Dynamic temperature control system based on multi-stage series electric heating furnace temperature control algorithm

Abstract

The invention relates to a dynamic temperature control system based on a multi-stage series electric heating furnace temperature control algorithm, and relates to the technical field of electric heating furnaces, and the system comprises the following steps: obtaining basic design information and position serial number identification of K-stage electric heaters of a target electric heating furnace, carrying out multi-stage temperature control scheme identification, and generating a multi-stage temperature control scheme, k transverse dynamic temperature control constraints and K longitudinal dynamic temperature control constraints are generated, K electric heater center monitoring temperatures and K real-time temperature rise values are obtained, double-constraint optimization is carried out on the K distribution powers, K target distribution powers are obtained, and dynamic temperature control is carried out. The technical problems that in the prior art, a multi-stage series electric heating furnace is low in temperature control reliability and low in dynamic temperature control accuracy are solved, and the technical effect of improving the temperature control reliability is achieved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electric heating furnaces, and in particular to a dynamic temperature control system based on a temperature control algorithm for a multi-stage series electric heating furnace. Background Art

[0002] During the production process, when the temperature sensor of an electric heater at a certain stage in a multi-stage series electric heating furnace fails, the actual temperature of the heater cannot be monitored in time. When the temperature is much higher than the preset value, it is easy to cause overheating damage to the metal material, which in turn triggers the safety interlock protection system, causing the entire production line to shut down for several hours for maintenance. At the same time, due to the thermal coupling effect between the electric heaters at each stage, when the temperature control of an electric heater at a certain stage deviates, it will directly affect the electric heaters at subsequent stages, making the temperature control of the entire system complicated and difficult to stabilize. The prior art has technical problems such as low temperature control reliability and low dynamic temperature control accuracy of multi-stage series electric heating furnaces. Summary of the invention

[0003] The present disclosure provides a dynamic temperature control system based on a temperature control algorithm for a multi-stage series electric heating furnace, which is used to solve the technical problems of low temperature control reliability and low dynamic temperature control accuracy of the multi-stage series electric heating furnace in the prior art.

[0004] In view of the above problems, the present disclosure provides a dynamic temperature control system based on a temperature control algorithm of a multi-stage series electric heating furnace, the system comprising: A basic design information acquisition module is used to obtain the basic design information and position serial number identification of the K-class electric heater of the target electric heating furnace; A multi-level temperature control scheme generation module is used to obtain a preset output temperature threshold, identify a multi-level temperature control scheme based on basic design information and position serial number identification, and generate a multi-level temperature control scheme; A dynamic temperature control constraint setting module is used to generate K lateral dynamic temperature control constraints and K longitudinal dynamic temperature control constraints according to a multi-level temperature control scheme; A real-time temperature rise value acquisition module is used to monitor the inlet and outlet temperatures and surface multi-point temperatures of the K-level electric heater in real time, and obtain the central monitoring temperatures of K electric heaters and K real-time temperature rise values; A target allocated power acquisition module is used to perform dual-constraint optimization on K allocated powers in a multi-stage temperature control scheme based on the K electric heater center monitoring temperatures and the K real-time temperature rise values ​​to obtain K target allocated powers; The dynamic control module is used to transmit the K target allocated powers to the dynamic adjustment unit to complete power redistribution and dynamic temperature control.

[0005] One or more technical solutions provided in this disclosure have at least the following technical effects or advantages: The present invention obtains the basic design information and position serial number identification of the K-level electric heater of the target electric heating furnace, then obtains the preset output temperature threshold, combines the basic design information and the position serial number identification to identify the multi-level temperature control scheme, generates the multi-level temperature control scheme, and then generates K horizontal dynamic temperature control constraints and K longitudinal dynamic temperature control constraints according to the multi-level temperature control scheme, and then performs real-time monitoring of the inlet and outlet temperatures and surface multi-point temperatures of the K-level electric heater, obtains the central monitoring temperatures of the K electric heaters and K real-time temperature rise values, performs dual-constraint optimization on the K allocated powers in the multi-level temperature control scheme based on the central monitoring temperatures of the K electric heaters and the K real-time temperature rise values, obtains K target allocated powers, and then transmits the K target allocated powers to the dynamic adjustment unit to complete power redistribution and dynamic temperature control. The technical effect of improving the temperature control reliability of the multi-level series electric heating furnace is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0007] Figure 1 A schematic diagram of the structure of a dynamic temperature control system based on a temperature control algorithm for a multi-stage series electric heating furnace provided in an embodiment of the present disclosure; Figure 2 A schematic flow chart of a dynamic temperature control method based on a temperature control algorithm for a multi-stage series electric heating furnace provided in an embodiment of the present disclosure.

[0008] Explanation of the reference numerals: basic design information acquisition module 11 , multi-stage temperature control scheme generation module 12 , dynamic temperature control constraint setting module 13 , real-time temperature rise value acquisition module 14 , target allocated power acquisition module 15 , dynamic control module 16 . DETAILED DESCRIPTION

[0009] The present disclosure provides a dynamic temperature control system based on a temperature control algorithm for a multi-stage series electric heating furnace, so as to solve the technical problems of low temperature control reliability and low dynamic temperature control accuracy of a multi-stage series electric heating furnace in the prior art.

[0010] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0011] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, system, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, systems, products or devices.

[0012] Examples, such as Figure 1 As shown, the present disclosure provides a dynamic temperature control system based on a multi-stage series electric heating furnace temperature control algorithm for performing the following Figure 2 The dynamic temperature control method based on the temperature control algorithm of a multi-stage series electric heating furnace shown in the figure comprises: The basic design information acquisition module 11 is used to obtain the basic design information and position serial number identification of the K-class electric heater of the target electric heating furnace; In a possible embodiment, the target electric heating furnace is a multi-stage series electric heating furnace, including a K-class electric heating furnace. The basic design information acquisition module 11 is used to extract K basic design information of the K-class electric heating furnace from the structural design information of the target electric heating furnace, and provide data support for subsequent temperature control. Preferably, the target temperature to be heated by each stage of the electric heating furnace is different, so the model of each stage of the electric heating furnace is different, and each has a corresponding basic design information. Among them, the K basic design information reflects the structural design of the K-class electric heating furnace, including the rated power and heating area of ​​the electric heater.

[0013] In one embodiment, each stage of electric heater is an independent heating unit, which may have different characteristics such as power, size, heating efficiency, etc. The position of each electric heater in the electric heating furnace is identified by a unique position serial number, which is used to distinguish and identify different electric heaters, and helps the system to accurately obtain the specific position of each electric heater in the multi-stage series electric heating furnace.

[0014] A multi-level temperature control scheme generating module 12 is used to obtain a preset output temperature threshold, identify a multi-level temperature control scheme in combination with basic design information and position serial number identification, and generate a multi-level temperature control scheme; Furthermore, the steps performed by the multi-stage temperature control scheme generating module 12 also include: Obtaining multiple sample preset output temperature thresholds, K sample basic design information and K sample position serial number identifiers, and multiple sample multi-level temperature control schemes as training data; Performing supervised training on the encoder and the decoder based on the training data, and continuously updating the network parameters of the encoder and the decoder during the training until a preset number of iterations is met, thereby obtaining a trained temperature control scheme identifier; The temperature control scheme identifier is used to perform multi-level temperature control scheme identification on the preset output temperature threshold, basic design information and the position serial number identifier to generate the multi-level temperature control scheme.

[0015] In one embodiment, the preset output temperature threshold is extracted according to the heating task of the target electric heating furnace. The preset output temperature threshold is the output temperature range of the target electric heating furnace. Then, based on the preset output temperature threshold, K basic design information and the K position sequence number identifiers, the temperature control scheme identifier is used to determine the heating temperature control scheme of the K-level electric heater in the target electric heating furnace to obtain the multi-level temperature control scheme. The multi-level temperature control scheme is a scheme that limits the power allocated to the K-level electric heater and the heating conditions.

[0016] Optionally, multiple sample preset output temperature thresholds, K sample basic design information and K sample position serial number identifiers, and multiple sample multi-level temperature control schemes are obtained as training data. Then, an encoder is constructed to encode the input data (preset output temperature threshold, basic design information and position serial number identifier) ​​into an intermediate representation, and a decoder is constructed to decode the multi-level temperature control scheme (including K temperature rise thresholds, K temperature thresholds and K allocated powers) from the intermediate representation. The encoder and decoder are supervised and trained using the prepared training data. During the training process, the encoder converts the input data into an internal representation, and the decoder attempts to recover the multi-level temperature control scheme from this representation. By comparing the multi-level temperature control scheme output by the decoder with the actual multi-level temperature control scheme in the training data, the loss function (such as mean square error, cross entropy, etc.) is calculated, and the network parameters of the encoder and decoder are updated based on the gradient of the loss function. During the training process, iterations are continuously performed, and each iteration adjusts the network parameters according to the value of the current loss function to reduce the prediction error. The iteration will continue until the preset number of iterations is met or other stopping conditions are met (such as the loss function value no longer decreases significantly). After enough iterations, the encoder and decoder will learn how to extract effective information from the input data and generate a reasonable multi-level temperature control solution, and then a trained temperature control solution identifier will be obtained.

[0017] Furthermore, the trained temperature control scheme identifier is used to process the preset output temperature threshold, K basic design information and the K position serial number identifiers, and the input data is encoded into an intermediate representation by the encoder, and then the decoder decodes the multi-level temperature control scheme from this representation. This scheme includes the temperature rise threshold, temperature threshold and allocated power for each level of electric heater. It is realized by combining the supervised training method of deep learning to automatically learn how to generate a reasonable multi-level temperature control scheme from the input data. The technical effect of improving the accuracy and efficiency of the temperature control scheme and reducing the cost of manual intervention and trial and error is achieved.

[0018] A dynamic temperature control constraint setting module 13, used to generate K lateral dynamic temperature control constraints and K longitudinal dynamic temperature control constraints according to a multi-level temperature control scheme; A real-time temperature rise value acquisition module 14 is used to monitor the inlet and outlet temperatures and surface multi-point temperatures of the K-level electric heater in real time, and obtain the central monitoring temperatures of K electric heaters and K real-time temperature rise values; In a possible embodiment, the K temperature rise thresholds are respectively used as K horizontal dynamic temperature control constraints, so as to achieve the goal of horizontally constraining the temperature control of the target electric heating furnace from the temperature rise range that can be heated in sequence by the K-level electric heaters. Furthermore, the K temperature thresholds are used as K vertical dynamic temperature control constraints, so as to achieve the goal of vertically constraining the range of heating temperature that can be satisfied by the K-level electric heaters in each stage of heating.

[0019] In one possible embodiment, temperature sensors are used to perform real-time temperature monitoring at the entrances and exits of K-level electric heaters and at K initial surface monitoring point arrays to obtain the K real-time surface monitoring temperature sets, K real-time outlet temperatures, and K real-time inlet temperatures. Furthermore, the central monitoring temperature of each level of electric heater is determined by further processing the K real-time surface monitoring temperature sets (such as weighted average, interpolation calculation, etc.). These central monitoring temperatures will be used for subsequent temperature control. Among them, the central monitoring temperatures of the K electric heaters are temperature values ​​that reflect the actual temperature conditions of the K-level electric heaters.

[0020] Furthermore, the steps performed by the real-time temperature rise value obtaining module 14 also include: Get the preset initial surface monitoring point quantity; Extracting K surface areas based on the K basic design information, identifying the monitoring point layout intervals according to the K surface areas and the preset number of initial surface monitoring points, and arranging the initial surface monitoring points according to the identification results to obtain K initial surface monitoring point arrays; In a preset monitoring window, continuously monitoring the temperature of the K-level electric heater according to the K initial surface monitoring point arrays to obtain a set of K initial temperature monitoring sequences; Performing temperature gradient calculations on the K initial temperature monitoring sequence sets respectively to obtain K temperature gradient sets; Screening monitoring points based on the K temperature gradient sets to determine K surface monitoring point arrays; Real-time monitoring of multi-point surface temperature is performed according to the K surface monitoring point arrays to generate K real-time surface monitoring temperature sets, and central monitoring temperatures of the K real-time surface monitoring temperature sets are identified to obtain the central monitoring temperatures of the K electric heaters.

[0021] Furthermore, the steps performed by the real-time temperature rise value obtaining module 14 also include: Extract K temperature gradient minimum values ​​from the K temperature gradient sets respectively, and use the K temperature gradient minimum values ​​as simulated watering points for water injection, and as the water surface rises, obtain K first water surfaces until it rises to a preset temperature gradient, and use the multiple temperature gradients submerged by the K first water surfaces as K first temperature gradient sets, wherein the K temperature gradient sets include K initial surface monitoring point location identifiers; Performing nearest neighbor fusion on the K first temperature gradient sets to generate K first region segmentation ridge lines, wherein the K first region segmentation ridge lines rise as the water surface rises; Continue to inject water until the water level rises to a preset temperature gradient to obtain K second water surfaces, and the multiple temperature gradients submerged by the K second water surfaces are taken as K second temperature gradient sets; Performing nearest neighbor fusion on the K second temperature gradient sets to generate K second region segmentation ridge lines, wherein the K second region segmentation ridge lines rise as the water surface rises; After multiple nearest neighbor fusions, the water surface submerges the K maximum temperature gradients in the K temperature gradient sets, and obtains K regional segmentation ridge line sets; The K temperature gradient sets are screened for monitoring points based on the K region segmentation ridge line sets to obtain the K surface monitoring point arrays.

[0022] Furthermore, the steps performed by the real-time temperature rise value obtaining module 14 also include: Using the K region segmentation ridge line sets to perform region segmentation on the K temperature gradient sets to generate K temperature gradient segmentation region sets; Calculating the mean value of the temperature gradient in each of the K temperature gradient divided area sets to obtain the mean value sets of the K divided areas; Respectively calculate the ratio of the mean of each divided area in the K divided area mean sets to the sum of the means of the corresponding divided area mean sets, and use the calculation results as the distribution coefficient of the divided area monitoring points to obtain the K divided area monitoring point distribution coefficient sets; Obtaining a preset number of real-time surface monitoring points, and multiplying the K divided area monitoring point distribution coefficient sets by the preset number of real-time surface monitoring points, respectively, to obtain a set of the number of real-time surface monitoring points in the K divided areas; Based on the number set of real-time surface monitoring points in the K divided areas, monitoring points are randomly selected from the K temperature gradient divided area sets to generate the K surface monitoring point arrays.

[0023] In one embodiment, based on the experience or design requirements of those skilled in the art, an initial number of surface monitoring points is preset for arranging monitoring points on the surface of the electric heater, that is, the preset initial number of surface monitoring points. Further, based on K basic design information (such as the size, shape, heating element layout, etc. of the electric heater), K surface areas are extracted, and the arrangement intervals of the monitoring points are calculated based on the preset initial number of surface monitoring points and the surface area. Optionally, the surface area is divided by the preset initial number of surface monitoring points to obtain the monitoring area area of ​​each monitoring point, and the monitoring area is set to a square, and the side length of the area is obtained according to the monitoring area area, that is, the monitoring point arrangement interval. According to the identified monitoring point arrangement intervals, the initial monitoring points are arranged on the surface of the electric heater to form an array of K initial surface monitoring points.

[0024] In a preset monitoring window (a continuous monitoring time period preset by a person skilled in the art), a temperature sensor is used to continuously monitor the temperature of the K initial surface monitoring point arrays, and the temperature data of each monitoring point is recorded to form a set of K initial temperature monitoring sequences. The K initial temperature monitoring sequence sets reflect the monitoring temperature changes of each initial surface monitoring point in the K initial surface monitoring point arrays in the preset monitoring window.

[0025] The temperature gradients of K initial temperature monitoring sequence sets are calculated to evaluate the change trend and distribution of the electric heater surface temperature. The temperature gradient set reflects the spatial distribution characteristics of the electric heater surface temperature.

[0026] Then, based on the K temperature gradient sets, the initial surface monitoring points are screened, and those points that can more accurately reflect the central temperature of the electric heater or the representative temperature are retained to form an array of K surface monitoring points. The optimized array of K surface monitoring points is used to monitor the surface temperature of the electric heater in real time to generate K real-time surface monitoring temperature sets. The central monitoring temperature of each level of electric heater is identified by further processing the K real-time surface monitoring temperature sets (such as weighted average, interpolation calculation, etc.). These central monitoring temperatures will be used for subsequent temperature control.

[0027] In one embodiment, since different mountains in a mountainous area have different heights, when water penetrates into the mountainous area from the lowest point of the mountainous area, two different parts will appear in the mountainous area, one part is the water collection area, and the other part is the dividing ridge line. The dividing ridge line is used to separate the mountainous area with water from the mountainous area without water penetration, and as the water injection continues, the water level rises and the dividing ridge line also rises until all mountainous areas are submerged by the water surface, thereby achieving the goal of separating mountainous areas of different heights.

[0028] The temperature gradient is simulated as the mountain height, and K mountain areas can be simulated according to the K temperature gradient sets, and there are multiple temperature gradient points in each area. The temperature gradient value of each point can correspond to a certain height on the mountain. The temperature gradient reflects the temperature change of each level of electric heater at a certain monitoring point. Exemplarily, the temperature gradient can be 5°C, 10°C, etc., and the temperature gradient set in each mountain area ranges from 0°C to 50°C. Therefore, the mountain height in each mountain area can range from flat land (0°C) to the highest point (50°C). Thus, different mountain morphologies of K mountain areas can be obtained according to the K temperature gradient sets.

[0029] Water is poured from the lowest point of the K mountainous areas (that is, the lowest point of the valley or mountainous area), such as the temperature gradient corresponding to the lowest point of the first mountainous area is 0°C, the temperature gradient corresponding to the lowest point of the second mountainous area is 5°C, etc. As the water is poured continuously, the water level will continue to rise until it submerges the highest point of the mountainous area. Therefore, K temperature gradient minimum values ​​are extracted from the K temperature gradient sets respectively, and water is poured with the K temperature gradient minimum values ​​as the simulated watering points. As the water level rises, K first water surfaces are obtained when it rises to a preset temperature gradient (the maximum temperature gradient that can be divided into one area with the K temperature gradient minimum values ​​set by technicians in this field, such as 15°C), and the multiple temperature gradients submerged by the K first water surfaces are taken as K first temperature gradient sets. That is, all temperature gradients between the temperature gradient at the lowest point and the temperature gradient at the lowest point superimposed by 15°C are summarized to obtain the K first temperature gradient sets. Among them, the K temperature gradient sets include K initial surface monitoring point positioning identifiers. The K first temperature gradient sets are subjected to nearest neighbor fusion to generate K first region segmentation ridge lines, wherein the K first region segmentation ridge lines rise as the water surface rises. Optionally, adjacent temperature gradients of the K first temperature gradient sets are identified according to the K initial surface monitoring point positioning identifiers, and the adjacent temperature gradients are fused into one region, and then the edge of each region is used as part of the segmentation ridge line to obtain the K first region segmentation ridge lines.

[0030] Continue to inject water until the water level rises to a preset temperature gradient to obtain K second water surfaces, and use the multiple temperature gradients submerged by the K second water surfaces as K second temperature gradient sets. Based on the same principle as the K first area segmentation ridge lines, perform nearest neighbor fusion on the K second temperature gradient sets to generate K second area segmentation ridge lines, wherein the K second area segmentation ridge lines rise as the water level rises.

[0031] After multiple nearest neighbor fusions, until the water surface submerges the K maximum values ​​of the temperature gradients in the K temperature gradient sets, K regional segmentation ridge line sets are obtained. The K regional segmentation ridge line sets divide the K temperature gradient sets into multiple different regions, and the range of the temperature gradient in each region is different. Based on the K regional segmentation ridge line sets, the K temperature gradient sets are screened for monitoring points to obtain the K surface monitoring point arrays.

[0032] In a possible embodiment, the K temperature gradient sets are divided into regions using the K region segmentation ridge line sets to generate K temperature gradient divided region sets. The temperature gradient range in each temperature gradient divided region is different, such as the temperature gradient of a temperature gradient divided region in a temperature gradient divided region set is between 5°C and 20°C, and the temperature gradients of the remaining temperature gradient divided regions are between 21°C and 35°C, between 36°C and 50°C, etc. Then, the mean of the temperature gradient in the region is calculated for the K temperature gradient divided region sets respectively, and the mean sets of K divided regions are obtained. For example, the temperature gradient of a temperature gradient divided region is between 5°C and 20°C, including 10 temperature gradients, namely 5°C, 15°C, 6°C, 9°C, 12°C, 16°C, 11°C, 15°C, 13°C, and 11°C. Then the mean of the divided regions is 11.3°C. The K divided area mean value sets reflect the average temperature gradient of each temperature gradient divided area in the K temperature gradient divided area sets.

[0033] The ratio of the mean value of each divided area in the K divided area mean value sets to the sum of the means of the corresponding divided area mean value sets is calculated respectively, and the calculation results are used as the distribution coefficients of the divided area monitoring points to obtain the K divided area monitoring point distribution coefficient sets. The distribution coefficient of each divided area monitoring point reflects the importance of the temperature fluctuation degree of each divided area to the electric heater. The larger the coefficient, the higher the importance.

[0034] The preset number of real-time surface monitoring points is obtained (the number of surface monitoring points for real-time monitoring is preset by a person skilled in the art), and the K divided area monitoring point distribution coefficient sets are respectively multiplied by the preset number of real-time surface monitoring points to obtain the K divided area real-time surface monitoring point number sets, and then based on the K divided area real-time surface monitoring point number sets, monitoring points are randomly selected from the K temperature gradient divided area sets to generate the K surface monitoring point arrays. The technical effect of reliably setting the surface monitoring points according to the real-time temperature gradient distribution of each level of electric heater is achieved.

[0035] In one embodiment, by calculating the difference between the real-time outlet temperature and the real-time inlet temperature of each electric heater, K real-time temperature rise values ​​are obtained. These temperature rise values ​​reflect the current heating efficiency or effect of the electric heater, thereby achieving the goal of providing reliable data support for subsequent dual-constraint optimization.

[0036] The target allocated power acquisition module 15 is used to perform dual-constraint optimization on the K allocated powers in the multi-stage temperature control scheme based on the K electric heater center monitoring temperatures and the K real-time temperature rise values ​​to obtain K target allocated powers; In one possible embodiment, by utilizing the target allocated power acquisition module 15, it is ensured that the power allocated by the system to the K-level electric heater of the target electric heating furnace can simultaneously meet the horizontal (i.e., temperature control between different electric heaters) and vertical (i.e., internal temperature control of a single electric heater) dynamic temperature control constraints.

[0037] Optionally, the dual-constraint optimization process requires that the K real-time temperature rise values ​​of the K-level electric heaters must satisfy the K horizontal dynamic temperature control constraints, and the central monitoring temperatures of the K electric heaters must satisfy the K vertical dynamic temperature control constraints. In other words, when optimizing the K allocated powers based on the central monitoring temperatures of the K electric heaters and the K real-time temperature rise values, the working effect of the K-level electric heaters that perform power redistribution must satisfy the K horizontal dynamic temperature control constraints and the K vertical dynamic temperature control constraints. The technical effect of improving the reliability of dynamic temperature control is achieved.

[0038] Furthermore, the steps performed by the target allocated power obtaining module 15 also include: Using the K longitudinal dynamic temperature control constraints, constraint identification is performed on the central monitoring temperatures of the K electric heaters to determine K longitudinal deviation factors and K longitudinal deviation directions; Using the K lateral dynamic temperature control constraints, the K real-time temperature rise values ​​are subjected to constraint identification, and K lateral deviation factors and K lateral deviation directions are determined; Based on the K lateral deviation factors, K lateral deviation directions, K longitudinal deviation factors and K longitudinal deviation directions, the K allocated powers are double-constrained optimized to obtain the K target allocated powers, wherein the double-constrained optimized search requires that the K real-time temperature rise values ​​of the K-level electric heaters need to satisfy the K lateral dynamic temperature control constraints, and the central monitoring temperatures of the K electric heaters need to satisfy the K longitudinal dynamic temperature control constraints during the optimized search.

[0039] Furthermore, the steps performed by the target allocated power obtaining module 15 also include: Using an allocated power adjustment unit to identify the K lateral deviation factors, the K lateral deviation directions, the K longitudinal deviation factors, the K longitudinal deviation directions, and the K allocated powers, and determine K adjusted allocated powers; Performing temperature control simulation on the K-level electric heater based on the K adjusted allocated powers, and determining whether the K temperature control simulation results satisfy the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints; If so, the K adjusted allocated powers are used as the K target allocated powers.

[0040] Furthermore, the steps performed by the target allocated power obtaining module 15 also include: If not, deviation identification is performed according to the K temperature control simulation results and the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints to obtain K simulated lateral deviation factors, K simulated lateral deviation directions, K simulated longitudinal deviation factors, and K simulated longitudinal deviation directions; Using the allocated power adjustment unit to identify the K simulated lateral deviation factors, the K simulated lateral deviation directions, the K simulated longitudinal deviation factors, the K simulated longitudinal deviation directions and the K adjusted allocated powers, and determine K stage allocated powers; When the temperature control simulation results of the K stage allocated powers satisfy the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints, the K stage allocated powers are used as the K target allocated powers.

[0041] In a possible embodiment, the K longitudinal dynamic temperature control constraints are used to identify the K electric heater center monitoring temperatures, and K lateral deviation factors and K longitudinal deviation directions are determined, that is, the difference between the K electric heater center monitoring temperatures and the K temperature thresholds is calculated, and the calculation results are used as K longitudinal deviation factors. When the K electric heater center monitoring temperatures are greater than the K temperature thresholds, the K longitudinal deviation directions are positive; when the K electric heater center monitoring temperatures are less than or equal to the K temperature thresholds, the K longitudinal deviation directions are negative.

[0042] Based on the same principle, the K real-time temperature rise values ​​are constrained and identified using the K lateral dynamic temperature control constraints to determine K lateral deviation factors and K lateral deviation directions. In other words, the difference between the K real-time temperature rise values ​​and the K temperature rise thresholds is calculated, and the calculation results are used as K lateral deviation factors. When the K real-time temperature rise values ​​are greater than the K temperature rise thresholds, the K lateral deviation directions are positive; when the K real-time temperature rise values ​​are less than or equal to the K temperature rise thresholds, the K lateral deviation directions are negative. The goal of providing optimization scales and optimization directions for subsequent optimization is achieved.

[0043] The K lateral deviation factors, K lateral deviation directions, K longitudinal deviation factors, K longitudinal deviation directions and the K allocated powers are identified by using the allocated power adjustment unit to determine K adjusted allocated powers. The allocated power adjustment unit is used to intelligently adjust and optimize the K allocated powers and output K adjusted allocated powers.

[0044] Then, the K-class electric heater is subjected to temperature control simulation with the K adjusted allocated powers, the heating condition of the K-class electric heater under the K adjusted allocated powers is determined, and K temperature control simulation results are obtained. Exemplarily, a temperature control simulation environment is set for the K-class electric heater through COMSOL Multiphysics or MATLAB, which can simulate the temperature response of the electric heater according to a given power input (i.e., K adjusted allocated powers). The calculated K adjusted allocated powers are used as input and respectively allocated to the K-class electric heaters. In the simulation environment, each electric heater will simulate the temperature change according to its allocated power. In the simulation environment, the temperature control simulation process is started. This process will calculate and simulate the temperature change of each electric heater over time based on the physical properties of the electric heater (such as heat capacity, thermal conductivity, etc.) and the input power. Thereby obtaining the temperature control simulation result.

[0045] After the temperature control simulation is completed, collect and record the simulated temperature data of each electric heater. These data may include temperature time series, central monitoring temperature, real-time temperature rise value, etc. Among them, the K temperature control simulation results include K simulated surface monitoring temperature sets, K simulated outlet temperatures and K simulated inlet temperatures. Based on the same analysis principle as above, obtain K simulated temperature rise values ​​and K simulated electric heater central monitoring temperatures. Then, determine whether the K simulated temperature rise values ​​and the K simulated electric heater central monitoring temperatures meet the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints. If they meet, use the K adjusted allocated powers as the K target allocated powers.

[0046] If not, deviation identification is performed based on the K temperature control simulation results and the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints to obtain K simulated lateral deviation factors, K simulated lateral deviation directions, K simulated longitudinal deviation factors, and K simulated longitudinal deviation directions. Optionally, the difference between the K simulated electric heater center monitoring temperatures and the K temperature thresholds in the K temperature control simulation results is calculated, and the calculation results are used as K simulated longitudinal deviation factors. When the K simulated electric heater center monitoring temperatures are greater than the K temperature thresholds, the K simulated longitudinal deviation directions are positive; when the K simulated electric heater center monitoring temperatures are less than or equal to the K temperature thresholds, the K simulated longitudinal deviation directions are negative.

[0047] Optionally, the difference between the K simulated real-time temperature rise values ​​and the K temperature rise thresholds is calculated, and the calculation result is used as the K simulated lateral deviation factors. When the K simulated real-time temperature rise values ​​are greater than the K temperature rise thresholds, the K simulated lateral deviation directions are positive; when the K simulated real-time temperature rise values ​​are less than or equal to the K temperature rise thresholds, the K simulated lateral deviation directions are negative.

[0048] The K simulated lateral deviation factors, K simulated lateral deviation directions, K simulated longitudinal deviation factors, K simulated longitudinal deviation directions and the K adjusted allocated powers are identified by the allocated power adjustment unit to determine K stage allocated powers. When the temperature control simulation results of the K stage allocated powers satisfy the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints, the K stage allocated powers are used as the K target allocated powers.

[0049] Optionally, multiple sample lateral deviation factor sets, multiple sample lateral deviation direction sets, multiple sample longitudinal deviation factor sets, multiple sample longitudinal deviation direction sets, multiple sample allocation power sets, and multiple sample adjustment allocation power sets are obtained as training data, and supervised training is performed on a framework built based on a convolutional neural network until the output converges, thereby obtaining the trained allocation power adjustment unit.

[0050] Preferably, the power allocation adjustment unit includes an input layer, a convolution layer, a pooling layer and a fully connected layer. The loss function is used to perform loss analysis on the training process of the power allocation adjustment unit, wherein the loss function is ,in, Allocate power to the sample, is the predicted allocated power output during the training of the allocated power adjustment unit, and N is the number of training samples. When the loss function reaches the minimum value, the training converges, and the allocated power adjustment unit is obtained.

[0051] The dynamic control module 16 is used to transmit the K target allocated powers to the dynamic adjustment unit for power redistribution and dynamic temperature control.

[0052] Optionally, the dynamic adjustment unit is used to reallocate the power of the K-level electric heater according to the K target allocated powers, and after the allocation is completed, the heating conditions of the K-level electric heater can meet the preset output temperature threshold and temperature rise thresholds and temperature thresholds of the target electric heating furnace, thereby achieving the technical effect of improving the temperature control accuracy of the target electric heating furnace.

[0053] In summary, the embodiments of the present disclosure have at least the following technical effects: The present invention obtains the basic design information and position serial number identification of the K-level electric heater of the target electric heating furnace, then obtains the preset output temperature threshold, combines the basic design information and the position serial number identification to identify the multi-level temperature control scheme, generates the multi-level temperature control scheme, and then generates K horizontal dynamic temperature control constraints and K longitudinal dynamic temperature control constraints according to the multi-level temperature control scheme, and then performs real-time monitoring of the inlet and outlet temperatures and surface multi-point temperatures of the K-level electric heater, obtains the central monitoring temperatures of the K electric heaters and K real-time temperature rise values, performs dual-constraint optimization on the K allocated powers in the multi-level temperature control scheme based on the central monitoring temperatures of the K electric heaters and the K real-time temperature rise values, obtains K target allocated powers, and then transmits the K target allocated powers to the dynamic adjustment unit to complete power redistribution and dynamic temperature control. The technical effect of improving the temperature control reliability of the multi-level series electric heating furnace is achieved.

[0054] It should be noted that the above-mentioned sequence of the embodiments of the present disclosure is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

[0056] This specification and the accompanying drawings are merely exemplary illustrations of the present disclosure and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present disclosure. Obviously, those skilled in the art may make various modifications and variations to the present disclosure without departing from the scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the present disclosure and its equivalents, the present disclosure is intended to include these modifications and variations.

Claims

1. A dynamic temperature control system based on a temperature control algorithm for a multi-stage series electric heating furnace, characterized in that: The system comprises: A basic design information acquisition module is used to obtain the basic design information and position serial number identification of the K-class electric heater of the target electric heating furnace; A multi-level temperature control scheme generation module is used to obtain a preset output temperature threshold, identify a multi-level temperature control scheme based on basic design information and position serial number identification, and generate a multi-level temperature control scheme; A dynamic temperature control constraint setting module is used to generate K lateral dynamic temperature control constraints and K longitudinal dynamic temperature control constraints according to a multi-level temperature control scheme; A real-time temperature rise value acquisition module is used to monitor the inlet and outlet temperatures and surface multi-point temperatures of the K-level electric heater in real time, and obtain the central monitoring temperatures of K electric heaters and K real-time temperature rise values; A target allocated power acquisition module is used to perform dual-constraint optimization on K allocated powers in a multi-stage temperature control scheme based on the K electric heater center monitoring temperatures and the K real-time temperature rise values ​​to obtain K target allocated powers; The dynamic control module is used to transmit the K target allocated powers to the dynamic adjustment unit to complete power redistribution and dynamic temperature control.

2. The dynamic temperature control system based on the temperature control algorithm of the multi-stage series electric heating furnace according to claim 1 is characterized in that: The steps performed by the real-time temperature rise value obtaining module also include: Arrange initial surface monitoring points to obtain K initial surface monitoring point arrays, perform continuous temperature monitoring and temperature gradient calculation within a preset monitoring window to obtain K temperature gradient sets, and perform monitoring point screening to determine K surface monitoring point arrays; Real-time monitoring of multi-point surface temperature is performed according to the K surface monitoring point arrays, K real-time surface monitoring temperature sets are generated, central monitoring temperature identification is performed, and central monitoring temperatures of the K electric heaters are obtained.

3. The dynamic temperature control system based on the temperature control algorithm of the multi-stage series electric heating furnace according to claim 2 is characterized in that: The steps performed by the real-time temperature rise value obtaining module also include: Extracting K temperature gradient minimum values ​​from the K temperature gradient sets respectively, performing watering simulation, and obtaining K first temperature gradient sets; Perform nearest neighbor fusion on the K first temperature gradient sets to generate K first region segmentation ridge lines; Continue to inject water, after multiple nearest neighbor fusions, until the K maximum temperature gradients in the K temperature gradient sets are submerged, obtain K regional segmentation ridge line sets, and screen the monitoring points of the K temperature gradient sets to obtain the K surface monitoring point arrays.

4. The dynamic temperature control system based on the temperature control algorithm of the multi-stage series electric heating furnace according to claim 3 is characterized in that: The steps performed by the real-time temperature rise value obtaining module also include: Using the K regional segmentation ridge line sets to divide the K temperature gradient sets into regions, and calculating the mean of the temperature gradient in the regions to obtain the mean value sets of the K divided regions, and obtaining the distribution coefficient sets of the monitoring points of the K divided regions based on the mean value sets of the K divided regions; Divide the number of monitoring points according to the preset number of real-time surface monitoring points and the K divided area monitoring point distribution coefficient sets to obtain the K divided area real-time surface monitoring point number sets; Monitoring points are randomly selected based on the number sets of real-time surface monitoring points in the K divided areas to generate the K surface monitoring point arrays.

5. The dynamic temperature control system based on the temperature control algorithm of the multi-stage series electric heating furnace according to claim 1 is characterized in that: The steps performed by the target allocated power acquisition module also include: K longitudinal dynamic temperature control constraints and K transverse dynamic temperature control constraints are used to identify the central monitoring temperatures of K electric heaters and K real-time temperature rise values, respectively, to determine the deviation factor and deviation direction; The K allocated powers are optimized with dual constraints based on the deviation factor and the deviation direction to obtain the K target allocated powers.

6. The dynamic temperature control system based on the temperature control algorithm of the multi-stage series electric heating furnace according to claim 5 is characterized in that: The steps performed by the target allocated power acquisition module also include: Using the allocated power adjustment unit to identify the deviation factor and the deviation direction, and determine K adjusted allocated powers, wherein the deviation factor includes K lateral deviation factors and K longitudinal deviation factors, and the deviation direction includes K lateral deviation directions and K longitudinal deviation directions; Performing temperature control simulation on the K-level electric heater based on the K adjusted allocated powers, and determining whether the K temperature control simulation results satisfy the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints; If so, the K adjusted allocated powers are used as the K target allocated powers.

7. The dynamic temperature control system based on the temperature control algorithm of the multi-stage series electric heating furnace according to claim 6 is characterized in that: The steps performed by the target allocated power acquisition module also include: If not, performing deviation identification according to the K temperature control simulation results and the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints to obtain a simulation deviation factor and a simulation deviation direction; Using the allocated power adjustment unit to identify the simulated deviation factor, the simulated deviation direction and the K adjusted allocated powers, and determine the K stage allocated powers; When the temperature control simulation results of the K stage allocated powers satisfy the K lateral dynamic temperature control constraints and the K longitudinal dynamic temperature control constraints, the K stage allocated powers are used as the K target allocated powers.

8. The dynamic temperature control system based on the temperature control algorithm of the multi-stage series electric heating furnace according to claim 1 is characterized in that: The steps performed by the multi-stage temperature control scheme generation module also include: A temperature control identifier is pre-built to identify a multi-level temperature control solution based on the preset output temperature threshold, basic design information and the position serial number identifier, and generate the multi-level temperature control solution.

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