A diffusion furnace control system for electrothermal alloy processing

Abstract

This invention discloses a control system for a diffusion furnace used in electrothermal alloy processing, comprising a temperature acquisition unit, a core controller, and a heating execution unit. The core controller integrates a multi-temperature zone collaborative temperature compensation module, a resistance temperature dynamic compensation module, an adaptive PID calculation module, and a main / auxiliary heating switching logic judgment module. The multi-temperature zone collaborative temperature compensation module dynamically compensates for the setpoints of the furnace opening / tail temperature zones based on the furnace temperature zone; the resistance temperature dynamic compensation module compensates for the power signal according to the real-time resistance value of the heating element, offsetting the power attenuation caused by resistance temperature drift; the adaptive PID calculation module tunes the PID parameters online using a BP neural network; when the temperature and rate of change of each temperature zone meet the switching conditions, the main / auxiliary heating switching module controls the main heating module to exit and the auxiliary heating module to start operation. This invention improves the temperature control accuracy during the holding stage while ensuring the heating rate, and also improves the uniformity of the axial temperature field in the furnace.

Description

Technical Field

[0001] This invention relates to the technical field of electrothermal alloy heat treatment equipment, specifically to a diffusion furnace control system for electrothermal alloy processing. Background Technology

[0002] Electrothermal alloys (such as iron-chromium-aluminum alloys and nickel-chromium alloys) are the basic materials for manufacturing electrothermal elements and are widely used in industrial heating equipment such as ceramic kilns, high-temperature heat treatment furnaces, and laboratory resistance furnaces. After smelting and forming, electrothermal alloys typically require heat treatment processes such as annealing, solution treatment, and secondary diffusion to eliminate internal stress, improve microstructure, and obtain a uniform solid solution, thereby ensuring the physical and mechanical properties of the final product. Diffusion heat treatment is one of the key processes in the production of electrothermal alloys; the precision of its temperature control and the uniformity of the temperature field directly determine the microstructure uniformity and performance consistency of the alloy product.

[0003] A search revealed that existing temperature control methods for electrothermal alloy diffusion furnaces mainly suffer from the following technical defects: 1. Poor temperature uniformity: The electric heating alloy diffusion furnace has a long furnace body (usually several meters), and there is significant heat loss at the furnace mouth and tail, resulting in uneven axial temperature distribution in the furnace chamber. Existing control methods lack effective multi-temperature zone coordinated control means. Each temperature zone operates independently and does not communicate with each other, making it impossible to actively compensate for heat loss at the furnace mouth and tail. This results in inconsistent heat treatment effects on materials at different locations in the furnace, seriously affecting the uniformity of product quality.

[0004] 2. There is a lack of specific compensation strategies for the resistance-temperature characteristics of electrothermal alloy materials. When electrothermal alloys are used as heating elements, their resistivity changes significantly with temperature. Existing control methods do not consider the impact of this characteristic on heating power control, which causes the actual output power to deviate from the target value as the temperature rises, resulting in a decrease in temperature control accuracy.

[0005] 3. The heating power requirements for the heating and holding stages differ greatly, and there is a lack of segmented switching strategies. The heating stage requires high power for rapid heating, while the holding stage only requires low power for precise maintenance. Existing control methods use the same heating module and control parameters throughout the entire process, which cannot balance the rapid heating stage with the high precision of the holding stage, resulting in either slow heating or poor holding accuracy.

[0006] 4. The temperature control has insufficient adaptive capability. Once the control parameters of the existing control method are set, they are fixed in the system. When the operating conditions change (such as aging of heating elements or fluctuations in grid voltage), the system cannot automatically adjust the control strategy to adapt to the new operating conditions.

[0007] To address this, a diffusion furnace control system for electrothermal alloy processing is proposed. Summary of the Invention

[0008] The purpose of this invention is to provide a diffusion furnace control system for electrothermal alloy processing, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a diffusion furnace control system for electrothermal alloy processing, comprising a diffusion furnace, wherein the diffusion furnace is provided with at least three temperature zones along the furnace body axial direction, including a furnace mouth temperature zone, a furnace middle temperature zone, and a furnace tail temperature zone, and the control system comprising: The temperature acquisition unit consists of thermocouples and matching transmitters distributed in each temperature zone, used to acquire the furnace temperature and heating element temperature in each temperature zone in real time. The core controller integrates a multi-temperature zone collaborative temperature compensation module, a resistance temperature dynamic compensation module, an adaptive PID calculation module, and a main / auxiliary thermal switching logic judgment module. The heating execution unit includes a main heating power drive module and an auxiliary heating power drive module; The multi-temperature zone collaborative temperature compensation module is configured to: use the measured temperature of the furnace temperature zone as a reference benchmark for temperature field uniformity, calculate the temperature difference between the furnace mouth temperature zone or furnace tail temperature zone and the furnace temperature zone in real time, and when the temperature difference exceeds a preset threshold, adjust the temperature setting value of the lagging temperature zone upward according to the product of the temperature difference and the dynamic compensation coefficient, and the dynamic compensation coefficient is adaptively adjusted according to the changing trend of the temperature difference. The resistance temperature dynamic compensation module is configured to: calculate the current resistance value of the heating element based on the real-time temperature of the heating element and the resistance temperature coefficient of the heating alloy, and compensate the basic power control signal based on the ratio of the current resistance value to the reference resistance value, so as to offset the power attenuation caused by the resistance temperature drift of the heating element. The main and auxiliary heat switching logic judgment module is configured to: when the deviation between the temperature of each temperature zone and the target heat preservation temperature is less than or equal to the first threshold, and the temperature change rate of each temperature zone is less than or equal to the second threshold, send a switching command to the heating execution unit to make the main heating power drive module stop output or switch to a low power maintenance state, and the auxiliary heating power drive module is put into operation. The adaptive PID calculation module is configured to use the current temperature deviation, the rate of change of deviation, and the second-order change of deviation as node inputs of the BP neural network input layer, tune the PID parameters of each temperature zone online through the BP neural network, and output a basic power control signal based on the tuned PID parameters. This basic power control signal serves as the reference input of the resistance temperature dynamic compensation module.

[0010] Preferably, the dynamic compensation coefficient is determined according to the following formula: ; ; in, This represents the compensation coefficient for the current sampling period; Indicates the basic compensation coefficient; Indicates the adjustment factor; and This represents the temperature difference between the first two sampling periods; and the compensation coefficient is limited to between 0.3 and 0.8.

[0011] Preferably, the current resistance value is calculated using the following formula: ; in, This indicates the resistance value at the current temperature; This indicates the resistance value at 20℃; This indicates the temperature coefficient of resistance of the electrothermal alloy. Indicates the current temperature of the heating element; The compensated power control signal is: ; in, This indicates the basic power control signal.

[0012] Preferably, the dynamic compensation coefficients of the furnace mouth temperature zone and the furnace tail temperature zone are set independently, and the basic compensation coefficient of the furnace mouth temperature zone is greater than that of the furnace tail temperature zone, so as to compensate for the greater heat loss at the furnace mouth.

[0013] Preferably, the BP neural network has a three-layer structure, with 3 nodes in the input layer, 5 nodes in the hidden layer, and 3 nodes in the output layer; the three nodes in the input layer input the current temperature deviation, the rate of change of deviation, and the second-order change of deviation, respectively; the three nodes in the output layer output the proportional gain, integral gain, and derivative gain, respectively; the weights of the BP neural network are adjusted online using the gradient descent method.

[0014] Preferably, the adaptive PID calculation module takes the furnace temperature as the controlled variable and outputs the main loop control quantity according to the positional PID algorithm; then, it takes the heating element temperature as the controlled variable and outputs the secondary loop basic power control signal according to the incremental PID algorithm.

[0015] Preferably, it also includes a protective atmosphere control unit, which consists of a mass flow meter, a pressure transmitter, and a regulating valve. The core controller outputs a regulating valve opening command according to a PI algorithm based on the deviation between the protective gas flow setpoint and the mass flow meter feedback value; and outputs an exhaust or intake regulating valve control command according to a PID algorithm based on the deviation between the micro-positive pressure setpoint and the pressure transmitter feedback value.

[0016] Preferably, the core controller also has a built-in safety monitoring module. When the temperature of any temperature zone exceeds the sum of the target insulation temperature and the safety margin, the safety monitoring module immediately outputs a heating stop command to the heating execution unit.

[0017] Preferably, during the cooling control phase, the core controller outputs cooling control commands according to the temperature range of the current furnace temperature, following a segmented rule: the high-temperature range outputs a first cooling power, the medium-temperature range outputs a second cooling power, and the low-temperature range outputs a third cooling power, with the second cooling power being between the first cooling power and the third cooling power.

[0018] Preferably, the first threshold is The second threshold is The preset threshold for temperature difference is .

[0019] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention utilizes a multi-temperature zone collaborative temperature compensation module to calculate the temperature difference between the furnace opening and the furnace tail in real time, using the furnace temperature zone as a benchmark. When the temperature difference exceeds a threshold, the set value of the lagging temperature zone is actively increased based on a dynamic compensation coefficient. The compensation coefficient is adaptively adjusted according to the temperature difference trend, and the coefficients for the furnace opening and furnace tail are set independently. The steady-state temperature difference of each temperature zone is controlled within 1℃, effectively solving the problem of product quality dispersion caused by the uneven axial temperature field of a long furnace body.

[0020] 2. This invention uses a dynamic resistance temperature compensation module to calculate the current resistance value based on the real-time temperature of the heating element and the resistance temperature coefficient of the electrothermal alloy, and compensates the power signal in real time according to the ratio of the current resistance to the reference resistance. This eliminates the power attenuation caused by the positive resistance temperature characteristics of the electrothermal alloy under voltage source driving, and maintains temperature control accuracy throughout the entire temperature range.

[0021] 3. This invention employs a segmented strategy of heating with a main heating module and maintaining heat with an auxiliary heating module, while requiring temperature deviation during switching conditions. And the rate of temperature change Ensure the temperature stabilizes before switching; leverage the high power of the main module during the heating phase, and utilize the low thermal inertia of the auxiliary module during the heat preservation phase. Maintain high precision within the range.

[0022] 4. This invention uses temperature deviation, deviation change rate, and deviation quadratic change as inputs to a BP neural network, and tunes the proportional, integral, and derivative gains of the PID controller online. The weights are adjusted in real time using the gradient descent method, so that the control parameters are automatically optimized as the heating element ages, the power grid fluctuates, and other operating conditions change. The system robustness is significantly better than that of traditional fixed-parameter PID control. Attached Figure Description

[0023] Figure 1This is a schematic diagram of the axial temperature zone distribution structure of the diffusion furnace of the present invention; Figure 2 This is a signal flow diagram of the control system of the present invention; Figure 3 This is the complete control logic diagram of the control system of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0025] Please see Figures 1 to 3 This invention provides a technical solution for a diffusion furnace control system for electrothermal alloy processing: A control system for a diffusion furnace used in electrothermal alloy processing mainly includes the following functional units: Temperature acquisition unit: Consists of thermocouples and matching transmitters distributed along the furnace body axis in each temperature zone (including the furnace mouth temperature zone, the furnace middle temperature zone, and the furnace tail temperature zone, with no less than 3 temperature zones), used to acquire furnace temperature in real time. and heating element temperature ; Core controller: It adopts an industrial-grade embedded controller (such as PLC or dedicated industrial computer), which integrates a BP neural network algorithm module, a multi-temperature zone collaborative compensation module, an adaptive PID calculation module, a main-auxiliary heat switching logic judgment module and a safety monitoring module. Heating execution unit: includes a main heating power drive module (high-power thyristor power regulator, corresponding to the main heating element) and an auxiliary heating power drive module (low-power precision power regulator, corresponding to the auxiliary heating element), used to receive power control signals from the controller and drive the heating element; Protective atmosphere control unit: Consists of a mass flow meter, a pressure transmitter and corresponding regulating valves, used for closed-loop control of protective gas flow and furnace micro-positive pressure; Human-machine interface: used for operators to preset and modify process parameters, and to display the system operating status in real time; After the control system is powered on, the core controller automatically executes the full-process control according to the following timing logic: data acquisition and initialization stage → heating control stage (including multi-temperature zone collaborative compensation) → main and auxiliary heat switching stage → heat preservation control stage → cooling control stage. The core controller automatically determines the switching conditions between each stage based on the preset process parameters and the real-time temperature data.

[0026] As one embodiment of the present invention, please refer to Figures 1 to 3 The following section uses the diffusion heat treatment process of iron-chromium-aluminum alloy (Cr20Al5) as an example to explain in detail the specific collaborative working process of each unit in the control system.

[0027] Data acquisition and initialization phase After the control system is started, the core controller first executes the following initialization logic steps; Step 1.1: Read the process parameters preset by the operator through the human-machine interface, including: heating rate. Target insulation temperature Insulation time Cooling rate Micro-positive pressure setting value , Protective gas flow setting value Temperature difference action threshold (This embodiment takes) Safety margin (This embodiment takes) ), and store the above parameters in the controller's non-volatile memory; Step 1.2: The core controller reads the current furnace temperature values ​​of each temperature zone along the axial distribution of the furnace body through the temperature acquisition unit. ( , (Total number of temperature zones) and temperature values ​​of furnace wall heating elements ; Step 1.3: The core controller initializes the temperature setpoints for each temperature zone. (Ambient temperature), initial compensation coefficient (Initial value is preset to 0.5), initialize the weights and biases of the internal BP neural network; Step 1.4: Set the sampling period for the core controller. (In this embodiment, 1 second is used) to start the timed sampling interrupt.

[0028] Temperature control stage The temperature rise control phase includes the following control tasks, which are completed collaboratively by the core controller and the corresponding execution units: Step 2.1: In each sampling period ( After analog-to-digital conversion, the temperature acquisition unit uploads the following data to the core controller: measured furnace temperature values ​​for each temperature zone. ( ), measured temperature values ​​of heating elements in each temperature zone ( (Units are all) .

[0029] Step 2.2: The core controller determines the temperature setpoint for each temperature zone based on the current time. Compared with the measured temperature value The temperature deviation value for each temperature zone is calculated using the following formula: ; in, Indicates the first The sampling period Temperature deviation for each temperature zone; a positive value indicates that the actual temperature is lower than the set value. Indicates the first The sampling period The temperature setpoint for each temperature zone is determined by the core controller during the heating phase based on a preset heating rate. It is calculated and generated in real time with the current time; Indicates the first The sampling period Measured furnace temperatures in each temperature zone.

[0030] Step 2.3: During the heating process, the core controller compares the measured temperatures of each temperature zone in real time and calls the multi-temperature zone collaborative temperature compensation module to perform the following compensation calculations: Step 2.3.1: Calculate the temperature difference between the furnace mouth / tail temperature zone and the furnace middle temperature zone. The specific calculation formula is shown below: ; in, Indicates the first The measured temperature of the furnace temperature zone in each sampling cycle is used as a reference benchmark for temperature field uniformity. Indicates the first Measured temperature of furnace mouth or furnace tail temperature zone in each sampling cycle; Step 2.3.2: When the temperature difference exceeds the preset threshold, the core controller performs temperature setpoint compensation, as detailed below: when hour: ; when At that time, keep the set value unchanged: ; in The temperature difference action threshold is used in this embodiment. .

[0031] Step 2.3.3: Adaptive adjustment of dynamic compensation coefficients, details of which are as follows: Compensation coefficient in the core controller It is not a fixed value, but is dynamically adjusted based on historical temperature data and differences in temperature zone location. The specific calculation formula is shown below: ; ; in, This represents the basic compensation coefficient, which is 0.5 in this embodiment; Indicates the first The adjustment amount of the compensation coefficient for each sampling period; This represents the adjustment factor, which is set to 0.1 in this embodiment; and This represents the temperature difference between the first two sampling periods; The core controller performs amplitude limiting on the calculated compensation coefficient to ensure that its value is always between 0.3 and 0.8; Compensation logic explanation: When the temperature in the furnace mouth or furnace tail zone continuously lags behind the temperature in the furnace middle zone (temperature difference exceeds...) The system automatically increases the temperature setpoint of the lagging temperature zone by the product of the temperature difference and the dynamic compensation coefficient. Since the heat loss at the furnace mouth and the furnace tail is different, the compensation coefficient of each temperature zone can be set independently. The compensation coefficient is dynamically adjusted in the range of 0.3 to 0.8 - the coefficient increases when the lag trend intensifies and decreases when the lag trend eases, so as to avoid overcompensation.

[0032] Step 2.4: For each temperature zone, the core controller will calculate the current temperature deviation. The historical values ​​are input into the internal BP neural network module to tune the PID control parameters for that temperature range online. , and ; Step 2.4.1: A three-layer backpropagation (BP) neural network is adopted, with a 3-5-3 structure (3 nodes in the input layer, 5 nodes in the hidden layer, and 3 nodes in the output layer), as shown below: Input layer: Select the following 3 variables as network input, and denote the output of the input layer node as... ( Its value is directly equal to the input value, and the specific formula is as follows: ; ; ; Hidden layer: Hidden layer number 1 Nodes ( The weighted input and output of ) are as follows: ; ; Output layer: Output layer number Nodes ( The weighted input and output of ) are as follows: ; ; The output of the output layer node is mapped to the effective PID parameter range through the core controller engineering calibration to obtain the adaptive control parameters for each temperature zone. The specific formula is shown below: ; ; ; in, , and These represent the upper limits of the proportional, integral, and derivative gains preset in the controller parameter table based on the thermal inertia of the furnace body.

[0033] Step 2.4.2: Online adjustment of network weights, details of which are as follows: The network performance metric function is the sum of squared system errors at the current moment, and the specific calculation formula is as follows: ; The core controller adjusts the output layer weights online using the gradient descent method. The specific calculation formula is shown below: ; The hidden layer weight adjustment is calculated using the following formula: ; in, Indicates the learning rate; This represents the momentum factor; both sets of data are preset in the controller parameter table based on the on-site commissioning conditions.

[0034] Step 2.5: The core controller performs cascaded PID calculations based on the tuned parameters and outputs dynamic power compensation commands to the heating execution unit based on the resistance-temperature characteristics of the electrothermal alloy. Step 2.5.1: The main loop uses furnace temperature as the controlled variable and employs a positional PID algorithm. The specific calculation formula is shown below: ; in, This indicates the main circuit output, i.e., the target temperature of the heating element; The sampling period.

[0035] Step 2.5.2: The secondary loop uses the heating element temperature as the controlled variable and employs an incremental PID algorithm. The specific calculation formula is shown below: ; ; ; in, This indicates that the secondary circuit output, i.e. the basic power control signal (dimensionless, representing a percentage of rated power), will serve as the reference input for subsequent resistor compensation. This indicates the measured temperature of the heating element; , and This indicates that the PID parameters for the secondary loop are fixed; in this embodiment, they are set to 1.5, 0.08, and 0.03, respectively.

[0036] Step 2.5.3: The core controller calculates the current resistance value of the electrothermal alloy based on the real-time temperature of the heating element using the following formula: ; in, Indicates the first The resistance value of the heating element in each temperature zone at the current temperature; Indicates the first The resistance value of the heating element in each temperature zone at 20℃; The temperature coefficient of resistance of an electrothermal alloy ( ), iron-chromium-aluminum alloy ; Indicates the first The current temperature of the heating element in each temperature zone.

[0037] The core controller calculates the final power control signal output to the heating actuator based on the resistance change. The specific calculation formula is shown below: ; The above formula is based on the constant power principle. This makes the compensated power control signal proportional to the current resistance. When the compensation coefficient increases, As the power output increases, the core controller correspondingly increases the power control signal output to the heating actuator, thereby precisely offsetting the power attenuation caused by resistance temperature drift.

[0038] Compensation logic explanation: Because the heating alloy has a positive temperature coefficient of resistance, the resistance of the heating element increases as the temperature rises. Increase. In voltage source drive mode, to ensure that the actual Joule thermal power accurately follows the target power. The voltage must be increased as the resistance increases.

[0039] Step 2.6: Throughout the entire heating process, the safety monitoring module built into the core controller continuously executes the following monitoring logic: Preset safe temperature threshold Its value is equal to the target insulation temperature. With safety margin (In this embodiment, 50°C) sum of the temperatures in any temperature zone When this happens, the safety monitoring module immediately outputs a heating stop command to the heating execution unit, cutting off the heating power supply.

[0040] Main and auxiliary heat switching stage When the temperature in each temperature zone approaches the target insulation temperature, the core controller executes the main / auxiliary heat switching logic as follows: Step 3.1: The core controller determines in real time whether all of the following main / auxiliary hot-switching conditions are met: Condition 1 (temperature deviation condition) ,( ); Condition 2 (Temperature Change Rate Condition) ,( ); in, This represents the temperature deviation threshold; in this embodiment, it is taken as... ; Indicates the first The temperature change rate of each temperature zone is calculated by the core controller by fitting the temperature values ​​of the most recent sampling points. This represents the threshold for the rate of temperature change; in this embodiment, it is taken as... .

[0041] Switching logic description: Condition 1 ensures that the temperature of each temperature zone is close to the target value, and condition 2 ensures that the temperature tends to be stable rather than fluctuating drastically. The core controller will only perform the switch when both conditions are met at the same time. This avoids switching too early before the temperature is stable, which would cause temperature control failure, and also avoids switching when the temperature fluctuates greatly, which would cause oscillation.

[0042] Step 3.2: When both of the above conditions are met simultaneously, the core controller sends a switching command to the heating execution unit: The main heating power drive module (a high-power thyristor power regulator responsible for rapid heating) stops outputting or switches to a low-power maintenance state. The auxiliary heating power drive module (a small-power precision regulator responsible for precise temperature control) has been put into operation.

[0043] Switching principle explanation: The main heating module has high power and high thermal inertia, which is suitable for rapid heating but not for fine adjustment; the auxiliary heating module has low power and low thermal inertia, which heats up slowly but has high adjustment accuracy. By switching to the auxiliary heating module when the target temperature is approached, the low thermal inertia of the auxiliary heating module is used to achieve high-precision temperature maintenance, taking into account both the rapid heating stage and the high precision of the heat preservation stage.

[0044] Thermal insulation control stage After the main and auxiliary heat exchangers switch is completed, the control system enters the heat preservation control stage, as shown in the following steps: Step 4.1: During the heat preservation stage, the auxiliary heating power drive module performs temperature maintenance control. The core controller continues to call the adaptive PID calculation and resistance temperature dynamic compensation logic from the previous steps, but switches the target weight of the BP neural network parameter tuning module from "rapid heating" to "high-precision maintenance". In this embodiment, the steady-state temperature fluctuation of each temperature zone during the heat preservation stage is controlled within... Within.

[0045] Step 4.2: When the temperature in each temperature zone stabilizes at... Once within the specified range, the core controller begins the heat preservation timing. During the heat preservation period, the protective atmosphere control unit continuously executes the following closed-loop control. Step 4.2.1: The core controller calculates the preset protective gas flow rate. Receive feedback signal from mass flow meter The valve opening command is output according to the following PI algorithm, and the specific calculation formula is shown below: ; in, and This represents the PID parameters for gas flow control, where the discretized integral term is implicitly multiplied by the sampling period. .

[0046] Step 4.2.2: The core controller determines the micro-positive pressure setpoint. Receive feedback signal from pressure transmitter The system outputs control commands for the exhaust or intake regulating valves according to the following PID algorithm to maintain stable furnace pressure. The specific calculation formula is shown below: ; in, , and This represents the PID parameters for pressure control.

[0047] Step 4.2.3: When the heat preservation time reaches the preset heat preservation time... At that time, the core controller automatically enters the cooling control phase.

[0048] Cooling control phase After the heat preservation process is completed, the core controller automatically enters the cooling control phase, as shown in the following steps: Step 5.1: The core controller cools down at the preset rate. Gradually reduce the temperature setpoints for each temperature zone: ; The core controller monitors the actual cooling rate in real time: ; in, This indicates the number of sampling points used for computation. When... Deviation Exceeding the set threshold (in this embodiment, it is taken as...) When this happens, the core controller automatically adjusts the control signal output to the cooling actuator.

[0049] Step 5.2: The core controller outputs cooling control commands based on the current furnace temperature zone: when the temperature is higher than... When the temperature is at zero, the cooling power is zero; when the temperature is at... to During this period, the rated cooling power output is... When the temperature is lower than At that time, the rated cooling power output is .

[0050] In this embodiment, the base value of the cooling control signal is given by the segmentation rule in step S13, and in actual operation, the core controller determines the value according to... and The deviation is adjusted by adding a fine-tuning amount to the base value to achieve a precise closed loop for the rate.

[0051] Abnormal operating condition handling methods The core controller's built-in anomaly diagnosis module monitors the system's operating status in real time and executes the following processing logic; 1. Temperature overshoot handling: When At that moment, the core controller immediately sends a command to the heating execution unit to reduce the heating power. Until the temperature returns to the normal range, among which This indicates the allowable overshoot amount; in this embodiment, it is taken as... .

[0052] 2. Sensor anomaly handling: When the anomaly diagnosis module detects an abnormal temperature signal (including temperature sudden change exceeding the normal range, open circuit or short circuit of the temperature signal), the core controller immediately stops the heating output and outputs an alarm signal through the human-machine interface.

[0053] 3. Power outage recovery: When power is restored after an unexpected power outage, the core controller reads the process status data stored in the non-volatile memory and automatically selects whether to continue the interrupted process or restart the entire process based on the current process status.

[0054] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control system for a diffusion furnace for electrothermal alloy processing, comprising a diffusion furnace, wherein the diffusion furnace is provided with at least three temperature zones along the furnace body axial direction, including a furnace mouth temperature zone, a furnace middle temperature zone, and a furnace tail temperature zone, characterized in that: The control system includes: The temperature acquisition unit consists of thermocouples and matching transmitters distributed in each temperature zone, used to acquire the furnace temperature and heating element temperature in each temperature zone in real time. The core controller integrates a multi-temperature zone collaborative temperature compensation module, a resistance temperature dynamic compensation module, an adaptive PID calculation module, and a main / auxiliary thermal switching logic judgment module. The heating execution unit includes a main heating power drive module and an auxiliary heating power drive module; The multi-temperature zone collaborative temperature compensation module is configured to: use the measured temperature of the furnace temperature zone as a reference benchmark for temperature field uniformity, calculate the temperature difference between the furnace mouth temperature zone or furnace tail temperature zone and the furnace temperature zone in real time, and when the temperature difference exceeds a preset threshold, adjust the temperature setting value of the lagging temperature zone upward according to the product of the temperature difference and the dynamic compensation coefficient, and the dynamic compensation coefficient is adaptively adjusted according to the changing trend of the temperature difference. The resistance temperature dynamic compensation module is configured to: calculate the current resistance value of the heating element based on the real-time temperature of the heating element and the resistance temperature coefficient of the heating alloy, and compensate the basic power control signal based on the ratio of the current resistance value to the reference resistance value, so as to offset the power attenuation caused by the resistance temperature drift of the heating element. The main and auxiliary heat switching logic judgment module is configured to: when the deviation between the temperature of each temperature zone and the target heat preservation temperature is less than or equal to the first threshold, and the temperature change rate of each temperature zone is less than or equal to the second threshold, send a switching command to the heating execution unit to make the main heating power drive module stop output or switch to a low power maintenance state, and the auxiliary heating power drive module is put into operation. The adaptive PID calculation module is configured to use the current temperature deviation, the rate of change of deviation, and the second-order change of deviation as node inputs of the BP neural network input layer, tune the PID parameters of each temperature zone online through the BP neural network, and output a basic power control signal based on the tuned PID parameters. This basic power control signal serves as the reference input of the resistance temperature dynamic compensation module.

2. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: The dynamic compensation coefficient is determined according to the following formula: ； ； in, This represents the compensation coefficient for the current sampling period; Indicates the basic compensation coefficient; Indicates the adjustment factor; and This represents the temperature difference between the first two sampling periods; and the compensation coefficient is limited to between 0.3 and 0.

8.

3. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: The current resistance value is calculated using the following formula: ； in, This indicates the resistance value at the current temperature; This indicates the resistance value at 20℃; Indicates the temperature coefficient of resistance of an electrothermal alloy; Indicates the current temperature of the heating element; The compensated power control signal is: ； in, This indicates the basic power control signal.

4. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: The dynamic compensation coefficients for the furnace mouth temperature zone and the furnace tail temperature zone are set independently. The basic compensation coefficient for the furnace mouth temperature zone is greater than that for the furnace tail temperature zone, in order to compensate for the greater heat loss at the furnace mouth.

5. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: The BP neural network has a three-layer structure: 3 nodes in the input layer, 5 nodes in the hidden layer, and 3 nodes in the output layer. The three nodes in the input layer take the current temperature deviation, the rate of change of the deviation, and the second-order change of the deviation as inputs, respectively. The three nodes in the output layer output the proportional gain, integral gain, and derivative gain, respectively. The weights of the BP neural network are adjusted online using the gradient descent method.

6. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: The adaptive PID calculation module takes the furnace temperature as the controlled variable and outputs the main loop control quantity according to the positional PID algorithm; then, it takes the heating element temperature as the controlled variable and outputs the secondary loop basic power control signal according to the incremental PID algorithm.

7. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: It also includes a protective atmosphere control unit, which consists of a mass flow meter, a pressure transmitter, and a regulating valve. The core controller outputs the regulating valve opening command according to the deviation between the protective gas flow setpoint and the mass flow meter feedback value using a PI algorithm; and outputs the exhaust or intake regulating valve control command according to the deviation between the micro positive pressure setpoint and the pressure transmitter feedback value using a PID algorithm.

8. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: The core controller also has a built-in safety monitoring module. When the temperature of any temperature zone exceeds the sum of the target insulation temperature and the safety margin, the safety monitoring module immediately outputs a heating stop command to the heating execution unit.

9. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: During the cooling control phase, the core controller outputs cooling control commands according to the temperature range of the current furnace temperature, following a segmented rule: the first cooling power is output for the high temperature range, the second cooling power is output for the medium temperature range, and the third cooling power is output for the low temperature range. The second cooling power is between the first cooling power and the third cooling power.

10. The control system for a diffusion furnace for electrothermal alloy processing according to claim 1, characterized in that: The first threshold is The second threshold is The preset threshold for temperature difference is .

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