Intelligent temperature control system of magnetic tile roller way electric kiln

Abstract

The application discloses an intelligent temperature control system of a magnetic tile roller electric kiln and relates to the technical field of electric kiln temperature control.The application solves the technical problem that the temperature detection has errors and the temperature control has time delay, which leads to inaccurate temperature control and affects the sintering efficiency in the prior art.The application calibrates the temperature data of the thermocouple through a temperature calibration model, then controls the heating element according to the calibrated temperature data, and performs predictive analysis based on the calibrated temperature data, and presets the heating element control time according to the analysis result.The application can reduce the probability of operator's misoperation while ensuring the accuracy of the temperature data.The application can accurately calculate the time based on the calibrated temperature data by flexibly setting the parameters in the formula according to the sintering link, and the temperature control module can reasonably set the temperature control time based on the consideration of the time delay, thereby improving the temperature control precision of the magnetic tile roller electric kiln.

Description

Technical Field

[0001] This invention belongs to the field of electric kiln temperature control, and relates to temperature control technology for magnetic tile roller electric kilns, specifically an intelligent temperature control system for magnetic tile roller electric kilns. Background Technology

[0002] Magnetic tile roller kilns are lightweight, continuous industrial kilns widely used for the rapid firing of chemical powders, ceramic substrates, and other products. They offer advantages such as low energy consumption, short firing cycles, and low labor costs. However, precise temperature control in magnetic tile roller kilns remains a pressing issue that needs to be addressed.

[0003] Existing technology (patent application number 2019113955719) discloses a method for controlling the sintering temperature of a roller kiln. This method determines the target temperature zone and optimal temperature change rate based on the material of the roller kiln lining, calculates the target temperature at each preset time point, and controls the heating element to achieve the target temperature at each preset time point. This ensures a stable temperature rise while reducing the impact on the power grid and improving the service life of the heating element. However, existing technology cannot avoid temperature detection errors when controlling the temperature of a magnetic tile roller kiln. Furthermore, the feedback mechanism for temperature control introduces time delays, resulting in inaccurate temperature control and affecting sintering efficiency. Therefore, an intelligent temperature control system for magnetic tile roller kilns is urgently needed. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an intelligent temperature control system for magnetic tile roller electric kilns, which solves the technical problems of temperature detection errors and time delays in temperature control in the prior art, resulting in inaccurate temperature control and affecting sintering efficiency.

[0005] To achieve the above objectives, a first aspect of the present invention provides an intelligent temperature control system for a magnetic tile roller kiln, including a central control module, and a temperature detection module and a temperature control module connected thereto; wherein the temperature detection module is connected to a thermocouple, and the temperature control module is connected to a heating element.

[0006] The central control module acquires the sintering process of the target preform and sets the target temperature for each sintering stage; the sintering stage includes free heating, constant rate heating, sintering holding, constant rate cooling, or free cooling.

[0007] The central control module sends a temperature detection signal to the temperature detection module based on the current sintering stage; the temperature detection module acquires temperature data through thermocouples, calibrates the temperature data, and then returns it to the central control module.

[0008] The central control module performs predictive analysis on temperature data and sends temperature control signals to the temperature control module based on the analysis results to achieve temperature control; among them, predictive analysis is the predictive temperature control switching node.

[0009] Preferably, the central control module is communicatively and / or electrically connected to the temperature detection module and the temperature control module, respectively; and the central control module is also used to write and update the temperature control program.

[0010] The temperature detection module collects temperature data through several thermocouples installed in the magnetic tile roller kiln; the temperature control module controls the heating element according to the temperature control signal.

[0011] Preferably, the central control module identifies the target preform via a camera and extracts the sintering process of the target preform from its internally stored data; and

[0012] The optimal heating rate and optimal cooling rate are determined based on the material properties of the heating element.

[0013] Preferably, the central control module generates a temperature detection signal based on the current sintering stage; the temperature detection module acquires and calibrates temperature data based on the temperature detection signal, including:

[0014] Several sets of temperature data are obtained using several thermocouples;

[0015] The temperature calibration model is invoked to calibrate several sets of temperature data, and the calibrated sets of temperature data are fed back to the central control module; wherein, the temperature calibration model is established based on an artificial intelligence model.

[0016] Preferably, establishing the temperature calibration model based on an artificial intelligence model includes:

[0017] The temperature error of the thermocouple is simulated under various operating conditions, and standard training data is generated by integrating the data. The standard training data includes environmental data, thermoelectric potential, and corresponding temperature error.

[0018] Environmental data and thermoelectric potential are labeled as model input data, and the corresponding temperature error is labeled as model output data. The artificial intelligence model is trained using the model input data and model output data to obtain a temperature calibration model. The artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0019] Preferably, the central control module performs predictive analysis based on temperature data, including:

[0020] Acquire the calibrated temperature data and the optimal temperature change rate, labeled WD and WBS respectively; where the optimal temperature change rate includes the optimal heating rate or the optimal cooling rate.

[0021] The time WZS to reach the next temperature conversion node is calculated using the formula WZS=α×(JWD-WD) / WBS+T; where α takes the value of 0 or 1, JWD is the target temperature corresponding to the next conversion node, and T is the holding time.

[0022] Preferably, the central control module generates a temperature control signal based on the predictive analysis results and sends it to the temperature control module, including:

[0023] When WZS≤ZSY, it is determined that the next temperature transition node is about to be reached, and a temperature control signal is generated; where ZSY is a transition time threshold set based on experience, and ZSY≥5s;

[0024] The time WZS for reaching the next temperature conversion node and the temperature control signal are sent to the temperature control module.

[0025] Preferably, the temperature control module controls the heating power of the heating element based on a temperature control signal, including:

[0026] Receive temperature control signals and calculate the temperature control timing based on the time WZS of the next temperature conversion node and the signal delay;

[0027] When the temperature control time is reached, the heating power of the heating element is controlled according to the set program.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] 1. This invention constructs a temperature calibration model based on standard training data obtained through simulation. The temperature data of thermocouples is calibrated using this model. Then, the heating element is controlled based on the calibrated temperature data. Furthermore, predictive analysis is performed based on the calibrated temperature data, and the control timing of the heating element is preset according to the analysis results. This invention ensures accurate temperature data while reducing the probability of operator error and improving the sintering efficiency of the magnetic tile roller kiln.

[0030] 2. This invention designs a formula to calculate the time to reach the next temperature transition node. By flexibly setting the parameters in the formula according to the sintering process, the time can be accurately calculated based on the calibrated temperature data. The temperature control module can reasonably set the temperature control time based on the time delay, thereby improving the temperature control accuracy of the magnetic tile roller kiln. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram illustrating the working steps of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Please see Figure 1 The first aspect of this invention provides an intelligent temperature control system for a magnetic tile roller kiln, including a central control module, and a temperature detection module and a temperature control module connected thereto; the temperature detection module is connected to a thermocouple, and the temperature control module is connected to a heating element; the central control module acquires the sintering process of the target blank and sets the target temperature for each sintering stage; the central control module sends a temperature detection signal to the temperature detection module based on the current sintering stage; the temperature detection module acquires temperature data through the thermocouple, calibrates the temperature data, and returns it to the central control module; the central control module performs predictive analysis on the temperature data and sends a temperature control signal to the temperature control module based on the analysis results to achieve temperature control.

[0035] Current technology for temperature control in magnetic tile roller kilns typically involves observing the internal temperature using instruments. Operators then adjust the heating power of the heating elements based on the observed temperature and their experience. However, this existing technology fails to account for temperature errors from thermocouple measurements and the time delays caused by operator judgment, resulting in inaccurate temperature control of the magnetic tile roller kiln.

[0036] This invention constructs a temperature calibration model based on standard training data obtained through simulation. The temperature data of thermocouples is calibrated using this model, and then the heating element is controlled based on the calibrated temperature data. Furthermore, predictive analysis is performed based on the calibrated temperature data, and the control timing of the heating element is preset according to the analysis results. This invention ensures accurate temperature data while reducing the probability of operator error and improving the sintering efficiency of the magnetic tile roller kiln.

[0037] In this invention, the central control module communicates and / or is electrically connected to the temperature detection module and the temperature control module respectively; and the central control module is also used to write and update the temperature control program; the temperature detection module collects temperature data through several thermocouples set in the magnetic tile roller kiln; the temperature control module controls the heating element according to the temperature control signal.

[0038] The central control module is used for overall monitoring and control of the magnetic tile roller kiln. It is primarily responsible for processing various data and providing early warnings. It obtains calibrated temperature data from the temperature detection module, analyzes it, and then controls the heating power of the heating elements through the temperature control module. The temperature detection module collects temperature data inside the kiln via thermocouples, calibrates it, and sends it to the central control module. The temperature control module mainly controls the heating power of the heating elements to regulate the temperature inside the kiln. The heating elements are the same as those used in existing magnetic tile roller kilns.

[0039] The sintering process in this invention includes free heating, constant-rate heating, sintering heat preservation, constant-rate cooling, or free cooling. Of course, the operator can also customize the sintering process. The central control module will determine which sintering process is in based on parameters such as firing time and temperature, and will monitor and control the sintering process accordingly.

[0040] In this invention, the central control module identifies the target preform using a camera, extracts the sintering process of the target preform from internally stored data, and determines the corresponding optimal heating rate and optimal cooling rate based on the material properties of the heating element. The optimal heating efficiency / optimal cooling rate is determined by comprehensively considering material properties, energy consumption, temperature change rate, etc.

[0041] One of the problems solved by this invention is to address the temperature measurement error of thermocouples. For example, the output thermoelectric potential of K-type thermocouples made of nickel-chromium-nickel-silicon materials and J-type thermocouples made of iron-copper-nickel materials have a corresponding relationship with the temperature difference, but it is not a linear relationship, but a curve relationship. Therefore, the temperature data output by the thermocouples has a certain error and needs to be calibrated.

[0042] In a preferred embodiment, the central control module generates a temperature detection signal based on the current sintering stage; the temperature detection module acquires and calibrates temperature data based on the temperature detection signal, including: acquiring several sets of temperature data through several thermocouples; calling a temperature calibration model to calibrate the several sets of temperature data; and feeding back the calibrated several sets of temperature data to the central control module.

[0043] Several thermocouples are installed in the magnetic tile roller kiln to collect temperature data. The data is then analyzed to determine the temperature inside the kiln. Therefore, each thermocouple collects a set of temperature data. After acquiring the temperature data, the corresponding temperature calibration model is called to calculate the temperature error of that set of temperature data. The calibrated temperature data is obtained by adding (or subtracting) the temperature data and the temperature error.

[0044] In an optional embodiment, establishing a temperature calibration model based on an artificial intelligence model includes: simulating the temperature error of thermocouples under various operating conditions and integrating to generate standard training data; labeling environmental data and thermoelectric potential as model input data and labeling the corresponding temperature error as model output data; training the artificial intelligence model using the model input data and model output data to obtain the temperature calibration model.

[0045] The working conditions of thermocouples under various operating conditions are simulated. Temperature error is extracted from the simulation results. Combined with environmental data (temperature, humidity, air pressure, etc. of the electric kiln on the magnetic tile roller conveyor), thermoelectric potential and corresponding temperature error, several sets of data can be generated and integrated to form standard training data.

[0046] An artificial intelligence model is trained based on standard training data, and the trained model is designated as the temperature calibration model. The temperature calibration model can be obtained through the central control module or the temperature detection module. After acquisition, a connection between the temperature calibration model and the thermocouple should be established based on the simulation for easy reference.

[0047] The second problem solved by this invention is to address the time delay caused by manual operation or existing automated operation. For example, when a worker observes the temperature data and determines that the heating power needs to be adjusted, the heating power is adjusted immediately. However, a time delay will occur during the worker's analysis and operation, which is not conducive to precise firing.

[0048] In a preferred embodiment, the central control module performs predictive analysis based on temperature data, including: acquiring calibrated temperature data and the optimal temperature change rate, labeled as WD and WBS respectively; and calculating the time WZS to reach the next temperature transition node using the formula WZS=α×(JWD-WD) / WBS+T.

[0049] In this embodiment, α is either 0 or 1, JWD is the target temperature corresponding to the next conversion node, and T is the holding time. For example, α = 1 in the constant rate heating or cooling stage, and α = 0 in the sintering holding stage; the holding time T is also set according to the sintering stage. It should be noted that the specific heating, cooling, or holding steps in the sintering stage should follow the set process, and the formula should be used appropriately.

[0050] To illustrate this embodiment: predict the temperature transition point from the constant rate heating stage to the sintering and heat preservation stage. At this point, the optimal heating rate, the target temperature corresponding to the temperature transition point, and the current temperature are known. According to the formula, the time required to switch to the sintering and heat preservation stage can be calculated.

[0051] In an optional embodiment, the central control module generates a temperature control signal based on the predictive analysis results and sends it to the temperature control module, including: when WZS≤ZSY, determining that the next temperature transition node is about to be reached, generating a temperature control signal; and sending the time WZS of reaching the next temperature transition node and the temperature control signal to the temperature control module.

[0052] If WZS = 5s ≤ ZSY = 5s, then a temperature control signal is generated at this moment, meaning that the next sintering stage can begin after 5s. At this point, WZS, the temperature control signal, and related time delay calculation data need to be sent to the temperature control module. The temperature control module controls the heating power of the heating element based on the temperature control signal, including: receiving the temperature control signal, calculating the temperature control time based on the time WZS of the next temperature transition node and the signal delay; and controlling the heating power of the heating element according to the set program when the temperature control time is reached.

[0053] When the temperature control module receives the temperature control signal, it calculates the signal transmission delay and combines it with the time WZS to preset the temperature control time. For example, if WZS = 5s and the delay is 1s, the temperature control module sets the temperature control time to 4s later. After the temperature control time is reached, the heating power of the heating element can be controlled according to the set program.

[0054] The data in the above formula are all calculated by removing the dimensions and taking the numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0055] Working principle of the invention:

[0056] The central control module acquires the sintering process of the target preform and sets the target temperature for each sintering stage; the sintering stage includes free heating, constant rate heating, sintering holding, constant rate cooling, or free cooling.

[0057] The central control module sends a temperature detection signal to the temperature detection module based on the current sintering stage; the temperature detection module acquires temperature data through thermocouples, calibrates the temperature data, and then returns it to the central control module.

[0058] The central control module performs predictive analysis on temperature data and sends temperature control signals to the temperature control module based on the analysis results to achieve temperature control.

[0059] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An intelligent temperature control system for a magnetic tile roller kiln, comprising a central control module, and a temperature detection module and a temperature control module connected thereto; wherein the temperature detection module is connected to a thermocouple, and the temperature control module is connected to a heating element; characterized in that: The central control module acquires the sintering process of the target preform and sets the target temperature for each sintering stage; the sintering stage includes free heating, constant rate heating, sintering holding, constant rate cooling, or free cooling. The central control module sends a temperature detection signal to the temperature detection module based on the current sintering stage; the temperature detection module acquires temperature data through thermocouples, calibrates the temperature data, and then returns it to the central control module. The central control module performs predictive analysis on temperature data and sends temperature control signals to the temperature control module based on the analysis results to achieve temperature control; among them, predictive analysis is the predictive temperature control switching node; The central control module identifies the target preform via a camera and extracts the sintering process of the target preform from its internally stored data; and The optimal heating rate and optimal cooling rate are determined based on the material properties of the heating element. The central control module performs predictive analysis based on temperature data, including: Acquire the calibrated temperature data and the optimal temperature change rate, labeled WD and WBS respectively; where the optimal temperature change rate includes the optimal heating rate or the optimal cooling rate. The time WZS to reach the next temperature conversion node is calculated using the formula WZS=α×(JWD-WD) / WBS+T; where α is 0 or 1, JWD is the target temperature corresponding to the next conversion node, and T is the holding time.

2. The intelligent temperature control system for the magnetic tile roller kiln according to claim 1, characterized in that, The central control module communicates and / or is electrically connected to the temperature detection module and the temperature control module, respectively; and the central control module is also used to write and update the temperature control program. The temperature detection module collects temperature data through several thermocouples installed in the magnetic tile roller kiln; the temperature control module controls the heating element according to the temperature control signal.

3. The intelligent temperature control system for the magnetic tile roller kiln according to claim 1, characterized in that, The central control module generates a temperature detection signal based on the current sintering process; the temperature detection module acquires and calibrates temperature data based on the temperature detection signal, including: Several sets of temperature data are obtained using several thermocouples; The temperature calibration model is invoked to calibrate several sets of temperature data, and the calibrated sets of temperature data are fed back to the central control module; wherein, the temperature calibration model is established based on an artificial intelligence model.

4. The intelligent temperature control system for the magnetic tile roller kiln according to claim 3, characterized in that, The temperature calibration model established based on the artificial intelligence model includes: The temperature error of the thermocouple is simulated under various operating conditions, and standard training data is generated by integrating the data. The standard training data includes environmental data, thermoelectric potential, and corresponding temperature error. Environmental data and thermoelectric potential are labeled as model input data, and the corresponding temperature error is labeled as model output data. The artificial intelligence model is trained using the model input data and model output data to obtain a temperature calibration model. The artificial intelligence model includes a BP neural network model or an RBF neural network model.

5. The intelligent temperature control system for the magnetic tile roller kiln according to claim 1, characterized in that, The central control module generates a temperature control signal based on the predictive analysis results and sends it to the temperature control module, including: When WZS≤ZSY, it is determined that the next temperature transition node is about to be reached, and a temperature control signal is generated; where ZSY is a transition time threshold set based on experience, and ZSY≥5s; The time WZS for reaching the next temperature conversion node and the temperature control signal are sent to the temperature control module.

6. The intelligent temperature control system for the magnetic tile roller kiln according to claim 5, characterized in that, The temperature control module controls the heating power of the heating element based on a temperature control signal, including: Receive temperature control signals and calculate the temperature control timing based on the time WZS of the next temperature conversion node and the signal delay; When the temperature control time is reached, the heating power of the heating element is controlled according to the set program.

Images