Industrial ceramic kiln based on combination of PID and AI fuzzy control methods and control method thereof

Abstract

The invention discloses an industrial ceramic kiln based on combination of PID and AI fuzzy control methods and a control method thereof.The industrial ceramic kiln comprises a kiln body, a heating device, a temperature sensor and an intelligent controller, the control method is executed by the intelligent controller, and the deviation e between the actual temperature (5) of a hearth and the target temperature and the change rate ec of the deviation e are calculated in real time; e and ec are compared with a threshold value e0 and a threshold value ec0 respectively, and when e is larger than e0 or ec0 is larger than ec0, a fuzzy control mode is started for rapid dynamic adjustment; and when e is less than or equal to e0 and e is less than or equal to ec0, switching to a PID control mode to carry out high-precision steady-state adjustment. According to the method, through a double-threshold self-adaptive switching mechanism, the advantages of high robustness of fuzzy control and high precision of PID control are effectively fused, the problem that dynamic performance and steady-state performance cannot be considered when traditional single control is used for coping with large-inertia and nonlinear objects of the kiln is solved, and the control quality of the sintering temperature and the product percent of pass are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of industrial ceramic production equipment technology, and in particular to an industrial ceramic kiln based on a combination of PID and AI fuzzy control methods. Background Technology

[0002] Industrial ceramics possess excellent properties such as high strength, high temperature resistance, and corrosion resistance, and are widely used in aerospace, machinery manufacturing, and electronics industries. In the production process of industrial ceramics, the temperature control of the kiln directly determines the performance of the ceramic product. Industrial ceramics require high-temperature sintering, and the accuracy, stability, and rationality of the heating and cooling rates during sintering have a significant impact on key performance indicators such as density, strength, and hardness. Precise and stable temperature control ensures a thorough and uniform sintering process for ceramic materials, thereby improving the mechanical and chemical properties of the product.

[0003] Temperature control in traditional industrial ceramic kilns often relies on a single PID control method. While PID control is widely used in industrial control due to its simple algorithm and ease of implementation, industrial ceramic kilns exhibit significant large inertia and pure time delay characteristics. Furthermore, the internal temperature field of the kiln is complex and subject to interference from various factors such as the load of the ceramic blanks, raw material composition, and ambient temperature and humidity, resulting in significant nonlinear characteristics. A single PID control method struggles to adapt to these complex changes. When operating conditions fluctuate, it is prone to problems such as large temperature overshoot, long settling time, and insufficient steady-state accuracy, leading to quality defects in ceramic products such as cracking, deformation, and substandard performance.

[0004] Furthermore, as an intelligent control method, AI fuzzy control does not require the establishment of precise mathematical models. It can form control rules based on the operator's experience and expert knowledge, and has strong adaptability to nonlinear and uncertain systems. However, after the system reaches steady state, AI fuzzy control is prone to certain steady-state errors due to the limitations of fuzzy subset partitioning and the number of control rules, making it difficult to meet the high temperature precision requirements of industrial ceramic sintering.

[0005] Therefore, combining the high-precision steady-state characteristics of PID control with the strong dynamic adaptability of AI fuzzy control, and giving full play to the advantages of both control methods, to improve the accuracy and stability of temperature control in industrial ceramic kilns, has become an urgent technical problem to be solved. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention aims to solve the problems of low temperature control accuracy, slow dynamic response, and weak anti-interference ability in existing industrial ceramic kilns using a single control method. It provides an industrial ceramic kiln based on a combination of PID and AI fuzzy control methods to achieve precise and stable temperature control, improve the pass rate of industrial ceramic products, and ensure product quality.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides an industrial ceramic kiln based on a combination of PID and fuzzy control methods, including a kiln body, a heating device, multiple temperature sensors, and an intelligent controller. The intelligent controller integrates dual-algorithm modules for fuzzy control and PID control, configured to: calculate the furnace temperature deviation *e* and the rate of change of deviation *ec* in real time; when |e|>e0 or |ec|>ec0, fuzzy control is activated for rapid dynamic adjustment; when |e|≤e0 and |ec|≤ec0, PID control is switched for high-precision steady-state adjustment. Through an intelligent switching mechanism based on real-time deviation and the rate of change of deviation, the system automatically adopts fuzzy control during the dynamic large deviation phase, leveraging its rapid response and strong robustness to effectively reduce overshoot and shorten adjustment time; during the steady-state small deviation phase, it automatically switches to PID control, utilizing its high-precision characteristics to eliminate steady-state errors. This achieves full-cycle optimized control of the kiln, which has large inertia and nonlinear characteristics, significantly improving temperature control quality and product qualification rate.

[0008] Furthermore, the heating device specifically includes multiple sets of burners and corresponding electric proportional regulating valves. The output signal of the intelligent controller is directly used to control the opening degree of the electric proportional regulating valve, realizing continuous, linear, and precise adjustment of the heating power. This setup tightly integrates advanced control algorithms with high-precision actuators, ensuring accurate implementation of control quantities, overcoming the coarseness problem of traditional on / off regulation, and providing a reliable execution foundation for the uniformity and stability of the temperature field.

[0009] Furthermore, the kiln also includes pressure sensors and fans. The intelligent controller integrates a pressure control loop, adjusting the fan speed according to the pressure deviation to achieve closed-loop control of the furnace pressure. This multi-variable collaborative control structure achieves synchronous stability of the temperature field and atmospheric pressure, avoiding interference to precision temperature control caused by pressure fluctuations, and further ensuring the integrity of the sintering process conditions.

[0010] Furthermore, the kiln also includes a conveying device whose conveying speed and direction are controllable and coordinated with the temperature control process; this integrated design achieves precise matching between the thermal process and material conveying, and optimizes sintering uniformity from both spatial and temporal dimensions by controlling the residence time and movement mode of the product in the key temperature zone.

[0011] Furthermore, the kiln also includes human-machine interaction equipment and communication modules for centralized setting of process parameters, real-time data monitoring, and historical data management. This digital interactive platform provides operators with a transparent and traceable process management environment, greatly facilitating parameter debugging, process monitoring, and quality analysis, and providing support for process standardization and continuous optimization.

[0012] Furthermore, the system supports remote monitoring and operation via remote control terminals; this function expands the accessibility and management flexibility of the equipment, supports remote diagnosis and maintenance, adapts to the needs of modern intelligent factory networked management, and improves the responsiveness and convenience of production management.

[0013] Furthermore, the fuzzy control algorithm module makes inference decisions based on a pre-set fuzzy rule base that is based on expert experience. This design enables the control system to encapsulate and reproduce the operational wisdom and heuristic knowledge of human experts, so that when faced with nonlinear and uncertain dynamics that are not precisely modeled, it can make reasonable and rapid judgments similar to those of experts. This is the core source of the system's strong adaptive capability.

[0014] Furthermore, the system also includes a PLC control module, which works in conjunction with the intelligent controller to be responsible for the underlying sequential control, safety interlocks, and equipment drive. This hierarchical collaborative architecture constitutes an organic combination of advanced algorithms and highly reliable logic, which not only ensures the performance of intelligent decision-making, but also ensures the absolute reliability and rapid response of all safety protection and interlock functions through the professional PLC, thereby improving the overall safety and operational stability of the entire system.

[0015] The present invention also provides a control method, comprising the steps of: initialization, acquiring temperature and calculating e and ec, selecting fuzzy or PID control mode based on the comparison results of e and ec with thresholds, executing the corresponding algorithm and outputting control signals, and cyclic feedback; the method is a specific programmatic implementation of the intelligence of the above system, which defines a complete, deterministic and automatically cyclically executed decision process, transforming innovative control ideas into stable industrial production logic.

[0016] Furthermore, in the control method, the switching thresholds e0 and ec0 can be adaptively adjusted according to different sintering process stages; this dynamic threshold management endows the switching mechanism with high-order capabilities of context awareness and dynamic optimization. By adopting differentiated switching strategies in different process stages (such as rapid heating, precision heat preservation, and programmed cooling), the control performance can be further refined, and the global performance throughout the entire cycle can be re-optimized. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2This is a control principle framework diagram of the present invention.

[0018] Figure 3 This is a flowchart of the process in this invention.

[0019] In the diagram: 1. Kiln body, 2. Heating device, 3. Temperature sensor, 4. Pressure sensor, 5. Conveying device, 6. Electric proportional regulating valve, 7. Fan, 8. Control cabinet, 9. Human-machine interface device, 10. PLC controller, 11. Remote control terminal, 12. Intelligent controller, 13. Communication module. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments. Example 1

[0021] See Figure 1-3 This embodiment constructs a basic intelligent temperature-controlled kiln system. The kiln body 1 is a tunnel-type structure lined with refractory material. The heating device 2 consists of multiple sets of gas burners. The temperature sensors 3 are multiple K-type thermocouples, distributed and installed inside the furnace. The core control device is an industrial intelligent controller 12. After the system is powered on, the controller 12 continuously collects the temperature and calculates the deviation *e* and the rate of change *ec* from the set value (e.g., 1200℃). For example, in the initial stage of heating, if *e* = -200℃ (|e| > preset *e0* = 50℃), fuzzy control is activated, and a larger power command is output for rapid heating. When the temperature rises to 1190℃, *e* = -10℃ (|e| ≤ 50℃) and the heating rate drops to *ec* = 1℃ / min (|ec| ≤ preset *ec0* = 2℃ / min), it automatically switches to PID control for fine-tuning, ultimately stabilizing the temperature within the range of 1200℃ ± 1℃. This basic system can already realize the core intelligent switching control function. Example 2

[0022] Based on Example 1, this embodiment further specifies the heating device 2. An electric proportional regulating valve 6 (such as a linear regulating valve with an input signal of 4-20mA) is installed on the gas pipeline of each burner group. The analog control signal output by the intelligent controller 12 (corresponding to U_fuzzy in fuzzy control or U_pid in PID control) is converted into a current signal, directly driving the valve core of the regulating valve 6 to actuate, thereby steplessly and linearly converting the 0%-100% control quantity into the corresponding gas flow rate. This replaces the traditional on / off control, achieving smooth and precise adjustment of the heating power. Example 3

[0023] This embodiment incorporates closed-loop pressure control. A pressure sensor 4 is installed at the kiln exhaust port to monitor the micro-positive pressure in the furnace in real time (e.g., a target value of 10 Pa). A variable frequency fan 7 is also configured for exhaust. The intelligent controller 12 runs a pressure PID control loop in parallel with the temperature control algorithm. It reads the pressure value, calculates the deviation from the set value, and outputs frequency commands to the frequency converter of the fan 7. By dynamically adjusting the exhaust volume, the furnace pressure is stabilized, making it unaffected by burner start-up / shutdown or changes in external wind pressure. Example 4

[0024] This embodiment integrates material conveying control. A high-temperature resistant roller conveyor driven by a variable frequency motor is installed inside the kiln as a conveying device 5. The PLC control module 10 or intelligent controller 12 controls the temperature and simultaneously adjusts the frequency of the roller conveyor motor according to the sintering process curve, thereby controlling the product conveying speed. For example, in the uniform temperature zone, a lower speed can be set to extend the holding time; furthermore, by programming the motor to control forward and reverse rotation, the product can be made to swing back and forth in this zone, promoting uniform cross-sectional temperature. Example 5

[0025] This embodiment implements human-machine interaction. An industrial touchscreen is installed on the control cabinet panel as the human-machine interaction device 9, and is connected to the intelligent controller 12 via an Ethernet switch (communication module 13). Operators can create and call different process recipes on the touchscreen 9. The recipes include target temperatures, pressures, conveyor speeds, and control parameters (such as e0, ec0, Kp, Ti, Td) for each stage. The screen displays all sensor data, control outputs, process stages, and historical curves in real time, and supports data recording and export. Example 6

[0026] This embodiment extends the remote access function. Within the factory's local area network, authorized engineers can securely log in to the system via a computer or tablet computer (remote control terminal 11) with dedicated client software installed, connecting to the aforementioned communication module 13 through the network. The remote terminal 11 can mirror or partially replicate the functions of the local human-machine interface device 9, enabling process monitoring, parameter viewing, alarm confirmation, and parameter modification within the authorized scope. Example 7

[0027] In the software of the intelligent controller 12, the fuzzy control algorithm module is configured as follows: the universe of discourse for temperature deviation e is defined as [-60, 60]℃, divided into seven fuzzy subsets: negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; the universe of discourse for deviation change rate ec is defined as [-5, 5]℃ / min, also divided into seven subsets; and the universe of discourse for output control quantity U is [0, 100]%. The rule base contains 49 rules, such as "if deviation e is negative large and change rate ec is zero, then control quantity U is positive large". These rules are derived from the summary and formalization of the optimal operating experience of the kiln. Example 8

[0028] This embodiment clarifies the system's layered architecture, employing a collaborative mode of "intelligent controller 12 + PLC control module 10". The intelligent controller 12 (such as an industrial computer) is responsible for running advanced fuzzy-PID switching algorithms, process management, and data logging. Another independent PLC control module 10 (such as a standard rack-mount PLC) receives enable commands from the intelligent controller 12 via a communication network and is specifically responsible for executing all underlying logic control and safety interlock functions, including the burner's automatic ignition sequence, flame monitoring, gas valve safety shut-off, and conveyor motor start / stop, ensuring the absolute reliability of the system's basic operations. Example 9

[0029] This embodiment solidifies the control method using software flow. The main program in the intelligent controller 12 executes the following steps in a loop: 1) Read the millivolt signals of each thermocouple and convert them into temperature values ​​to obtain the representative temperature T_actual. 2) Subtract T_actual from the current target temperature T_set read from the recipe to get e; 3) Calculate ec = (e - e_last) / Δt based on the current e and the previous e_last, and update e_last; 4) Judgment: If abs(e)>e0 or abs(ec)>ec0, then call the fuzzy control sub-function; otherwise, call the PID control sub-function. 5) After limiting the control quantity U returned by the sub-function, send it to the electric proportional control valve 6 through the analog output card; 6) Wait for the next sampling cycle and return to step 1. This cycle continues throughout the entire sintering process. Example 10

[0030] The software of the intelligent controller 12 pre-sets multiple process stages (such as "preheating 1", "rapid heating", "heat preservation", and "slow cooling"). Each stage is associated with a set of optimized switching thresholds (e0, ec0). For example, in the "rapid heating" stage: e0=80℃, ec0=8℃ / min, allowing fuzzy control to dominate; in the "heat preservation" stage: e0=10℃, ec0=1℃ / min, prompting the system to enter PID fine control as early as possible. During system operation, the current stage is automatically switched according to process time or events, and the corresponding threshold set is called, thereby achieving a more refined match between the control strategy and process requirements.

Claims

1. An industrial ceramic kiln based on a combination of PID and AI fuzzy control method, characterized by, The application relates to a sintering furnace, which comprises the following components: a kiln body (1) internally forming a hearth for sintering industrial ceramics; a heating device (2) arranged in the kiln body (1) and used for heating the hearth; a plurality of temperature sensors (3) distributedly arranged in different areas of the hearth and used for detecting the temperature of each point of the hearth in real time; an intelligent controller (12) in signal connection with the temperature sensors (3) and the heating device (2), wherein a fuzzy control algorithm module and a PID control algorithm module are integrated in the intelligent controller (12); wherein the intelligent controller (12) is configured to execute the following control method: the actual temperature detected by the temperature sensors (3) is acquired in real time, and the deviation e and the deviation change rate ec between the actual temperature and a preset target temperature are calculated; the absolute value of the deviation e is compared with a preset first threshold value e0, and the absolute value of the deviation change rate ec is compared with a preset second threshold value ec0; when |e|>e0 or |ec|>ec0, the fuzzy control algorithm module is enabled, fuzzy reasoning is carried out based on the deviation e and the deviation change rate ec, and a first control signal is output to the heating device (2) to quickly suppress the temperature deviation; when |e|<=e0 and |ec|<=ec0, the PID control algorithm module is enabled, proportional, integral and differential operations are carried out based on the deviation e, and a second control signal is output to the heating device (2) to accurately eliminate the steady-state error. The heating device (2) comprises a plurality of burners and an electric proportional regulating valve (6) connected to a fuel supply pipeline of each burner; the first control signal or the second control signal is used for adjusting the opening degree of the electric proportional regulating valve (6) and / or controlling the start-stop of the burners. The sintering furnace further comprises a pressure sensor (4) and a fan (7), the pressure sensor (4) is arranged in the kiln body (1) and used for detecting the pressure of the hearth in real time, the fan (7) is in communication with the kiln body (1) and used for adjusting the atmosphere and pressure in the hearth, the intelligent controller (12) is in signal connection with the pressure sensor (4) and the fan (7) and is configured to adjust the operating parameters of the fan (7) according to the deviation between the pressure value detected by the pressure sensor (4) and a preset target pressure, so that the closed-loop control of the pressure of the hearth is realized. The sintering furnace further comprises a conveying device (5) arranged in the hearth and used for conveying ceramic blanks; the conveying speed and direction of the conveying device (5) are controllable. The sintering furnace further comprises a man-machine interaction device (9) and a communication module (13), the man-machine interaction device (9) is connected with the intelligent controller (12) through the communication module (13) and is used for setting the target temperature, the first threshold value e0, the second threshold value ec0, PID control parameters and a sintering process curve and is used for displaying the temperature, pressure, equipment state information and historical data in real time. ​ ​ ​ ​ ​ 2. The industrial ceramic kiln based on the combination of PID and AI fuzzy control method according to claim 1, characterized in that: ​ 3. The industrial ceramic kiln based on the combination of PID and AI fuzzy control method according to claim 1, characterized in that: ​ 4. The industrial ceramic kiln based on the combination of PID and AI fuzzy control method according to claim 1, characterized in that: ​ 5. The industrial ceramic kiln based on PID and AI fuzzy control method combination according to claim 1, characterized in that, ​ 6. The industrial ceramic kiln based on the combination of PID and AI fuzzy control method according to claim 5, characterized in that, Also include remote control terminal (11), the remote control terminal (11) is connected with the communication module (13) through wired or wireless network, can remote access the function of the man-machine interactive device (9), realize the remote monitoring and operation of kiln.

7. The industrial ceramic kiln based on the combination of PID and AI fuzzy control method according to claim 1, characterized in that, In the fuzzy control algorithm module, the domain of the deviation e and the deviation change rate ec is divided into a plurality of fuzzy subsets, and reasoning is performed based on a preset fuzzy rule base, and the fuzzy rule base is established according to expert experience knowledge of industrial ceramic sintering.

8. The industrial ceramic kiln based on the combination of PID and AI fuzzy control method according to any one of claims 1-7, characterized in that, Also include PLC control module (10), the PLC control module (10) is connected with at least one signal of the intelligent controller (12), the heating device (2), the conveying device (5), for executing the start-stop, interlock, safety protection and sequence flow control logic of equipment.

9. A control method to be applied to the industrial ceramic kiln according to any one of claims 1-8, characterized in that, The method comprises the following steps: S1: system initialization, set target temperature T_set, deviation threshold e0, deviation change rate threshold ec0, and initialize PID control parameters and fuzzy control parameters; S2: real-time acquisition of actual temperature T_actual of multiple measurement points in the furnace; S3: calculate temperature deviation e = T_actual - T_set, and deviation change rate ec = de / dt; S4: determine the control mode: if |e|>e0 or |ec|>ec0, then enter step S5; if |e|≤e0 and |ec|≤ec0, then enter step S6; S5: adopt fuzzy control algorithm, take e and ec as input, after fuzzy, rule reasoning and clarification processing, calculate the first output control quantity U_fuzzy, and jump to step S7; S6: adopt PID control algorithm, take e as input, after proportional, integral, differential operation, calculate the second output control quantity U_pid; S7: according to the first output control quantity U_fuzzy or the second output control quantity U_pid, generate corresponding actuator control signal, adjust heating power; S8: return to step S2, loop until the process is completed.

10. The control method according to claim 9, characterized by, In step S4, the deviation threshold e0 and the deviation change rate threshold ec0 are adaptively adjusted according to different ceramic sintering process stages.

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