CN117889985A - A method and system for online temperature monitoring of processing flow - Google Patents

Abstract

The invention relates to the technical field of processing procedure monitoring, and particularly discloses a processing procedure online temperature monitoring method and system, wherein the method comprises the following steps: the temperature sensors are used for monitoring temperature data of corresponding position points of each temperature sensor in real time; the infrared image acquisition module is used for acquiring real-time infrared image information of the PAN precursor in each processing cavity; the temperature control data docking port is used for acquiring real-time temperature control data of each processing cavity; the circulating fan control data docking port is used for acquiring real-time circulating fan control data of each processing cavity; and the monitoring and early warning module is used for judging the temperature state of each reaction cavity according to the temperature data monitored by the temperature sensor and the real-time infrared image information, and carrying out abnormal analysis on the processing state according to comparison of the judging result with the real-time temperature control data and the circulating fan control data.

Description

A method and system for online temperature monitoring of processing flow

Technical Field

The present invention relates to the technical field of machining process monitoring, and in particular to a machining process online temperature monitoring method and system.

Background technique

Temperature monitoring is a common control indicator in the processing and production process. Deviations and abnormalities in temperature control will have a great impact on the quality of the product. For the processing of carbon fiber, it is necessary to pre-oxidize, low-temperature carbonize, high-temperature carbonize and graphitize the pre-treated polyacrylonitrile (PAN) fibers. In this process, it is necessary to accurately detect the ambient temperature state and product temperature state in each processing chamber, and then take timely measures when abnormalities occur to ensure product quality.

The temperature monitoring method mainly sets a temperature sensor to monitor the temperature in each processing chamber in real time, and then judges the accuracy of temperature control by comparing the temperature value with the set temperature. When the actual temperature in the processing chamber is significantly different from the controlled temperature, an early warning is issued, and the problems with the equipment are then dealt with.

Although the existing temperature monitoring method can judge the temperature of each processing chamber during the carbon fiber processing process, due to the sensitivity of PAN silk to temperature, the uniformity of the temperature distribution of each processing chamber has a great impact on the consistency of the product. At the same time, the existing temperature judgment method has poor sensitivity for abnormal judgment of temperature control. When there is a large difference between the actual temperature and the controlled temperature, it means that the product has been greatly affected, which will cause certain losses.

Summary of the invention

The purpose of the present invention is to provide a method and system for online temperature monitoring of a processing flow to solve the following technical problems:

How to improve the comprehensiveness and sensitivity of temperature monitoring for each reaction chamber.

The purpose of the present invention can be achieved through the following technical solutions:

A processing flow online temperature monitoring system, the system comprising:

Temperature sensors: each processing chamber is provided with several groups of temperature sensors for real-time monitoring of the temperature data of the corresponding position points of each temperature sensor;

Infrared image acquisition module, used to collect real-time infrared image information of PAN raw fibers in each processing chamber;

Temperature control data docking port, used to obtain real-time temperature control data of each processing chamber;

Circulation fan control data docking port, used to obtain real-time circulation fan control data of each processing chamber;

The monitoring and early warning module is used to judge the temperature status of each reaction chamber based on the temperature data monitored by the temperature sensor and the real-time infrared image information, and to perform abnormal analysis on the processing status based on the comparison of the judgment result with the real-time temperature control data and circulating fan control data;

The feedback adjustment module is used to perform feedback adjustment on the temperature control data and the circulating fan control data according to the results of the abnormal analysis.

Furthermore, the process of determining the temperature state of each reaction chamber includes:

Obtaining the mean value of the ambient temperature in the reaction chamber and the ambient temperature uniformity coefficient according to the temperature data monitored by the temperature sensor;

Identify the location of the PAN raw silk in the infrared image information, and obtain the dot matrix temperature mean and dot matrix temperature uniformity coefficient in the identification area according to the preset points;

The temperature field mean value of the reaction chamber is determined according to the ambient temperature mean value and the lattice temperature mean value, and the temperature field uniformity coefficient of the reaction chamber is determined according to the ambient temperature uniformity coefficient and the lattice temperature uniformity coefficient; the temperature state of each reaction chamber is judged according to the temperature field mean value and the temperature field uniformity coefficient.

Furthermore, the process of obtaining the ambient temperature uniformity coefficient includes:

By formula:

Calculate and obtain the ambient temperature uniformity coefficient s _ET (t) at the current time point;

The process of obtaining the lattice temperature uniformity coefficient includes:

By formula:

Calculate and obtain the lattice temperature uniformity coefficient s _PT (t) at the current time point;

The process of obtaining the temperature field average value includes:

By formula:

Calculate and obtain the average temperature field value T _field (t) at the current time point;

By formula:

Calculate and obtain the temperature field uniformity coefficient s _field (t) at the current time point;

Where n is the number of temperature sensors in the current reaction chamber, i∈[1,n], ET _i (t) is the real-time temperature obtained by the i-th sensor, Get the real-time temperature mean for all sensors, m is the number of points in the identification area, j∈[1,m], PT _i (t) is the real-time temperature at the jth point, /> is the mean temperature of all lattices, μ is the thermal conductivity, γ is the adjustment coefficient, and γ＞1.

Furthermore, the process of analyzing machining anomalies includes:

By formula:

Calculate and obtain the temperature control difference T _Δ (t) at the current time point;

Compare the temperature control difference T _Δ (t) with the first preset difference interval [T1 _Δ low, T1 _Δ up]:

like Then the temperature of the current reaction chamber is warned;

If T _Δ (t)∈[T1 _Δ low, T1 _Δ up], then T _Δ (t) is compared with the first preset difference interval [T2 _Δ low, T2 _Δ up]:

If T _Δ (t)∈[T2 _Δ low, T2 _Δ up], it is judged that the current reaction chamber temperature control state is normal;

like Then the current reaction chamber temperature control data is feedback-adjusted;

Wherein, p(t) is the real-time power of the reaction chamber temperature control device, f _T is the reaction chamber temperature diffusion function, and t1 is a preset fixed period.

Furthermore, the process of feedback-adjusting the current reaction chamber temperature control data includes:

Taking t1 as the cycle, the temperature control data of the next cycle is adjusted according to the temperature control difference of the previous cycle. The adjustment process includes:

By formula:

Calculate and obtain the adjustment amount Δp1 of the temperature control device of the reaction chamber in the current cycle;

Where T _Δ is the temperature control difference of the previous cycle, is the inverse function of f _T , W is the conditional coefficient, when When , W＝1, otherwise, W＝-1; μ is the incremental coefficient, and it satisfies 1.1＞μ＞1.

Furthermore, the process of analyzing processing anomalies also includes:

By formula:

Calculate and obtain the temperature uniformity difference s _Δ (t) at the current time point;

Compare the temperature uniformity difference s _Δ (t) with the preset uniformity difference interval [s1 _Δ , s2 _Δ ]:

If s _Δ (t)＜s1 _Δ , it is judged that the current temperature uniformity of the reaction chamber is normal;

If s _Δ (t)＞s2 _Δ , a temperature uniformity warning is issued for the current reaction chamber;

If s _Δ (t)∈[s1 _Δ ,s2 _Δ ], then take t1 as the period to determine whether the average power of the circulating fan control in the period reaches the preset critical value:

If it is reached, a temperature uniformity warning will be issued for the current reaction chamber;

If it is not reached, feedback adjustment is performed on the current temperature uniformity of the reaction chamber;

Wherein, t1 is a preset fixed period.

Furthermore, the process of feedback-adjusting the current temperature uniformity of the reaction chamber includes:

By formula:

Calculate and obtain the current cycle fan control power increase Δp2;

Among them, pm is the preset critical value, is the average power of the circulating fan control in the last cycle, s _Δ is the temperature uniformity difference in the last cycle, and τ is the preset fixed coefficient.

The invention discloses an online temperature monitoring method for a processing flow, wherein the method adopts an online temperature monitoring system for a processing flow to perform temperature monitoring and control process.

Beneficial effects of the present invention:

(1) The present invention can accurately judge the overall temperature state in the processing chamber through the monitoring process of multiple temperature sensors and product surface temperature in each processing chamber, thereby improving the comprehensiveness of the temperature monitoring process, and combining the monitored data with real-time temperature control data and circulating fan control data for analysis, so as to make more timely judgments on existing temperature control anomalies, that is, improve the sensitivity of abnormal warning. In addition, the feedback adjustment module performs feedback adjustment on the temperature control data and circulating fan control data according to the results of the abnormal analysis, and then adaptively self-adjusts the current temperature state within the controllable error range, thereby ensuring the accuracy of temperature control.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further described below in conjunction with the accompanying drawings.

FIG. 1 is a logic block diagram of an online temperature monitoring system for a processing flow of the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Please refer to FIG. 1 . In one embodiment, an online temperature monitoring system for a processing flow is provided. The system includes a temperature sensor, an infrared image acquisition module, a temperature control data docking port, a circulating fan control data docking port, a monitoring and early warning module, and a feedback adjustment module. In each processing chamber, a plurality of groups of temperature sensors are arranged to monitor the temperature data of the corresponding position points of each temperature sensor in real time. The infrared image acquisition module is used to collect the real-time infrared image information of the PAN raw silk in each processing chamber. Therefore, through the monitoring process of multiple temperature sensors in each processing chamber and the surface temperature of the product, the overall temperature state in the processing chamber can be accurately judged, thereby improving the comprehensiveness of the temperature monitoring process. The temperature control data docking port is used to obtain the real-time temperature control data of each processing chamber. The circulating fan The control data docking port is used to obtain the real-time circulating fan control data of each processing chamber. Therefore, the temperature state of each reaction chamber is judged by the monitoring and early warning module according to the temperature data monitored by the temperature sensor and the real-time infrared image information. The processing state is analyzed for abnormalities based on the comparison of the judgment result with the real-time temperature control data and the circulating fan control data. In this process, the monitored data is combined with the real-time temperature control data and the circulating fan control data for analysis, which can make more timely judgments on the existing temperature control abnormalities, that is, improve the sensitivity of the abnormal early warning. In addition, the feedback adjustment module performs feedback adjustment on the temperature control data and the circulating fan control data according to the results of the abnormal analysis, and then adaptively self-adjusts the current temperature state within the controllable error range, thereby ensuring the accuracy of temperature control.

The process of judging the temperature state of each reaction chamber includes: obtaining the mean value of the ambient temperature in the reaction chamber and the ambient temperature uniformity coefficient according to the temperature data monitored by the temperature sensor; wherein the process of obtaining the ambient temperature uniformity coefficient includes: using the formula:

Calculate and obtain the ambient temperature uniformity coefficient s _ET (t) at the current time point;

Where n is the number of temperature sensors in the current reaction chamber, i∈[1,n], ET _i (t) is the real-time temperature obtained by the i-th sensor, The real-time temperature mean is obtained for all sensors, so the environmental temperature uniformity coefficient can reflect the discreteness of the temperature values of temperature points at different positions, and then the current temperature distribution state can be judged by s _ET (t). When the temperature state distribution is relatively uniform, the s _ET (t) value is close to 0; then the position of the PAN raw fiber in the infrared image information is identified. It should be noted that the above identification process is based on the machine learning training model. The specific machine learning training model is obtained by training the working state images of different processing chambers, which will not be further described here. Then, the dot matrix temperature mean and the dot matrix temperature uniformity coefficient in the identification area are obtained according to the preset points; the process of obtaining the dot matrix temperature uniformity coefficient includes: through the formula:

Calculate and obtain the point temperature uniformity coefficient s _PT (t) at the current time point; where m is the number of points in the identification area, j∈[1, m], PT _i (t) is the real-time temperature at the jth point, is the average temperature of all the lattices. Therefore, the lattice temperature uniformity coefficient obtained by the above calculation can judge the temperature uniformity of the current temperature acting on the product, and then determine the average temperature field of the reaction chamber according to the average ambient temperature and the average lattice temperature. The formula is:

The mean value of the temperature field at the current time point T _field (t) is calculated; where μ is the thermal conductivity, and the temperature state in the current reaction chamber can be more accurately judged by the mean value of the temperature field; the temperature field uniformity coefficient of the reaction chamber is determined according to the ambient temperature uniformity coefficient and the lattice temperature uniformity coefficient; by the formula:

The temperature field uniformity coefficient s _field (t) at the current time point is calculated; wherein γ is an adjustment coefficient and satisfies γ>1. Therefore, this embodiment comprehensively considers the product temperature and the spatial temperature in the reaction chamber to accurately judge the overall temperature status and overall distribution status of the temperature field mean value and the temperature field uniformity coefficient, and the process of processing abnormality analysis includes:

By formula:

The temperature control difference T _Δ (t) at the current time point is calculated; wherein p(t) is the real-time power of the reaction chamber temperature control device, f _T is the reaction chamber temperature diffusion function, which is obtained by fitting the reaction chamber test data, and t1 is a preset fixed time period, which is selected and set according to empirical data. Therefore, the temperature control difference T _Δ (t) is compared with the first preset difference interval [T1 _Δ low, T1 _Δ up]: [T1 _Δ low, T1 _Δ up] is set according to the error tolerance condition in the test data. Therefore, if It means that there is a problem of abnormal temperature control in the differential process, and then a temperature warning is issued for the current reaction chamber; if T _Δ (t)∈[T1 _Δ low, T1 _Δ up], T _Δ (t) is compared with the first preset differential interval [T2 _Δ low, T2 _Δ up]: [T2 _Δ low, T2 _Δ up] is set according to the allowable error data fitting under the ideal state, and satisfies [T2 _Δ low, T2 _Δ up]∈[T1 _Δ low, T1 _Δ up]. Therefore, if T _Δ (t)∈[T2 _Δ low, T2 _Δ up], it is judged that the current reaction chamber temperature control state is normal; if/> Feedback adjustment is then performed on the current reaction chamber temperature control data. The process of feedback adjustment on the current reaction chamber temperature control data includes: taking t1 as a cycle, adjusting the temperature control data of the next cycle according to the temperature control difference of the previous cycle. The adjustment process includes:

By formula:

Calculate and obtain the adjustment amount Δp1 of the reaction chamber temperature control device in the current cycle; where T _Δ is the temperature control difference in the previous cycle, is the inverse function of f _T , W is the conditional coefficient, when/> When W=1, otherwise, W=-1; μ is the incremental coefficient and satisfies 1.1＞μ＞1. It is set according to the test data fitting and is used to correct the obtained adjustment amount. Therefore, through the above process, on the basis of judging that the temperature control abnormality occurs, different processing strategies can be adopted when the cumulative difference mean is too large. On the one hand, potential risks can be judged in time. On the other hand, when it is judged that the temperature is within the controllable range, the normal operation of the current processing process can be guaranteed by feedback adjustment, thereby ensuring the quality of the prepared product.

In addition, the process of processing abnormality analysis also includes: through the formula:

The temperature uniformity difference s _Δ (t) at the current time point is calculated; the temperature uniformity difference s _Δ (t) is compared with the preset uniformity difference interval [s1 _Δ , s2 _Δ ], and the preset uniformity difference interval is set according to the empirical data fitting. Therefore, if s _Δ (t) < s1 _Δ , it means that the current temperature distribution state is relatively uniform, and then it is judged that the temperature uniformity state of the current reaction chamber is normal; if s _Δ (t) > s2 _Δ , it means that the current temperature state is in an uncontrollable state, and then a temperature uniformity warning is issued for the current reaction chamber; if s _Δ (t) ∈ [s1 _Δ , s2 _Δ ], then with t1 as the period, it is judged whether the average value of the average power control of the circulating fan of the period reaches the preset critical value: if it reaches it, a temperature uniformity warning is issued for the current reaction chamber; if it does not reach it, the temperature uniformity of the current reaction chamber is feedback-adjusted, and the process of feedback-adjusting the temperature uniformity of the current reaction chamber includes:

By formula:

Calculate and obtain the current cycle fan control power increase Δp2; where pm is the preset critical value, is the mean value of the average power of the circulating fan control in the previous cycle, s _Δ is the temperature uniformity difference in the previous cycle, τ is the preset fixed coefficient, which is set according to the fitting of empirical data and should be used to correct the obtained increase. It should be noted that when the circulating fan control power is large, it will have a certain impact on the PAN raw yarn. Therefore, the preset critical value is set to avoid the influence of large airflow on the PAN raw yarn. Then, on the basis of ensuring the quality of the raw yarn, the operating performance of the temperature in the reaction chamber is adaptively adjusted by increasing the cycle circulating fan control power to ensure the overall quality of the product.

In one embodiment, a method for online temperature monitoring of a processing flow is provided. The method uses an online temperature monitoring system for a processing flow to perform temperature monitoring and control processes.

The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims (8)

1. A processing flow online temperature monitoring system, characterized in that the system comprises: Temperature sensors: each processing chamber is provided with several groups of temperature sensors for real-time monitoring of the temperature data of the corresponding position points of each temperature sensor; Infrared image acquisition module, used to collect real-time infrared image information of PAN raw fibers in each processing chamber; Temperature control data docking port, used to obtain real-time temperature control data of each processing chamber; Circulation fan control data docking port, used to obtain real-time circulation fan control data of each processing chamber; The monitoring and early warning module is used to judge the temperature status of each reaction chamber based on the temperature data monitored by the temperature sensor and the real-time infrared image information, and to perform abnormal analysis on the processing status based on the comparison of the judgment result with the real-time temperature control data and circulating fan control data; The feedback adjustment module is used to perform feedback adjustment on the temperature control data and the circulating fan control data according to the results of the abnormal analysis. 2. The online temperature monitoring system for a processing flow according to claim 1 is characterized in that the process of judging the temperature state of each reaction chamber comprises: Obtaining the mean value of the ambient temperature in the reaction chamber and the ambient temperature uniformity coefficient according to the temperature data monitored by the temperature sensor; Identify the location of the PAN raw silk in the infrared image information, and obtain the dot matrix temperature mean and dot matrix temperature uniformity coefficient in the identification area according to the preset points; The temperature field mean value of the reaction chamber is determined according to the ambient temperature mean value and the lattice temperature mean value, and the temperature field uniformity coefficient of the reaction chamber is determined according to the ambient temperature uniformity coefficient and the lattice temperature uniformity coefficient; the temperature state of each reaction chamber is judged according to the temperature field mean value and the temperature field uniformity coefficient. 3. The online temperature monitoring system for a processing flow according to claim 2, characterized in that the process of obtaining the ambient temperature uniformity coefficient comprises: By formula: Calculate and obtain the ambient temperature uniformity coefficient s _ET (t) at the current time point; The process of obtaining the lattice temperature uniformity coefficient includes: By formula: Calculate and obtain the lattice temperature uniformity coefficient s _PT (t) at the current time point; The process of obtaining the temperature field average value includes: By formula: Calculate and obtain the average temperature field value T _field (t) at the current time point; By formula: Calculate and obtain the temperature field uniformity coefficient s _field (t) at the current time point; Where n is the number of temperature sensors in the current reaction chamber, i∈[1,n], ET _i (t) is the real-time temperature obtained by the i-th sensor, Get the real-time temperature mean for all sensors, m is the number of points in the identification area, j∈[1,m], PT _i (t) is the real-time temperature at the jth point, /> is the mean temperature of all lattices, μ is the thermal conductivity, γ is the adjustment coefficient, and γ＞1. 4. The online temperature monitoring system for a processing flow according to claim 3 is characterized in that the process of analyzing processing abnormalities includes: By formula: Calculate and obtain the temperature control difference T _Δ (t) at the current time point; Compare the temperature control difference T _Δ (t) with the first preset difference interval [T1 _Δ low, T1 _Δ up]: like Then the temperature of the current reaction chamber is warned; If T _Δ (t)∈[T1 _Δ low, T1 _Δ up], then T _Δ (t) is compared with the first preset difference interval [T2 _Δ low, T2 _Δ up]: If T _Δ (t)∈[T2 _Δ low, T2 _Δ up], it is judged that the current reaction chamber temperature control state is normal; like Then the current reaction chamber temperature control data is feedback-adjusted; Wherein, p(t) is the real-time power of the reaction chamber temperature control device, f _T is the reaction chamber temperature diffusion function, and t1 is a preset fixed period. 5. The online temperature monitoring system for a processing flow according to claim 4 is characterized in that the process of feedback-adjusting the current reaction chamber temperature control data comprises: Taking t1 as the cycle, the temperature control data of the next cycle is adjusted according to the temperature control difference of the previous cycle. The adjustment process includes: By formula: Calculate and obtain the adjustment amount Δp1 of the temperature control device of the reaction chamber in the current cycle; Where T _Δ is the temperature control difference of the previous cycle, is the inverse function of f _T , W is the conditional coefficient, when When , W＝1, otherwise, W＝-1; μ is the incremental coefficient, and it satisfies 1.1＞μ＞1. 6. The online temperature monitoring system for a processing flow according to claim 3, characterized in that the process of analyzing processing abnormalities further comprises: By formula: Calculate and obtain the temperature uniformity difference s _Δ (t) at the current time point; Compare the temperature uniformity difference s _Δ (t) with the preset uniformity difference interval [s1 _Δ , s2 _Δ ]: If s _Δ (t)＜s1 _Δ , it is judged that the current temperature uniformity of the reaction chamber is normal; If s _Δ (t)＞s2 _Δ , a temperature uniformity warning is issued for the current reaction chamber; If s _Δ (t)∈[s1 _Δ ,s2 _Δ ], then take t1 as the period to determine whether the average power of the circulating fan control in the period reaches the preset critical value: If it is reached, a temperature uniformity warning will be issued for the current reaction chamber; If it is not reached, feedback adjustment is performed on the current temperature uniformity of the reaction chamber; Wherein, t1 is a preset fixed period. 7. The online temperature monitoring system for a processing flow according to claim 6, characterized in that the process of feedback-adjusting the current temperature uniformity of the reaction chamber comprises: By formula: Calculate and obtain the current cycle fan control power increase Δp2; Among them, pm is the preset critical value, is the average power value of the circulating fan control in the last cycle, s _Δ is the temperature uniformity difference in the last cycle, and τ is the preset fixed coefficient. 8. A method for online temperature monitoring of a processing flow, characterized in that the method adopts an online temperature monitoring system for a processing flow as described in any one of claims 1 to 7 to perform temperature monitoring and control processes.