Automatic temperature control method and control system for pot-type calcining furnace

Abstract

The invention relates to the technical field of calcining furnace temperature control, in particular to an automatic temperature control method and system for a pot-type calcining furnace, and the method comprises the steps: obtaining the pressure data of each layer of flame path in the pot-type calcining furnace and the temperature data of each charging bucket in each layer of flame path in real time; dividing the whole data acquisition time into a plurality of time periods; acquiring a comprehensive influence coefficient of each layer of flame path in each time period according to the difference degree between the temperature data of different charging buckets in each layer of flame path in each time period and the correlation degree between the pressure data of each layer of flame path in each time period and the adjacent time period and the temperature deviation state; and according to the similarity degree between the temperature distribution of each charging bucket in each time period and the average level of the comprehensive influence coefficients of all layers of flame paths in each time period, obtaining a temperature anomaly factor of each time period, and further obtaining a PID parameter of the next time period. According to the method, the weight of the rise time in the fitness function is adaptively adjusted according to the temperature anomaly feature, and the accuracy of temperature control is improved.

Description

Technical Field

[0001] This application relates to the field of calcining furnace temperature control technology, specifically to an automatic temperature control method and control system for a tank-type calcining furnace. Background Technology

[0002] A pot furnace is a core thermal equipment that uses high-temperature heating to induce physicochemical changes in materials. It is primarily used to remove moisture and volatile components, making the materials more robust and durable. Pot furnaces offer advantages such as low carbon loss and high-quality calcined coke, making them ideal for calcining petroleum coke in the aluminum industry. Precise temperature control within the furnace is particularly crucial for improving material properties.

[0003] The core working principle of a calcining furnace is to induce a series of chemical changes in materials through high temperatures, such as dehydration, decomposition, and oxidation, thereby improving the material's density and strength. Temperature and pressure distribution are crucial factors affecting the calcination results. In practical applications, if the material contains a high amount of water or the feed rate is too high, it will absorb a large amount of heat, causing a sharp drop in furnace temperature. Furthermore, blockages in the flues can lead to uneven heat distribution, all of which cause temperature fluctuations within the calcining furnace. Conventional automatic temperature control methods monitor the temperature in the flues and calcining chamber, automatically adjusting the reciprocating motion of the pressure plate to control gas flow and achieve automatic temperature control. However, these methods fail to adequately consider the impact of material feed rate and flue blockages, easily leading to uneven furnace temperatures, affecting calcination quality, and exhibiting a lack of precise temperature control. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide an automatic temperature control method and control system for a tank-type calcining furnace. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide an automatic temperature control method for a pot-type calcining furnace, the method comprising the following steps: Real-time acquisition of pressure data for each fire channel in the tank calciner and temperature data for each material tank in each fire channel; The entire data acquisition time was divided into multiple time periods; based on the rate of change and fluctuation of temperature data of each material tank in each fire channel in each time period, the temperature anomaly significance coefficient of each material tank in each fire channel in each time period was obtained; based on the difference of temperature anomaly significance coefficient between different material tanks in each fire channel in each time period, the first influence coefficient of each fire channel in each time period was obtained. Based on the degree of difference between the temperature data of different tanks in each fire channel at each time period, the temperature deviation coefficient of each fire channel at each time period is obtained; based on the correlation between the pressure data of each fire channel at each time period and the temperature deviation coefficient in the adjacent time periods, the second influence coefficient of each fire channel at each time period is obtained; and combined with the first influence coefficient of each fire channel at each time period, the comprehensive influence coefficient of each fire channel at each time period is obtained. Based on the similarity between the temperature distribution of each tank in each time period and the average level of the comprehensive influence coefficient of all fire channels in each time period, the temperature anomaly factor of each time period is obtained, and then the PID parameters of the next time period are obtained by using a genetic algorithm.

[0005] Preferably, the significant coefficient of temperature anomaly of each material tank in each fire channel at each time period refers to the product of the temperature change coefficient of each material tank in each fire channel at each time period and the coefficient of variation of the temperature data at each time period; wherein, the temperature change coefficient of each material tank in each fire channel at each time period refers to the mean of the absolute values ​​of the slopes of all data points in the fitted curve corresponding to the temperature data of each material tank in each fire channel at each time period.

[0006] Preferably, the first influence coefficient of each fire channel in each time period refers to the average of the absolute differences between the temperature anomaly significance coefficients of any two material tanks in each fire channel in each time period.

[0007] Preferably, the temperature deviation coefficient of each fire channel in each time period refers to the average DTW distance between the temperature data of any two material tanks in each fire channel in each time period.

[0008] Preferably, the second influence coefficient of each fire channel in each time period refers to the sum of the Spearman correlation coefficient and 1 between all average pressure values ​​and all temperature deviation coefficients of each fire channel in each time period and its neighboring time periods.

[0009] Preferably, the comprehensive influence coefficient of each fire channel in each time period refers to the product of the first influence coefficient and the second influence coefficient of each fire channel in each time period.

[0010] Preferably, the temperature anomaly factor for each time period refers to the ratio of the average of the comprehensive influence coefficients of all fire channels in each time period to the correlation of the tank room temperature in each time period.

[0011] Preferably, the temperature correlation between tanks in each time period refers to the average of the maximum mutual information coefficients between any two tanks in each time period; wherein, the temperature average sequence of each tank in each time period refers to the sequence composed of the temperature averages of all fire channels corresponding to each tank in each time period arranged in descending order of the fire channel layer number.

[0012] Preferably, the specific process of obtaining the PID parameters for the next time period using a genetic algorithm is as follows: the generated initial PID parameters are used as input to the genetic algorithm, the input to the fitness function of the genetic algorithm is the rise time and its weight, the overshoot and its weight, and the output is the fitness; when adjusting the fitness function of the genetic algorithm, the weight of the rise time is set to... Set the weight of the overshoot to ,in, Let be the normalized result of the temperature anomaly factor for the i-th time period, and Then, the optimized PID parameters are obtained using a genetic algorithm; the optimized PID parameters are used as the PID parameters for the (i+1)th time period to regulate the opening of the pull plate.

[0013] Secondly, embodiments of this application also provide an automatic control system for a pot furnace, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] This application has at least the following beneficial effects: This application proposes an automatic temperature control method and control system for a tank-type calcining furnace. By deeply analyzing the fluctuation and abnormal characteristics of the temperature distribution in each fire channel within the furnace under the influence of different material properties or fire channel blockage, as well as the correlation between temperature deviation characteristics and pressure data, a comprehensive influence coefficient is obtained. This coefficient accurately characterizes the probability of abnormal temperature data in each fire channel at different times. By analyzing the correlation characteristics of longitudinal temperature distribution between different material tanks and combining this with the comprehensive influence coefficient, a temperature anomaly factor for each time period is constructed. This provides a comprehensive analysis of the temperature distribution from both longitudinal and lateral perspectives, more accurately assessing the significance of temperature imbalance characteristics in the tank-type calcining furnace at different times. Based on the temperature anomaly factor for each time period, the weight of the rise time in the genetic algorithm is adjusted, thereby optimizing the opening control of the pull plates within the calcining furnace. Its advantage lies in reducing the impact of uneven material addition and fire channel blockage on internal temperature unevenness, helping to compensate for the shortcomings of insufficient temperature control and improving calcination quality. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of an automatic temperature control method for a pot-type calcining furnace according to an embodiment of this application; Figure 2 This is a flowchart illustrating the acquisition of temperature anomaly factors for different time periods, as provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automatic temperature control method and control system for a pot-type calcining furnace proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[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 pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the automatic temperature control method and control system for a pot-type calcining furnace provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of an automatic temperature control method for a pot furnace according to an embodiment of this application. The method includes the following steps: Step 1: Real-time acquisition of pressure data for each fire channel in the calciner and temperature data for each material tank in each fire channel.

[0021] This embodiment uses the petroleum coke canister calcination process as an example for the following analysis. In the calcination furnace, the material is indirectly heated through multiple fire channels. This requires the use of high-efficiency combustion technology to ensure complete combustion of the fuel within the fire channels, thereby providing a stable and uniform high-temperature environment for the calcination chamber. High-efficiency fuel combustion not only improves calcination efficiency but also reduces energy consumption and pollutant emissions, which is crucial for achieving the high-efficiency, energy-saving, and environmentally friendly goals of the canister calcination process. This embodiment adopts an eight-layer fire channel calcination furnace structure, where the fire channel system is arranged around the outer wall of multiple canisters, comprising eight layers from top to bottom. High-temperature flue gas flows within the fire channels, indirectly heating the material inside the canisters through the canister walls. To monitor the working status inside the calcination furnace in real time, data acquisition uses a layered acquisition method. Pressure transmitters are used to collect pressure data for each layer of fire channels, and intelligent photoelectric thermometers are used to collect temperature data for each canister in each layer of fire channels. In this embodiment, the data acquisition time interval is set to 2 seconds.

[0022] Step 2: Divide the entire data collection time into multiple time periods; based on the rate of change and fluctuation of temperature data of each material tank in each fire channel in each time period, obtain the temperature anomaly significance coefficient of each material tank in each fire channel in each time period; based on the difference in temperature anomaly significance coefficient between different material tanks in each fire channel in each time period, obtain the first influence coefficient of each fire channel in each time period.

[0023] Temperature control in a pot furnace is a major factor affecting the quality of calcined petroleum coke, and the preheating air intake is crucial for maintaining a constant temperature in the furnace's flue. Automatic temperature control of the furnace is achieved by automatically controlling the volatile components and the size of the hot air baffle, ensuring that the physicochemical properties of the calcined coke meet production requirements. However, the temperature within the furnace is influenced by a combination of factors, such as material moisture content, feed rate, and flue blockage, all of which can affect temperature uniformity. Therefore, the following analysis is conducted based on the potential temperature variation characteristics.

[0024] First, during the calcination process, the temperature changes within the tank calciner exhibit a certain degree of short-term, drastic fluctuations. For example, when the added material has a high moisture content or a large quantity, the newly added material absorbs some of the heat from the tank, causing a sharp drop in temperature. Additionally, intensified combustion may lead to a rapid temperature increase. Therefore, the entire data acquisition time is uniformly and continuously divided into multiple time periods according to a preset time length. Since the calcination process is relatively long and temperature changes have a certain time delay, this embodiment sets the preset time length to 2 minutes to obtain the short-term, drastic temperature change characteristics of each time period. Taking the j-th tank position in the k-th fire channel as an example, this embodiment uses a quadratic polynomial fitting technique to obtain the fitting curve of the tank's temperature data in the i-th time period. Then, the absolute value of the slope at each point on the fitting curve is calculated, and the average of all the absolute values ​​of the slopes is taken as the temperature change coefficient of the j-th tank in the k-th fire channel in the i-th time period, denoted as [missing information]. The result This reflects the drastic change in temperature data of the j-th material tank in the k-th fire channel during the i-th time period, and The larger the value, the more drastic the temperature data changes during that period. Furthermore, under good calcination conditions, the measured temperature of the tank fluctuates within a small range around the set temperature. The greater the abnormal interference during calcination, the greater the short-term drastic fluctuations in the temperature data. Therefore, the coefficient of variation of the temperature data of the j-th tank in the k-th fire channel during the i-th time period is calculated and denoted as [equation missing]. The result This reflects the degree of drastic fluctuation in the measured temperature at the location of the material tank.

[0025] As a preferred embodiment, based on the rate of change and fluctuation of temperature data of each material tank in each fire channel at each time period, the significant coefficient of temperature anomaly of each material tank in each fire channel at each time period is obtained, which is used to characterize the degree of anomaly of temperature data of each material tank in each fire channel at each time period.

[0026] In this embodiment, the significant coefficient of temperature anomaly of the j-th hopper in the k-th fire channel during the i-th time period is denoted as . Its formula is: In the formula, Let be the significance coefficient of the temperature anomaly of the j-th hopper in the k-th fire channel during the i-th time period. Let be the temperature change coefficient of the j-th hopper in the k-th fire channel during the i-th time period. Let be the coefficient of variation of the temperature data of the j-th hopper in the k-th fire channel during the i-th time period. The obtained... The larger the value, the more pronounced the rapid and dramatic temperature changes and vibrations in the tank due to the material properties or combustion intensity during that period.

[0027] Furthermore, the varying degrees of influence from material properties and combustion states at different locations of the material tanks exacerbate the uneven temperature distribution. To capture this characteristic, the absolute difference between the temperature anomaly significance coefficients of any two material tank locations in the k-th fire channel during the i-th time period is calculated. The average of all these absolute differences is taken as the first influence coefficient of the k-th fire channel during the i-th time period, denoted as [missing value]. The result This reflects the degree of difference in temperature anomaly characteristics among different material tanks in the k-th fire channel during the i-th time period. The larger the value, the greater the difference in temperature anomaly characteristics between different material tanks in the k-th fire channel during the i-th time period.

[0028] Step 3: Based on the degree of difference between the temperature data of different tanks in each fire channel at each time period, obtain the temperature deviation coefficient of each fire channel at each time period; based on the correlation between the pressure data and the temperature deviation coefficient of each fire channel at each time period and its adjacent time period, obtain the second influence coefficient of each fire channel at each time period, and combine it with the first influence coefficient of each fire channel at each time period to obtain the comprehensive influence coefficient of each fire channel at each time period.

[0029] Furthermore, during normal calcination, there is usually a certain temperature difference between different layers of flues, while the temperature of different feed tanks within the same flue should remain balanced. Significant deviations in the temperature distribution of different feed tanks can lead to unstable properties of the calcined petroleum coke. Inside the calcining tank, the petroleum coke feedstock flows downwards through multiple flues for calcination, and the feeding process is difficult to standardize, easily resulting in temperature differences between different feed tanks. Therefore, taking the k-th flue as an example, the DTW distance between the corresponding temperature data of any two feed tanks in the i-th time period is calculated. The average of all DTW distances is taken as the temperature deviation coefficient of the k-th flue in the i-th time period, denoted as [missing value]. The result The larger the value, the greater the temperature difference between different tanks in the k-th fire channel during the i-th time period.

[0030] Furthermore, the high volatile content of the petroleum coke feedstock in each flue can lead to the generation of a large amount of volatile components during calcination. These components undergo incomplete combustion within the flue, easily forming carbon deposits and tar, which can then clog the flue. This causes the pressure in the flue to gradually increase, preventing the heat generated by combustion from dissipating properly and further exacerbating the uneven temperature distribution at different locations within the same flue. Therefore, it can be concluded that when flue blockage occurs, there is a certain positive correlation between flue pressure and its temperature deviation.

[0031] To determine the impact of flue blockage on temperature unevenness, the following steps are taken: First, the N preceding time periods of the i-th time period are defined as the nearest neighbor time periods of the i-th time period. The value of N is set to the range [12, 15], and in this embodiment, N is 12. If the number of nearest neighbor time periods of the i-th time period is less than N, then the actual time periods existing before the i-th time period are used as the nearest neighbor time periods. The average pressure data of the k-th flue in each time period is calculated and denoted as the average pressure value of the k-th flue in each time period. The sum of the Spearman correlation coefficients between all average pressure values ​​of the k-th flue in the i-th time period and its nearest neighbor time periods and all temperature deviation coefficients, plus 1, is used as the second influence coefficient of the k-th flue in the i-th time period. The Spearman correlation coefficient is incremented by 1 to prevent the second influence coefficient from being negative and affecting subsequent calculations. The resulting second influence coefficient reflects the degree of influence of the blockage of the k-th flue in the i-th time period on its temperature unevenness.

[0032] The larger the value of the second influence coefficient, the stronger the positive correlation between the pressure data and the temperature deviation coefficient of the k-th fire channel. This indicates that the temperature unevenness of the k-th fire channel is more affected by the fire channel blockage, and thus the temperature data deviation of the k-th fire channel in the i-th time period is greater.

[0033] As a preferred embodiment, the comprehensive influence coefficient of each layer of fire channel in each time period is obtained based on the first influence coefficient and the second influence coefficient of each fire channel in each time period. This comprehensive influence coefficient is used to characterize the degree of influence of the uneven temperature distribution of different material tanks in each layer of fire channel in each time period on the combined effects of different material properties and fire channel blockage.

[0034] In this embodiment, the product of the first influence coefficient and the second influence coefficient of the k-th fire channel in the i-th time period is taken as the comprehensive influence coefficient of the k-th fire channel in the i-th time period. The larger the value of the comprehensive influence coefficient, the greater the degree of comprehensive influence of the uneven temperature distribution of different tanks in the fire channel in the i-th time period on the material properties or the blockage of the fire channel.

[0035] Step 4: Based on the similarity between the temperature distribution of each tank in each time period and the average level of the comprehensive influence coefficient of all fire channels in each time period, obtain the temperature anomaly factor for each time period, and then use the genetic algorithm to obtain the PID parameters for the next time period.

[0036] Furthermore, in the tank calciner, the temperature of different fire channels in different layers is distributed in a stepped manner along the material's movement direction. For example, the top of the tank is the inlet, and the bottom is the outlet. The material undergoes preheating, high-temperature calcination, and cooling processes as it moves from top to bottom within the tank. This results in a stepped temperature distribution across different fire channels within a single tank. To avoid batch-to-batch fluctuations in the quality of the petroleum coke output, besides ensuring good temperature consistency within the same fire channel layer, the overall temperature distribution across all tanks should also be relatively similar. Therefore, the average temperature of all fire channels corresponding to each tank in the i-th time period is arranged in descending order of fire channel layer number to obtain the average temperature sequence of each tank in the i-th time period. Then, the maximum mutual information coefficient between the average temperature sequences of any two tanks in the i-th time period is calculated. The average of all maximum mutual information coefficients is taken as the inter-tank temperature correlation in the i-th time period, denoted as . The result The larger the value, the closer the overall temperature state of different material tanks in the calcining furnace during the i-th time period.

[0037] In summary, the comprehensive influence coefficient reflects the temperature non-uniformity of a single-layer flue in a pot furnace, while the temperature correlation coefficient between material tanks reflects the correlation characteristics of the longitudinal temperature distribution among different material tanks in the pot furnace. Therefore, based on the average level of the comprehensive influence coefficients of all flue layers in each time period, and the temperature correlation coefficients between material tanks in each time period, temperature anomaly factors for each time period are obtained to characterize the significance of the temperature non-uniformity characteristics of the pot furnace in each time period. The flowchart for obtaining the temperature anomaly factors for each time period is shown below. Figure 2 As shown.

[0038] In this embodiment, the temperature anomaly factor for the i-th time period is denoted as... Its formula is: In the formula, Let i be the temperature anomaly factor for the i-th time period. Let be the average of the comprehensive influence coefficients of all fire channels in the i-th time period. Let be the correlation coefficient of tank room temperature in the i-th time period. The obtained... The larger the value, the more significant the temperature imbalance within the calcining furnace during that period.

[0039] Furthermore, the temperature regulation within the calciner is optimized based on temperature anomaly factors at different time periods. This application utilizes PID control technology combined with a genetic algorithm to regulate the opening degree of the pull plate, thereby improving the accuracy of temperature control. Specifically, a set of initial PID parameters is generated using the Ziegler-Nichols method and used as input to the genetic algorithm; the input to the fitness function in the genetic algorithm is the rise time and its weight, overshoot and its weight, and the output is the fitness; when adjusting the fitness function of the genetic algorithm, the weight of the rise time is set to... Set the weight of overshoot as follows: ,in, Let be the normalized result of the temperature anomaly factor for the i-th time period, and The proportional, integral, and derivative coefficients in the optimized PID algorithm are then obtained using a genetic algorithm. These optimized PID parameters are used as the PID parameters for the (i+1)th time period to regulate the opening degree of the pull plate during that time period, thereby achieving temperature control. In this embodiment, the sigmoid function is used for normalization. It should be noted that when generating the initial PID parameters, the proportional, integral, and derivative coefficients in this embodiment are set to ranges of [0,1], [0,0.1], and [0,10], respectively. Both the Ziegler-Nichols method and the genetic algorithm are well-known techniques, and their specific processes will not be elaborated further.

[0040] The larger the temperature anomaly factor for the i-th time period, the more significant the temperature imbalance anomaly in the calcining furnace during that time period, and the greater its impact on calcination quality. In this case, a larger factor needs to be set. Parameters are set to reduce the rise time of the response, thereby improving the control response speed; if the temperature anomaly factor for the i-th time period is smaller, it indicates that the overall temperature inside the calcining furnace is more balanced during that time period, and a smaller parameter can be set. This increases the weight of overshoot, thereby reducing the overshoot in the system response and preventing large overshoots that could lower temperature control accuracy. Optimizing the opening control of the pull plates inside the calcining furnace using these methods helps compensate for insufficient temperature control precision and improves calcination quality.

[0041] Based on the same inventive concept as the above method, this application embodiment also provides an automatic control system for a tank calcining furnace, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described automatic temperature control methods for a tank calcining furnace.

[0042] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0043] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0044] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. An automatic temperature control method for a pot-type calcining furnace, characterized in that, The method includes the following steps: Real-time acquisition of pressure data for each fire channel in the tank calciner and temperature data for each material tank in each fire channel; The entire data acquisition time was divided into multiple time periods; based on the rate of change and fluctuation of temperature data of each material tank in each fire channel in each time period, the temperature anomaly significance coefficient of each material tank in each fire channel in each time period was obtained; based on the difference of temperature anomaly significance coefficient between different material tanks in each fire channel in each time period, the first influence coefficient of each fire channel in each time period was obtained. Based on the degree of difference between the temperature data of different tanks in each fire channel at each time period, the temperature deviation coefficient of each fire channel at each time period is obtained; based on the correlation between the pressure data of each fire channel at each time period and the temperature deviation coefficient in the adjacent time periods, the second influence coefficient of each fire channel at each time period is obtained; and combined with the first influence coefficient of each fire channel at each time period, the comprehensive influence coefficient of each fire channel at each time period is obtained. Based on the similarity between the temperature distribution of each tank in each time period and the average level of the comprehensive influence coefficient of all fire channels in each time period, the temperature anomaly factor of each time period is obtained, and then the PID parameters of the next time period are obtained by using a genetic algorithm.

2. The automatic temperature control method for a pot-type calcining furnace as described in claim 1, characterized in that, The significant coefficient of temperature anomaly for each material tank in each fire channel at each time period refers to the product of the temperature change coefficient of each material tank in each fire channel at each time period and the coefficient of variation of the temperature data at each time period; wherein, the temperature change coefficient of each material tank in each fire channel at each time period refers to the mean of the absolute values ​​of the slopes of all data points in the fitted curve corresponding to the temperature data of each material tank in each fire channel at each time period.

3. The automatic temperature control method for a pot-type calcining furnace as described in claim 1, characterized in that, The first influence coefficient of each fire channel in each time period refers to the average of the absolute differences between the temperature anomaly significance coefficients of any two tanks in each fire channel in each time period.

4. The automatic temperature control method for a pot-type calcining furnace as described in claim 1, characterized in that, The temperature deviation coefficient of each fire channel at each time period refers to the average DTW distance between the temperature data of any two tanks in each fire channel at each time period.

5. The automatic temperature control method for a pot-type calcining furnace as described in claim 1, characterized in that, The second influence coefficient of each fire channel in each time period refers to the sum of the Spearman correlation coefficient and 1 between all average pressure values ​​and all temperature deviation coefficients of each fire channel in each time period and its neighboring time period.

6. The automatic temperature control method for a pot-type calcining furnace as described in claim 1, characterized in that, The comprehensive influence coefficient of each fire channel at each time period refers to the product of the first influence coefficient and the second influence coefficient of each fire channel at each time period.

7. The automatic temperature control method for a pot-type calcining furnace as described in claim 1, characterized in that, The temperature anomaly factor for each time period refers to the ratio of the average of the comprehensive influence coefficients of all fire channels in each time period to the correlation of the tank room temperature in each time period.

8. The automatic temperature control method for a pot-type calcining furnace as described in claim 7, characterized in that, The temperature correlation between tanks in each time period refers to the average of the maximum mutual information coefficients between any two tanks in each time period; wherein, the temperature mean sequence of each tank in each time period refers to the sequence composed of the temperature mean of all fire channels corresponding to each tank in each time period arranged in order of fire channel layer from top to bottom.

9. The automatic temperature control method for a pot-type calcining furnace as described in claim 1, characterized in that, The specific process of obtaining the PID parameters for the next time period using a genetic algorithm is as follows: Obtain the initial PID parameters and use them as input to the genetic algorithm; use the rise time and its weight, and the overshoot and its weight as input to the fitness function in the genetic algorithm, and output the fitness; when adjusting the fitness function of the genetic algorithm, set the weight of the rise time to... Set the weight of the overshoot to ,in, Let be the normalized result of the temperature anomaly factor for the i-th time period, and Then, the optimized PID parameters are obtained using a genetic algorithm; the optimized PID parameters are used as the PID parameters for the (i+1)th time period to regulate the opening of the pull plate.

10. An automatic control system for a pot-type calcining furnace, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the automatic temperature control method for a pot furnace as described in any one of claims 1-9.

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