Multi-zone intelligent temperature control system for pet extruder

Abstract

The present application relates to the technical field of industrial control system, specifically to a PET extruder multi-region intelligent temperature regulation system, which comprises a temperature field parameter acquisition module, which acquires the input power, real-time temperature value, temperature change rate value and temperature difference value between adjacent temperature zones of each heating element of the extruder, and obtains a temperature zone correlation state set; the temperature zone correlation state set is summarized and mapped to establish a temperature field response characteristic. In the present application, through correlation analysis of multi-region temperature state, temperature regulation is upgraded from single-region independent adjustment to dynamic optimization based on thermal dynamics coupling relationship, improving the coordination of multi-region temperature regulation. In combination with the heat transfer rate between temperature zones, temperature response delay time and temperature response gain coefficient, dynamic parameters required for temperature regulation can be calculated in real time, instead of relying on fixed set values, adapting to different operating conditions and improving regulation flexibility.

Description

Technical Field

[0001] The present 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 automated systems used to monitor, control, and optimize industrial production processes. They encompass multiple subsystems, including programmable logic controllers (PLCs), distributed control systems (DCSs), industrial computer control systems, and embedded control systems. This technology is widely used in industries such as manufacturing, energy, chemicals, metallurgy, and automation equipment, 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] Existing temperature control systems for PET extruders primarily rely on independent control of each temperature zone, ignoring the thermal coupling between these zones. This results in the inability to correct for inter-zone interactions during temperature adjustment in real time, leading to the accumulation of local temperature deviations and impacting overall stability. Furthermore, temperature predictions often rely on static rules or extrapolation of historical data, failing to consider real-time heat transfer effects. This results in poorly accurate predictions of temperature trends and a lag in adjustment strategies. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of 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 solution: The multi-zone intelligent temperature control system for PET extruder includes:

[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 extruder heating element 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 a 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 a 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;

[0009] The control sequence generation module calls the control optimization parameter, compares the optimized power value with the real-time temperature value of the temperature zone, obtains the power compensation amount, and corrects the power compensation amount and the target temperature value of each zone to establish the control instruction sequence.

[0010] Preferably, the obtaining step of the temperature zone correlation state set is:

[0011] The real-time monitoring data set of each temperature zone is obtained by monitoring the input power of the heating element of each temperature zone of the extruder, measuring the real-time temperature value and temperature change rate value of each temperature zone, and measuring the temperature difference between adjacent temperature zones.

[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 correlation mapping between the temperature zones is generated.

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

[0014] Preferably, the obtaining step of the temperature field response characteristic is:

[0015] Based on the temperature zone correlation state set, multivariate nonlinear regression analysis is performed, and the correlation index R is calculated, and the formula is:

[0016]

[0017] Where, P i represents the input power of the i-th temperature zone, T i represents the real-time temperature value of the i-th temperature zone, ΔT i represents the temperature difference between the i-th temperature zone and the adjacent temperature zone, γ i represents the adjustment index of the i-th temperature zone, c i represents the correction coefficient of the i-th 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 characteristic is constructed.

[0019] Preferably, the obtaining step of the temperature zone coupling parameter group is:

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

[0021]

[0022] and

[0023]

[0024] and

[0025]

[0026] wherein, R i represents the correlation degree index of the ith temperature zone, T base represents the reference temperature value, T current represents the current temperature value;

[0027] The coupling parameter group of each temperature zone is formed by combining the heat transfer rate, the temperature response delay time and the temperature response gain coefficient, and the temperature zone coupling parameter group is obtained.

[0028] Preferably, the obtaining step of the temperature zone mapping relationship is:

[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] wherein, M i is the comprehensive mapping value of the ith temperature zone, Q i is the input power of the heating element of the ith 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 ith temperature zone;

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

[0033] Preferably, the obtaining step of the temperature trend sequence is:

[0034] The temperature zone mapping relationship is called, variable initialization is performed based on the flow field calculation model, the initial temperature distribution and the boundary condition of each temperature zone are set to the model calculation domain, and the flow field calculation input set is generated;

[0035] Based on the flow field calculation input set, the temperature field calculation is performed, the temperature value and the heating element input power of each temperature zone are mapped to the boundary condition of the flow field equation, the numerical iteration calculation is performed, the temperature gradient field distribution is established, and the iteration error is adjusted through the calculation of the heat transfer dynamic characteristics between the temperature zones, so that the temperature gradient field distribution is obtained.

[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 steps of obtaining the control optimization parameters are:

[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 to generate 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 of the temperature interval. The adjustment is made according to the temperature change rate and the heat transfer response time between the temperature intervals 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, an 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 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 status 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. Combined with 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 in the heat transfer process between the temperature intervals is reduced, the control accuracy is improved, and it is ensured that the temperature after power adjustment tends 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 solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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's heating elements to obtain a temperature zone associated state set; the temperature zone associated state set is summarized and mapped 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 of each temperature zone, and obtains the temperature zone coupling parameter group. The temperature zone coupling parameter group is linearly superimposed 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 calculation 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. The real-time temperature value and temperature change rate of each temperature zone are measured at the same time. The temperature difference between adjacent temperature zones is measured to obtain a real-time monitoring data set for each temperature zone.

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

[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 is 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 measurements. 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 obtain the rate of change. As for the temperature difference between 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 threshold of 50°C, 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 heating element input power, temperature change rate and adjacent temperature zone temperature difference data one by one. First, the power value and temperature change rate at the same time point are calculated for the preliminary linear correlation coefficient. If the correlation coefficient is greater than the critical value 0.6 formed by multiple tests, it is considered to be a strong correlation, otherwise it is considered to be a weak correlation. Weakly associated data is further investigated for possible nonlinear effects using partial correlation test. For the temperature difference of adjacent temperature zones, whether it is within the interval of 0°C to 50°C is the basic judgment basis. If the temperature difference repeatedly deviates from this interval at multiple times, this data is marked as abnormal and reanalyzed in combination with power input and temperature change rate. The degree of mutual influence of different temperature zones in heat transfer is identified by comparing their correlation, and finally the possible interaction dependency information of each temperature zone is sorted out according to the detected effective correlation and abnormal marking. The information is grouped according to the temperature zone and the correlation strength of power and temperature is classified in the same record structure, and the temperature zone data correlation mapping is generated.

[0060] According to the temperature zone data correlation mapping, the correlation strength and abnormal marking data between the same group of temperature zones are summarized, and the temperature zone pairs with correlation strength higher than 0.6 are regarded as potential thermal coupling significant regions. The temperature change and power input of these regions are cross-compared to extract the key features that may cause temperature linkage and define them as feature labels. For example, the phenomenon of high power input accompanied by large fluctuations in adjacent temperature can be marked as a certain correlation feature. Then all the feature labels of different temperature zones are sorted and combined to form multiple feature items. If there are a large number of repeated features between some temperature zones, it can be determined that they have stable coupling effect. The 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 the temperature zone correlation state set.

[0061] The acquisition steps of the temperature field response characteristics are:

[0062] Based on the temperature zone correlation state set, multivariate nonlinear regression analysis is performed to calculate the correlation degree index, and the formula is:

[0063]

[0064] Where, P i represents the input power of the i-th temperature zone, T i represents the real-time temperature value of the i-th temperature zone, represents the temperature difference between the i-th temperature zone and the adjacent temperature zone, γ i represents the adjustment index of the i-th temperature zone, c iRi represents a correction coefficient of the ith temperature zone, R represents a correlation index, and n represents a total number of temperature zones;

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

[0066] Specifically, the formula has the advantages that by simultaneously considering P i , T i , γ i , c i and other quantitative indicators, the heat transfer coupling characteristics and temperature difference information can be introduced into the same expression, so that the correlation index R reflecting the multi-temperature zone linkage relationship is obtained, and the index can be used for subsequent coupling analysis of each temperature zone.

[0067] The acquisition steps of the P i parameter are as follows:

[0068] First, a power detection device is installed in each temperature zone of the extruder, real-time voltage and current are read and multiplied, so that the input power value at the current time is obtained, then the power values within a period of time are summarized, a power collection sequence is generated in chronological order, and the segments with excessive noise fluctuations in the sequence are checked point by point, if the current of some time is higher than the rated range listed in the equipment manual (for example, 0A to 10A), the power data at the corresponding time is excluded, and the typical value of P i is obtained by selecting a continuous stable period on the remaining power sequence, finally, the power collection sequences of different temperature zones are respectively averaged or peaked by the time period statistical method to form the input power parameters meeting the calculation requirements, for example, in the case of n=4, P1=1200W, P2=1500W, P3=900W, and P4=1100W can be obtained, which correspond to the power input of temperature zone 1 to temperature zone 4 within a certain period of time.

[0069] The acquisition steps of the T i parameter are as follows:

[0070] Under the same sampling frequency, real-time temperature values are recorded by temperature sensors located in each temperature zone, if the temperature sensor range is between 0℃ and 300℃, the highest temperature inside the extruder is checked before operation to ensure that it does not exceed the range, the temperature collection sequence of each temperature zone during operation is counted and abnormality is checked, the records of temperature instantaneous rise to the upper limit of the range are checked one by one, and the abnormal points caused by instantaneous interference are removed by comparing the temperature difference of adjacent regions before and after the time, after verifying the completeness of the collected data, the temperature values of the stable operation stage are selected to represent T i , for example, after detection and investigation, T1=180℃, T2=200℃, T3=220℃, and T4=210℃ are obtained.

[0071] ΔT i The parameter acquisition step is:

[0072] The parameter is determined based on the temperature difference of adjacent temperature zones. By comparing the temperature records of temperature zone i and temperature zone i±1 at each time point 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 exceeds a certain empirical threshold (for example, 60°C), it is further confirmed whether the temperature sensor reading at this time is within the normal range (0°C to 300°C). After confirming that there is no error, the value is recorded in sequence, and finally the value representing the temperature difference between temperature zones is selected to enter the formula operation. For example, in the n=4 scenario, we get In use, it is used in the form of absolute value to participate in subsequent operations.

[0073] γ i The parameter acquisition step is:

[0074] The adjustment index is usually determined by a thermal response test experiment. The power input is kept constant in a specified temperature zone, and the temperature change curve over time is measured. The trend characteristics of temperature rise or fall are recorded, and the material characteristics and heat transfer rate are combined to give a numerical calibration for each temperature zone. By comparing the temperature response under different power levels, if the temperature rise speed is close to linear in a certain interval, the adjustment index can be set to about 1.0 to 1.3. If there is a large nonlinearity, a higher or lower index value can be used. After sorting out the comparison results, the adjustment indexes of each temperature zone are determined, for example, γ1=1.2, γ2=1.1, γ3=1.3, γ4=1.2, and the applicable conditions are recorded, such as the corresponding power range and material flow characteristics.

[0075] c i The parameter acquisition step is:

[0076] The correction coefficient is quantitatively processed by quantifying the heat loss in the heat transfer process of the temperature zone and the structural characteristics of the equipment. First, detect the external heat dissipation in the installation environment of the equipment, record the wind speed, machine body heat dissipation efficiency and other data, and then calculate the heat dissipation difference between the high temperature stage and the low temperature stage in the power and temperature collection sequence to obtain the comprehensive correction factor of different temperature zones. If the heat dissipation around a certain temperature zone is relatively more than other areas, a higher c i is given to amplify the influence of heat loss in this area. Finally, the specific numerical value of c1

[0077] =0.5, c2=0.6, c3=0.55, c4=0.52, for example, during actual inspection, the surface temperature of the extruder is detected by a thermal imager, and the correction coefficients are obtained by recording the 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, the operator or monitoring system will register it. 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] Then calculate the denominator:

[0090]

[0091] The corresponding c i With P i +T i Multiply the four items, add them together, 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, which when squared is approximately 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 approximately 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 testers 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 range of 0W to 5000W and the temperature comparison range of 0°C to 300°C. The data items that exceed or fall 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 a continuous abnormal input power in the temperature zone. Separate group statistics are performed for the periods when the temperature difference and power difference are both high. 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 above 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 obtained by the final screening 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 greater than 1.5 is classified as a high coupling range. The temperature change rate and heat distribution difference are registered and compared respectively. Finally, based on the reference to the above group statistical conclusions, the heat transfer characteristics of each temperature zone are summarized, and the conclusion is entered into the system to obtain the temperature field response characteristics.

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

[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 it integrates 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 heat transfer speed, temperature delay and thermal coupling amplification relationship 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 seconds during the operation of the equipment and comparing them, recording the change trend of each temperature zone at 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 testing 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 detected more than 5 times in this period, and each detection result remains relatively stable, R can be determined i The final value is 1.63, and other temperature zones can also be executed in the same way to obtain their respective R i .

[0113] T base The parameter acquisition step is:

[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 best processing interval (about 170-220°C) listed in the process requirements, and then multiple comparisons are performed on the continuous recorded values of the temperature sensor within 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 operates most stably near 180°C and more than 20 times of data checking shows no more than 5°C deviation, T base = 180°C and apply it to the subsequent formula.

[0115] T current The parameter acquisition step is:

[0116] This parameter represents the temperature record of the current temperature zone during actual operation, which can be directly selected from the latest value of the online acquisition result of the temperature sensor, or can be represented by the average value of multiple acquisitions within a short period of time (for example, 10-30 seconds). If the temperature reading is higher than 300°C during acquisition, the data should be checked against the safety range of the equipment (usually 0-300°C), and 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, the reasonable data segment is retained and a simple smoothing process is performed to obtain T current , for example, most readings in multiple acquisitions are concentrated between 210°C and 212°C, and finally T current = 210°C for formula calculation.

[0117] Calculation process:

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

[0119]

[0120] Then calculate D1:

[0121]

[0122] Then calculate G1:

[0123]

[0124] The results show that for the first temperature zone, when the correlation index R1 is 1.63, the reference temperature T base is 180℃, and the current temperature T current 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 operation can be performed for other temperature zones, and then the V i , D i , and G i of each temperature zone are combined and recorded as a coupling parameter group. A larger or smaller value often means that the thermal characteristics of the temperature zone have more obvious differences, which can provide a reference for subsequent judgment of the thermal coupling characteristics of the temperature zone.

[0125] To form the coupling parameter group of each temperature zone in combination with the heat transfer rate, the temperature response delay time, and the temperature response gain coefficient, it is first necessary to search in the V i record already registered above to see if there are multiple times when the value exceeds 0.01. If there are multiple times when the value exceeds 0.01, it indicates that the temperature zone has a faster transfer rate during the test period. In combination with the corresponding delay time D i , a comparison is made. For example, when D i is greater than 20, it indicates that a longer waiting period is needed to reach the current test target temperature. In this case, the statistical items of this part of the temperature zone can be separately marked in the sampling sequence, and then cross-arranged with the gain coefficient G i obtained above. For the temperature zone i, when the record entry of G i is obviously higher than 0.15, it indicates that the thermal coupling amplification is more obvious. Therefore, it is necessary to check whether the actual power input is within the power range required by the process, for example, within the range of 0W to 3000W, and compare the power input of the adjacent temperature zone. After multiple comparisons, the corresponding time stamps of the rate value, the delay time, and the gain coefficient are marked in the record table, and they are combined into a complete coupling parameter group entry and marked as the index items belonging to the same temperature zone. Finally, by summarizing these entries, the coupling parameter group of each temperature zone is obtained.

[0126] The acquisition steps of the temperature zone mapping relationship are as follows:

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

[0128]

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

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

[0131] Specifically, the formula has the beneficial effect that by simultaneously including Q i , G i , T i , D i and H i and other quantitative indicators of different dimensions, the power input, temperature gain characteristics, real-time temperature level, delay attribute and coupling interference degree can be comprehensively considered under the same operation expression, thereby providing more intuitive numerical support for the overall thermal coupling relationship of multiple temperature zones.

[0132] The acquisition steps of the Q i parameter are as follows:

[0133] The parameter represents the actual input power of the heating element of 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 at each temperature zone of the extruder to measure the working voltage and real-time current of the heating rod or resistance wire at the temperature zone, and then the voltage and current are collected at intervals of 1 second or less within a specified monitoring period (for example, 10 minutes). All collected voltage and current data are recorded in chronological order, and the instantaneous power value at each sampling time is obtained through multiplication operation. After removing abnormal noise points, the remaining power value sequence is divided into time periods and counted, and if the power value is found to be stable between 1200W and 1300W within a certain period of time, the average value or the median of multiple measurement values can be selected to represent Q i , the typical input power of the temperature zone. The above operation can ensure that the power value is accurate and repeatable. For example, in actual measurement, the input power Q i of a certain temperature zone is 1250W, which can be used in subsequent comprehensive mapping value calculation.

[0134] The acquisition steps of the G i parameter are as follows:

[0135] This parameter represents the temperature response gain coefficient. It is derived from the correlation index and the reference temperature data obtained earlier, and is calculated by relating the correlation index to the reference temperature. During operation, the researcher will first identify the specific processing temperature range of the material in the extruder, and determine one or more reference temperature intervals according to the equipment specifications, then use the correlation index R i and the reference temperature to determine In actual collection, a large number of temperature sampling data and correlation index of each temperature zone are recorded first, and then these data are segmented for statistics. For example, in a stable production state, the equipment is frequently maintained at a reference temperature interval of 180°C, and according to the R i value obtained earlier, the statistical value is about 1.63, then G i can be calculated, which is about 0.121, indicating that the temperature response of this temperature zone has certain amplification characteristics. If multiple detections all show the same result, this value will be recorded as the fixed gain coefficient of the temperature zone for subsequent continuous use.

[0136] T i The acquisition steps of this parameter are:

[0137] This parameter represents the real-time temperature value of the i-th temperature zone at the current collection time, which 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 in the field, and the temperature inside the temperature zone is recorded every second or shorter time interval. All records are saved in a list sorted by time in ascending order. To eliminate intermittent abnormal readings, such as short-term drift caused by sudden external impact or loose sensor connection, the sudden change point needs to be screened out after comparing several sampling points before and after. Then the remaining temperature sequence is aggregated in intervals, and if most stable values are found to be between 208°C and 212°C, T i can be selected according to the average value or mode of multiple measurement results. For example, in a stable production environment, the temperature of temperature zone i is finally determined to be 210°C, which is used as the real-time temperature input required by the formula for subsequent operation of the comprehensive mapping value.

[0138] D i The acquisition steps of this parameter are:

[0139] This parameter is used to characterize the temperature response delay time of the i-th temperature zone, which can be calculated from the relationship between the heat transfer rate, the reference temperature and the current temperature, and the correlation index obtained earlier, i.e. by statistical methods in different time intervals. When the extruder is started and gradually warmed up, the operator will record multiple points on the temperature growth curve of each temperature zone to determine the time interval required from the reference temperature to the current temperature, and then combine the confirmed Ri The time difference is allocated to the delay analysis to generate D i The specific value. To ensure the consideration of external environmental conditions, a representative average value is usually taken in multiple measurements. For example, in recent multiple collections, it is found that the average time consumption of the temperature zone temperature from 180℃ to 210℃ is 30 seconds, and according to the correlation index 1.63 before, the comparison is brought in, and D i is about 18.40, which can be used as its delay characteristics in daily production conditions.

[0140] H i The parameter acquisition step is:

[0141] The parameter represents the coupling influence factor, mainly reflecting the interaction strength between the i-th temperature zone and the surrounding temperature zones. During field detection, the power difference, temperature difference and heat transfer time between adjacent temperature zones are compared comprehensively, and a coupling characteristic data table is established, recording the number of larger temperature difference points, the number of similar heat flow channels, etc. between each temperature zone and adjacent temperature zones, and then according to the coupling classification standard in the experience or technical manual, a preliminary evaluation range is selected. If the temperature synchronization change rate of the i-th temperature zone with the adjacent area exceeds a certain threshold, for example, more than 10 times of temperature synchronization increase appears within 10 minutes, and the power input fluctuation difference between them is controlled within 300W, then the temperature zone can be determined as a highly coupled area, and a higher H i value is assigned. When quantifying, the coupling data table is counted and scored, and the scoring result of the coupling degree is summarized and converted into a numerical factor H i . For example, after a complete statistics, it is found that a certain temperature zone and the surrounding area show strong linkage characteristics, so H i is determined as 0.50, which is the final coupling influence factor that can be 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] First calculate the numerator part:

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

[0147] = 4351.25

[0148] Then the denominator part is calculated:

[0149]

[0150] The comprehensive mapping value M i is:

[0151]

[0152] This result shows that the i-th temperature zone, under the joint action of current power, temperature, gain, delay and coupling factors, has a comprehensive mapping value of about 3893.92, which is used to represent the relative position of the current temperature zone in the thermal characteristic analysis. When greater than 3000, it indicates that the superimposed result of power input and temperature delay of the temperature zone is relatively prominent, and when less than 1000, it indicates that the comprehensive effect of power and delay is relatively limited. If different M i values are calculated for other temperature zones, the thermal coupling distribution mode between multiple temperature zones can be further clarified by comparing the sizes of each temperature zone.

[0153] According to the comprehensive mapping value and in combination with the spatial distribution of each temperature zone and the heat transfer relationship, first, the physical location number of each temperature zone and the interval distance between it and the adjacent temperature zone need to be queried in the recorded temperature zone layout data, and then the M i value at the corresponding time point in the recorded temperature field and power distribution is annotated to the location identifier of each temperature zone, and by comparing the M i value difference of adjacent temperature zones, it can be judged whether abnormal fluctuations occur. If the M i of a certain temperature zone is continuously higher than 3000 while the M jWhen the value is between 1000 and 2000, it indicates that the thermal coupling effect of the temperature zone with the surrounding area is unbalanced. In the marking, the power input, temperature gain, and delay characteristics of the area are summarized and compared with the coupling influence factors of the adjacent temperature zones. Different colors or symbols are used to distinguish areas above the preset threshold. The threshold range is usually derived from the compilation of previous production data. If the value exceeds 3000 multiple times and the interval is less than 60 seconds, it indicates that the power and temperature coupling characteristics of the temperature zone in this period are very obvious. These information will be sorted into a comparison table, summarized and compared by 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, such as 0℃ to 300℃, are also included. After comparison, it can be determined whether the temperature zone continues to meet the device safety operation standard in this high mapping value stage. If there is a safety concern, the corresponding power allocation should be adjusted lower or higher and resampled. Finally, after analyzing the M i distribution of all temperature zones and their differences, a systematic temperature zone mapping relationship can be formed, and the coupling state distribution of each temperature zone under different working conditions is noted, thereby establishing the mapping relationship dataset of the extruder multi-temperature zone.

[0154] The temperature trend sequence acquisition step is:

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

[0156] Based on the flow field calculation input set, perform temperature field calculation, map the temperature values of each temperature zone and the input power of the heating element to the boundary conditions of the flow field equation, perform numerical iteration calculation, establish the temperature gradient field distribution, and adjust the iteration error by calculating the heat transfer dynamic characteristics between temperature zones to obtain the temperature gradient field distribution;

[0157] Based on the temperature gradient field distribution, extract the time sequence characteristics of each temperature zone in the temperature field, calculate the temperature change rate at each time step, and analyze the heat transfer path between temperature zones. Combined with the temperature change mode in the time dimension, the temperature trend sequence is obtained.

[0158] Specifically, the call temperature zone mapping relationship, first need to view the previously obtained temperature zone location information and each temperature zone power and temperature distribution data, combined with these data on the flow field calculation model for the initialization of each variable, in the process to determine the material flow direction, temperature zone space coordinates and boundary conditions and other specific parameters, and in the actual working area of the extruder calibration fluid flow range, for example, the flow boundary set as the material and screw between the main heat transfer area and in the area set density, viscosity and thermal conductivity and other properties, the temperature value and power distribution of each boundary introduced uniform time coordinate to facilitate subsequent iterative operation, if a temperature zone in the previous record contains multiple temperature fluctuations beyond the specified value, for example, 280℃, then need to focus on the flow field calculation for the temperature zone marked and arranged shorter time step for iteration, this specified value can be set by the detection equipment in the range of 0℃ to 300℃ limit temperature, after the working range of the heating element also need to combine the power and current data for calibration, 0W to 5000W set as the operating power interval and match the corresponding temperature range 0℃ to 300℃, then further these area characteristics and geometric boundary combined, the grid elements divided in the model one by one identified known temperature distribution and power input conditions, next the initial temperature value of multi-temperature zone according to the average temperature recorded or select the temperature peak in the short cycle as the initial start temperature, after the boundary conditions and initial temperature of all temperature zones corresponding to the discrete points on the model, form a complete variable initialization scheme and saved to the flow field calculation input set, generate flow field calculation input set.

[0159] Based on the flow field calculation input set, the flow field equation is calculated according to the obtained heating power information and real-time temperature record, and the specific process includes first discretizing the flow field equation (Navier-Stokes equation or heat conduction equation) and expressing the heat transfer between each grid element and its adjacent element through a coefficient matrix, then gradually mapping the temperature value of each temperature zone and the heating power at the corresponding time to the boundary conditions of the model, and checking the difference between the current temperature gradient distribution and the last time after each iteration, if the error exceeds 3℃, it is considered that the iteration step needs to be increased or the time step needs to be reduced, the 3℃ value is obtained by comparing the actual temperature sensor measurement data through repeated tests, which can well reflect the temperature change trend in this interval, and the heat exchange between each temperature zone is calculated in the iteration, each grid element records the heat flux and corrects the distribution of the heat transfer path according to the fluid viscosity, and the temperature value of the local grid element is interpolated to reduce the numerical oscillation while approaching the stable state, and once the global error is reduced to within 1℃, the temperature results of each temperature zone grid are summarized to form a global temperature distribution map, and the heat transfer path between each temperature zone can be found by layer-by-layer comparison, if the cumulative heat transfer of a certain temperature zone is significantly higher than that of the adjacent region, it will be marked at the corresponding time step, and finally the temperature fluctuation record of multiple iterations is combined to adjust the error estimation between each temperature zone and obtain a relatively stable temperature gradient field distribution, and the temperature gradient field distribution is obtained.

[0160] Based on the temperature gradient field distribution, a time sequence is extracted in each temperature zone for several time steps, and the corresponding temperature change rate is calculated based on the sequence, if the temperature rise amplitude continuously exceeds 2℃ per second at multiple time points, it is marked in the time sequence table, this exceeding standard can be found by pre-test that most normal temperature rise rates are in the range of 0℃ to 2℃ per second, and exceeding is considered as faster temperature rise behavior, then the temperature rate data and the recorded power value are matched and compared, to identify which time points and heat transfer paths may have strong correlation, and the correlation is identified according to the relative position between temperature zones, for example, if the temperature rise rate increases synchronously between adjacent temperature zones, it is marked as a common heat transfer area, the temperature sequence in the area is compared and divided into time periods, the rate difference in the process of continuous temperature rise or drop is retained, and the specific time step information is recorded in comparison with the flow field grid level, after completing the multi-period comparison, the temperature change curves of all temperature zones are summarized on the same time axis to find the regularity of any repetitive rate fluctuation, combined with the screened heat transfer paths, a multi-dimensional analysis of temperature trend is formed, and then the temperature rise, constant temperature or temperature drop of each temperature zone is expressed in the form of time sequence and the key heat transfer characteristics of adjacent temperature zones are added, finally these combined information is integrated into a unified data structure, and the temperature trend sequence is obtained.

[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 based on the heat transfer characteristics of the temperature interval. Adjustments are made according to the temperature change rate and the heat transfer response time between the temperature intervals 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. Then, these temperature values ​​are matched with the temperature target value of each zone and the difference is calculated. 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 smoothed in combination with the data of adjacent time periods in subsequent processing. After confirming the data quality, the same time step will be marked. 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 obtain the rise and fall amplitude per unit time. Then a time rate distribution column is formed. In order to distinguish the speed of temperature rise, it is necessary to compare the empirical threshold, such as the rise or fall interval of 2°C per second. This value is set based on 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 to the time axis to generate a real-time deviation record and summarized into 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 categorized by zone number and placed in a comparison table. The increase or decrease in deviation over time is recorded for each zone entry. The differences at different times and between zones are then compared. For example, if the deviation value exceeds 5°C (the tolerance standard established through preliminary production research) multiple times in a row, such entries are classified as periods of significant deviation increase, and the onset and duration of the deviation are marked. The previously archived heat transfer paths are then used to search for heat transfer response records for adjacent zones during the same time period. If adjacent zones also show significant deviation changes at similar times, this is further determined to be a local coupling deviation. These zones are grouped together to analyze the transfer characteristics between the zones and retrieve the corresponding relationship between power supply and temperature response delay. The required adjustment amount for each zone is determined by gradually querying the temperature change rate for each period and comparing it with the previously recorded response time. A rapidly increasing deviation in a zone indicates a relatively high adjustment amount, requiring a corresponding power increase or decrease in subsequent steps. After determining the adjustment amounts for all zones, the results are summarized into a continuous parameter sequence, and the overall deviation change trend over the test period is verified. Finally, these adjustment amounts are integrated into a unified adjustment amount parameter set.

[0167] Based on the adjustment parameter set, staff will refer to the previously generated temperature interval heat transfer response time and allocate the adjustment amount of each temperature zone to the corresponding control channel. At the same time, they will perform a matching check between the power range and the temperature safety threshold. For example, they will ensure that the power distribution in the range of 0W to 5000W is consistent with the temperature 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 will be constrained and the parameters of the adjacent temperature zones will be refreshed to maintain overall executableness. Then, a control command format is generated for each adjustment amount entry that has completed the check, and 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 the 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 temperature zones to obtain the power compensation 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, it is first 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 is greater than the threshold value corresponding to 5°C in multiple time steps, record these abnormal moments and combine them with the temperature trend sequence obtained in the previous article to check whether there are large fluctuations in the heating or cooling rate. If it is indeed found that the heating or cooling exceeds 2°C 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. Then, after summarizing the difference information of all temperature zones, establish a unified comparison table with the temperature zone number as the index. In the table, register the difference size and frequency of occurrence for each temperature zone to facilitate subsequent power correction, and finally abstract these difference entries into a power comparison result set.

[0173] Based on the power ratio result set, the temperature zones need to be divided according to the size of the power-temperature difference gradient and whether the difference value is concentrated in a certain time period. By comparing the time series, it can be found that the difference value of some temperature zones is concentrated in the initial temperature rise or high temperature maintenance period. At this time, according to the recorded temperature response characteristics, the power input of the corresponding temperature zone is adjusted locally. If a temperature zone continuously appears a difference value greater than 5℃ within five minutes, it indicates that the power compensation required by the temperature zone may be higher. In order to accurately determine this value, the temperature trend curve of the temperature zone at different time steps is compared with the target temperature confirmed earlier. If the temperature deviation is continuously rising and the deviation rate is higher than 1℃ per second, the section is defined as the key compensation period. In this period, the power increment is selected according to the rated capacity of the heating element registered earlier, and the actual adjustment frequency is recorded. Then these adjustment items are summarized together with the power changes of other temperature zones and put into the comparison table for interactive comparison. If it is found that the heat transfer coupling of adjacent temperature zones causes another side to appear excessive temperature fluctuations, the power compensation of the corresponding temperature zone also needs to be corrected in time. Finally, the compensation values obtained are recorded in a special parameter list and marked with specific application period to obtain the power compensation parameter set.

[0174] When converting the power compensation parameter set into instructions that can be directly issued to the extruder heating element, the power correction range and effective time period need to be attached to the label of each temperature zone. For example, the compensation amount of a certain temperature zone is 300W and requires to take effect within the next 20-30 seconds. When compiling the instruction table, the power correction value of the temperature zone and the corresponding time boundary are written into the record, and a sequence number is assigned. If multiple temperature zones have power compensation requirements in adjacent time intervals, these instructions are sorted in order to avoid large-scale concentrated adjustment at the same time. If it is detected during comparison that the power correction may exceed the rated safety range, the content needs to be marked in the item and the next comparison logic needs to be warned. After confirming that all compensation values are within the acceptable range, all instructions are arranged in order according to the temperature zone number to generate a set of executable control strategy format and 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 extruder heating element 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 a 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 a 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; 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 calculation 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. The real-time temperature value and temperature change rate of each temperature zone are measured at the same time. The temperature difference between adjacent temperature zones is measured to obtain a real-time monitoring data set for each temperature zone. Based on the real-time monitoring data set of each temperature zone, the relationship between the heating element input power, real-time temperature value, temperature change rate and temperature difference is analyzed to analyze the mutual influence between temperature zones and generate the data association map between temperature zones; According to the temperature interval 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, a multivariate nonlinear regression analysis is performed to calculate the association index. The formula is: Among them, P i Represents the input power in the i-th temperature zone, T i Represents the real-time temperature value of the i-th temperature zone, ΔT i represents the temperature difference between the i-th temperature zone and the adjacent temperature zone, γ i represents the regulation index of the i-th temperature zone, c i represents the correction coefficient of the i-th 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: 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 i-th 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 i-th 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 i-th 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: Calling the temperature zone mapping relationship, initializing variables based on the flow field calculation model, and setting the initial temperature distribution and boundary conditions of each temperature zone to the model calculation domain to generate a flow field calculation input set; Based on the flow field calculation input set, a 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, a numerical iterative calculation is performed, a 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 for 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 to generate 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 of the temperature interval. The adjustment is made according to the temperature change rate and the heat transfer response time between the temperature intervals 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 for obtaining the control instruction sequence are: Based on the control optimization parameters, an 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 parameter set; The power compensation parameter set is converted into executable control strategy instructions to establish a control instruction sequence.

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