Continuous temperature control method suitable for high-temperature heating furnace

Abstract

The invention relates to the technical field of high-temperature heating furnace temperature control, in particular to a temperature continuous control method suitable for a high-temperature heating furnace, which comprises the following steps: analyzing temperature data collected by different instruments, calculating a temperature change overlap ratio by combining neighborhood differences, and integrating the temperature data to obtain a temperature change overlap ratio; a characteristic curve is selected through historical temperature curve fitting, and a heating section and a heat preservation section are determined; determining an initial PID parameter based on the overlap ratio of the current temperature data and the historical data and the PID parameter distribution; finally, the proportional gain parameter is adjusted by controlling the temperature fluctuation characteristic of the period and the difference between the temperature fluctuation characteristic and the characteristic curve, and the method aims at achieving accurate control over temperature oscillation and deviation elimination in the heating process of the high-temperature furnace.

Description

Technical Field

[0001] This application relates to the field of temperature control technology for high-temperature heating furnaces, and specifically to a method for continuous temperature control suitable for high-temperature heating furnaces. Background Technology

[0002] High-temperature heating furnaces, a general term for certain high-temperature heating devices, are common industrial equipment used in various industries. They are not only used as energy-saving electric furnaces in the metal processing industry, but also in industries such as construction and medicine. With the continuous advancement of industrial technology, the temperature control requirements for high-temperature heating furnaces are gradually increasing in their application areas.

[0003] In the sintering process of tungsten-molybdenum powder metallurgy in high-temperature heating furnaces, PID algorithms are commonly used for continuous temperature control. However, in terms of PID parameter tuning, traditional methods rely on experience or static settings, which are difficult to adapt to the dynamic characteristics changes at different stages of the sintering process. This can easily lead to temperature overshoot, oscillation, or sluggish response, affecting control accuracy and efficiency and increasing energy consumption. Summary of the Invention

[0004] In view of the above, it is necessary to provide a continuous temperature control method suitable for high-temperature heating furnaces to solve the above problems.

[0005] One embodiment of this application provides a method for continuous temperature control of a high-temperature heating furnace, the method comprising: During the heating process in the high-temperature heating furnace, the sequences of temperature data collected at all times by two different instruments are respectively denoted as the first temperature data sequence and the second temperature data sequence. The system presets the temperature switching range and the neighborhood of temperature data, filters out the temperature data collected by two instruments that simultaneously meet the temperature switching range at each time, and obtains the temperature change overlap between the temperature data collected at the corresponding time by combining the difference distribution between the neighborhood data; the time with the largest temperature change overlap is taken as the switching point, and the first temperature data sequence and the second temperature data sequence are integrated; based on the integrated temperature data from multiple historical heating processes, the reference heating process for the current heating is selected, and the heating process of the high-temperature furnace is divided into heating segment and heat preservation segment; The PID parameters of the PID controller are obtained during each heating process of the high-temperature heating furnace. The distribution of temperature change overlap between temperature data of the same sequence is analyzed in the integrated temperature data of the current heating process and the historical heating process. The initial proportional gain parameters of the current heating and holding stages are determined by combining the proportional gain parameters in the PID parameters of the historical heating and holding stages. The control cycle is preset. Based on the fluctuation characteristics of the integrated temperature data of each control cycle and the intersection characteristics with the temperature data of the reference heating process, the constrained temperature oscillation characteristic value is obtained. For each control cycle, the deviation elimination degree is determined based on the difference distribution characteristics between the integrated temperature data and the temperature data of the current reference heating process. The constrained temperature oscillation characteristic value and the deviation elimination degree are compared with the initial proportional gain parameter, and the proportional gain parameter of the PID algorithm for the next control cycle is adjusted.

[0006] Specifically, obtaining the temperature change overlap between the temperature data collected at corresponding times involves: For two temperature data points in the first and second temperature data sequences that are within the same sampling time range, calculate the absolute value of the difference between the temperature data points at the same time in the neighborhood temperature sequences of the two temperature data points. Then, sum all the absolute values ​​of the differences between the two temperature data points and take the reciprocal to obtain the degree of overlap of temperature changes between the two temperature data points at the same sampling time.

[0007] Specifically, the process of integrating the first temperature data sequence and the second temperature data sequence involves: combining the temperature data before and after the switching point in the first temperature data sequence with the temperature data after the switching point in the second temperature sequence to form a temperature sequence; and using the temperature data within the temperature sequence as the integrated temperature data. The temperature data of the first sequence is acquired by a thermocouple, and the temperature data of the second sequence is acquired by an infrared thermometer.

[0008] Wherein, the sequence of temperature data from the current heating reference process satisfies the minimum sum of DTW distances between it and the sequence of temperature data integrated from the remaining heating processes.

[0009] The process of dividing the high-temperature furnace heating process into a heating section and a holding section is as follows: Obtain the inflection point of the fitted curve of the temperature data during the current heating process, and obtain the first inflection point and the second inflection point. The time period before the first inflection point is taken as the heating period, and the time period after the first inflection point and before the second inflection point is taken as the heat preservation period.

[0010] Specifically, the process of determining the initial proportional gain parameters for the current heating and holding sections is as follows: The proportional gain parameter that appears most frequently in the historical heating phase is used as the initial proportional gain parameter for the PID control algorithm in the heating phase. Obtain the sum of the temperature change overlap between the integrated temperature data of the insulation section during the current heating process and the integrated temperature data of the insulation section during the historical heating process. Select the proportional gain parameter of the PID parameter of the insulation section during the historical heating process with the largest sum of temperature change overlap as the initial proportional gain parameter of the PID control algorithm of the insulation section during the current heating process.

[0011] Specifically, the obtained constrained temperature oscillation characteristic value is as follows: Based on the fluctuation characteristics of the integrated temperature data for each control cycle, and combined with the intersection characteristics between the temperature data and the reference heating process temperature data during the current heating, the temperature oscillation characteristic value for each control cycle is obtained. The sequence of integrated temperature data within each control cycle is denoted as the local temperature data sequence. Its first-order difference sequence is obtained, and the number of adjacent elements with different positive and negative signs in the first-order difference sequence is obtained. The absolute value of the difference between each adjacent temperature data in the local temperature data sequence is calculated and divided by the time interval between adjacent temperature data to obtain the temperature change rate. All the obtained temperature change rates are accumulated and multiplied by the number to obtain the constraint factor for each control cycle. Divide the temperature oscillation characteristic value by the normalized constraint factor, and normalize the resulting ratio to obtain the constrained temperature oscillation characteristic value.

[0012] Specifically, obtaining the temperature oscillation characteristic value for each control cycle is as follows: Obtain the percentage of extreme points in the local temperature data sequence for each control cycle; calculate the sum of the absolute values ​​of the differences between all temperature data within the control cycle and the temperature data at the corresponding moment in the current reference heating process; In the current heating process, for the temperature data at time i, if it satisfies the following conditions: the temperature data at time i-1 is greater than the temperature data at time i-1 in the current heating reference process, and the temperature data at time i+1 is less than the temperature data at time i+1 in the current heating reference process, then the temperature data at time i is a temperature change feature point; the number of temperature change feature points in each control cycle is counted. For each control cycle, the result of positively fusing the percentage of the number, the sum, and the number of temperature change feature points is used as the temperature oscillation feature value for each control cycle.

[0013] Specifically, determining the deviation elimination degree for each control cycle involves: For the integrated temperature data in the control cycle, calculate the absolute value of the difference between each temperature data and the temperature data at the corresponding moment in the current heating reference process, and record it as the deviation value of each temperature data. Calculate the absolute value and minimum value of the difference between the deviation value of the temperature data and the deviation value of the adjacent previous temperature data. The ratio of the absolute value of the difference to the minimum value is used as the degree of change of the deviation value of each temperature data. The ratio of the deviation value to the degree of change of the deviation value is calculated, and the negative correlation mapping result obtained by accumulating the ratios obtained from all temperature data is used as the degree of deviation elimination for each control cycle.

[0014] Specifically, adjusting the proportional gain parameter of the PID algorithm for the next control cycle involves: The initial proportional gain parameters obtained in the heating and holding stages are used as the proportional gain parameters for the first control cycle of the heating and holding stages. The formula for adjusting the proportional gain parameter is: In the formula: The proportional gain parameter representing the control period; This indicates the preset adjustment factor; This represents the temperature oscillation characteristic value after the control period constraint; Indicates the degree of deviation elimination in the control cycle; This represents the cumulative sum of the deviations of temperature data within the control cycle; This represents the sum of all temperature change rates within the control cycle; This represents the proportional gain parameter for the next control cycle. , All are preset thresholds, among which and ; This represents the normalization function.

[0015] This application has at least the following beneficial effects: This application combines thermocouples and infrared thermometers to work together. Based on the calculation of the overlap of temperature changes at the same sampling time point and its neighboring temperature data, it dynamically determines the optimal switching point with the highest overlap within a preset switching range, achieving smooth and seamless integration of sensor data. This effectively solves the problems of insufficient temperature measurement accuracy and switching shock in the high-temperature segment, laying the foundation for precise control. Secondly, by utilizing the integrated temperature data sequence of historical sintering processes and obtaining the temperature data of the current heating reference process as an ideal reference, it accurately divides the heating and holding stages, better supporting the temperature control strategy and ensuring more refined heating and holding processes, thereby improving the reliability and efficiency of the entire process. Finally, based on this characteristic curve and historical experience, initial PID parameters are set, and in each control cycle, the temperature oscillation characteristic value is constructed and constrained to reflect the oscillation intensity and deviation elimination degree. Accordingly, the proportional gain parameter of the PID is adaptively and dynamically adjusted to effectively suppress temperature oscillation, reduce overshoot, and accelerate deviation elimination, significantly improving the temperature control accuracy, response speed, and stability of the high-temperature heating furnace in complex sintering processes, ultimately reducing energy consumption. Attached Figure Description

[0016] Figure 1 A flowchart of a continuous temperature control method for a high-temperature heating furnace provided in this application; Figure 2 A flowchart illustrating the acquisition process of the heating section and the insulation section provided in this application. Detailed Implementation

[0017] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0019] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0021] This application proposes a continuous temperature control method suitable for high-temperature heating furnaces, applied in the field of high-temperature heating furnace temperature control technology, as detailed in the appendix. Figure 1 The method includes the following steps: S1: During the heating process in the high-temperature heating furnace, the sequences of temperature data collected at all times by two different instruments are respectively denoted as the first temperature data sequence and the second temperature data sequence.

[0022] A thermocouple is installed at the center of the furnace chamber of the high-temperature heating furnace to collect the temperature at various times inside the furnace. The temperature data acquired at all times are combined to form the first temperature data sequence of the high-temperature heating furnace.

[0023] Since the temperature detection temperature of thermocouples is typically 2300℃, which is lower than the maximum temperature of 2500℃ during the sintering process in a high-temperature heating furnace, this application also uses an infrared thermometer to collect temperature data from the high-temperature heating furnace. The infrared thermometer's sensor is installed outside the high-temperature heating furnace, pointing towards the area inside the furnace that needs to be monitored. By precisely aligning it with the heating area inside the furnace, the infrared thermometer can acquire the furnace temperature in real time, obtaining a second temperature data sequence.

[0024] This completes the collection of relevant temperature data for the high-temperature heating furnace.

[0025] S2: Preset the temperature switching range and the neighborhood of temperature data, filter out the temperature data collected by the two instruments that simultaneously meet the temperature switching range at each time, and combine the difference distribution between the neighborhood data to obtain the temperature change overlap between the temperature data collected at the corresponding time; take the time with the largest temperature change overlap as the switching point, and integrate the first temperature data sequence and the second temperature data sequence; based on the integrated temperature data in the historical multiple heating processes, filter out the reference heating process for the current heating, and divide the high-temperature furnace heating process to obtain the heating segment and the heat preservation segment.

[0026] In continuous temperature control of high-temperature heating furnaces, PID control is the most widely used algorithm, and its parameter tuning directly determines the stability, response speed, and accuracy of the control system. This application controls the temperature of the high-temperature heating furnace during the sintering process by adaptively adjusting the parameters of the PID algorithm.

[0027] This application addresses the sintering process of tungsten-molybdenum powder metallurgy materials after pressing in a high-temperature heating furnace. During this process, the temperature changes from room temperature to high temperature. While thermocouples are typically used for temperature measurement in the low-temperature range, their accuracy decreases sharply with increasing temperature in the high-temperature range. Therefore, an infrared thermometer with a temperature measurement range of 1000℃ to 3200℃ should be used for temperature measurement in the high-temperature range. When thermocouples and infrared thermometers work together, a switching point needs to be set. Before the switching point, temperature control is performed using temperature data collected by the thermocouples; after the switching point, control is performed using temperature data collected by the infrared thermometer. This application uses 1400℃ to 1600℃ as the temperature switching range and analyzes the temperature data when the temperature reaches this range. The analysis here uses temperature data collected by the thermocouples at the same time. Temperature data collected by infrared thermometer For example, obtain respectively Point and The 20 nearest neighboring data points of a given temperature data point in its sampling time sequence are denoted as the neighboring data points of that data point. This number of 20 is arbitrarily set; implementers can adjust the number of neighboring data points according to actual circumstances, and this application does not impose any restrictions on this. The sequence of all neighboring data points for each data point is denoted as the neighborhood temperature sequence.

[0028] For the temperature data at the same sampling time in the first temperature data sequence and the second temperature data sequence that are within the temperature switching range, calculate the absolute value of the difference between the temperature data at the same time in the neighborhood temperature sequences of the two temperature data. Then, sum all the absolute values ​​of the differences obtained from the two temperature data and take the reciprocal to obtain the degree of overlap of temperature changes between the two temperature data at the same sampling time.

[0029] It should be understood that the greater the overlap of temperature changes, the closer the temperatures collected by the thermocouple and the infrared thermometer are. In this case, the temperature sampling accuracy is higher, and the impact on the temperature control system is smaller when switching temperature data. Conversely, the smaller the overlap of temperature changes, the greater the temperature difference between the two types of sensors, the lower the temperature sampling accuracy, and the greater the impact on the temperature control system when switching temperature data.

[0030] The temperature data with the highest degree of overlap in temperature changes is selected as the switching point. If there are multiple temperature data with the highest degree of overlap, the temperature data closest to the center of the temperature switching range is selected as the switching point. The two temperature data sequences can then be integrated to obtain a temperature sequence. The temperature data at and before the switching point in the temperature sequence are temperature data collected by thermocouples, while the temperature data after the switching point are temperature data collected by infrared thermometers. It should be understood that the temperature sequence is composed of the integrated temperature data corresponding to the heating process. All temperature data mentioned thereafter are integrated temperature data.

[0031] In each historical sintering operation of tungsten-molybdenum powder metallurgy using a high-temperature heating furnace, a corresponding integrated historical temperature sequence can be obtained. The fitted curve of the historical temperature sequence is recorded as a temperature change curve, where the vertical axis represents temperature data and the horizontal axis represents time. It should be understood that each historical sintering operation yields a temperature change curve from room temperature to the end of the process. Statistical analysis of the obtained historical temperature sequences generates temperature data for the current reference heating process. The sequence of temperature data from the current reference heating process satisfies the condition that the sum of the DTW distances between the current and all obtained historical temperature sequences is minimized. Based on the deviation between the temperature data of the current reference heating process and the real-time temperature value, the PID parameters are adaptively adjusted, effectively improving the temperature control accuracy and efficiency of the high-temperature heating furnace.

[0032] The temperature changes in the high-temperature furnace during sintering are typically divided into three stages: heating, holding, and cooling. Therefore, the inflection point of the sequence of temperature data from the reference heating process is detected using the first derivative method, resulting in two inflection points. These are designated as the first and second inflection points in chronological order of data acquisition. The time period before the first inflection point is considered the heating stage, and the time period after the first inflection point and before the second inflection point is considered the holding stage. This application primarily focuses on PID control for the heating and holding stages. It should be noted that if multiple inflection points exist, the two inflection points closest to the sintering temperature of tungsten-molybdenum powder metallurgy (2000℃) are designated as the first and second inflection points in chronological order.

[0033] The flowcharts for obtaining the heating section and the heat preservation section are as follows: Figure 2 As shown.

[0034] S3: Obtain the PID parameters of the PID controller during each heating process of the high-temperature heating furnace. Analyze the distribution of temperature change overlap between temperature data of the same sequence in the integrated temperature data of the current heating process and the historical heating process. Combine the proportional gain parameters in the PID parameters of the historical heating and holding stages to determine the initial proportional gain parameters of the current heating and holding stages. Preset the control cycle. Based on the fluctuation characteristics of the integrated temperature data of each control cycle and the intersection characteristics with the temperature data of the reference heating process, obtain the constrained temperature oscillation characteristic value.

[0035] In the sintering process of tungsten and molybdenum powder metallurgy, a large amount of power is required for rapid heating during the heating stage, while precise and stable temperature control is required during the holding stage. The corresponding PID parameters are different.

[0036] First, the initial parameters for the PID control algorithm of the heating and holding stages are obtained based on the historical temperature control data of tungsten-molybdenum powder metallurgy sintering. For the heating stage of the current sintering process, the proportional gain parameter, which appears most frequently in the historical temperature control data of the heating stage, is used as the initial proportional gain parameter for the PID control algorithm of the heating stage. Then, the sum of the temperature change overlap between the integrated temperature data of the holding stage during the current heating process and the integrated temperature data of the holding stage during the historical heating process is obtained. The proportional gain parameter of the PID parameter of the holding stage during the historical heating process, which has the largest sum of temperature change overlap, is selected as the initial proportional gain parameter for the PID control algorithm of the holding stage during the current heating process.

[0037] Furthermore, the initial PID parameters are adaptively adjusted to obtain the control period of the PID parameters during the real-time sintering process (from room temperature to the end of sintering) under the control of the initial PID parameters. In this application, this period is set to 30 seconds. The implementer can acquire the local temperature data sequence within the adjustment period. Here, it is assumed that the local temperature data sequence is Q. The local temperature data sequence acquired within the control period is analyzed to construct the temperature oscillation characteristic value Z within the control period. Its formula is as follows: In the formula: A represents the number of extreme points of the integrated temperature data within the control cycle. The extreme point extraction algorithm is a well-known method and will not be elaborated here. N represents the number of integrated temperature data within the control cycle. The larger the value, the more temperature data peaks and troughs there are within the control period, and the greater the oscillation characteristic value; m represents the number of temperature change characteristic points within the control period; This represents the absolute value of the difference between the a-th extreme point and the ideal temperature within the control cycle. The larger the value, the more severe the overshoot and the greater the temperature oscillation characteristic value.

[0038] The method for obtaining temperature change feature points is as follows: During the heating process, for the temperature data at time i, if the temperature data at time i-1 is greater than the temperature data at time i-1 in the current heating reference process, and the temperature data at time i+1 is less than the temperature data at time i+1 in the current heating reference process, then the temperature data at time i is the temperature change feature point.

[0039] It should be understood that the larger the temperature oscillation characteristic value, the more obvious the continuous temperature oscillation phenomenon is under the control of the PID parameter, indicating that the proportional gain is too large or the integral time is too small; the smaller the temperature oscillation characteristic value, the weaker the continuous temperature oscillation phenomenon is under the control of the PID parameter, indicating that the proportional gain is too small or the integral time is too large.

[0040] Frequent output jitter in PID parameter control can lead to large temperature oscillation characteristic values. However, this frequent output jitter is not caused by excessive proportional gain or insufficient integral time, but rather by excessive derivative time. Therefore, this application constructs a constraint factor based on the frequent output jitter phenomenon to constrain the temperature oscillation characteristic value. This constraint ensures that the temperature oscillation characteristic value accurately reflects the temperature oscillation phenomenon and reduces the interference caused by frequent jitter due to excessive derivative time. The constraint factor is denoted as Y, and its formula is as follows: In the formula: u represents the number of times the temperature change direction of the temperature data changes within the control cycle. For example, if the initial number of changes is 0, the number of changes is incremented by 1 when any data point satisfies the condition that its temperature change direction is inconsistent with the temperature change direction of its adjacent temperature data; N represents the number of integrated temperature data within the control cycle. This represents the rate of temperature change of the nth integrated temperature data point within the control cycle. Specifically, the rate of temperature change is the absolute value of the temperature difference between a given temperature data point and the previous temperature data point, divided by the time interval between the two data points. The constrained temperature oscillation characteristic value is... In the formula: Z represents the initial temperature oscillation characteristic value, and Y represents the normalized constraint factor. The constrained temperature oscillation characteristic value, This represents the normalization function.

[0041] It should be noted that the direction of temperature change at data point a can be obtained based on the temperature value of data point b at the next moment. If the temperature at point b is greater than or equal to the temperature at point a, then the temperature change at point a is positive; if the temperature at point b is less than the temperature at point a, then the temperature change at point a is negative. The specific method for obtaining the number of changes in the direction of temperature change is as follows: obtain the first-order difference sequence of the local temperature data sequence of the control period, and take the number of adjacent elements with different positive and negative signs in the first-order difference sequence as the number of changes in the direction of temperature change for the corresponding control period.

[0042] It should be understood that the larger the constraint factor, the stronger the correlation between the temperature oscillation characteristic value and the frequent output jitter phenomenon, and the weaker the actual temperature oscillation phenomenon; conversely, the smaller the constraint factor, the weaker the correlation between the temperature oscillation characteristic value and the frequent output jitter phenomenon, and the stronger the actual temperature oscillation phenomenon.

[0043] S4: For each control cycle, determine the deviation elimination degree for each control cycle based on the difference distribution characteristics between the integrated temperature data and the temperature data of the current reference heating process; compare the constrained temperature oscillation characteristic value and the deviation elimination degree for each control cycle, and adjust the proportional gain parameter of the PID algorithm for the next control cycle in combination with the initial proportional gain parameter.

[0044] When determining the proportional gain parameter in a PID algorithm based on temperature oscillation characteristics, the integral time parameter still has an impact. In this application, to further determine whether the issue lies with the integral time parameter or the proportional gain parameter, a deviation elimination degree is constructed based on the elimination of accumulated deviations. This degree is then used to further adjust the PID parameters. The integral time is used to eliminate historical accumulated deviations. When the integral time is too large, the integral action is weak, resulting in slow elimination of steady-state error and sluggish system response. Conversely, when the integral time is too small, the integral action is too strong, easily leading to overshoot and oscillation. Therefore, for temperature data within the control cycle, the deviation elimination degree F is constructed using the following formula: In the formula, N represents the number of integrated temperature data points within the control cycle; The absolute value of the difference between the nth temperature data and the temperature data at the corresponding moment in the current heating process is denoted as the deviation value. The deviation value of the nth temperature data is expressed as the degree of change between the nth temperature data and the adjacent previous temperature data. The method for obtaining the degree of change of the deviation value is as follows: calculate the absolute value and the minimum value of the difference between the deviation value of the temperature data and the deviation value of the adjacent previous temperature data; and take the ratio of the absolute value of the difference to the minimum value as the degree of change of the deviation value.

[0045] It should be understood that, The larger the value, the larger the deviation of the temperature data, but the smaller the change in the degree of deviation. This indicates a larger static deviation of the temperature data, meaning a larger historical cumulative deviation and a smaller degree of deviation elimination. A larger degree of deviation elimination means that the temperature deviation is eliminated faster under the current PID control parameters, and the better the effect of the integral time. If oscillation occurs, it is more likely caused by an insufficient integral time. Conversely, a smaller degree of deviation elimination means that the temperature deviation is eliminated slower under the current PID control parameters, and the worse the effect of the integral time. If oscillation occurs, it is more likely caused by an excessively large proportional gain.

[0046] Furthermore, the proportional gain parameter of the PID algorithm in the adaptive continuous temperature control of the high-temperature heating furnace is adjusted. The formula for the adjusted proportional gain parameter is: In the formula: The proportional gain parameter representing the control period; This indicates the preset adjustment factor; This represents the temperature oscillation characteristic value after the control period constraint; Indicates the degree of deviation elimination in the control cycle; This represents the cumulative sum of the deviations of temperature data within the control cycle; This represents the sum of all temperature change rates within the control cycle; This represents the proportional gain parameter for the next control cycle. , All are preset thresholds, among which and ; Represents the normalization function; The larger the value, the greater the likelihood that the current temperature data is oscillating, and the more likely the oscillation is to be caused by an excessively large proportional gain parameter; The larger the value, the faster the current temperature deviation is eliminated; conversely, the lower the rate of temperature change, the greater the likelihood that the proportional gain coefficient is too small. In this application, f=0.1 is set. and The threshold values ​​are set to 0.7 and 0.3, respectively, and the implementer can adjust them as needed.

[0047] The adjusted proportional gain parameter is used as the proportional gain parameter of the PID algorithm in the next control cycle of the high-temperature heating furnace to control the temperature of the furnace, improving temperature control accuracy and efficiency while reducing energy consumption. The high-temperature heating furnace mainly consists of three parts: the furnace body structure, the temperature measurement system, and the control system. The furnace body structure includes the furnace chamber and heating elements. The furnace chamber is insulated with multi-layer refractory materials, with a maximum withstand temperature ≥2500℃. The heating elements are surround-type silicon molybdenum rods or graphite heating elements, with an adjustable power range of 0–100kW. The temperature measurement system includes a thermocouple array and an infrared thermometer. The thermocouple array consists of S-type thermocouples distributed on the inner wall of the furnace chamber and the material support platform, monitoring the temperature of multiple low-temperature zones in real time. The infrared thermometer is installed in the optical window on the side wall of the furnace body, with its focus aligned with the central heating zone. The control system includes a core controller, actuators, and data interaction. The core controller is an embedded industrial PLC running an adaptive PID algorithm. The actuators are solid-state relays that adjust the heating element current, with a response time ≤20ms. Data interaction involves uploading temperature data to the host computer in real time, generating sintering process monitoring curves and parameter adjustment logs.

[0048] By continuously controlling the temperature during the sintering process in the high-temperature heating furnace through adaptive adjustment of PID parameters, a controllable high-temperature thermal environment is provided. This promotes the metallurgical bonding and densification of tungsten and molybdenum powder particles through diffusion mechanisms, forming and maintaining a uniform and stable thermal field. This ensures consistent heating of all parts of the material, preventing deformation or uneven performance. The precise execution of the preset heating-holding-cooling process curves, especially the stringent requirements for temperature stability during the holding stage, directly determines the key properties of the final sintered body, such as grain size, density, strength, and conductivity. This significantly improves the accuracy, efficiency, and stability of the high-temperature heating furnace in performing its core thermal functions during complex sintering processes, ultimately reducing energy consumption.

[0049] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0050] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for continuous temperature control suitable for high-temperature heating furnaces, characterized in that, The method includes the following steps: During the heating process in the high-temperature heating furnace, the sequences of temperature data collected at all times by two different instruments are respectively denoted as the first temperature data sequence and the second temperature data sequence. The system presets the temperature switching range and the neighborhood of temperature data, filters out the temperature data collected by two instruments that simultaneously meet the temperature switching range at each time, and obtains the temperature change overlap between the temperature data collected at the corresponding time by combining the difference distribution between the neighborhood data; the time with the largest temperature change overlap is taken as the switching point, and the first temperature data sequence and the second temperature data sequence are integrated; based on the integrated temperature data from multiple historical heating processes, the reference heating process for the current heating is selected, and the heating process of the high-temperature furnace is divided into heating segment and heat preservation segment; The PID parameters of the PID controller are obtained during each heating process of the high-temperature heating furnace. The distribution of temperature change overlap between temperature data of the same sequence is analyzed in the integrated temperature data of the current heating process and the historical heating process. The initial proportional gain parameters of the current heating and holding stages are determined by combining the proportional gain parameters in the PID parameters of the historical heating and holding stages. The control cycle is preset. Based on the fluctuation characteristics of the integrated temperature data of each control cycle and the intersection characteristics with the temperature data of the reference heating process, the constrained temperature oscillation characteristic value is obtained. For each control cycle, the deviation elimination degree is determined based on the difference distribution characteristics between the integrated temperature data and the temperature data of the current reference heating process. The constrained temperature oscillation characteristic value and the deviation elimination degree are compared with the initial proportional gain parameter, and the proportional gain parameter of the PID algorithm for the next control cycle is adjusted.

2. The continuous temperature control method for a high-temperature heating furnace as described in claim 1, characterized in that, The degree of overlap in temperature changes between the temperature data collected at corresponding times is specifically as follows: For two temperature data points in the first and second temperature data sequences that are within the same sampling time range, calculate the absolute value of the difference between the temperature data points at the same time in the neighborhood temperature sequences of the two temperature data points. Then, sum all the absolute values ​​of the differences between the two temperature data points and take the reciprocal to obtain the degree of overlap of temperature changes between the two temperature data points at the same sampling time.

3. The continuous temperature control method for a high-temperature heating furnace as described in claim 1, characterized in that, The process of integrating the first temperature data sequence and the second temperature data sequence is as follows: the temperature data before and after the switching point in the first temperature data sequence is combined with the temperature data after the switching point in the second temperature sequence to form a temperature sequence, and the temperature data within the temperature sequence is used as the integrated temperature data. The temperature data of the first sequence is collected by a thermocouple, and the temperature data of the second sequence is collected by an infrared thermometer.

4. The continuous temperature control method for a high-temperature heating furnace as described in claim 1, characterized in that, The sequence of temperature data from the current heating reference process satisfies the minimum sum of DTW distances between it and the sequence of temperature data integrated from the remaining heating processes.

5. The continuous temperature control method for a high-temperature heating furnace as described in claim 1, characterized in that, The process of dividing the high-temperature furnace heating process into heating section and holding section is as follows: Obtain the inflection point of the fitted curve of the temperature data during the current heating process, and obtain the first inflection point and the second inflection point. The time period before the first inflection point is taken as the heating period, and the time period after the first inflection point and before the second inflection point is taken as the heat preservation period.

6. The continuous temperature control method for a high-temperature heating furnace as described in claim 1, characterized in that, The process of determining the initial proportional gain parameters for the current heating and holding sections is as follows: The proportional gain parameter that appears most frequently in the historical heating phase is used as the initial proportional gain parameter for the PID control algorithm in the heating phase. Obtain the sum of the temperature change overlap between the integrated temperature data of the insulation section during the current heating process and the integrated temperature data of the insulation section during the historical heating process. Select the proportional gain parameter of the PID parameter of the insulation section during the historical heating process with the largest sum of temperature change overlap as the initial proportional gain parameter of the PID control algorithm of the insulation section during the current heating process.

7. The continuous temperature control method for a high-temperature heating furnace as described in claim 1, characterized in that, The obtained constrained temperature oscillation characteristic value is specifically: Based on the fluctuation characteristics of the integrated temperature data for each control cycle, and combined with the intersection characteristics between the temperature data and the reference heating process temperature data during the current heating, the temperature oscillation characteristic value for each control cycle is obtained. The sequence of integrated temperature data within each control cycle is denoted as the local temperature data sequence. Its first-order difference sequence is obtained, and the number of adjacent elements with different positive and negative signs in the first-order difference sequence is obtained. The absolute value of the difference between each adjacent temperature data in the local temperature data sequence is calculated and divided by the time interval between adjacent temperature data to obtain the temperature change rate. All the obtained temperature change rates are accumulated and multiplied by the number to obtain the constraint factor for each control cycle. Divide the temperature oscillation characteristic value by the normalized constraint factor, and normalize the resulting ratio to obtain the constrained temperature oscillation characteristic value.

8. The continuous temperature control method for a high-temperature heating furnace as described in claim 7, characterized in that, The temperature oscillation characteristic value obtained for each control cycle is specifically as follows: Obtain the percentage of extreme points in the local temperature data sequence for each control cycle; calculate the sum of the absolute values ​​of the differences between all temperature data within the control cycle and the temperature data at the corresponding moment in the current reference heating process; In the current heating process, for the temperature data at time i, if it satisfies the following conditions: the temperature data at time i-1 is greater than the temperature data at time i-1 in the current heating reference process, and the temperature data at time i+1 is less than the temperature data at time i+1 in the current heating reference process, then the temperature data at time i is a temperature change feature point; the number of temperature change feature points in each control cycle is counted. For each control cycle, the result of positively fusing the percentage of the number, the sum, and the number of temperature change feature points is used as the temperature oscillation feature value for each control cycle.

9. A continuous temperature control method for a high-temperature heating furnace as described in claim 7, characterized in that, The determination of the deviation elimination degree for each control cycle is specifically as follows: For the integrated temperature data in the control cycle, calculate the absolute value of the difference between each temperature data and the temperature data at the corresponding moment in the current heating reference process, and record it as the deviation value of each temperature data. Calculate the absolute value and minimum value of the difference between the deviation value of the temperature data and the deviation value of the adjacent previous temperature data. The ratio of the absolute value of the difference to the minimum value is used as the degree of change of the deviation value of each temperature data. The ratio of the deviation value to the degree of change of the deviation value is calculated, and the negative correlation mapping result obtained by accumulating the ratios obtained from all temperature data is used as the degree of deviation elimination for each control cycle.

10. The continuous temperature control method for a high-temperature heating furnace as described in claim 9, characterized in that, The adjustment of the proportional gain parameter of the PID algorithm for the next control cycle is specifically as follows: The initial proportional gain parameters obtained in the heating and holding stages are used as the proportional gain parameters for the first control cycle of the heating and holding stages. The formula for adjusting the proportional gain parameter is: In the formula: The proportional gain parameter representing the control period; This indicates the preset adjustment factor; This represents the temperature oscillation characteristic value after the control period constraint; Indicates the degree of deviation elimination in the control cycle; This represents the cumulative sum of the deviations of temperature data within the control cycle; This represents the sum of all temperature change rates within the control cycle; This represents the proportional gain parameter for the next control cycle. , All are preset thresholds, among which and ; This represents the normalization function.

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