Intelligent temperature control system for step-by-step catalytic cracking reactor

Abstract

The invention relates to the technical field of intelligent control of chemical reactors, and discloses an intelligent temperature control system for a step-by-step catalytic cracking reactor, which comprises a temperature field modeling module, a sensor configuration module, an index definition module, a control execution module, an analysis and diagnosis module and the like. The temperature field modeling module constructs a standard temperature field distribution model through infrared thermal imaging and generates virtual temperature points; the sensor configuration module is used for arranging a real temperature point and acquiring a temperature gradient and a temperature point missing rate; the index definition module sets a preset temperature index based on the thermodynamic parameters; the control execution module realizes steady state, dynamic response, heat balance and composite regulation and control; and the analysis and diagnosis module carries out fault tracing on the target which does not pass regulation and control and adjusts a strategy. The system further comprises a data acquisition module and a communication interface module, so that data storage and external communication are realized. The method improves the temperature control precision and the system self-adaption capability, and is suitable for the complex temperature control scene of the catalytic cracking reaction.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of chemical reactors, in particular to an intelligent temperature control system for a step-by-step catalytic cracking reactor. Background Art

[0002] In the field of chemical production, catalytic cracking reaction is an important process link, and the precise control of its reaction temperature directly affects product quality, reaction efficiency and equipment safety. Traditional reactor temperature control methods have many limitations and are difficult to meet the precise control requirements under complex working conditions. From the perspective of temperature monitoring, traditional methods usually use single-point or limited-point temperature sensors, which cannot fully and real-time reflect the temperature field distribution inside the reactor. The internal structure of the catalytic cracking reactor is complex, especially in the catalyst bed area, where the temperature distribution is significantly uneven. Single-point monitoring can easily miss temperature anomalies in key areas, resulting in the inability to timely detect problems such as local overheating or heat flow blockage, which may lead to risks such as increased side reactions, catalyst deactivation, and even equipment failure. In terms of control strategies, traditional control systems often use fixed-parameter PID control algorithms, which lack the ability to adapt to dynamic operating conditions. When dynamic changes such as temperature increases, decreases, or temperature fluctuations occur during the reaction process, traditional control methods struggle to quickly respond and adjust control parameters, which can easily lead to temperature overshoot or prolonged adjustment times, affecting the stability and consistency of the reaction. For example, under variable operating conditions, such as changes in raw material composition or adjustments to reaction load, traditional systems are unable to accurately simulate temperature response behavior, making it difficult to ensure that the reaction proceeds stably within the target temperature range.

[0003] In terms of heat balance control, traditional technologies pay insufficient attention to the axial and radial heat balance of the reactor. During catalytic cracking reactions, heat generation and transfer are complex, and heat distribution varies significantly across different axial sections and radial regions. Traditional control systems lack the means to accurately calculate and control axial and radial heat balance factors, which can easily lead to uneven heat distribution within the reactor, affecting reaction uniformity and conversion rate. This can also increase thermal stress on the equipment and shorten its service life.

[0004] Furthermore, traditional systems suffer from significant deficiencies in fault diagnosis and control strategy adjustment. When temperature control anomalies occur, traditional methods struggle to quickly trace and locate the fault, preventing timely analysis of the path and cause of the abnormal temperature difference. This results in inefficient troubleshooting and increases the time and cost of production interruptions. Furthermore, traditional systems lack the ability to dynamically adjust control strategies, preventing them from optimizing control parameters based on real-time monitoring data and diagnostic results, making them difficult to adapt to changing production needs.

[0005] The advancement of intelligent and automated chemical industry demands higher precision, real-time performance, and adaptability in temperature control of catalytic cracking reactors. Existing technologies are limited in temperature field modeling, sensor configuration, multi-dimensional control strategies, and fault diagnosis. A more advanced and intelligent temperature control system is urgently needed to address these challenges, improve the efficiency and stability of catalytic cracking reactions, and reduce production costs and safety risks. Summary of the Invention

[0006] The object of the present invention is to provide an intelligent temperature control system for a step-by-step catalytic cracking reactor to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent temperature control system for a step-by-step catalytic cracking reactor, the system comprising: Temperature field modeling module: Scan the internal structure of the standard reactor to obtain a standard temperature field distribution model, construct a spatial coordinate system with the geometric center of the temperature field distribution model, use the temperature field distribution model as a virtual temperature model, use the spatial coordinate system as a reference control frame, and create virtual temperature points of the virtual temperature model at preset intervals. The virtual temperature points include virtual basic temperature points, virtual key temperature points, and virtual matching temperature points; Sensor configuration module: Connected to the temperature field modeling module, the reactor to be controlled is defined as the control target, the control target is placed in the reference control frame, the real temperature points of the control target are arranged at preset intervals, including basic temperature points, key temperature points, and matching temperature points, the virtual temperature model is aligned with the control target, and the temperature difference between the real temperature point and the virtual temperature point is defined as the temperature gradient; Index definition module: connects with the sensor configuration module to obtain the thermodynamic parameters of the standard reactor and define the preset temperature index based on the thermodynamic parameters; Control execution module: connected to the indicator definition module, performs temperature control on the control target, including steady-state control, dynamic response control, thermal balance control and compound control. Dynamic response control includes heating rate control, cooling rate control and temperature fluctuation suppression control. Analysis and diagnosis module: Connected with the control execution module, it traces the faults of control targets that fail dynamic response regulation and thermal balance regulation, generates diagnostic reports and adjusts the control strategy.

[0008] Preferably, the temperature field modeling module includes: The internal structure of the standard reactor is scanned by infrared thermal imaging to construct a standard temperature field distribution model; a spatial coordinate system is established based on the geometric center of the temperature field distribution model to generate virtual temperature points including virtual basic temperature points, virtual key temperature points and virtual matching temperature points. The virtual temperature points evenly cover the entire temperature field distribution model and the spacing between adjacent virtual temperature points is equal. The virtual temperature points in the catalyst bed area of ​​the reactor are used as virtual key temperature points, and the virtual temperature points in the axial three-divided section of the reactor are used as virtual matching temperature points. The coordinate set of the virtual temperature points is recorded. ,in is the number of virtual temperature points; The temperature field distribution model is used as a virtual temperature model, and the spatial coordinate system is used as a reference control frame.

[0009] Preferably, the sensor configuration module includes: Define the reactor to be controlled as the control target, locate the control target in the reference control frame, and arrange the real temperature points including basic temperature points, key temperature points and matching temperature points. The real temperature points evenly cover the entire control target and the spacing between adjacent real temperature points is equal. The real temperature points in the catalyst bed area of ​​the control target are used as key temperature points, and the real temperature points in the axial trisection section of the control target are used as matching temperature points. Record the coordinate set of the real temperature points. ,in is the number of real temperature points; Coincidentally aligning the virtual matching temperature point with the matching temperature point to achieve alignment of the control target with the virtual temperature model; Define the temperature difference between the real temperature point and the virtual temperature point as the temperature gradient , including the base temperature point gradient , key temperature point gradient , matching temperature point gradient , the base temperature point and the matching temperature point are defined as non-critical temperature points, and the temperature gradient of the non-critical temperature point is ; The temperature difference between the virtual base temperature point and the base temperature point is , the temperature difference between the virtual key temperature point and the key temperature point is , the temperature difference between the virtual matching temperature point and the matching temperature point is ; Get the number of virtual temperature points and the number of true temperature points ,definition and The ratio is the temperature point missing rate .

[0010] Preferably, the indicator definition module includes: obtaining the thermodynamic parameters of the standard reactor including material thermal conductivity, specific heat capacity, reaction activation energy, target operating temperature range, and maximum temperature rise rate limit; defining preset temperature indicators according to the thermodynamic parameters, including a preset temperature gradient threshold, a preset temperature point missing rate threshold, a preset temperature rise rate threshold, and a preset thermal equilibrium deviation threshold.

[0011] Preferably, the control execution module includes: Perform steady-state control, dynamic response control, and thermal balance control on the control target positioned in the reference control frame; The steps of steady-state control are: obtaining the coordinates and number of real temperature points and virtual temperature points, calculating the temperature gradient and temperature point missing rate ,like and If the preset temperature gradient threshold and the preset temperature point missing rate threshold are met at the same time, it is determined that the control target passes the steady-state control; Construct dynamic response control and thermal balance control environment for the control target through steady-state control, perform heating rate control, cooling rate control and heat distribution uniformity control, and output the control results in quantitative data; Composite regulation is performed on the control target through dynamic response regulation and thermal balance regulation to simulate the temperature response behavior under variable operating conditions.

[0012] Preferably, the steps of constructing a dynamic response control and thermal balance control environment, executing control and outputting quantitative data are: Define the dynamic response factor of the control target Including temperature response factor , cooling response factor , thermal balance factor ; The temperature response factor Including key area warming factor and non-key area warming factor, cooling response factor Including key area cooling factor and non-key area cooling factor, thermal balance factor Including axial heat balance factor and radial heat balance factor; Build a dynamic response control environment, including step temperature increase instructions, step temperature decrease instructions, and sinusoidal temperature disturbance instructions applied to the control target; Define the temperature response test result as the heating rate of the key area and heating rates in non-critical areas The cooling response test results are the cooling rate of the key area and cooling rate in non-critical areas , the thermal balance test results are axial temperature difference and radial temperature difference ; like 、 、 、 、 、 If the corresponding preset temperature rise rate threshold and the preset thermal balance deviation threshold are met, it is determined that the control target is controlled through dynamic response regulation and thermal balance regulation.

[0013] Preferably, the steps of performing compound regulation are: The comprehensive control stability coefficient of the control target is calculated as:

[0014] in, is the comprehensive control stability coefficient, is the number of variable working condition scenarios, and is the weight coefficient, Indicates the The temperature response factor under different working conditions is: Indicates the Thermal balance factor under variable operating conditions.

[0015] Preferably, the analysis and diagnosis module includes: Obtain control data for control targets that fail dynamic response control and thermal balance control, and calculate the temperature gradient direction vector ,in is the coordinate vector of the virtual temperature point, is the coordinate vector of the real temperature point; based on Analyze the conduction path of abnormal temperature differences and locate heat flow blocking areas or local overheating areas; divide the control target into a finite number of equal-thickness segments along the axial direction, define a preset temperature point density threshold, count the number of temperature points in each segment, calculate the segment temperature point density, and perform material thermal conductivity verification on segments that exceed the preset threshold.

[0016] Preferably, the system further comprises: The data acquisition module is connected to the temperature field modeling module and the sensor configuration module to periodically obtain the virtual temperature point coordinate set and the real temperature point coordinate set , construct the temperature difference distribution matrix, store the temperature difference distribution matrix in the local database, where the data acquisition cycle is synchronized with the execution cycle of dynamic response control, and the dimension of the temperature difference distribution matrix is ​​the same as the number of virtual temperature points consistent.

[0017] Preferably, the system further comprises: The communication interface module is connected to the external distributed control system, encapsulates the control results of the control execution module and the diagnosis report of the analysis and diagnosis module into industrial protocol data packets, and transmits them to the distributed control system via Ethernet.

[0018] Compared with the prior art, the present invention has the following beneficial effects: In terms of temperature field modeling and monitoring, the temperature field modeling module constructs a standard temperature field distribution model through infrared thermal imaging scanning, and establishes a spatial coordinate system with the geometric center to generate evenly covered virtual temperature points, in which the catalyst bed area is set as a virtual key temperature point, and the axial three-section section is set as a virtual matching temperature point, thus realizing the refined modeling of the temperature distribution inside the reactor. The sensor configuration module aligns the reactor to be controlled with the virtual temperature model, arranges real temperature points corresponding to the virtual temperature points, and obtains data such as temperature gradient and temperature point missing rate in real time. This precise modeling and point arrangement method based on the spatial coordinate system can comprehensively and real-time reflect the temperature status of each area inside the reactor, especially the temperature changes in key areas, solving the problem that traditional single-point or limited-point monitoring cannot fully perceive the temperature field distribution, and provides a reliable data basis for precise control.

[0019] In terms of multi-dimensional control strategy, the control execution module realizes the organic combination of steady-state control, dynamic response control, thermal balance control and compound control. Steady-state control ensures the temperature control accuracy of the reactor under stable operating conditions by judging whether the temperature gradient and temperature point loss rate meet the preset threshold. Dynamic response control monitors the temperature increase and decrease rates and axial and radial temperature differences in key and non-key areas in real time by applying step temperature increase and decrease instructions and sinusoidal temperature disturbance instructions, ensuring that the reactor can respond to temperature changes quickly and stably under dynamic conditions and effectively suppress temperature fluctuations. Thermal balance control ensures uniform heat distribution inside the reactor and improves the uniformity and conversion rate of the reaction by accurately calculating and controlling the axial thermal balance factor and the radial thermal balance factor. Compound control simulates the temperature response behavior under variable operating conditions and calculates the comprehensive control stability coefficient, enabling the system to adapt to changes in various complex operating conditions, significantly improving the system's adaptability and robustness.

[0020] In terms of fault diagnosis and control strategy optimization, the analysis and diagnosis module calculates the temperature gradient direction vector and analyzes the conduction path of abnormal temperature differences, enabling rapid location of heat flow blockages or localized overheating. Furthermore, by axially segmenting the reactor into equal-thickness sections, calculating the temperature point density, and performing material thermal conductivity verification, the accuracy and efficiency of fault diagnosis are further improved. The diagnostic report generated based on the diagnostic results enables timely adjustment of the control strategy, achieving a closed-loop management system from fault detection to diagnosis and control strategy optimization. This shortens fault handling time, reduces the risk of production interruptions, and improves system reliability and stability.

[0021] The data acquisition module and communication interface module further enhance the system's data management and interaction capabilities. The data acquisition module periodically acquires virtual and real temperature point coordinates, constructs a temperature difference distribution matrix, and stores it in a local database, supporting the system's historical data tracing and analysis. The communication interface module encapsulates control results and diagnostic reports into industrial protocol data packets and transmits them to the distributed control system via Ethernet. This enables seamless integration with external distributed control systems, improves the automation and intelligence level of the production process, and facilitates production managers to monitor the reactor's operating status in real time and conduct global production scheduling and management.

[0022] The present invention comprehensively improves the accuracy, real-time nature and adaptability of temperature control in catalytic cracking reactors through innovative temperature field modeling methods, multi-dimensional control strategies, intelligent fault diagnosis mechanisms, and comprehensive data management and interaction capabilities. This is of great significance for improving chemical production efficiency, ensuring product quality, and reducing production costs and safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a working principle diagram of the intelligent temperature control system for the step-by-step catalytic cracking reactor of the present invention; Figure 2 The workflow diagram for the control execution module; Figure 3 Flowchart of dynamic response control and thermal balance control. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0025] See also Figure 1-Figure 3 The present invention relates to an intelligent temperature control system for a step-by-step catalytic cracking reactor. The system includes a temperature field modeling module, a sensor configuration module, an indicator definition module, a control execution module, and an analysis and diagnosis module. Each module works in coordination to achieve intelligent control of the reactor temperature. The details are as follows: Temperature Field Modeling Module: This module obtains a standard temperature field distribution model by scanning the internal structure of a standard reactor. A spatial coordinate system is constructed with the geometric center of this model as the reference control frame, while the temperature field distribution model is used as a virtual temperature model. Virtual temperature points are created within the virtual temperature model at preset intervals. These virtual temperature points include virtual base temperature points, virtual key temperature points, and virtual matching temperature points. The virtual key temperature points are used to characterize the temperature characteristics of the reactor's catalyst bed region, while the virtual matching temperature points are set at the reactor's axial trisection section.

[0026] The Sensor Configuration Module connects to the Temperature Field Modeling Module, defines the reactor to be controlled as the control target and places it in the reference control framework. Real temperature points are placed on the control target at preset intervals, including base temperature points, key temperature points, and matching temperature points. The key temperature points correspond to the catalyst bed region of the control target, and the matching temperature points are located at the axial trisection of the control target. After aligning the virtual temperature model with the control target, the temperature gradient is defined as the temperature difference between the real and virtual temperature points.

[0027] Index definition module: Connects to the sensor configuration module to obtain the thermodynamic parameters of the standard reactor, such as material thermal conductivity, specific heat capacity, reaction activation energy, target operating temperature range, maximum temperature rise rate limit, etc., and defines preset temperature indicators based on these parameters.

[0028] Control Execution Module: This module connects to the indicator definition module and performs temperature control on the control target. Control methods include steady-state control, dynamic response control, thermal balance control, and compound control. Dynamic response control includes heating rate control, cooling rate control, and temperature fluctuation suppression control.

[0029] Analysis and diagnosis module: Connected with the control execution module, it traces the faults of control targets that fail dynamic response regulation and thermal balance regulation, generates diagnostic reports and adjusts the control strategy.

[0030] The present invention will be further described below in conjunction with Examples 1 to 5: Example 1: In the specific implementation of the temperature field modeling module, infrared thermal imaging technology is used to scan the internal structure of a standard reactor to construct a standard temperature field distribution model. Infrared thermal imaging technology can obtain temperature distribution information on the surface of an object in a non-contact manner. By collecting and processing temperature data from different areas within the reactor, a visual temperature field distribution image is formed. This image can clearly reflect the temperature differences and distribution patterns in various parts of the reactor.

[0031] A spatial coordinate system is established with the geometric center of the standard temperature field distribution model as the origin. This spatial coordinate system uses a three-dimensional rectangular coordinate system, with the reactor's axial direction as the Z axis, the radial directions as the X axis and the Y axis, and the geometric center as the origin of the coordinate system (0,0,0). By establishing this spatial coordinate system, a unified reference framework is provided for subsequent analysis of the temperature field distribution model and the positioning of virtual temperature points.

[0032] Within the established spatial coordinate system, virtual temperature points are generated within the virtual temperature model at preset intervals. These virtual temperature points evenly cover the entire temperature field distribution model, and the spacing between adjacent virtual temperature points is equal. The value of the preset spacing is determined by the size of the reactor and the required temperature control accuracy. For example, for larger reactors, the preset spacing can be appropriately increased, but it must ensure that the virtual temperature points can fully reflect the distribution characteristics of the temperature field. For reactors with higher temperature control precision requirements, the preset spacing needs to be reduced to improve the accuracy of temperature monitoring and control.

[0033] Virtual temperature points include virtual base temperature points, virtual key temperature points, and virtual matching temperature points. Among them, the virtual temperature points in the reactor's catalyst bed area serve as virtual key temperature points. The catalyst bed is the core area of ​​the catalytic cracking reaction, and its temperature distribution directly affects the reaction rate and product distribution. Therefore, the temperature in this area requires focused monitoring and control. By densely placing virtual key temperature points in the catalyst bed area, more precise temperature information can be obtained, providing a more detailed basis for subsequent temperature control.

[0034] Virtual temperature points along the reactor's axial trisection are used as virtual matching temperature points. The reactor is divided into three equal sections along the axial direction (i.e., the Z-axis). A cross section is taken in the middle of each section, representing the axial trisection section. Setting virtual matching temperature points on these sections facilitates subsequent alignment with the matching temperature points of the reactor to be controlled. This ensures spatial consistency between the virtual temperature model and the reactor to be controlled, enabling accurate comparison and analysis of temperature differences between the two.

[0035] The coordinate set of all virtual temperature points is recorded as ,in is the number of virtual temperature points. The coordinates of each virtual temperature point are determined by its X, Y, and Z axis coordinate values ​​in the spatial coordinate system. For example, the virtual temperature point The coordinates are By recording the coordinate set of the virtual temperature points, the position of each virtual temperature point in the virtual temperature model can be accurately located.

[0036] After generating the virtual temperature points, the temperature field distribution model is used as a virtual temperature model. This virtual temperature model represents the temperature distribution of the standard reactor under ideal operating conditions and serves as a reference for subsequent temperature control of the reactor under control. The spatial coordinate system serves as a reference control framework, providing a unified spatial reference for positioning the reactor under control and distributing the temperature points.

[0037] When generating virtual temperature points, it's crucial to ensure uniform coverage of the entire temperature field distribution model. This uniform coverage avoids blind spots in temperature monitoring and ensures a comprehensive and accurate representation of the temperature field's distribution characteristics. By properly setting the preset spacing and number of virtual temperature points, the distribution density of these points is essentially consistent across the reactor's axial, radial, and circumferential directions, ensuring accurate and reliable temperature field modeling.

[0038] When setting virtual key temperature points, in addition to selecting the catalyst bed region, the catalyst bed structure and reaction characteristics must also be considered. For example, if the catalyst bed is divided into different zones, such as the inlet zone, reaction zone, and outlet zone, it may be necessary to set virtual key temperature points in each sub-zone to more closely monitor temperature changes in different areas. Furthermore, the density of virtual key temperature points can be adjusted based on the intensity of the reaction within the catalyst bed, with more virtual key temperature points set in areas with intense reactions to improve temperature monitoring accuracy.

[0039] Virtual matching temperature points are set at the axial trisection sections. This is because the axial trisection section can evenly divide the reactor into multiple sections along the axial direction. This facilitates alignment of the matching temperature points of the reactor to be controlled with the virtual matching temperature points in the subsequent sensor configuration module, thereby achieving spatial alignment of the virtual temperature model and the reactor to be controlled. The distribution of virtual matching temperature points on each axial trisection section also needs to be uniform. For example, virtual matching temperature points can be set at regular angular intervals on the section to form a uniform distribution along the circumference.

[0040] When constructing a standard temperature field distribution model, the reactor's boundary conditions and heat conduction characteristics must also be considered. For example, the reactor's outer shell may dissipate heat, resulting in lower temperatures near the outer shell. Therefore, when generating virtual temperature points, it is necessary to appropriately increase the density of virtual temperature points near the outer shell to more accurately reflect temperature changes in that area. Furthermore, the density of virtual temperature points should also be increased near the heating or cooling elements within the reactor due to the large temperature gradients.

[0041] When recording the coordinates of virtual temperature points, precise coordinate measurement and recording methods are required to ensure the accuracy of the coordinate values. By combining infrared thermal imaging equipment with computer software, the coordinate information of virtual temperature points can be automatically acquired and stored in a database for subsequent access and analysis. Furthermore, to facilitate the management and identification of different types of virtual temperature points, virtual basic temperature points, virtual key temperature points, and virtual matching temperature points can be categorized and identified in the database, for example, by adding different tags or attribute fields.

[0042] Example 2: The sensor configuration module is implemented as follows: The reactor to be controlled is defined as the control target and precisely placed within the reference control frame constructed by the temperature field modeling module using three-dimensional spatial positioning technology. The reference control frame uses a three-dimensional rectangular coordinate system with the geometric center of the standard reactor as the origin, the Z axis as the axial direction, and the X and Y axes as the radial directions. The reactor to be controlled is positioned to ensure that its geometric center coincides with the origin of the reference control frame and that the axial and radial directions are fully aligned with the coordinate system to eliminate the impact of spatial position deviation on temperature control.

[0043] After completing the positioning of the control target, the real temperature points are arranged on the control target at a preset spacing. The preset spacing is consistent with the preset spacing of the virtual temperature points in the temperature field modeling module to ensure that the two are comparable in spatial resolution. The real temperature points are also divided into three categories: basic temperature points, key temperature points, and matching temperature points. Among them, the catalyst bed area of ​​the control target is the core area of ​​temperature control. The real temperature points in this area are used as key temperature points to monitor the temperature changes in the core area of ​​the reaction in real time; the real temperature points of the axial three-section section of the control target (that is, the middle section of each section after the reactor is divided into three sections along the axial direction) are used as matching temperature points to achieve spatial alignment with the virtual matching temperature points in the virtual temperature model.

[0044] The layout of the real temperature points must follow the principle of uniform coverage, that is, all real temperature points are evenly distributed in the axial, radial and circumferential directions of the control target, and the spacing between adjacent real temperature points is equal. For example, in the axial direction, from the top to the bottom of the reactor, the temperature points are arranged in sequence with a preset spacing; in the radial direction, with the central axis of the reactor as the reference, the temperature points are evenly distributed on the circumference of the circle at different radial positions. The circumferential spacing angle can be set according to the control accuracy requirements (such as 30°, 45°, etc.) to ensure that the temperature points in each section can fully reflect the temperature distribution characteristics of the section. The coordinate set of the recorded real temperature points is ,in is the number of real temperature points, each real temperature point Coordinates Corresponding to its spatial position in the reference control frame, coordinate acquisition can be achieved through a high-precision sensor positioning system or three-dimensional modeling software combined with physical measurement.

[0045] The alignment of the virtual temperature model and the control target is achieved by aligning virtual matching temperature points with matching temperature points. The specific operation is as follows: First, the coordinate set of virtual matching temperature points on the axial trisection of the virtual temperature model is extracted, and the coordinate set of matching temperature points on the corresponding axial trisection of the control target is simultaneously obtained. Then, a coordinate transformation algorithm (such as translation and rotation) is used to adjust the attitude of the control target in the reference control frame so that the matching temperature points of the control target and the virtual matching temperature points coincide in spatial position. This alignment process requires the use of a computer-assisted algorithm, which iteratively minimizes the coordinate error between the matching temperature points to ensure that the virtual temperature model and the control target are fully aligned in space.

[0046] Temperature gradient The definition is based on the temperature difference between the real temperature point and the virtual temperature point, which is divided into three categories: the temperature difference between the virtual basic temperature point and the corresponding basic temperature point is the basic temperature point gradient The temperature difference between the virtual key temperature point and the corresponding key temperature point is the key temperature point gradient , the temperature difference between the virtual matching temperature point and the corresponding matching temperature point is the matching temperature point gradient Since the base temperature point and the matching temperature point are non-critical temperature points, their temperature gradients are uniformly recorded as , used to distinguish the temperature difference between critical and non-critical areas. The temperature gradient is calculated for each real temperature point and its corresponding virtual temperature point. The real temperature point's temperature is collected in real time by a sensor and then subtracted from the preset temperature value of the virtual temperature point in the standard model (or the model temperature value obtained through interpolation). This yields the temperature gradient for each point.

[0047] Temperature point missing rate The calculation involves the number of virtual temperature points and the number of true temperature points , and its calculation formula is This parameter is used to measure the integrity of the actual temperature point layout on the control target: hour, , indicating that the number of real temperature points is exactly the same as that of virtual temperature points; when hour, , which reflects that there are missing temperature points in the control target, which may be caused by factors such as sensor failure, limited installation location or reactor structure differences. The introduction of temperature point missing rate helps to evaluate the effectiveness of the temperature monitoring system. When the preset threshold is exceeded, the system can trigger an early warning and prompt additional sensor deployment or check the equipment status.

[0048] When arranging true temperature points, consider the sensor type and installation method. Because critical temperature points are located in the catalyst bed area, special high-temperature and corrosion-resistant sensors (such as armored thermocouples) may be required. Customized mounting brackets should be used to ensure that the sensor probe penetrates deep into the bed to obtain accurate temperature data. Conventional temperature sensors can be used for non-critical temperature points (such as base and matching temperature points) and installed in reserved detection holes in the reactor shell or axial section. During installation, ensure the seal between the sensor and the reactor wall to prevent heat leakage from affecting measurement accuracy.

[0049] For the matching temperature point layout of the axial three-section section, it is necessary to ensure that the number of temperature points in each section is consistent with the number of virtual matching temperature points and the spatial distribution pattern is the same. For example, if the virtual matching temperature points are arranged with a radius of 、 ( ), with 12 temperature points arranged on each circle (circumferential interval of 30°). The corresponding cross-section of the control target needs to have an equal number of temperature points arranged at the same radius and with the same intervals to ensure that the spatial distribution of the matching temperature points completely matches the virtual model and avoid alignment errors caused by distribution differences.

[0050] During coordinate recording, a one-to-one correspondence between real and virtual temperature points must be established. For key temperature points, this correspondence is achieved through geometric position matching (e.g., coordinates of specific areas within the catalyst bed); for matching temperature points, this is achieved through axial cross-sectional position and radial radius matching; and for basic temperature points, a uniform correspondence is achieved through the spatial coordinates of non-critical areas. This correspondence requires the use of a database management system, which assigns a corresponding virtual temperature point number to each real temperature point to facilitate subsequent calculations of temperature gradients and missing rates.

[0051] Furthermore, the sensor configuration module must have dynamic adjustment capabilities. If the structure of the reactor being controlled differs from a standard reactor (e.g., diameter difference, catalyst bed height variation, etc.), the actual temperature points can be re-assigned by modifying the preset spacing or adjusting the temperature point distribution pattern to ensure the system can adapt to different models or modified reactors. Furthermore, the system must regularly verify the validity of the actual temperature points, detecting sensor failures or drift by comparing the temperature values ​​of adjacent points and analyzing the rationality of temperature gradients.

[0052] Example 3: The implementation method of the indicator definition module is as follows: First, it is necessary to fully obtain the thermodynamic parameters of the standard reactor. These parameters are the basis for defining the preset temperature indicators, covering material thermal conductivity, specific heat capacity, reaction activation energy, target operating temperature range, maximum temperature rise rate limit, etc. The thermal conductivity of the material reflects the ability of the reactor shell and internal components to conduct heat. Its value is determined by standard heat conduction experiments, such as the flat plate method or hot wire method. The corresponding test standard is selected according to the type of material (such as stainless steel, ceramic, etc.) and specifications to ensure the accuracy and comparability of the data. The specific heat capacity parameter is used to calculate the heat absorption and release capacity of each component of the reactor when the temperature changes. It is measured by calorimetry and the specific value is determined in combination with the chemical composition and physical state (such as solid or liquid) of the material.

[0053] The reaction activation energy is a key kinetic parameter for catalytic cracking reactions. Its value is obtained through catalytic reaction kinetic experiments. It is usually calculated by fitting the reaction rate constants at different temperatures using the Arrhenius equation. The target operating temperature range is determined according to the process requirements of the catalytic cracking reaction. The lower limit is the minimum temperature to ensure the start of the reaction, and the upper limit is the maximum temperature to avoid catalyst deactivation or intensification of side reactions. This range needs to be set in combination with the process manual and historical operating data. The maximum temperature rise rate limit is a parameter set to prevent a sudden rise in reactor temperature from causing safety risks or affecting product quality. Its value takes into account the thermal capacity of the reactor, the power of the heating equipment, and the responsiveness of the control system.

[0054] After obtaining the above thermodynamic parameters, it is necessary to define the preset temperature indicators based on these parameters, including the preset temperature gradient threshold, the preset temperature point missing rate threshold, the preset temperature rise rate threshold, and the preset thermal equilibrium deviation threshold. The preset temperature gradient threshold is used to limit the allowable temperature difference range between the real temperature point and the virtual temperature point. Its setting needs to comprehensively consider the control accuracy requirements and thermodynamic characteristics of the reactor. For critical temperature points (such as the catalyst bed area), since temperature changes have a significant impact on the reaction, the preset temperature gradient threshold needs to be set to a smaller value; for non-critical temperature points (such as the base temperature point and the matching temperature point), the threshold can be appropriately relaxed. The process of determining this threshold needs to refer to the temperature distribution data of the standard reactor under stable operation, and adjust it by statistically analyzing the historical temperature difference fluctuation range of each temperature point and combining it with the process safety margin.

[0055] The preset temperature point missing rate threshold is used to measure the completeness of the actual temperature point layout, and its value is related to the monitoring accuracy requirements of the reactor. When the temperature point missing rate is too high, it may lead to incomplete temperature field modeling, affecting the accuracy of the control strategy. For example, for large reactors, the preset temperature point missing rate threshold can be set to 10%, that is, the difference between the number of actual temperature points and the number of virtual temperature points is allowed to not exceed 10% of the total number of virtual temperature points; for small precision reactors, this threshold can be tightened to 5%. The setting of the threshold must take into account the actual feasibility of sensor installation to avoid increasing hardware costs and installation complexity due to excessive pursuit of completeness.

[0056] The preset temperature-rise rate threshold is used to limit the reactor's heating rate. Its value is directly related to the maximum temperature-rise rate limit and is typically set at 80% to 90% of the maximum temperature-rise rate limit to provide a certain control buffer. For example, if the maximum temperature-rise rate limit is 5°C / min, the preset temperature-rise rate threshold can be set at 4.5°C / min. The determination of this threshold must be considered in conjunction with reaction kinetics to avoid excessively rapid temperature rises, which can cause reactants to pass through the catalyst bed before they have fully reacted, thus affecting product yield and selectivity. Furthermore, the regulation accuracy of the heating equipment and the response delay of the control system must be considered.

[0057] The preset thermal balance deviation threshold is used to evaluate the temperature uniformity of the reactor in the thermal equilibrium state, including axial thermal balance deviation and radial thermal balance deviation. The axial thermal balance deviation is measured by the average temperature difference of each axial section of the reactor, and the radial thermal balance deviation is measured by the temperature difference at different radial positions within the same section. For example, the axial thermal balance deviation threshold can be set to ±3°C, which requires that the average temperature difference between adjacent axial sections does not exceed 3°C; the radial thermal balance deviation threshold is ±2°C, which means that the temperature difference between any two points in the same section does not exceed 2°C. The setting of these thresholds needs to refer to the thermal balance data of the standard reactor during stable operation, and combine the principles of heat transfer to analyze the heat conduction and convection characteristics in the reactor.

[0058] When defining preset temperature indicators, it's necessary to establish correlations between parameters. For example, the preset temperature gradient threshold is related to the thermal conductivity of the material. When the reactor uses a material with a higher thermal conductivity, heat transfer is faster and the temperature gradient may be smaller, so the threshold can be appropriately tightened. Conversely, if the material has a lower thermal conductivity, the temperature gradient may be larger, and the threshold needs to be relaxed. For another example, the preset temperature rise rate threshold is related to the specific heat capacity and the thermal capacity of the reactor. Reactors with a larger specific heat capacity require more heat to heat up, resulting in a slower temperature rise rate, so a higher threshold can be set. Reactors with a smaller specific heat capacity have a faster temperature rise rate, so a lower threshold needs to be set to avoid temperature overshoot.

[0059] Furthermore, preset temperature indicators must be adjustable to accommodate varying process conditions or changes in reactor status. For example, if a change in catalyst type causes a change in the reaction activation energy, the target operating temperature range and temperature rise rate threshold must be reassessed. If the shell material or internal structure changes after reactor maintenance or modification, the thermal conductivity and specific heat capacity of the material must be remeasured, and the temperature gradient and thermal equilibrium deviation threshold adjusted accordingly. The system can provide a modification interface for preset temperature indicators through a human-computer interface, allowing operators to adjust parameters based on actual conditions and record adjustment history for traceability.

[0060] To ensure the rationality of the preset temperature indicators, multiple rounds of verification and optimization were required. First, based on the historical operating data of a standard reactor, temperature changes under different operating conditions were simulated to verify whether the preset indicators could effectively distinguish between normal operation and abnormal conditions. Second, a thermal model of the reactor was established using simulation software. Different thermodynamic parameters and disturbance conditions were input to analyze the impact of the preset indicators on the control system performance. Finally, no-load and loaded tests were conducted on the actual reactor to observe the actual performance of each indicator during the temperature control process. The preset indicators were fine-tuned based on the test results.

[0061] Example 4: The implementation of the control execution module is as follows: First, steady-state control is performed for the control target located in the reference control frame. This process requires obtaining the coordinates and number of the real temperature point and the virtual temperature point, and calculating the temperature gradient. and temperature point missing rate , to determine whether the control target meets the preset conditions. Temperature gradient is the temperature difference between the real temperature point and the corresponding virtual temperature point, reflecting the difference between the control target temperature distribution and the standard model; the temperature point missing rate By formula Calculate, where is the number of virtual temperature points, is the actual number of temperature points, which is used to measure the integrity of the temperature point layout. and If both the preset temperature gradient threshold and the preset temperature point missing rate threshold are met at the same time, the control target is determined to have passed steady-state control and entered the subsequent dynamic response control and thermal balance control stages; if not, the system returns to the sensor configuration module to check the temperature point layout or re-align the model.

[0062] For the control target through steady-state control, it is necessary to build a dynamic response control and thermal balance control environment to perform heating rate control, cooling rate control and heat distribution uniformity control. In this process, the dynamic response factor of the control target is first defined. , including the temperature response factor , cooling response factor , thermal balance factor Among them, the temperature response factor Subdivided into key regional warming factors and non-critical area warming factors , which correspond to the heating rates of the catalyst bed area and other non-critical areas; the cooling response factor Subdivided into key area cooling factors and non-critical area cooling factors , used to characterize the cooling rate in different regions; thermal balance factor Including axial heat balance factor and radial heat balance factor , measured by the average temperature difference of adjacent axial sections of the reactor and the maximum temperature difference at different radial positions within the same section.

[0063] The dynamic response control environment simulates temperature changes in actual operating conditions by applying step temperature increase commands, step temperature decrease commands, and sinusoidal temperature disturbance commands. The step temperature increase command gradually increases the control target temperature from the initial value to the set value at a constant rate, for example, from 200°C to 500°C at a rate of 1°C / min. This is used to test the reactor's responsiveness during a linear temperature increase. The step temperature decrease command decreases the temperature at a constant rate, such as from 500°C to 300°C at a rate of 2°C / min. This examines the stability of the cooling process. The sinusoidal temperature disturbance command simulates temperature fluctuations under variable operating conditions by inputting a periodic temperature fluctuation signal (such as a sine wave with a frequency of 0.1Hz and an amplitude of ±10°C). This tests the system's ability to suppress temperature oscillations.

[0064] During the application of the above instructions, real-time acquisition of temperature response test results (temperature rise rate in key areas) , heating rate in non-critical areas ), cooling response test results (cooling rate in key areas , cooling rate in non-critical areas ) and thermal balance test results (axial temperature difference , radial temperature difference For example, the heating rate in the key area The temperature change rate of the key temperature points in the catalyst bed area is calculated to obtain the heating rate of the non-key area. Take the average rate of change of non-critical temperature points; axial temperature difference is the average temperature difference of the adjacent three-divided sections in the axial direction, and the radial temperature difference is the temperature difference between the maximum radius and the central axis in the same section. 、 、 、 The temperature rise rate does not exceed the preset threshold (such as 3°C / min in critical areas and 5°C / min in non-critical areas), and 、 If the preset thermal balance deviation threshold is met (such as axial temperature difference ≤ 4°C, radial temperature difference ≤ 3°C), the control target is determined to be controlled through dynamic response regulation and thermal balance regulation; if not, the analysis and diagnosis module is triggered to trace the fault.

[0065] For the control targets that have been regulated by the first two controls, compound regulation is further performed to simulate the temperature response behavior under variable operating conditions. Compound regulation applies different temperature disturbance patterns in sequence through multiple preset variable operating conditions scenarios (such as changes in raw material composition, feed flow fluctuations, catalyst activity decay, etc.), such as superimposing random temperature fluctuations during the step-by-step heating process, or introducing step-type load changes during the constant temperature stage. The system calculates the comprehensive regulation stability coefficient of the control target To evaluate its stability under multiple working conditions, the calculation formula is:

[0066] in, It is a comprehensive control stability coefficient, and its value range reflects the temperature control stability of the system under variable working conditions; The number of variable working conditions is set according to actual process requirements (for example, including 5 to 10 typical working conditions); and are weight coefficients, representing the importance of temperature response factor and thermal balance factor (e.g. 、 , which can be determined through process expert experience or analytic hierarchy process); Indicates the The temperature response factor under each variable working condition scenario comprehensively reflects the deviation of the heating / cooling rate under the working condition; Indicates the The thermal balance factor under a variable working condition reflects the temperature uniformity deviation under the working condition. The system can quantitatively evaluate the overall performance of the control target under complex working conditions.

[0067] During the control process, all test results are output as quantified data, including real-time temperature values ​​at each temperature point, temperature gradients, heating / cooling rates, temperature differentials, and stability coefficients. This data is stored in real time in a local database via the data acquisition module, forming a historical record for traceability and analysis. The control execution module also collaborates with the communication interface module to encapsulate control results and abnormal status information into industrial protocol data packets, which are then transmitted via Ethernet to an external distributed control system for remote monitoring and coordinated control.

[0068] In addition, the control execution module has an adaptive adjustment function. When it detects that the temperature response of the control target exceeds the preset index, the system automatically adjusts the control parameters, such as increasing the heating / cooling power, changing the temperature command waveform or adjusting the weight coefficient. 、 For control targets that have repeatedly failed to pass regulation, the system automatically triggers an analysis and diagnosis process. Combining data such as the temperature gradient direction vector and the segment temperature point density, it locates areas of abnormal heat flow and generates suggestions for adjusting the control strategy.

[0069] Example 5: The implementation of the analysis and diagnosis module is as follows: When the control target fails to pass the dynamic response control and thermal balance control, the system automatically triggers the analysis and diagnosis process. First, the control data of the control target is obtained, including the real temperature point coordinate set. , virtual temperature point coordinate set , real-time temperature value and temperature gradient of each temperature point and dynamic response factor Based on these data, the temperature gradient direction vector is calculated , the formula is ,in is the virtual temperature point coordinate vector (such as ), is the coordinate vector of the real temperature point (such as ), which represents the spatial offset direction and distance of the real temperature point relative to the virtual temperature point and is used to analyze the conduction path of the abnormal temperature difference.

[0070] Taking a catalytic cracking reactor as an example, it is assumed that the key temperature gradient of the catalyst bed area is found in the dynamic response control. If the temperature is continuously too high (such as exceeding the preset threshold ±2°C), the temperature gradient direction vector is calculated. , find the coordinate vector of the real temperature point in the area Deviates from the virtual temperature point coordinate vector in both the axial (Z axis) and radial (X axis) directions , indicating that the abnormal temperature difference may be transmitted in both the axial and radial directions. Further analysis of the vector components reveals that if the Z-axis component is positive (i.e., the actual temperature point is above the virtual temperature point) and the X-axis component is positive (biased to the right of the reactor), the heat flow blockage can be preliminarily located to the upper right of the catalyst bed. This may be caused by excessive catalyst packing density or uneven distribution of heat transfer elements in this area.

[0071] For further verification, the control target is divided into a finite number of equal thickness segments along the axial direction (for example, the axial length of the reactor is divided into 10 segments, each with a thickness of ), define a preset temperature point density threshold (such as 5 temperature points per cubic meter). Count the number of temperature points in each section and calculate the section temperature point density (number of temperature points / section volume). If the temperature point density of a section (such as section 3) is 8 / cubic meter, which exceeds the preset threshold of 5 / cubic meter, it indicates that the temperature points in this section are too densely distributed, there may be sensor redundancy, or the temperature changes in this section are complex. At this time, the material thermal conductivity of this section is calibrated. By comparing the measured thermal conductivity of the reactor shell material in this section with the standard value (such as the standard thermal conductivity of stainless steel material is 16W / (m·K)), if the measured value is 12W / (m·K), which is significantly lower than the standard value, it can be determined that the thermal conductivity of the material in this section has decreased due to corrosion or scaling, which in turn causes local temperature anomalies.

[0072] In another scenario, if the control target is the axial temperature difference in thermal balance control When the temperature exceeds a preset threshold (e.g., greater than 4°C), analysis of the temperature gradient direction vector reveals a negative Z-axis offset between the temperature gradient direction vectors of the middle and top axial sections (the actual temperature point is lower than the virtual temperature point), indicating that the temperature in the middle section is too low. After axially dividing the reactor into five sections, the temperature point density in the middle section was found to be 4 per cubic meter, below the preset threshold of 5 per cubic meter, potentially leading to inadequate temperature monitoring in this section. While material thermal conductivity verification is not triggered at this point, a prompt is issued to increase the number of temperature points in this section to improve monitoring accuracy.

[0073] The analysis and diagnosis module also needs to make a comprehensive judgment based on historical data and control instructions. For example, if the heating rate of a non-critical area is A sudden increase in temperature, while the temperature gradient direction vector indicates that the true temperature in that area is shifting toward the heating element, may be due to abnormal heating element power or control algorithm parameter drift. By retrieving the heating element's power output data and comparing it with the preset heating rate curve, you can quickly locate control parameter deviations and adjust the PID controller parameters.

[0074] During fault tracing, the system employs a hierarchical diagnostic logic: First, the temperature gradient direction vector is used to determine the spatial distribution and conduction direction of abnormal temperature differences, preliminarily locating areas of heat flow blockage or overheating. Next, segment temperature point density analysis is performed to eliminate monitoring blind spots or sensor placement issues. Finally, material thermal conductivity verification or equipment status checks are performed in high-risk sections, such as checking for agglomeration in the catalyst bed and blockage in heat transfer pipes. This multi-dimensional diagnostic approach gradually narrows the scope of the fault and improves diagnostic efficiency.

[0075] After the diagnosis is complete, the system generates a detailed diagnostic report, including the coordinates of the abnormal temperature difference area, the temperature gradient directional vector component, the statistical results of the temperature point density in the section, the material thermal conductivity verification data, and the suspected cause of the fault (such as "heat flow blockage in the upper right of the catalyst bed, possibly due to catalyst accumulation" and "material thermal conductivity decreases in the axial middle section, recommending cleaning of scale"). The diagnostic report also suggests control strategy adjustments, such as increasing local cooling air volume in the heat flow blockage area, adjusting the power distribution of the heating element, or arranging equipment maintenance to address the problem of decreased material thermal conductivity.

[0076] The analysis and diagnosis module works in conjunction with the data acquisition module to store all data generated during the diagnosis process (such as temperature gradient direction vectors, segment temperature point density, and measured material thermal conductivity values) in a local database, forming a fault case library. This library can be used to train machine learning models. By analyzing historical fault data, the system's ability to identify new anomalies is enhanced, enabling continuous optimization of diagnostic strategies.

[0077] During this process, the communication interface module encapsulates the diagnostic report into an industrial protocol data packet and transmits it via Ethernet to an external distributed control system (DCS) or operator station. This allows engineers to obtain detailed information about reactor temperature anomalies in real time and remotely adjust control strategies. For example, if a diagnostic report indicates that the thermal conductivity of a section of material has decreased and requires cleaning, the DCS can automatically switch to a backup reactor or trigger an online cleaning procedure.

[0078] The analysis and diagnosis module uses temperature gradient directional vector analysis, segment temperature point density statistics, and material thermal conductivity verification, combined with spatial coordinate offsets and data anomalies in specific cases, to accurately trace the source of control targets that fail regulation. This module not only locates physical faults such as heat flow blockage and localized overheating, but also identifies sensor placement defects and control parameter deviations. Through layered diagnostic logic and data-driven diagnostic methods, it provides reliable troubleshooting support for intelligent temperature control systems.

[0079] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0080] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A temperature intelligent control system for a step-by-step catalytic cracking reactor, characterized in that: include: Temperature field modeling module: Scan the internal structure of the standard reactor to obtain a standard temperature field distribution model, construct a spatial coordinate system with the geometric center of the temperature field distribution model, use the temperature field distribution model as a virtual temperature model, use the spatial coordinate system as a reference control frame, and create virtual temperature points of the virtual temperature model at preset intervals. The virtual temperature points include virtual basic temperature points, virtual key temperature points, and virtual matching temperature points; Sensor configuration module: Connected to the temperature field modeling module, the reactor to be controlled is defined as the control target, the control target is placed in the reference control frame, the real temperature points of the control target are arranged at preset intervals, including basic temperature points, key temperature points, and matching temperature points, the virtual temperature model is aligned with the control target, and the temperature difference between the real temperature point and the virtual temperature point is defined as the temperature gradient; Index definition module: connects with the sensor configuration module to obtain the thermodynamic parameters of the standard reactor and define the preset temperature index based on the thermodynamic parameters; Control execution module: connected to the indicator definition module, performs temperature control on the control target, including steady-state control, dynamic response control, thermal balance control and compound control. Dynamic response control includes heating rate control, cooling rate control and temperature fluctuation suppression control. Analysis and diagnosis module: Connected with the control execution module, it traces the faults of control targets that fail dynamic response regulation and thermal balance regulation, generates diagnostic reports and adjusts the control strategy.

2. The intelligent temperature control system for a step-by-step catalytic cracking reactor according to claim 1, characterized in that: The temperature field modeling module includes: The internal structure of the standard reactor is scanned by infrared thermal imaging to construct a standard temperature field distribution model; a spatial coordinate system is established based on the geometric center of the temperature field distribution model to generate virtual temperature points including virtual basic temperature points, virtual key temperature points and virtual matching temperature points. The virtual temperature points evenly cover the entire temperature field distribution model and the spacing between adjacent virtual temperature points is equal. The virtual temperature points in the catalyst bed area of ​​the reactor are used as virtual key temperature points, and the virtual temperature points in the axial three-divided section of the reactor are used as virtual matching temperature points. The coordinate set of the virtual temperature points is recorded. ,in is the number of virtual temperature points; The temperature field distribution model is used as a virtual temperature model, and the spatial coordinate system is used as a reference control frame.

3. The intelligent temperature control system for a stepwise catalytic cracking reactor according to claim 1, characterized in that: The sensor configuration module includes: Define the reactor to be controlled as the control target, locate the control target in the reference control frame, and arrange the real temperature points including basic temperature points, key temperature points and matching temperature points. The real temperature points evenly cover the entire control target and the spacing between adjacent real temperature points is equal. The real temperature points in the catalyst bed area of ​​the control target are used as key temperature points, and the real temperature points in the axial trisection section of the control target are used as matching temperature points. Record the coordinate set of the real temperature points. ,in is the number of real temperature points; Coincidentally aligning the virtual matching temperature point with the matching temperature point to achieve alignment of the control target with the virtual temperature model; Define the temperature difference between the real temperature point and the virtual temperature point as the temperature gradient , including the base temperature point gradient , key temperature point gradient , matching temperature point gradient , the base temperature point and the matching temperature point are defined as non-critical temperature points, and the temperature gradient of the non-critical temperature point is ; The temperature difference between the virtual base temperature point and the base temperature point is , the temperature difference between the virtual key temperature point and the key temperature point is , the temperature difference between the virtual matching temperature point and the matching temperature point is ; Get the number of virtual temperature points and the number of true temperature points ,definition and The ratio is the temperature point missing rate .

4. The intelligent temperature control system for a step-by-step catalytic cracking reactor according to claim 1, characterized in that: The indicator definition module includes: Obtain the thermodynamic parameters of the standard reactor, including material thermal conductivity, specific heat capacity, reaction activation energy, target operating temperature range, and maximum temperature rise rate limit; The preset temperature indicators are defined according to thermodynamic parameters, including a preset temperature gradient threshold, a preset temperature point missing rate threshold, a preset temperature rise rate threshold, and a preset thermal balance deviation threshold.

5. The intelligent temperature control system for a step-by-step catalytic cracking reactor according to claim 3, characterized in that: The control execution module includes: Perform steady-state control, dynamic response control, and thermal balance control on the control target positioned in the reference control frame; The steps of steady-state control are: obtaining the coordinates and number of real temperature points and virtual temperature points, calculating the temperature gradient and temperature point missing rate ,like and If the preset temperature gradient threshold and the preset temperature point missing rate threshold are met at the same time, it is determined that the control target passes the steady-state control; Construct dynamic response control and thermal balance control environment for the control target through steady-state control, perform heating rate control, cooling rate control and heat distribution uniformity control, and output the control results in quantitative data; Composite regulation is performed on the control target through dynamic response regulation and thermal balance regulation to simulate the temperature response behavior under variable operating conditions.

6. The intelligent temperature control system for a stepwise catalytic cracking reactor according to claim 5, characterized in that: The steps of constructing a dynamic response control and thermal balance control environment, executing control and outputting quantitative data are as follows: Define the dynamic response factor of the control target Including temperature response factor , cooling response factor , thermal balance factor ; The temperature response factor Including key area warming factor and non-key area warming factor, cooling response factor Including key area cooling factor and non-key area cooling factor, thermal balance factor Including axial heat balance factor and radial heat balance factor; Build a dynamic response control environment, including step temperature increase instructions, step temperature decrease instructions, and sinusoidal temperature disturbance instructions applied to the control target; Define the temperature response test result as the heating rate of the key area and heating rates in non-critical areas The cooling response test results are the cooling rate of the key area and cooling rate in non-critical areas , the thermal balance test results are axial temperature difference and radial temperature difference ; like 、 、 、 、 、 If the corresponding preset temperature rise rate threshold and the preset thermal balance deviation threshold are met, it is determined that the control target is controlled through dynamic response regulation and thermal balance regulation.

7. The intelligent temperature control system for a stepwise catalytic cracking reactor according to claim 5, characterized in that: The steps of performing compound regulation are: The comprehensive control stability coefficient of the control target is calculated as: in, is the comprehensive control stability coefficient, is the number of variable working condition scenarios, and is the weight coefficient, Indicates the The temperature response factor under different working conditions is: Indicates the Thermal balance factor under variable operating conditions.

8. The intelligent temperature control system for a step-by-step catalytic cracking reactor according to claim 1, characterized in that: The analysis and diagnosis module includes: Obtain control data for control targets that fail dynamic response control and thermal balance control, and calculate the temperature gradient direction vector ,in is the coordinate vector of the virtual temperature point, is the coordinate vector of the real temperature point; based on Analyze the conduction path of abnormal temperature differences and locate heat flow blocking areas or local overheating areas; divide the control target into a finite number of equal-thickness segments along the axial direction, define a preset temperature point density threshold, count the number of temperature points in each segment, calculate the segment temperature point density, and perform material thermal conductivity verification on segments that exceed the preset threshold.

9. The intelligent temperature control system for a step-by-step catalytic cracking reactor according to claim 3, characterized in that: The system further comprises: The data acquisition module is connected to the temperature field modeling module and the sensor configuration module to periodically obtain the virtual temperature point coordinate set and the real temperature point coordinate set , construct the temperature difference distribution matrix, store the temperature difference distribution matrix in the local database, where the data acquisition cycle is synchronized with the execution cycle of dynamic response control, and the dimension of the temperature difference distribution matrix is ​​the same as the number of virtual temperature points consistent.

10. The intelligent temperature control system for a step-by-step catalytic cracking reactor according to claim 5, characterized in that: The system further comprises: The communication interface module is connected to the external distributed control system, encapsulates the control results of the control execution module and the diagnosis report of the analysis and diagnosis module into industrial protocol data packets, and transmits them to the distributed control system via Ethernet.

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