Furnace body cooling system of double-tube vacuum vertical furnace

Abstract

The invention provides a double-tube vacuum vertical furnace body cooling system which comprises a main cooling water circulation unit, a water pipe network is arranged on the periphery of the outer wall of a furnace body, and the whole outer surface of the furnace body is covered with the water pipe network attached to the outer wall of the furnace body for cooling; the auxiliary cooling water circulation unit is connected with the cooling water output end of the active cooling water circulation unit, directional cooling is carried out on calibration components in the furnace body through the auxiliary cooling water circulation unit, a plurality of temperature sensors are arranged on the outer wall of the furnace body and in the furnace body, and the temperature sensors collect the environment temperature and send the environment temperature to the main cooling water circulation unit. The data are imported into the comprehensive processing unit; a pressure monitoring module, a water quality processing module and a control module are arranged in the comprehensive processing unit, and the pressure monitoring module collects pressure data of the main cooling water circulation unit and feeds back the collected data to the control module and the water quality processing module.

Description

Technical Field

[0001] The invention belongs to the field of furnace body cooling, and in particular relates to a double-tube vacuum vertical furnace body cooling system. Background Art

[0002] Double-tube vacuum vertical furnace is an important heat treatment equipment widely used in metallurgy, chemical industry, materials and other fields. Among them, the furnace cooling system is a key component to ensure the normal operation of the furnace and extend its service life. The traditional furnace cooling system mainly adopts water cooling or air cooling, but with the continuous improvement of industrial production requirements for heat treatment quality and efficiency, the existing cooling system has exposed many shortcomings and urgently needs improvement and innovation.

[0003] At present, the cooling system of the double-tube vacuum vertical furnace has the following main problems: Low cooling efficiency: Although the traditional water cooling system has a good cooling effect, it consumes a lot of water resources and is prone to cause local overcooling of the furnace, affecting the uniformity of heat treatment. Although the air cooling system is energy-saving and environmentally friendly, its cooling capacity is limited and it is difficult to meet the cooling needs of large furnaces. Insufficient temperature control accuracy: The existing cooling system often uses simple switch control or rough temperature adjustment, which is difficult to achieve accurate control of the furnace temperature, which easily leads to unstable heat treatment quality of the workpiece. Uneven cooling: Due to the complex structure of the furnace body and uneven distribution of heat load, it is difficult for conventional cooling systems to perform differentiated cooling for different parts, which easily causes uneven distribution of furnace temperature and affects the heat treatment effect. Low system reliability: During long-term operation, the cooling system is prone to pipeline blockage, leakage and other faults, affecting production continuity and safety. Low energy utilization: Traditional cooling systems often discharge heat directly into the environment, causing energy waste, which does not meet the requirements of energy conservation and emission reduction. Insufficient intelligence: Most cooling systems lack intelligent monitoring and adaptive adjustment functions, and it is difficult to automatically optimize cooling parameters according to process requirements and environmental changes. High maintenance cost: Complex piping systems and frequent fault repairs increase the maintenance cost and downtime of equipment. Poor adaptability: Existing cooling systems are often designed for specific working conditions and are difficult to flexibly respond to the cooling needs of different heat treatment processes. Insufficient environmental friendliness: The cooling medium used in some cooling systems pollutes the environment and does not conform to the development trend of green manufacturing. Low space utilization: Traditional cooling systems occupy a large space, affecting the compact design of the furnace body and the optimization of the factory layout.

[0004] In view of the above problems, the development of a new efficient, precise and intelligent furnace cooling system has become an important direction for the development of double-tube vacuum vertical furnace technology. Researchers are exploring a variety of innovative solutions, such as new technologies such as phase change material cooling, heat pipe cooling, microchannel cooling, and the introduction of advanced technologies such as artificial intelligence and the Internet of Things to achieve precise control, uniform distribution and intelligent adjustment of furnace cooling. At the same time, it is also necessary to consider energy conservation and environmental protection, reliability improvement, simplified maintenance and other factors to comprehensively improve the performance and efficiency of double-tube vacuum vertical furnaces and meet the urgent needs of modern industrial production for high-quality heat treatment. Summary of the invention

[0005] The present invention proposes a double-tube vacuum vertical furnace cooling system, which mainly solves the following technical problems: improves cooling efficiency and uniformity, covers the entire furnace body through the main and auxiliary cooling systems. Achieves precise temperature control, uses multiple sensors and integrated processing units. Enhances system reliability, through pressure monitoring and water quality treatment. Improves intelligence, adopts multi-stage cooling schemes and predictive control, optimizes energy utilization, through heat exchangers and intelligent regulation.

[0006] The technical solution of the present invention is implemented as follows: a double-tube vacuum vertical furnace body cooling system includes a main cooling water circulation unit, a water pipe network is arranged around the outer wall of the furnace body, and the entire outer surface of the furnace body is covered by the water pipe network attached to the outer wall of the furnace body for cooling; an auxiliary cooling water circulation unit is connected to the cooling water output end of the active cooling water circulation unit, and the calibrated components inside the furnace body are directionally cooled through the auxiliary cooling water circulation unit, and a number of temperature sensors are arranged on the outer wall of the furnace body and the furnace body, and the temperature sensors collect ambient temperature and import the data into the comprehensive processing unit; the comprehensive processing unit is built-in with a pressure monitoring module, a water quality treatment module and a control module, wherein the pressure monitoring module collects pressure data of the main cooling water circulation unit, and feeds the collected data back to the control module and the water quality treatment module; the water quality treatment module is built-in with a heat exchanger, and after receiving the pressure data When the data exceeds the set threshold, the heat exchanger is started to assist in cooling the pipes of the cooling water circulation unit; the control module receives data from the temperature sensor and the pressure monitoring module, and a standard parameter matching table is built into the control module. The standard parameter matching table associates the temperature data, pressure data with the water pump parameters in the main cooling water circulation unit and the auxiliary cooling water circulation unit, and the control module sends the corresponding water pump parameters to the main cooling water circulation unit and the auxiliary cooling water circulation unit according to the data, and adjusts the water pump. The control module establishes a multi-stage cooling scheme according to the temperature sensitivity of different areas of the furnace body, adopts optimized cooling for temperature-sensitive areas, and standard cooling for non-critical areas, and predicts the trend of furnace temperature changes by analyzing historical operating data and current status, and pre-controls the water pump actions in the main cooling water circulation unit and the auxiliary cooling water circulation unit according to the change trend.

[0007] This double-tube vacuum vertical furnace cooling system has many significant innovations and advantages compared to existing technologies. The system adopts a dual cooling strategy combining the main cooling water circulation unit and the auxiliary cooling water circulation unit. It not only achieves comprehensive cooling through the water pipe network that fits the outer wall of the furnace body, but also provides directional cooling for key components inside the furnace body. This design greatly improves the accuracy and efficiency of cooling.

[0008] The system sets up multiple temperature sensors inside and outside the furnace to realize all-round temperature monitoring. This comprehensive data collection method provides a solid foundation for subsequent intelligent control. The system introduces a comprehensive processing unit, which integrates the pressure monitoring module, water quality treatment module and control module to realize comprehensive monitoring and management of the cooling system. In particular, the setting of the pressure monitoring module can timely detect abnormal conditions in the cooling system and improve the safety and reliability of the system. The intelligent start-up mechanism of the heat exchanger in the water quality treatment module automatically assists in cooling when the pressure exceeds the threshold. This design enhances the system's adaptability and stability. The control module adopts an innovative design of a standard parameter matching table to associate temperature data, pressure data and water pump parameters to achieve more accurate and efficient cooling control. The system introduces the concept of a multi-stage cooling scheme, and adopts differentiated cooling strategies according to the temperature sensitivity of different areas of the furnace. This refined cooling method not only improves cooling efficiency, but also optimizes energy use.

[0009] The system has predictive control capabilities. It predicts temperature change trends by analyzing historical data and current status and adjusts cooling parameters in advance. This forward-looking control strategy greatly improves the system's response speed and cooling effect.

[0010] In general, this cooling system builds a highly intelligent, precise and efficient furnace cooling system by comprehensively using advanced sensor technology, intelligent control algorithms, differentiated cooling strategies and predictive control. It can not only better protect the furnace body and improve production efficiency, but also optimize the use of energy, representing an important trend of industrial furnace cooling technology developing towards intelligence and refinement.

[0011] As a preferred embodiment, the control module adopts an adaptive PID control algorithm to dynamically adjust the control parameters according to the temperature change characteristics of the outer wall of the furnace body and different parts of the furnace body, and adjust the output parameters of the water pump in the main cooling water circulation unit or the auxiliary cooling water circulation unit in real time.

[0012] As a preferred embodiment, while establishing a standard parameter matching table, the control module performs correlation analysis on the temperature data and pressure data, marks the parameters that exceed the correlation threshold, and simultaneously issues an alarm to start the emergency cooling module, maximize the output of the water pump in the main cooling water circulation unit or the auxiliary cooling water circulation unit, and shut down the furnace heating for maintenance.

[0013] As a preferred implementation, the temperature sensor data set at the calibration component position inside the furnace body is calibrated separately, and a mathematical model of heat transfer inside the furnace body is simultaneously established. The temperature distribution inside the furnace body is dynamically estimated through a Kalman filter or a particle filter state estimation algorithm combined with the furnace body physical model and real-time measurement data.

[0014] As a preferred implementation, after the mathematical model is established, the mathematical model is trained by a neural network algorithm to establish the relationship between input parameters and output parameters, and after the dynamic estimation is completed, the mathematical model is optimized and corrected by comparing the output of the virtual sensor with the actual measurement results performed regularly.

[0015] As a preferred embodiment, the mathematical model uses any one of a finite element model, a statistical model or a machine learning model to estimate the temperature, and performs real-time calculation of the data in the model through distributed computing, and automatically adjusts the model parameters according to the running time of the furnace.

[0016] After adopting the above technical scheme, the beneficial effects of the present invention are: Improved cooling efficiency: Through the combination of the main cooling water circulation unit and the auxiliary cooling water circulation unit, comprehensive and accurate cooling is achieved, and the overall cooling efficiency is significantly improved. Accurate temperature control: The setting of multi-point temperature sensors enables the system to accurately monitor the temperature of each part of the furnace body and achieve more accurate temperature control. Enhanced equipment safety: The introduction of the pressure monitoring module can detect abnormal conditions in time, improving the safety and reliability of the system. Energy utilization optimization: The multi-stage cooling scheme adopts differentiated cooling strategies according to the temperature sensitivity of different areas, optimizes energy use, and reduces operating costs. Enhanced adaptive ability: The intelligent startup mechanism of the heat exchanger in the water quality treatment module improves the adaptive ability of the system and can better cope with temperature fluctuations. Predictive maintenance: By analyzing historical data to predict temperature change trends, the system can adjust parameters in advance, achieve preventive maintenance, and extend the life of the equipment. Simplified operation: The intelligent control system automatically adjusts the water pump parameters, reduces manual intervention, and simplifies the operation process. Improved production efficiency: Accurate temperature control and efficient cooling system help optimize production processes, improve product quality and production efficiency. Enhanced flexibility: The system can flexibly adjust the cooling strategy according to different production needs and environmental conditions, and has strong adaptability. Data analysis capabilities: The data collected and analyzed by the system can be used to continuously optimize the cooling strategy and process parameters and promote process improvement. Environmental friendliness: By optimizing cooling efficiency and energy use, resource waste is reduced and environmental impact is reduced. Cost savings: In the long run, efficient cooling systems and preventive maintenance can significantly reduce operating and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example:

[0021] like Figure 1 As shown, a double-tube vacuum vertical furnace body cooling system comprises a main cooling water circulation unit, a water pipe network is arranged around the outer wall of the furnace body, and the entire outer surface of the furnace body is covered by the water pipe network attached to the outer wall of the furnace body for cooling; an auxiliary cooling water circulation unit is connected to the cooling water output end of the active cooling water circulation unit, and the calibration components inside the furnace body are directionally cooled by the auxiliary cooling water circulation unit, and a plurality of temperature sensors are arranged on the outer wall of the furnace body and the furnace body, and the temperature sensors collect the ambient temperature and import the data into the comprehensive processing unit; the comprehensive processing unit is built-in with a pressure monitoring module, a water quality treatment module and a control module, wherein the pressure monitoring module collects pressure data of the main cooling water circulation unit, and feeds the collected data back to the control module and the water quality treatment module; the water quality treatment module is built-in with a heat exchanger, and upon receiving pressure data exceeding a set threshold value, start the heat exchanger to assist in cooling the pipes of the cooling water circulation unit; the control module receives data from the temperature sensor and the pressure monitoring module, and has a built-in standard parameter matching table in the control module. The standard parameter matching table associates the temperature data, pressure data with the water pump parameters in the main cooling water circulation unit and the auxiliary cooling water circulation unit, and the control module sends the corresponding water pump parameters to the main cooling water circulation unit and the auxiliary cooling water circulation unit according to the data, and adjusts the water pump. The control module establishes a multi-stage cooling scheme according to the temperature sensitivity of different areas of the furnace body, adopts optimized cooling for temperature-sensitive areas, and standard cooling for non-critical areas, and predicts the temperature change trend of the furnace body by analyzing historical operation data and current status, and pre-controls the water pump action in the main cooling water circulation unit and the auxiliary cooling water circulation unit according to the change trend.

[0022] The working principle and operation steps of this double-tube vacuum vertical furnace cooling system are as follows:

[0023] The system sets up a water pipe network around the outer wall of the furnace body through the main cooling water circulation unit to achieve comprehensive cooling of the entire outer surface of the furnace body. At the same time, the auxiliary cooling water circulation unit is connected to the main cooling water circulation unit for directional cooling of specific components inside the furnace body. This dual cooling design can control the furnace body temperature more comprehensively and accurately, ensure the temperature of each part of the furnace body is balanced, and prevent local overheating. The system sets up multiple temperature sensors on the outer wall and inside of the furnace body to monitor the ambient temperature in real time and transmit the data to the integrated processing unit. The built-in pressure monitoring module of the integrated processing unit collects pressure data from the main cooling water circulation unit. These real-time data provide the basis for the intelligent control of the system, enabling the system to make timely adjustments according to actual conditions.

[0024] The heat exchanger built into the water treatment module automatically starts when it detects that the pressure data exceeds the set threshold, and assists in cooling the pipes of the main cooling water circulation unit. This design can prevent the cooling water temperature from being too high, which will lead to a decrease in cooling effect, and can also avoid damage to the pipe system due to excessive water temperature.

[0025] The control module is the core of the system. It receives data from the temperature sensor and pressure monitoring module and compares it with the built-in standard parameter matching table. The matching table associates the temperature data, pressure data and the water pump parameters in the cooling water circulation unit, so that the control module can send the optimal water pump parameters to the cooling water circulation unit according to real-time data to achieve precise adjustment. This intelligent control method can adjust the cooling intensity according to the actual needs of the furnace body, which not only ensures the cooling effect, but also avoids energy waste caused by overcooling.

[0026] The system establishes a multi-level cooling scheme based on the temperature sensitivity of different areas of the furnace, using optimized cooling for temperature-sensitive areas and standard cooling for non-critical areas. This differentiated cooling strategy can optimize resource allocation and improve the overall efficiency of the system while ensuring temperature control of key parts.

[0027] The system predicts the temperature change trend of the furnace by analyzing historical operation data and current status, and controls the water pump action in advance based on the prediction results. This predictive control can respond to temperature changes in advance, make the cooling process smoother, and reduce the impact of temperature fluctuations on the furnace and process.

[0028] The reasons for this setting and operation are as follows: The dual-pipe cooling design can achieve more precise temperature control to meet the cooling needs of different parts. Real-time data acquisition and intelligent control systems can dynamically adjust cooling parameters according to actual conditions, improve system response speed and control accuracy. Multi-level cooling solutions and differentiated control strategies can optimize resource allocation and improve system efficiency. Predictive control can respond to temperature changes in advance, making the entire cooling process more stable and controllable.

[0029] This design not only ensures accurate control of the furnace temperature, but also improves the energy efficiency of the system, extends the life of the equipment, and provides a more stable temperature environment for the process. At the same time, the system is highly intelligent and automated, which can reduce human operating errors and improve production efficiency and product quality. In general, the design of this cooling system fully considers the characteristics and needs of the vacuum vertical furnace, and through advanced control strategies and intelligent design, it achieves efficient, accurate and reliable temperature control, providing a guarantee for high-quality production processes.

[0030] The control module adopts an adaptive PID control algorithm to dynamically adjust the control parameters according to the temperature change characteristics of the outer wall and different parts of the furnace body, and adjust the output parameters of the water pump in the main cooling water circulation unit or the auxiliary cooling water circulation unit in real time. This control module design using an adaptive PID control algorithm has significant innovation and advantages over the existing technology. Traditional cooling systems usually use PID controllers with fixed parameters, which cannot flexibly respond to complex and changing cooling needs. However, this adaptive PID control algorithm can dynamically adjust the control parameters according to the temperature change characteristics of the outer wall and different parts of the furnace body, achieving more accurate and flexible cooling control. This dynamic adjustment capability enables the system to respond quickly to temperature changes and improves cooling efficiency and accuracy.

[0031] The design takes into account the temperature variation characteristics of different parts of the furnace body. This differentiated control strategy far exceeds the traditional unified control method. By adjusting the control parameters according to the specific needs of different areas, the system can provide more precise control of temperature-sensitive areas while ensuring the overall cooling effect, effectively preventing local overheating or overcooling. The real-time adjustment of the output parameters of the water pump in the main cooling water circulation unit or the auxiliary cooling water circulation unit reflects the high flexibility and real-time performance of the system. This design can not only better adapt to changes in the working state of the furnace body, but also optimize energy use and avoid unnecessary waste of cooling resources. This adaptive control method can learn and adapt to long-term working modes. As the operating time increases, the control effect will continue to optimize, which is a feature that fixed parameter PID controllers do not have. This design provides a basis for the intelligence and automation of the system, and can be combined with other advanced technologies such as machine learning and predictive maintenance to further improve the overall performance of the system.

[0032] In general, the application of this adaptive PID control algorithm represents an important trend for industrial cooling systems to develop in a smarter, more precise and more efficient direction. It not only improves the cooling effect, but also optimizes energy use, enhances the adaptability and reliability of the system, and provides strong support for improving production efficiency and product quality.

[0033] While establishing a standard parameter matching table, the control module performs correlation analysis on the temperature data and pressure data, marks the parameters that exceed the correlation threshold, and issues an alarm at the same time, starts the emergency cooling module, maximizes the output of the water pump in the main cooling water circulation unit or the auxiliary cooling water circulation unit, and shuts down the furnace heating for maintenance. The design of this control module has significant innovation and advantages over the existing technology. Not only is a standard parameter matching table established, but also a correlation analysis mechanism for temperature data and pressure data is introduced. This multi-parameter correlation analysis method far exceeds traditional single parameter monitoring. The correlation analysis process includes the following steps: The system compares the changing trends of temperature and pressure data in real time, calculates the correlation coefficient between them, and compares them with the preset correlation pattern under normal operating conditions. When an abnormal correlation between temperature and pressure changes is detected, the system will determine that this indicates a potential fault or abnormality.

[0034] An innovative point is to mark the parameters that exceed the associated threshold. This marking mechanism has multiple functions: it can quickly identify potential problem areas and provide focus for subsequent analysis and maintenance; the marked data can be used to establish a long-term fault mode library, which helps the system to continuously learn and optimize fault prediction capabilities; marking can also be used to generate detailed fault reports to help technicians locate and solve problems more quickly. When the system detects an abnormality, it not only issues an alarm, but also automatically starts the emergency cooling module. This active response mechanism greatly improves the safety of the system. Maximizing the water pump output while shutting down the furnace heating for maintenance reflects the system's rapid response capability and safety-first design concept. This design can not only effectively prevent equipment damage, but also minimize production interruption time.

[0035] This design provides the basis for predictive maintenance. By analyzing the marked abnormal data, the system can gradually build a fault prediction model and achieve a transition from passive response to active prevention. Another advantage of this design is its flexibility and scalability. The correlation threshold can be adjusted according to different production needs and equipment characteristics, so that the system can adapt to different working environments and production requirements. This innovative control module design builds a safer, more efficient and intelligent furnace cooling system through multi-parameter correlation analysis, intelligent labeling, rapid response and preventive measures.

[0036] The temperature sensor data set at the calibration component position inside the furnace body is calibrated separately, and a mathematical model of heat transfer inside the furnace body is established simultaneously. The temperature distribution inside the furnace body is dynamically estimated through the Kalman filter or particle filter state estimation algorithm, combined with the furnace body physical model and real-time measurement data. The temperature sensor data at the calibration component position inside the furnace body is calibrated separately, which reflects the refined management of temperature control in key areas. This approach not only improves the accuracy of temperature monitoring, but also provides reliable basic data for subsequent temperature distribution estimation. Secondly, the simultaneous establishment of a mathematical model of heat transfer inside the furnace body is an important innovation. This mathematical model includes factors such as the thermal conductivity characteristics, geometric structure, and heat source distribution of the furnace body material, and can simulate the heat transfer process inside the furnace body.

[0037] The process of associating the calibration data with the mathematical model is as follows: the mathematical model provides a theoretical prediction of the temperature distribution inside the furnace; the calibrated temperature sensor data is used as the actual observation value to correct and optimize this theoretical model. This combination of theory and practice enables the model to more accurately reflect the actual working state of the furnace. The use of Kalman filters or particle filters for state estimation is another important innovation. These advanced filtering algorithms can effectively handle noise and uncertainty in the system and improve the accuracy of temperature estimation. Kalman filters are suitable for linear systems, while particle filters are more suitable for processing nonlinear, non-Gaussian complex systems. When these filters are working, they will continuously compare and fuse the model predictions with the actual measurement data to obtain the optimal state estimate.

[0038] Specifically, the mathematical model provides a priori estimates, the real-time measurement data provides observations, and the filter continuously updates the state estimate through iterative calculations, ultimately obtaining a dynamic estimate of the temperature distribution inside the furnace. The advantage of this approach is that it not only utilizes the limited actual measurement points, but also makes full use of the predictive power of the physical model to make reliable temperature estimates for areas that are not directly measured. In addition, this design is adaptive and scalable. As the running time increases, the system can continuously optimize the mathematical model and filter parameters to improve the accuracy of the estimate. At the same time, this approach also provides a basis for intelligent control and predictive maintenance of the furnace, which can predict potential hot spots or cold spots and take measures in advance to prevent problems from occurring.

[0039] After the mathematical model is established, the mathematical model is trained through the neural network algorithm to establish the relationship between the input parameters and the output parameters. After the dynamic estimation is completed, the mathematical model is optimized and corrected by comparing the output of the virtual sensor with the actual measurement results performed regularly.

[0040] Training mathematical models through neural network algorithms is an important breakthrough. Traditional mathematical models are usually based on known physical principles and empirical formulas, while this method introduces machine learning into the model building process and can capture more complex and nonlinear system behaviors. The use of neural networks allows the model to automatically learn the complex relationship between input parameters (such as heat source intensity, cooling water flow, etc.) and output parameters (such as temperature distribution at each point). This self-learning ability far exceeds the traditional fixed parameter model.

[0041] Establishing the relationship between input and output parameters not only improves the accuracy of the model, but also enhances its adaptability and generalization. This means that the model can better handle temperature predictions under various operating conditions, including some rare or extreme situations. After completing the dynamic estimation, the mathematical model is optimized and corrected by comparing the output of the virtual sensor with the actual measurement results, which is an innovative application of a closed-loop feedback mechanism. Traditional systems usually lack this ability to continuously optimize, and this design ensures that the model can continuously improve its accuracy over time. The concept of virtual sensors is itself an innovation, which uses mathematical models and limited actual measurement points to infer the temperature of locations that are difficult to measure directly, greatly expanding the monitoring capabilities of the system. The practice of regularly taking actual measurements and comparing them with the output of the virtual sensor not only verifies the accuracy of the model, but also provides opportunities for continuous improvement. This dynamic correction mechanism enables the system to adapt to the influence of long-term factors such as equipment aging and process changes, and maintain long-term accuracy and reliability. This design provides a strong foundation for predictive maintenance and intelligent control. Through the continuously optimized mathematical model, the system can more accurately predict potential problems such as hot spot formation or reduced cooling system efficiency, thereby achieving preventive maintenance. At the same time, the optimized model can be used to formulate more accurate control strategies to improve production efficiency and product quality.

[0042] Another advantage of this approach is its scalability and adaptability. As new data accumulates, the model can continue to learn and improve, adapting to different operating conditions and equipment states. This adaptive capability is difficult to achieve in traditional fixed model systems. This innovative design builds a highly intelligent and adaptive furnace temperature monitoring and prediction system by combining neural networks, virtual sensors, and dynamic correction mechanisms. It not only greatly improves the accuracy and reliability of temperature control, but also provides new features for the intelligence and optimization of industrial processes, representing an important trend in the development of industrial control systems towards artificial intelligence and data-driven.

[0043] The mathematical model uses any one of the finite element model, statistical model or machine learning model to estimate the temperature, and uses distributed computing to perform real-time calculations on the data in the model, and automatically adjusts the model parameters according to the running time of the furnace. The flexible selection method of using finite element model, statistical model or machine learning model is an important breakthrough. Traditional systems usually rely on only a single type of model, and this multi-model selection can select the most suitable model type according to the specific application scenario and data characteristics. Finite element models are good at processing complex geometric structures and physical processes, statistical models are suitable for processing large amounts of historical data, and machine learning models excel in capturing nonlinear relationships and processing high-dimensional data. This flexibility enables the system to better adapt to different types of furnaces and process requirements.

[0044] Another significant innovation is the real-time calculation of data in the model through distributed computing. Traditional systems are limited by computing resources and have difficulty processing a large number of complex calculations in real time. The introduction of distributed computing has greatly improved the computing power and response speed of the system, allowing complex temperature estimation models to run in a real-time environment. This not only improves the accuracy and real-time performance of temperature control, but also creates conditions for the application of more complex optimization algorithms and predictive models. Automatic adjustment of model parameters based on the operating time of the furnace is an important adaptive feature. Traditional models usually use fixed parameters and are difficult to adapt to the influence of long-term factors such as equipment aging and process changes. This automatic adjustment mechanism ensures that the model can maintain its accuracy and effectiveness over time.

[0045] Specifically, the system records the temperature distribution characteristics under different operating time, analyzes the parameter change trend, and dynamically adjusts the model parameters accordingly. This approach not only improves the long-term accuracy of the model, but also provides information for equipment life prediction and maintenance planning. This design demonstrates strong scalability and adaptability. As new data continues to accumulate and computing power increases, the system can be easily upgraded to more complex and accurate models without major changes to the infrastructure. This flexibility is difficult to achieve in traditional fixed model systems. This approach lays the foundation for truly intelligent and autonomous control. Through continuous learning and adaptation, the system can not only accurately control temperature, but also predict potential problems, optimize energy use, and even autonomously adjust production parameters to adapt to different production needs.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. Double-tube vacuum vertical furnace cooling system, characterized in that: It includes a main cooling water circulation unit with a water pipe network arranged around the outer wall of the furnace body, and the water pipe network attached to the outer wall of the furnace body covers the entire outer surface of the furnace body for cooling; an auxiliary cooling water circulation unit is connected to the cooling water output end of the active cooling water circulation unit, and the calibration components inside the furnace body are directionally cooled through the auxiliary cooling water circulation unit, and a number of temperature sensors are arranged on the outer wall of the furnace body and inside the furnace body, and the temperature sensors collect the ambient temperature and import the data into the comprehensive processing unit; the comprehensive processing unit is equipped with a pressure monitoring module, a water quality treatment module and a control module, wherein the pressure monitoring module collects pressure data of the main cooling water circulation unit, and feeds the collected data back to the control module and the water quality treatment module; the water quality treatment module is equipped with a heat exchanger, and when the received pressure data exceeds the set threshold, the heat exchanger is started Auxiliary cooling is performed on the pipes of the cooling water circulation unit; the control module receives data from the temperature sensor and the pressure monitoring module, and a standard parameter matching table is built in the control module. The standard parameter matching table associates the temperature data, pressure data with the water pump parameters in the main cooling water circulation unit and the auxiliary cooling water circulation unit. The control module sends corresponding water pump parameters to the main cooling water circulation unit and the auxiliary cooling water circulation unit according to the data, and adjusts the water pump. The control module establishes a multi-stage cooling scheme according to the temperature sensitivity of different areas of the furnace body, adopts optimized cooling for temperature-sensitive areas, and standard cooling for non-critical areas, and predicts the temperature change trend of the furnace body by analyzing historical operation data and current status, and pre-controls the water pump action in the main cooling water circulation unit and the auxiliary cooling water circulation unit according to the change trend.

2. The double-tube vacuum vertical furnace cooling system according to claim 1, characterized in that: The control module adopts an adaptive PID control algorithm to dynamically adjust control parameters according to the temperature change characteristics of the outer wall of the furnace body and different parts of the furnace body, and adjusts the output parameters of the water pump in the main cooling water circulation unit or the auxiliary cooling water circulation unit in real time.

3. The double-tube vacuum vertical furnace cooling system according to claim 1, characterized in that: While establishing a standard parameter matching table, the control module performs correlation analysis on the temperature data and pressure data, marks the parameters that exceed the correlation threshold, and simultaneously issues an alarm, starts the emergency cooling module, maximizes the output of the water pump in the main cooling water circulation unit or the auxiliary cooling water circulation unit, and shuts down the furnace heating for maintenance.

4. The double-tube vacuum vertical furnace cooling system according to claim 1, characterized in that: The temperature sensor data set at the calibration component position inside the furnace body is calibrated separately, and a mathematical model of heat transfer inside the furnace body is established simultaneously. The temperature distribution inside the furnace body is dynamically estimated through the Kalman filter or particle filter state estimation algorithm, combined with the furnace body physical model and real-time measurement data.

5. The double-tube vacuum vertical furnace cooling system according to claim 4, characterized in that: After the mathematical model is established, the mathematical model is trained through the neural network algorithm to establish the relationship between the input parameters and the output parameters. After the dynamic estimation is completed, the mathematical model is optimized and corrected by comparing the output of the virtual sensor with the actual measurement results performed regularly.

6. The double-tube vacuum vertical furnace cooling system according to claim 4, characterized in that: The mathematical model uses any one of a finite element model, a statistical model or a machine learning model to estimate the temperature, and uses distributed computing to perform real-time calculations on the data in the model, and automatically adjusts the model parameters according to the operating time of the furnace.

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