and control flexibility is improved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial control systems, and in particular to a multi-zone intelligent temperature control system for a PET extruder. Background Art

[0002] Industrial control systems are automation systems used to monitor, control and optimize industrial production processes, covering multiple subsystems such as programmable logic controllers (PLCs), distributed control systems (DCSs), industrial computer control systems and embedded control systems. This technology field is widely used in manufacturing, energy, chemical, metallurgy, automation equipment and other industries, aiming to improve production efficiency, optimize energy consumption and enhance product quality; the multi-zone intelligent temperature control system for PET extruders is an intelligent temperature management solution based on industrial control systems.

[0003] The existing PET extruder temperature control system mainly adopts the method of independent control of each temperature zone, ignoring the thermal coupling relationship between temperature zones, resulting in the inability to correct the mutual influence between zones in the temperature adjustment process in real time, which easily leads to the accumulation of local temperature deviations and affects the overall stability. At the same time, temperature prediction mostly relies on static rules or historical data extrapolation, and fails to comprehensively consider the real-time heat transfer effect, resulting in the difficulty of accurately reflecting the temperature trend in the prediction results, and the adjustment strategy has a lag. Therefore, improvements are needed. Summary of the invention

[0004] The purpose of the invention is to solve the shortcomings in the prior art and to propose a multi-zone intelligent temperature control system for a PET extruder.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: The multi-zone intelligent temperature control system of PET extruder comprises:

[0006] The temperature field parameter acquisition module collects the input power, real-time temperature value, temperature change rate value and temperature difference between adjacent temperature zones of the heating element of each temperature zone of the extruder to obtain a temperature zone associated state set; summarizes and maps the temperature zone associated state set to establish a temperature field response feature;

[0007] The twin model construction module calls the temperature field response characteristics, calculates the heat transfer rate, temperature response delay time and temperature response gain coefficient between each temperature zone, and obtains the temperature zone coupling parameter group; linearly superimposes the temperature zone coupling parameter group with the real-time temperature value of each zone and the input power of the heating element to establish a temperature zone mapping relationship;

[0008] The temperature trend prediction module calls the temperature zone mapping relationship, substitutes the temperature and power input values ​​of each temperature zone into the flow field equation, calculates the temperature gradient field distribution, and obtains the temperature trend sequence; calculates the temperature deviation and adjustment amount according to the temperature trend sequence and the temperature target value of each zone, and establishes the control optimization parameters;

[0009] The control sequence generation module calls the control optimization parameters, compares the optimized power value with the real-time temperature value of the temperature zone, obtains the power compensation amount, performs correction operation on the power compensation amount and the target temperature value of each zone, and establishes a control instruction sequence.

[0010] Preferably, the steps of acquiring the temperature zone association state set are:

[0011] The input power of the heating element in each temperature zone of the extruder is monitored in real time, and the real-time temperature value and temperature change rate value of each temperature zone are measured at the same time, and the temperature difference between adjacent temperature zones is measured to obtain the real-time monitoring data set of each temperature zone;

[0012] Based on the real-time monitoring data set of each temperature zone, the relationship between the input power of the heating element, the real-time temperature value, the temperature change rate and the temperature difference is analyzed, the mutual influence between the temperature zones is analyzed, and the data association mapping between the temperature zones is generated;

[0013] According to the temperature zone data association mapping, the relationship characteristics between the temperature zones are analyzed, and the relationship characteristics are integrated and constructed into a state set to obtain a temperature zone association state set.

[0014] Preferably, the steps of acquiring the temperature field response characteristics are:

[0015] Based on the temperature zone association state set, multivariate nonlinear regression analysis is performed to calculate the association index, and the formula is:

[0016]

[0017] Among them, P i represents the input power in the ith temperature zone, T i Represents the real-time temperature value of the i-th temperature zone, ΔT i represents the temperature difference between the ith temperature zone and the adjacent temperature zone, γ i represents the regulation index of the ith temperature zone, c i represents the correction coefficient of the ith temperature zone, R represents the correlation index, and n represents the total number of temperature zones;

[0018] According to the correlation index, the heat transfer characteristics of each temperature zone are identified and the temperature field response characteristics are constructed.

[0019] Preferably, the steps of obtaining the temperature zone coupling parameter group are:

[0020] Based on the temperature field response characteristics, the heat transfer rate V of each temperature zone is calculated. i , Temperature response delay time D i and the temperature response gain coefficient G i , the calculation formula is:

[0021]

[0022] and

[0023]

[0024] and

[0025]

[0026] Among them, R i Represents the correlation index of the i-th temperature zone, T base Represents the reference temperature value, T current Represents the current temperature value;

[0027] The heat transfer rate, the temperature response delay time and the temperature response gain coefficient are combined to form a coupling parameter group for each temperature zone, thereby obtaining a temperature zone coupling parameter group.

[0028] Preferably, the steps of obtaining the temperature zone mapping relationship are:

[0029] Based on the temperature zone coupling parameter group, the comprehensive mapping value in the temperature zone is calculated, and the expression is:

[0030]

[0031] Among them, M i is the comprehensive mapping value of the ith temperature zone, Q i is the input power of the heating element in the i-th temperature zone, G i is the temperature response gain coefficient of the ith temperature zone, T i is the real-time temperature value of the ith temperature zone, D i is the temperature response delay time of the ith temperature zone, H i is the coupling influence factor of the i-th temperature zone;

[0032] According to the comprehensive mapping value, combined with the spatial distribution and heat transfer relationship of each temperature zone, a temperature zone mapping relationship is established.

[0033] Preferably, the steps of acquiring the temperature trend sequence are:

[0034] The temperature zone mapping relationship is called, variables are initialized based on the flow field calculation model, and the initial temperature distribution and boundary conditions of each temperature zone are set to the model calculation domain to generate a flow field calculation input set;

[0035] Based on the flow field calculation input set, the temperature field calculation is performed, the temperature value of each temperature zone and the input power of the heating element are mapped to the boundary conditions of the flow field equation, the numerical iterative calculation is performed, the temperature gradient field distribution is established, and the iterative error is adjusted by calculating the dynamic characteristics of heat transfer between each temperature zone to obtain the temperature gradient field distribution;

[0036] Based on the temperature gradient field distribution, the time series characteristics of each temperature zone in the temperature field are extracted, the temperature change rate at each time step is calculated, and the heat transfer path between the temperature zones is analyzed. Combined with the temperature change pattern in the time dimension, the temperature trend sequence is obtained.

[0037] Preferably, the step of obtaining the control optimization parameters is:

[0038] Calling the temperature trend sequence, and obtaining the temperature target value of each zone, parsing the temperature trend based on time series analysis, extracting the temperature change rate at each time step, and calculating the real-time deviation of the current temperature state relative to the target temperature, and generating a temperature deviation data set;

[0039] Based on the temperature deviation data set, the deviation change trend of each temperature zone is analyzed, and the required adjustment amount is calculated in combination with the heat transfer characteristics between the temperature zones, and the adjustment is performed according to the temperature change rate and the heat transfer response time between the temperature zones to obtain the adjustment amount parameter set;

[0040] Based on the adjustment parameter set, control optimization parameters are established, and the temperature adjustment requirements of each temperature zone are converted into control strategy input to form control optimization parameters.

[0041] Preferably, the steps of acquiring the control instruction sequence are:

[0042] Based on the control optimization parameters, the optimized power value is obtained, and the real-time temperature value of each temperature zone is extracted. Based on the relationship between the current temperature state and the target power input, the difference between the optimized power value and the real-time temperature value of the temperature zone is calculated to generate a power comparison result set;

[0043] Based on the power comparison result set, the power compensation amount is calculated, the input power of the heating element in each temperature zone is adjusted, and the power control error is corrected according to the temperature response characteristics and the heat transfer effect between the temperature zones to obtain a power compensation amount parameter set;

[0044] The power compensation parameter set is converted into executable control strategy instructions to establish a control instruction sequence.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, through the correlation analysis of the temperature states of multiple regions, the temperature control is upgraded from the independent adjustment of a single region to the dynamic optimization based on the thermodynamic coupling relationship, thereby improving the coordination of the temperature control of multiple regions. By combining the heat transfer rate, temperature response delay time and temperature response gain coefficient between the temperature intervals, the dynamic parameters required for temperature control can be calculated in real time, rather than relying on fixed set values, to adapt to different operating conditions and improve the flexibility of control. In the process of calculating the temperature deviation and the adjustment amount at the same time, not only the temperature change trend of a single region is considered, but also the mutual influence in the heat transfer process is combined to reduce the interference of local temperature fluctuations on the overall control strategy. Through the dynamic calculation of the power compensation amount, the overshoot or undershoot phenomenon that occurs in the heat transfer process between the temperature intervals is reduced, the control accuracy is improved, and the temperature after power adjustment is ensured to be stable. The matching method of the power compensation amount and the target temperature value is corrected to form a dynamic adjustment strategy, so that the power distribution is more accurate and the energy waste and temperature control error caused by the fixed compensation method are avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] See also Figure 1 The present invention provides a technical solution: a multi-zone intelligent temperature control system for a PET extruder includes:

[0050] The temperature field parameter acquisition module collects the input power, real-time temperature value, temperature change rate value and temperature difference between adjacent temperature zones of the extruder heating element to obtain the temperature zone associated state set; summarizes and maps the temperature zone associated state set to establish the temperature field response characteristics;

[0051] The twin model construction module calls the temperature field response characteristics, calculates the heat transfer rate, temperature response delay time and temperature response gain coefficient between each temperature zone, and obtains the temperature zone coupling parameter group; linearly superimposes the temperature zone coupling parameter group with the real-time temperature value of each zone and the input power of the heating element to establish the temperature zone mapping relationship;

[0052] The temperature trend prediction module calls the temperature zone mapping relationship, substitutes the temperature and power input values ​​of each temperature zone into the flow field equation, calculates the temperature gradient field distribution, and obtains the temperature trend sequence; calculates the temperature deviation and adjustment amount based on the temperature trend sequence and the temperature target value of each zone, and establishes the control optimization parameters;

[0053] The control sequence generation module calls the control optimization parameters, compares the optimized power value with the real-time temperature value of the temperature zone, obtains the power compensation amount, performs correction operation on the power compensation amount and the target temperature value of each zone, and establishes the control instruction sequence.

[0054] The steps to obtain the temperature zone associated status set are:

[0055] The input power of the heating element in each temperature zone of the extruder is monitored in real time, and the real-time temperature value and temperature change rate value of each temperature zone are measured at the same time, and the temperature difference between adjacent temperature zones is measured to obtain the real-time monitoring data set of each temperature zone;

[0056] Based on the real-time monitoring data set of each temperature zone, the relationship between the input power of the heating element, the real-time temperature value, the temperature change rate and the temperature difference is analyzed, the mutual influence between the temperature zones is analyzed, and the data association mapping between the temperature zones is generated;

[0057] According to the data association mapping between temperature zones, the relationship characteristics between temperature zones are analyzed, and the relationship characteristics are integrated and constructed into a state set to obtain the temperature zone association state set.

[0058] Specifically, the rated voltage and current range of the heating element in each temperature zone are clarified according to the specifications of the extruder, and power detection and temperature sensors are deployed at the corresponding positions for real-time data collection. The temperature sensor can select a thermocouple or thermistor with a range of 0°C to 300°C as the main measurement tool. If the actual temperature measurement exceeds 300°C, it needs to be limited and marked according to the limit value indicated in the equipment manual. The power detection part obtains the input power data of the heating element by reading the current and voltage and multiplying them. Sampling is performed at fixed time intervals, such as 1 second, and the power value collected is recorded together with the temperature at the same time point. In order to further obtain the temperature change rate, it is necessary to perform two adjacent At the sampling moment, the temperature is subtracted and divided by the time interval. For example, the current temperature is subtracted from the temperature at the previous moment and then divided by 1 second to get the rate of change. As for the temperature difference of adjacent temperature zones, the temperature of the two temperature zones at the same sampling moment is subtracted to form the corresponding difference entry. If the temperature difference exceeds the empirically set 50°C threshold, the accuracy of the sensor installation needs to be re-checked and the sampling records before and after are compared. This 50°C value is an estimated value given after comprehensive consideration of the extruder temperature zone configuration and material properties in multiple production practices. After confirmation, multiple fields such as power data, real-time temperature, temperature change rate and temperature difference are combined into a unified monitoring data structure to obtain a real-time monitoring data set for each temperature zone.

[0059] Based on the real-time monitoring data set of each temperature zone, the coupling relationship between these variables is evaluated by comparing the data such as the input power of the heating element, the temperature change rate and the temperature difference between adjacent temperature zones one by one. First, the linear correlation coefficient of the power value and the temperature change rate at the same time point is calculated. If the correlation coefficient is greater than the critical value of 0.6 formed by multiple experiments, it can be regarded as a strong correlation, otherwise it is regarded as a weak correlation. The weak correlation data will be further checked for possible nonlinear effects using the partial correlation test method in the future. For the temperature difference between adjacent temperature zones, whether it is within the range of 0℃ to 50℃ is the basic judgment basis. If the temperature difference obviously deviates from this range at multiple moments, this section of data needs to be marked as abnormal and re-analyzed in combination with the power input and temperature change rate. The degree of mutual influence on heat transfer is identified by comparing the correlation between different temperature intervals. Finally, the possible interactive dependence information between each temperature interval is sorted out according to the detected effective correlation relationship and the abnormal marking situation. This information is grouped by temperature zone pairs and the correlation strength classification of power and temperature is noted in the same record structure to generate a temperature interval data association map.

[0060] According to the data association mapping between temperature zones, the association strength and abnormal marking data between the same group of temperature zones are summarized, and then the temperature zone pairs with association strength higher than 0.6 are regarded as potential thermal coupling significant areas. The temperature changes and power inputs in these areas are cross-compared, and the key features that may lead to temperature linkage are extracted and defined as feature labels. For example, the phenomenon of high power input accompanied by large temperature fluctuations in adjacent temperature zones can be marked as a certain association feature. Subsequently, all feature labels of different temperature zones are sorted out and combined to form multiple feature items. If a large number of repeated features appear between certain temperature zones, it can be determined that they have a stable coupling effect. This determination result is recorded in the relationship set and classified and merged with other feature items. When the feature information of all temperature zones is collected, it can be integrated into a unified state structure to obtain a temperature zone association state set.

[0061] The steps for obtaining the temperature field response characteristics are:

[0062] Based on the temperature zone association state set, multivariate nonlinear regression analysis is performed to calculate the association index. The formula is:

[0063]

[0064] Among them, P i represents the input power in the ith temperature zone, T i Represents the real-time temperature value of the i-th temperature zone, represents the temperature difference between the ith temperature zone and the adjacent temperature zone, γ i represents the regulation index of the ith temperature zone, c irepresents the correction coefficient of the ith temperature zone, R represents the correlation index, and n represents the total number of temperature zones;

[0065] According to the correlation index, the heat transfer characteristics of each temperature zone are identified and the temperature field response characteristics are constructed.

[0066] Specifically, the formula is beneficial in that by simultaneously considering P i , T i , γ i 、c i Multiple quantitative indicators such as heat transfer coupling characteristics and temperature difference information can be introduced into the same expression, so as to obtain the correlation index R that can reflect the linkage relationship of multiple temperature zones. This index can be used for subsequent coupling analysis of each temperature zone.

[0067] P i The steps to obtain the parameters are:

[0068] First, a power detection device is installed in each temperature zone of the extruder to read the real-time voltage and current and multiply them to obtain the input power value at the current moment. Then, the power values ​​over a period of time are summarized, and a power acquisition sequence is generated in chronological order. The segments in the sequence with excessive noise fluctuations are checked point by point. If the current at certain moments is higher than the rated range listed in the equipment manual (for example, 0A to 10A), the power data at the corresponding moment will be excluded, and a continuous stable period will be selected on the remaining power sequence to obtain P i The typical value is obtained, and finally the power acquisition sequence of different temperature zones is averaged or peaked by the time period statistics method to form the input power parameters that meet the calculation requirements. For example, when n=4, P1=1200W, P2=1500W, P3=900W, and P4=1100W can be obtained. These values ​​correspond to the power input of temperature zones 1 to 4 in a certain time period.

[0069] T i The steps to obtain the parameters are:

[0070] At the same sampling frequency, the real-time temperature value is recorded by the temperature sensor located in each temperature zone. If the temperature sensor range is between 0℃ and 300℃, check before operation that the maximum temperature inside the extruder does not exceed this range. The temperature acquisition sequence of each temperature zone during operation is statistically analyzed and abnormalities are checked. The records of the temperature instantaneously rising to the upper limit of the range are confirmed one by one, and the temperature difference between the previous and next moments and the adjacent areas is compared to eliminate abnormal points caused by instantaneous interference. After verifying that the collected data is complete, the temperature value of the stable operation stage is selected to represent T i For example, after testing and troubleshooting, it was found that T1 = 180°C, T2 = 200°C, T3 = 220°C, and T4 = 210°C.

[0071] ΔT i The steps to obtain the parameters are:

[0072] This parameter is determined based on the temperature difference between adjacent temperature zones. By comparing the temperature records of temperature zone i with temperature zone i±1 moment by moment and calculating the difference, temperature zone 1 can be compared with temperature zone 2, temperature zone 2 can be compared with temperature zone 3, and so on. If ΔT i If the temperature exceeds a certain empirical threshold (e.g. 60°C), it is necessary to further confirm whether the temperature sensor reading is within the normal range (0°C to 300°C). After confirmation, the value is recorded in sequence, and finally select the value that can characterize the temperature difference between the temperature intervals to enter the formula calculation. For example, in the scenario of n=4, we get When used, it participates in subsequent calculations in the form of absolute value.

[0073] γ i The steps to obtain the parameters are:

[0074] The adjustment index is usually determined through a thermal response test experiment. The power input is kept constant in a specified temperature zone and the temperature curve over time is measured. The trend characteristics of temperature rise or fall are recorded. Then, a numerical calibration is given to each temperature zone in combination with material properties and heat transfer rate. The temperature responses recorded in the experiment at different power levels are compared. If the temperature rise rate is close to linear in a certain interval, the adjustment index can be set at around 1.0 to 1.3. If a large nonlinearity occurs, a higher or lower index value can be used. After sorting out multiple comparison results, the adjustment index of each temperature zone is determined, for example, γ1=1.2, γ2=1.1, γ3=1.3, γ4=1.2, and its applicable conditions are recorded, such as the corresponding power range and material flow characteristics.

[0075] c i The steps to obtain the parameters are:

[0076] The correction factor quantifies the heat loss and equipment structural characteristics during the heat transfer process in the temperature zone. First, the external heat dissipation is detected in the equipment installation environment, and data such as wind speed and body heat dissipation efficiency are recorded. Then, the heat dissipation difference between the high temperature stage and the low temperature stage is calculated in the power and temperature acquisition sequence to obtain the comprehensive correction factor for different temperature zones. If the heat dissipation around a certain temperature zone is greater than that of other areas, a higher C is assigned. i It is used to amplify the heat loss effect in this area, and finally the specific value of c1 can be determined.

[0077] =0.5, c2=0.6, c3=0.55, c4=0.52. For example, during the actual inspection, the outer surface temperature of the extruder is detected with the help of a thermal imager, and this set of correction factors is obtained by combining the record of heat loss per minute.

[0078] The steps to obtain the n parameter are:

[0079] This parameter indicates the total number of temperature zones, which can be obtained by referring to the temperature zone design drawing or technical manual of the extruder itself. After confirming the segmentation of each independent temperature zone, it is registered by the operator or the monitoring system. Here, the example is n=4. If the equipment has 5 or 6 other temperature zones, the power, temperature, difference and other data of each temperature zone need to be included in the formula for calculation.

[0080] Calculation process:

[0081] First calculate the numerator:

[0082]

[0083] Among them, for each i, we calculate After getting several values, add them up. In the example:

[0084] When i=1, About 29.04;

[0085] When i=2, About 28.02;

[0086] When i=3, About 14.96;

[0087] When i=4, About 17.54;

[0088] Adding these values ​​gives a numerator of approximately 89.56;

[0089] Next, calculate the denominator:

[0090]

[0091] The corresponding c i With P i +T i Do the product, then add the four results and take the square root. In this example:

[0092] c1(P1+T1)=0.5×(1200+180)=690

[0093] c2(P2+T2)=0.6×(1500+200)=1020

[0094] c3(P3+T3)=0.55×(900+220)=616

[0095] c4(P4+T4)=0.52×(1100+210)=681.2

[0096] The sum is 3007.2, and the square root is about 54.84;

[0097] Divide the numerator by the denominator:

[0098]

[0099] The results show that in a multi-temperature zone system with n=4, through the joint calculation of the selected power, temperature, adjustment index, correction coefficient and temperature difference information, a correlation index R value of about 1.63 can be obtained. The higher the value, the tighter the thermal coupling of the current temperature zones. When R is lower than 1, it can be regarded as weak thermal coupling or insufficient transfer characteristics between local temperature zones. A value between 1 and 2 means that the coupling strength is moderate. According to the needs of different production processes, the power input or adjustment method can be adjusted in further analysis.

[0100] The heat transfer conditions of each temperature zone are summarized according to the correlation index R obtained earlier. After recording the temperature distribution data, the test personnel of each temperature zone will refer to the power and temperature corresponding table generated in the previous step to make multiple comparisons of the temperature values ​​in the same time period and the temperature difference of adjacent temperature zones. If it is found during the comparison process that the temperature difference of adjacent temperature zones continues to exceed the empirical threshold of 40°C, it is determined that the temperature zone may have a higher heat transfer rate, and the temperature zone data of the corresponding time period are marked in the statistical record. By checking the power transmission record, the difference in input power of adjacent temperature zones can be added to the reference range, such as the power comparison of 0W to 5000W range, and the temperature comparison of 0℃ to 300℃ range. The data items exceeding or below these ranges are recorded and summarized in combination with the temperature difference and the correlation index. Then, after multiple comparisons, it is checked whether there is continuous abnormal input power in the temperature zone, and separate group statistics are performed for the time periods with high temperature difference and power difference. In the process, the recorded contents such as the adjustment index and correction coefficient need to be checked. If it is found that the temperature zone has a significant temperature change in the stage with a larger adjustment index, it may indicate that the local material flow rate has changed. In order to clarify the specific impact range of this change, the statisticians classify these temperature zones into the same category in the grouping table and indicate the detection time and temperature difference details in the field. The adjustment index and correction coefficient obtained earlier are combined with the adjacent temperature zone time series for further splitting, and multiple detection items are checked against each other. Then, the heat transfer law finally screened out is recorded. After that, according to the performance of each temperature zone under different test conditions, the temperature interval correlation index is divided again. The temperature zone combination with a value between 1.0 and 1.5 is classified as a moderate coupling range, and the temperature zone combination with a value exceeding 1.5 is classified as a high coupling range. The temperature change rate and heat distribution difference are registered for comparison. Finally, the heat transfer characteristics of each temperature zone are summarized based on the reference to the aforementioned group statistical conclusions, and the conclusions are entered into the system to obtain the temperature field response characteristics.

[0101] The steps to obtain the temperature zone coupling parameter group are as follows:

[0102] Based on the temperature field response characteristics, calculate the heat transfer rate V in each temperature zone i , Temperature response delay time D i and the temperature response gain coefficient G i , the calculation formula is:

[0103]

[0104] and

[0105]

[0106] and

[0107]

[0108] Among them, R i Represents the correlation index of the i-th temperature zone, T base Represents the reference temperature value, T current Represents the current temperature value;

[0109] The heat transfer rate, the temperature response delay time and the temperature response gain coefficient are combined to form a coupling parameter group for each temperature zone, thereby obtaining a temperature zone coupling parameter group.

[0110] Specifically, the formula is beneficial in that by integrating R i 、T base and T current Multiple quantitative indicators such as temperature delay and thermal coupling amplification relationship can be used to simultaneously evaluate the speed of heat transfer in multiple temperature zones of the extruder, which helps to determine the overall coupling parameter group of each temperature zone.

[0111] R i The steps to obtain the parameters are:

[0112] This parameter comes from the correlation index result obtained previously. The power sampling values ​​and temperature sampling values ​​of each temperature zone of the extruder obtained previously are brought into the correlation calculation formula to complete the calculation and obtain R i The specific operation includes collecting current and temperature by second during the operation of the equipment and comparing them, recording the change trend of each temperature zone under different power levels and corresponding temperature conditions in a time series, and then calculating R one by one according to the existing multi-temperature zone correlation calculation method. i If the monitoring period includes 10 minutes of sample data, 600 records can be obtained. After summarizing these records, noise removal and interval inspection are performed. For example, when the power of a certain temperature zone is stable between 1200W and 1500W and the temperature is between 180℃ and 220℃, the correlation index R of a single period can be obtained in the process of segmented statistics.i =1.63 and tested more than 5 times during this period, and R can be determined after the test results remain relatively stable each time i The final value is 1.63. The same method can be used for other temperature zones to obtain their respective R i .

[0113] T base The steps to obtain the parameters are:

[0114] The reference temperature value is usually determined by considering the specific processing requirements of the raw materials in the extruder. For example, when extruding PET materials, a specific reference temperature curve can be selected from the optimal processing range (approximately 170°C to 220°C) listed in the process requirements. Then, multiple comparisons are performed on the continuous recorded values ​​of the temperature sensor over a period of time to determine the most common or most suitable temperature range of the extruder under a relatively stable material flow state, thereby obtaining a reusable reference temperature value. If the equipment runs most stably around 180°C and the data has not deviated by more than 5°C after more than 20 checks, T can be registered. base =180°C and apply it to the subsequent formulas.

[0115] T current The steps to obtain the parameters are:

[0116] This parameter indicates the temperature record of the current temperature zone during actual operation. The latest value can be directly selected from the online acquisition results of the temperature sensor, or the average value can be taken after multiple acquisitions within a short period of time (e.g., 10 to 30 seconds) to represent the current temperature zone status. If the temperature reading is found to be higher than 300°C during the acquisition period, the data should be checked against the equipment safety range (usually 0 to 300°C). For readings exceeding the range, the data records of the surrounding time should be compared to prevent extreme instantaneous measurement errors from being mixed into subsequent analysis. Finally, reasonable data segments are retained and briefly smoothed to obtain T. current For example, in multiple acquisitions, most of the readings are concentrated between 210℃ and 212℃, and finally T current =210℃ for calculation.

[0117] Calculation process:

[0118] When i=1, calculate V1 first:

[0119]

[0120] Then calculate D1:

[0121]

[0122] Calculate G1 again:

[0123]

[0124] The results show that for the first temperature zone, when the correlation index R1 is 1.63 and the reference temperature T base is 180℃, current temperature T current When the temperature is 210℃, the heat transfer rate V1 is about 0.00537, the temperature response delay time D1 is about 18.40, and the temperature response gain coefficient G1 is about 0.121. The same calculation can be performed for other temperature zones. Then, the V i , D i , G i The parameters are combined and recorded into a coupling parameter group. A large or small value often means that there are obvious differences in the thermal characteristics of the temperature zone, which can provide a reference for subsequent judgment of the thermal coupling characteristics of the temperature zone.

[0125] Combine the heat transfer rate, temperature response delay time and temperature response gain coefficient to form a coupling parameter group for each temperature zone. First, you need to register V i Check the records to see if there are multiple cases where the value exceeds 0.01. If the value exceeds 0.01 multiple times, it indicates that the temperature zone has a faster transmission rate during the test period. Combined with the corresponding delay time D i For comparison, for example, when D i When it is greater than 20, it means that a longer waiting period is required to reach the current test target temperature. For this, the statistical items of this temperature zone can be marked separately in the sampling sequence and then compared with the gain coefficient G obtained previously. i Cross sorting, where for temperature zone i, when G i When the record entry is significantly higher than 0.15, it means that the thermal coupling amplification is more obvious. Therefore, it is necessary to check whether the actual power input is within the power range that meets the process requirements, such as 0W to 3000W, and compare the power input of adjacent temperature zones. After multiple comparisons, the corresponding timestamps of the rate value, delay time and gain coefficient are noted in the record table, and they are merged into a complete coupling parameter group entry and the indicators belonging to the same temperature zone are marked. Finally, the coupling parameter group for each temperature zone is obtained by summarizing these entries.

[0126] The steps for obtaining the temperature zone mapping relationship are:

[0127] Based on the temperature zone coupling parameter group, the comprehensive mapping value in the temperature zone is calculated, and the expression is:

[0128]

[0129] Among them, M i is the comprehensive mapping value of the ith temperature zone, Q i is the input power of the heating element in the i-th temperature zone, G iis the temperature response gain coefficient of the ith temperature zone, T i is the real-time temperature value of the ith temperature zone, D i is the temperature response delay time of the ith temperature zone, H i is the coupling influence factor of the i-th temperature zone;

[0130] According to the comprehensive mapping value, combined with the spatial distribution and heat transfer relationship of each temperature zone, a temperature zone mapping relationship is established.

[0131] Specifically, the formula is beneficial in that by incorporating Q i , G i 、T i , D i and H i Quantitative indicators of different dimensions such as temperature gain can comprehensively examine power input, temperature gain characteristics, real-time temperature level, delay properties and coupling interference degree under the same operation expression, thereby providing more intuitive numerical support for the overall thermal coupling relationship of multiple temperature zones.

[0132] Q i The steps to obtain the parameters are:

[0133] This parameter represents the actual input power of the heating element in the i-th temperature zone, and its value needs to be obtained through continuous monitoring of voltage and current. First, a data acquisition device is installed in each temperature zone of the extruder to measure the working voltage and real-time current of the heating rod or resistance wire in this temperature zone, and then the voltage and current are collected at intervals of 1 second or less within a monitoring period of a specified duration (for example, 10 minutes). All collected voltage and current data will be recorded in chronological order, and the instantaneous power value at each sampling moment will be obtained by multiplication. Then, after removing the noise anomalies, the remaining power value sequence is divided into time periods and counted. If it is found that the power value is stable between 1200W and 1300W for a period of time, the average value or the median of multiple measurements can be selected to represent Q. i The typical input power in this temperature range. The above operation can ensure that the power value is accurate and repeatable. For example, in actual measurement, the input power Q in a certain temperature range can be obtained through data induction. i =1250W, which can be used in the subsequent calculation of the comprehensive mapping value.

[0134] G i The steps to obtain the parameters are:

[0135] This parameter represents the temperature response gain coefficient. It is derived from the correlation index and reference temperature data obtained previously, and is obtained by calculating the relationship between the correlation index and the reference temperature. During operation, researchers will first confirm the specific processing temperature area of ​​the extruder material, and determine one or more reference temperature sections according to the equipment manual, and then use the previously obtained correlation index R i With reference temperature The calculation method between In actual data collection, a large amount of temperature sampling data and correlation indicators of each temperature zone will be recorded first, and then these data will be segmented and counted. For example, in a stable production state, the equipment frequently maintains a reference temperature range of 180°C, and according to the R obtained previously i The statistical value is about 1.63, so G can be calculated i It is approximately equal to 0.121, indicating that the temperature response of this temperature zone has a certain amplification characteristic. If multiple tests show the same result, this value will be registered as the fixed gain coefficient of this temperature zone for subsequent use.

[0136] T i The steps to obtain the parameters are:

[0137] This parameter represents the real-time temperature value of the i-th temperature zone at the current collection moment, and needs to be measured online by a temperature sensor. A high-precision temperature sensor with a range of 0°C to 300°C can be selected on site, and the temperature inside the temperature zone is recorded at every second or shorter time interval. All records are saved in a list sorted in ascending order by time. In order to eliminate intermittent abnormal readings, such as short-term drift caused by sudden external impact or loose sensor connection, it is necessary to screen out the mutation points after comparing several sampling points before and after. After that, the remaining temperature series are interval aggregated. If it is found that most of the stable values ​​are between 208°C and 212°C, T can be selected based on the average or mode of multiple measurement results. i For example, in a stable production environment, the temperature of temperature zone i is ultimately determined to be 210°C and used as the real-time temperature input required by the formula for subsequent calculation of the comprehensive mapping value.

[0138] D i The steps to obtain the parameters are:

[0139] This parameter is used to characterize the temperature response delay time of the i-th temperature zone. Its value can be calculated from the relationship between the heat transfer rate, the reference temperature and the current temperature, and the correlation index obtained above, that is, through When the extruder is started and the temperature is gradually increased, the operator will make multiple records of the temperature growth curve of each temperature zone to determine the time interval from the reference temperature to the current temperature, and then combine the confirmed Ri Amortize the time difference to the latency analysis to generate D i To ensure that the external environment conditions are taken into consideration, a representative average value is generally taken from multiple measurements. For example, in recent multiple collections, it was found that it took an average of 30 seconds for the temperature zone temperature to increase from 180°C to 210°C. Based on the previous correlation index 1.63, D i It is approximately 18.40, and this value can then be quoted as its delay characteristic in daily production conditions.

[0140] H i The steps to obtain the parameters are:

[0141] This parameter represents the coupling influence factor, which mainly reflects the interaction intensity between the i-th temperature zone and the surrounding temperature zones. During on-site testing, it is necessary to make a comprehensive comparison of the power difference, temperature difference and heat transfer time between adjacent temperature zones, and establish a coupling characteristic data table to record the number of large temperature difference points, the number of similar heat flow channels and other information between each temperature zone and the adjacent temperature zone. Then, based on experience or the coupling classification standards in the technical manual, select a preliminary evaluation range. If for temperature zone i, the synchronous temperature change rate with the adjacent area exceeds a certain threshold, for example, more than 10 synchronous temperature increases occur within 10 minutes, and the power input fluctuation difference is controlled within 300W, then the temperature zone can be judged as a highly coupled area and a higher H is assigned to it. i When quantifying, the coupling data table will be statistically analyzed and scored, and then summarized to form the coupling degree score and converted into a numerical factor H. i For example, after a complete statistical analysis, it was found that a certain temperature zone and the surrounding areas showed strong linkage characteristics, so H i It is determined to be 0.50 as the coupling influence factor that can be finally called in the formula.

[0142] Calculation process:

[0143] Set known parameters:

[0144] Q i =1250W,G i =0.121,T i =210℃,D i =20,H i =0.50

[0145] Calculate the numerator first:

[0146] Q i ·G i +T i ·D i =1250×0.121+210×20=151.25+4200

[0147] =4351.25

[0148] Then calculate the denominator:

[0149]

[0150] Comprehensive mapping value M i for:

[0151]

[0152] The results show that the comprehensive mapping value of the ith temperature zone under the combined effects of current power, temperature, gain, delay and coupling factors is about 3893.92, which is used to indicate the relative position of the current temperature zone in the thermal characteristics analysis. When it is greater than 3000, it means that the result of the power input and temperature delay superposition in this temperature zone is relatively prominent. When it is less than 1000, it means that the comprehensive effect of power and delay is relatively limited. If other temperature zones also calculate different M i The thermal coupling distribution mode among multiple temperature zones can be further clarified by comparing the sizes of each temperature zone.

[0153] According to the comprehensive mapping value and combined with the spatial distribution of each temperature zone and the heat transfer relationship, it is necessary to first query the physical location number of each temperature zone and the distance between it and the adjacent temperature zone in the recorded temperature zone layout data, and then obtain the M at the corresponding time point in the temperature field and power distribution record. i The numerical value is annotated on the position mark of each temperature zone, and the M i The difference in values ​​can be used to determine whether abnormal fluctuations occur. If the M in a certain temperature zone changes during a period of continuous monitoring, i Continuously above 3000 and the M in adjacent temperature zones jIf the value is between 1000 and 2000, it means that the thermal coupling effect between the temperature zone and the surrounding areas is unbalanced. When marking, the power input, temperature gain and delay characteristics of the area will be summarized and compared with the coupling influence factors of the adjacent temperature zones, and different colors or symbols will be used to distinguish the areas above the preset threshold. The threshold range usually comes from the compilation of multiple batches of production measured data in the early stage. If it is found that the value exceeds 3000 many times and the interval is less than 60 seconds, it means that the power and temperature coupling characteristics of this temperature zone in this period are very obvious. This information will be sorted into a comparison table and summarized and compared by the temperature zone number. The actual power input range of the temperature zone, such as 0W to 3000W, and the temperature change sequence in the corresponding period must be included. Comparison with the effective range of 0℃ to 300℃, after comparison, it can be known whether this temperature zone continues to meet the equipment safety operation standards in this high mapping value stage. If there is a safety concern, the corresponding power allocation should be lowered or increased in further inspection and sampled again. Finally, after analyzing the M of all temperature zones, i After the distribution and their differences are determined, a systematic mapping relationship between temperature zones can be formed and the coupling state distribution of each temperature zone under different working conditions can be noted, thereby establishing a mapping relationship data set for multiple temperature zones of the extruder.

[0154] The steps to obtain the temperature trend series are:

[0155] Call the temperature zone mapping relationship, initialize the variables based on the flow field calculation model, set the initial temperature distribution and boundary conditions of each temperature zone to the model calculation domain, and generate the flow field calculation input set;

[0156] Based on the flow field calculation input set, the temperature field calculation is performed, the temperature value of each temperature zone and the input power of the heating element are mapped to the boundary conditions of the flow field equation, and numerical iterative calculation is performed to establish the temperature gradient field distribution. The iterative error is adjusted by calculating the dynamic characteristics of heat transfer between each temperature zone to obtain the temperature gradient field distribution;

[0157] Based on the temperature gradient field distribution, the time series characteristics of each temperature zone in the temperature field are extracted, the temperature change rate at each time step is calculated, and the heat transfer path between the temperature zones is analyzed. Combined with the temperature change pattern in the time dimension, the temperature trend sequence is obtained.

[0158] Specifically, to call the temperature zone mapping relationship, you first need to check the previously obtained temperature zone position information and the power and temperature distribution data of each temperature zone, and use these data to initialize the variables of the flow field calculation model. In this process, you need to determine the material flow direction, the spatial coordinates of the temperature zone, the boundary conditions and other specific parameters one by one, and calibrate the flow range of the fluid in the actual working area of ​​the extruder. For example, set the flow boundary as the main heat transfer area between the material and the screw and set the density, viscosity and thermal conductivity and other properties in this area. Introduce a unified time coordinate for the temperature value and power distribution of each boundary to facilitate subsequent iterative calculations. If a temperature zone contains multiple temperature fluctuations exceeding the specified value, such as 280°C, in the previous records, it is necessary to focus on this temperature zone and arrange a shorter time during the flow field calculation. The specified value can be set by the limit temperature of the detection equipment within the range of 0℃ to 300℃. Then the working range of the heating element should be calibrated in combination with the power and current data. 0W to 5000W is set as the operable power range and the corresponding temperature range of 0℃ to 300℃ is matched. Then these regional characteristics are further combined with the geometric boundaries, and the known temperature distribution and power input conditions are identified one by one for the grid units divided in the model. Next, the initial temperature values ​​of multiple temperature zones are recorded according to the statistical average temperature or the temperature peak value within a short period is selected as the initial starting temperature. After the boundary conditions and initial temperatures of all temperature zones are matched to the discrete points of the model, a complete variable initialization scheme is formed and saved in the flow field calculation input set to generate the flow field calculation input set.

[0159] Based on the flow field calculation input set, the flow field equation is numerically iterated according to the acquired heating power information and real-time temperature records. The specific process includes first discretizing the flow field equation (Navier-Stokes equation or heat conduction equation) and expressing the heat transfer relationship between each grid unit and its adjacent units through a coefficient matrix. Then, the temperature value of each temperature zone and the heating power at the corresponding moment are gradually mapped to the boundary conditions of the model. After each iteration, the difference between the current temperature gradient distribution and the previous moment is checked. If the error is found to exceed 3°C, it is considered that the number of iterations still needs to be increased or the time step needs to be reduced. The 3°C value is obtained by repeated experiments and compared with the actual temperature sensor measurement data to ensure that the temperature change can be well reflected within this range. The heat exchange between each temperature zone is calculated during the iteration. Each grid unit records the heat flux and corrects the distribution of the heat transfer path according to the fluid viscosity. While the iteration gradually approaches the stable state, the temperature value of the local grid unit is interpolated to reduce the numerical oscillation. Once the global error drops to less than 1°C, the temperature results of the grids in each temperature zone are summarized to form a global temperature distribution map. The heat transfer path between each temperature zone can be found by comparing layer by layer. If the cumulative heat transfer in a certain temperature zone is significantly higher than that in the adjacent area, this feature will be marked at the corresponding time step. Finally, by merging the temperature fluctuation records of multiple iteration results, the error estimate between each temperature zone can be adjusted and a relatively stable temperature gradient field distribution can be obtained.

[0160] Based on the temperature gradient field distribution, several time steps are extracted in each temperature zone to form a time series, and the corresponding temperature change rate is calculated based on the sequence. If the temperature rise is continuously monitored to exceed 2°C per second at multiple moments, it can be marked separately in the time series table. This exceeding standard can be found from previous tests that most normal heating rates are in the range of 0°C to 2°C per second. Exceeding this standard is considered a faster heating behavior. These temperature rate data are then matched and compared with the recorded power delivery values ​​to identify which moments may have a strong correlation with the heat transfer path, and are associated and marked according to the relative positions of the temperature intervals. For example, if the heating rates are synchronized between adjacent temperature zones, The increase will be marked as a common heat transfer area. The temperature series in this area will be compared and distinguished into time periods. The rate difference in the process of continuous temperature growth or decrease will be retained, compared with the flow field grid level and the specific time step information will be recorded. After completing the multi-time period comparison, the temperature change curves of all temperature zones will be summarized on the same time axis to find out the rules of any repetitive rate fluctuations. These screened heat transfer paths are combined to form a multi-dimensional analysis of the temperature trend, and then the heating, constant temperature or cooling form of each temperature zone is expressed in the form of a time series and the key heat transfer characteristics between adjacent temperature zones are attached. Finally, these combined information are integrated into a unified data structure to obtain a temperature trend sequence.

[0161] The steps to obtain the control optimization parameters are:

[0162] Call the temperature trend sequence and obtain the temperature target value of each zone. Analyze the temperature trend based on time series analysis, extract the temperature change rate at each time step, and calculate the real-time deviation of the current temperature state relative to the target temperature to generate a temperature deviation data set.

[0163] Based on the temperature deviation data set, the deviation change trend of each temperature zone is analyzed, and the required adjustment amount is calculated in combination with the heat transfer characteristics between the temperature zones. The adjustment is made according to the temperature change rate and the heat transfer response time between the temperature zones to obtain the adjustment amount parameter set.

[0164] Based on the adjustment parameter set, control optimization parameters are established, and the temperature adjustment requirements of each temperature zone are converted into control strategy input to form control optimization parameters.

[0165] Specifically, to call the temperature trend sequence and obtain the temperature target value of each zone, it is first necessary to extract the temperature change information of each time step from the previously recorded temperature trend sequence, and query the temperature value at the corresponding moment in the real-time monitoring data of each temperature zone, and then match these temperature values ​​with the temperature target value of each zone and calculate the difference. In order to avoid abnormal deviations caused by instantaneous fluctuations, it is necessary to perform a continuity check on the original temperature data before comparison. For example, check whether the maximum temperature fluctuation within 10 seconds is greater than 10°C and mark the records that may cause jumps. If it is found that the fluctuation is indeed higher than this standard, the record will be temporarily marked and combined with the data of adjacent time periods for smoothing in subsequent processing. After confirming the data quality, the same time step will be The temperature trend under the condition is compared with the target temperature point by point, and the temperature change rate is calculated by the ratio of the temperature difference to the time step. For example, the temperature at the current moment is subtracted from the temperature at the previous moment and then divided by the time interval to get the rise and fall amplitude per unit time, and then a time rate distribution column is formed. In order to distinguish the speed of heating, it is necessary to compare the empirical threshold, such as the rise or fall interval of 2°C per second. This value is set according to the average change range statistically calculated during multiple batches of production monitoring in the past. When it is higher than 2°C, it is registered as an accelerated rise and fall data entry and the specific time period is recorded. Finally, after collecting all the rate entries, the difference between the temperature and the target temperature can be mapped through the time axis to generate a real-time deviation record and summarized as a deviation list to form a set of temperature deviation data sets.

[0166] Based on the temperature deviation data set, the deviation data is first classified into a comparison table according to the temperature zone number, and the increase or decrease of the deviation over time is recorded under each temperature zone item. Then, the differences between different times and different temperature zones are compared. For example, when the deviation value is greater than 5°C for multiple consecutive times, which is the deviation tolerance standard given by the previous production survey, such items are listed as time periods with obvious deviation increases and the start and duration of the deviation are marked. Then, combined with the previously archived heat transfer path, the heat transfer response records of adjacent temperature zones in the same period are searched. If the adjacent temperature zones also show obvious deviation changes at similar times, they are further determined to be local coupling deviations. They are concentrated in the same group to analyze the transfer characteristics between temperature zones and retrieve the corresponding relationship between their power delivery and temperature response delay. The required adjustment amount for each temperature zone in the period is determined by gradually querying the temperature change rate of each period and comparing it with the previously recorded response time. If the deviation of a temperature zone increases rapidly, it means that the required adjustment amount is relatively higher, and the corresponding power supply needs to be increased or reduced in the subsequent links. After determining the adjustment amounts of all temperature zones, the results are summarized into a continuous parameter sequence and the total deviation change trend of the test time period is checked. Finally, these adjustment amounts are integrated to form a unified adjustment amount parameter set.

[0167] Based on the adjustment parameter set, the staff will refer to the previously generated heat transfer response time between temperature zones and allocate the adjustment amount of each temperature zone to the corresponding control channel. At the same time, a matching check will be performed between the power range and the temperature safety threshold. For example, the power distribution in the range of 0W to 5000W is guaranteed to be consistent with the temperature range in the range of 0℃ to 300℃. If it is found that the adjustment parameter of a certain temperature zone is obviously too large and is about to exceed the power range, it is necessary to check the temperature history data of the temperature zone at several times in the past and analyze whether there is a risk of power exceeding the limit. If it is indeed exceeded, the adjustment amount of the temperature zone is constrained and the parameters of the adjacent temperature zones are refreshed again to maintain the overall executability. Then, a control command format is generated for each adjustment entry that has completed the check, the temperature zone number is matched with the adjustment power, and the target temperature of the adjustment is recorded. Then, multiple commands are arranged in sequence to form an associated power allocation plan. After checking that there is no conflict, a set of control optimization parameters for subsequent execution can be obtained to form control optimization parameters.

[0168] The steps to obtain the control instruction sequence are:

[0169] Based on the control optimization parameters, the optimized power value is obtained, and the real-time temperature value of each temperature zone is extracted. Based on the relationship between the current temperature state and the target power input, the difference between the optimized power value and the real-time temperature value of the temperature zone is calculated to generate a power comparison result set;

[0170] Based on the power comparison result set, the power compensation amount is calculated, the input power of the heating element in each temperature zone is adjusted, and the power control error is corrected according to the temperature response characteristics and the heat transfer effect between the temperature zones to obtain the power compensation amount parameter set;

[0171] The power compensation parameter set is converted into executable control strategy instructions and a control instruction sequence is established.

[0172] Specifically, based on the control optimization parameters, first, it is necessary to filter out the latest target power value from the previously registered temperature zone power adjustment instructions and compare it with the real-time temperature data of each temperature zone. If it is found during the comparison that the temperature record exceeds the 300°C range set by the equipment experience for many consecutive times, the sensor status and power delivery record of the temperature zone must be checked. The power range must also be checked against the previously agreed 0W to 5000W range. Then, the current power value is matched with the temperature value of the corresponding temperature zone point by point to obtain a power-temperature correspondence table. Then, for each temperature zone, the absolute difference between the optimized power value and the actual temperature value is calculated and the continuity of the difference is checked. Check. If the difference in a single temperature zone in multiple time steps is greater than the threshold value corresponding to 5℃, these abnormal moments are recorded and combined with the temperature trend sequence obtained in the previous article to check whether there is a large fluctuation in the heating or cooling rate. If it is indeed found that the heating or cooling exceeds 2℃ per second and the power curve is not adjusted accordingly, it means that the power optimization in the previous stage may deviate from the temperature response. It is necessary to add tracking points in subsequent analysis, and then after summarizing the difference information of all temperature zones, establish a unified comparison table with the temperature zone number as the index, register the difference size and frequency of occurrence for each temperature zone in the table to facilitate subsequent power correction, and finally abstract these difference entries into a power comparison result set.

[0173] Based on the power comparison result set, it is necessary to first divide the temperature zones into gradients according to the size of the power-temperature difference and indicate whether the difference is concentrated in a certain time period. By comparing the time series, it can be found that the difference in some temperature zones is concentrated in the initial stage of heating or the high temperature stage of maintaining the temperature. At this time, the power input of the corresponding temperature zone is locally adjusted according to the recorded temperature response characteristics. If a temperature zone continues to have a difference greater than 5°C within five minutes, it indicates that the power compensation required for this temperature zone may be higher. In order to accurately determine this value, the temperature trend curve of the temperature zone at different time steps can be compared with the target temperature confirmed previously. If the temperature deviation continues to rise within a certain period of time and the deviation rate is higher than 1°C per second, this section is defined as a critical compensation period. During this period, the power increment is selected according to the rated capacity of the heating element registered previously and the actual number of adjustments used is noted. These adjustment items are then summarized and put into a comparison table together with the power changes in other temperature zones for interactive comparison. If it is found that the heat transfer coupling of adjacent temperature zones causes excessive temperature fluctuations on the other side, the power compensation amount of the corresponding temperature zone must be corrected in time. Finally, the summarized compensation values ​​are recorded in a special parameter list and marked with the specific application period to obtain a power compensation parameter set.

[0174] When converting the power compensation parameter set into the instruction content that can be directly issued to the extruder heating element, it is necessary to add the power correction range and effective time period after the label of each temperature zone. For example, if the compensation amount of a certain temperature zone is 300W and is required to take effect within the next 20 to 30 seconds, the power correction value of the temperature zone and the corresponding time boundary are written into the record when compiling the instruction table, and a sequential number is assigned to it. If there are multiple temperature zones with power compensation requirements in adjacent time intervals at the same time, these instructions are sorted in order to avoid large-scale centralized adjustments at the same time as much as possible. If it is detected during the comparison that the power correction may exceed the rated safety range, it is necessary to mark the content in the entry and let the next comparison logic perform an alert. After confirming that all compensation values ​​are within the acceptable range, all instructions are arranged in sequence according to the temperature zone number and a set of executable control strategy formats are generated to establish a control instruction sequence.

Claims

1. PET extruder multi-zone intelligent temperature control system, characterized by: The system comprises: The temperature field parameter acquisition module collects the input power, real-time temperature value, temperature change rate value and temperature difference between adjacent temperature zones of the heating element of each temperature zone of the extruder to obtain a temperature zone associated state set; summarizes and maps the temperature zone associated state set to establish a temperature field response feature; The twin model construction module calls the temperature field response characteristics, calculates the heat transfer rate, temperature response delay time and temperature response gain coefficient between each temperature zone, and obtains the temperature zone coupling parameter group; linearly superimposes the temperature zone coupling parameter group with the real-time temperature value of each zone and the input power of the heating element to establish a temperature zone mapping relationship; The temperature trend prediction module calls the temperature zone mapping relationship, substitutes the temperature and power input values ​​of each temperature zone into the flow field equation, calculates the temperature gradient field distribution, and obtains the temperature trend sequence; calculates the temperature deviation and adjustment amount according to the temperature trend sequence and the temperature target value of each zone, and establishes the control optimization parameters; The control sequence generation module calls the control optimization parameters, compares the optimized power value with the real-time temperature value of the temperature zone, obtains the power compensation amount, performs correction operation on the power compensation amount and the target temperature value of each zone, and establishes a control instruction sequence.

2. The multi-zone intelligent temperature control system for PET extruder according to claim 1, characterized in that: The steps for obtaining the temperature zone associated state set are: The input power of the heating element in each temperature zone of the extruder is monitored in real time, and the real-time temperature value and temperature change rate value of each temperature zone are measured at the same time, and the temperature difference between adjacent temperature zones is measured to obtain the real-time monitoring data set of each temperature zone; Based on the real-time monitoring data set of each temperature zone, the relationship between the input power of the heating element, the real-time temperature value, the temperature change rate and the temperature difference is analyzed, the mutual influence between the temperature zones is analyzed, and the data association mapping between the temperature zones is generated; According to the temperature zone data association mapping, the relationship characteristics between the temperature zones are analyzed, and the relationship characteristics are integrated and constructed into a state set to obtain a temperature zone association state set.

3. The multi-zone intelligent temperature control system for PET extruder according to claim 1, characterized in that: The steps for obtaining the temperature field response characteristics are: Based on the temperature zone association state set, multivariate nonlinear regression analysis is performed to calculate the association index, and the formula is: Among them, P i represents the input power in the ith temperature zone, T i Represents the real-time temperature value of the i-th temperature zone, ΔT i represents the temperature difference between the ith temperature zone and the adjacent temperature zone, γ i represents the regulation index of the ith temperature zone, c i represents the correction coefficient of the ith temperature zone, R represents the correlation index, and n represents the total number of temperature zones; According to the correlation index, the heat transfer characteristics of each temperature zone are identified and the temperature field response characteristics are constructed.

4. The multi-zone intelligent temperature control system for PET extruder according to claim 1, characterized in that: The steps for obtaining the temperature zone coupling parameter group are as follows: Based on the temperature field response characteristics, the heat transfer rate V of each temperature zone is calculated. i , Temperature response delay time D i and the temperature response gain coefficient G i , the calculation formula is: and and Among them, R i Represents the correlation index of the i-th temperature zone, T base Represents the reference temperature value, T current Represents the current temperature value; The heat transfer rate, the temperature response delay time and the temperature response gain coefficient are combined to form a coupling parameter group for each temperature zone, thereby obtaining a temperature zone coupling parameter group.

5. The multi-zone intelligent temperature control system for PET extruder according to claim 1, characterized in that: The steps for obtaining the temperature zone mapping relationship are: Based on the temperature zone coupling parameter group, the comprehensive mapping value in the temperature zone is calculated, and the expression is: Among them, M i is the comprehensive mapping value of the ith temperature zone, Q i is the input power of the heating element in the i-th temperature zone, G i is the temperature response gain coefficient of the ith temperature zone, T i is the real-time temperature value of the ith temperature zone, D i is the temperature response delay time of the ith temperature zone, H i is the coupling influence factor of the i-th temperature zone; According to the comprehensive mapping value, combined with the spatial distribution and heat transfer relationship of each temperature zone, a temperature zone mapping relationship is established.

6. The multi-zone intelligent temperature control system for PET extruder according to claim 1, characterized in that: The steps for obtaining the temperature trend sequence are: The temperature zone mapping relationship is called, variables are initialized based on the flow field calculation model, and the initial temperature distribution and boundary conditions of each temperature zone are set to the model calculation domain to generate a flow field calculation input set; Based on the flow field calculation input set, the temperature field calculation is performed, the temperature value of each temperature zone and the input power of the heating element are mapped to the boundary conditions of the flow field equation, the numerical iterative calculation is performed, the temperature gradient field distribution is established, and the iterative error is adjusted by calculating the dynamic characteristics of heat transfer between each temperature zone to obtain the temperature gradient field distribution; Based on the temperature gradient field distribution, the time series characteristics of each temperature zone in the temperature field are extracted, the temperature change rate at each time step is calculated, and the heat transfer path between the temperature zones is analyzed. Combined with the temperature change pattern in the time dimension, the temperature trend sequence is obtained.

7. The multi-zone intelligent temperature control system for PET extruder according to claim 1, characterized in that: The steps of obtaining the control optimization parameters are: Calling the temperature trend sequence, and obtaining the temperature target value of each zone, parsing the temperature trend based on time series analysis, extracting the temperature change rate at each time step, and calculating the real-time deviation of the current temperature state relative to the target temperature, and generating a temperature deviation data set; Based on the temperature deviation data set, the deviation change trend of each temperature zone is analyzed, and the required adjustment amount is calculated in combination with the heat transfer characteristics between the temperature zones, and the adjustment is performed according to the temperature change rate and the heat transfer response time between the temperature zones to obtain the adjustment amount parameter set; Based on the adjustment parameter set, control optimization parameters are established, and the temperature adjustment requirements of each temperature zone are converted into control strategy input to form control optimization parameters.

8. The multi-zone intelligent temperature control system for PET extruder according to claim 1, characterized in that: The steps of acquiring the control instruction sequence are: Based on the control optimization parameters, the optimized power value is obtained, and the real-time temperature value of each temperature zone is extracted. Based on the relationship between the current temperature state and the target power input, the difference between the optimized power value and the real-time temperature value of the temperature zone is calculated to generate a power comparison result set; Based on the power comparison result set, the power compensation amount is calculated, the input power of the heating element in each temperature zone is adjusted, and the power control error is corrected according to the temperature response characteristics and the heat transfer effect between the temperature zones to obtain a power compensation amount parameter set; The power compensation parameter set is converted into executable control strategy instructions to establish a control instruction sequence.

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