Stepwise catalytic cracking reactor temperature intelligent control system

Abstract

The present application relates to the technical field of intelligent control of chemical reactors, and discloses a temperature intelligent control system for a 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 lays out real temperature points, obtains temperature gradients and temperature point missing rates; the index definition module sets preset temperature indexes based on thermodynamic parameters; the control execution module realizes steady state, dynamic response, heat balance and composite regulation; and the analysis and diagnosis module traces faults and adjusts strategies for targets that do not pass regulation. The system further comprises a data acquisition module and a communication interface module, and realizes data storage and external communication. The present application improves temperature control precision and system adaptive capacity, and is suitable for complex temperature control scenarios of catalytic cracking reactions.

Description

TECHNICAL FIELD

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

[0002] In the field of chemical production, catalytic cracking reaction is an important process step, 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 cannot meet the precise control needs under complex working conditions. From the perspective of temperature monitoring, the traditional method usually uses single-point or limited-point temperature sensor layout, which cannot comprehensively 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, the temperature distribution is significantly uneven, and single-point monitoring can easily miss the temperature abnormalities in the key areas, leading to the inability to timely discover local overheating or heat flow blockage and other problems, which may in turn cause increased side reactions, catalyst deactivation, and even equipment failure risks

[0003] In terms of control strategy, the traditional control system mostly uses PID control algorithm with fixed parameters, lacking the ability to adaptively adjust to dynamic working conditions. When there are dynamic changes such as temperature rise, fall or fluctuations during the reaction process, the traditional control method is difficult to quickly respond and adjust the control parameters, easily leading to temperature overshoot or too long adjustment time, affecting the stability and consistency of the reaction. For example, under variable working conditions such as changes in raw material composition, reaction load adjustment, etc., the traditional system cannot accurately simulate the temperature response behavior, making it difficult to ensure that the reaction proceeds stably within the target temperature range.

[0004] In terms of heat balance regulation, the traditional technology lacks attention to the axial and radial heat balance of the reactor. During the catalytic cracking reaction process, the generation and transmission of heat are complex, and the heat distribution is significantly different in different axial sections and radial regions. The traditional control system lacks precise calculation and regulation means for axial and radial heat balance factors, which can easily lead to uneven heat distribution inside the reactor, affecting the uniformity and conversion rate of the reaction, and also may increase the thermal stress of the equipment, shortening the service life of the equipment.

[0005] In addition, the traditional system has obvious defects in fault diagnosis and control strategy adjustment. When temperature control is abnormal, the traditional method is difficult to quickly trace and locate the fault, and cannot timely analyze the conduction path and cause of abnormal temperature difference, leading to low fault handling efficiency and increasing the time and cost of production interruption. At the same time, the traditional system lacks dynamic adjustment ability of control strategy, and cannot timely optimize control parameters according to real-time monitoring data and diagnosis results, making it difficult to adapt to changing production needs.

[0006] With the development of intelligentization and automation in the chemical industry, higher requirements are placed on the precision, real-time performance, and self-adaptive ability of temperature control in catalytic cracking reactors. The existing technology has shortcomings in temperature field modeling, sensor configuration, multi-dimensional control strategy, and fault diagnosis, and there is an urgent need for a more advanced and intelligent temperature control system to solve these problems, in order to improve the efficiency and stability of catalytic cracking reactions, reduce production costs and safety risks. SUMMARY

[0007] The present application aims to provide an intelligent temperature control system for a step-by-step catalytic cracking reactor to solve the problems raised in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solution: an intelligent temperature control system for a step-by-step catalytic cracking reactor, comprising:

[0009] a temperature field modeling module: scanning the internal structure of a standard reactor to obtain a standard temperature field distribution model, constructing a spatial coordinate system with the geometric center of the temperature field distribution model, taking the temperature field distribution model as a virtual temperature model, taking the spatial coordinate system as a reference control framework, creating virtual temperature points of the virtual temperature model at a preset interval, the virtual temperature points including virtual basic temperature points, virtual key temperature points, and virtual matching temperature points;

[0010] a sensor configuration module: connected to the temperature field modeling module, defining a controlled reactor as a control target, placing the control target in the reference control framework, arranging real temperature points of the control target including basic temperature points, key temperature points, and matching temperature points at a preset interval, aligning the virtual temperature model with the control target, and defining the temperature difference between the real temperature points and the virtual temperature points as a temperature gradient;

[0011] an index definition module: connected to the sensor configuration module, obtaining thermodynamic parameters of the standard reactor, and defining preset temperature indexes based on the thermodynamic parameters;

[0012] a control execution module: connected to the index definition module, performing temperature regulation on the control target including steady-state regulation, dynamic response regulation, thermal balance regulation, and composite regulation, wherein the dynamic response regulation includes temperature rise rate regulation, temperature drop rate regulation, and temperature fluctuation suppression regulation;

[0013] an analysis and diagnosis module: connected to the control execution module, performing fault tracing on the control target that does not pass the dynamic response regulation and the thermal balance regulation, generating a diagnosis report and adjusting the control strategy.

[0014] Preferably, the temperature field modeling module comprises:

[0015] A standard temperature field distribution model was constructed by scanning the internal structure of a standard reactor using infrared thermal imaging. A spatial coordinate system was established based on the geometric center of the temperature field distribution model, generating virtual temperature points, including virtual base temperature points, virtual key temperature points, and virtual matching temperature points. These virtual temperature points uniformly cover the entire temperature field distribution model, with equal spacing between adjacent virtual temperature points. The virtual temperature points in the reactor catalyst bed region were designated as virtual key temperature points, and the virtual temperature points along the reactor's axial trisection were designated as virtual matching temperature points. The coordinate set of these virtual temperature points was recorded. ,in This represents the number of virtual temperature points.

[0016] The temperature field distribution model is used as a virtual temperature model, and the spatial coordinate system is used as a reference control frame.

[0017] Preferably, the sensor configuration module includes:

[0018] The reactor to be controlled is defined as the control target. The control target is located within a reference control frame, and real temperature points are set up, including base temperature points, critical temperature points, and matching temperature points. These real temperature points uniformly cover the entire control target, with equal spacing between adjacent points. The real temperature points in the catalyst bed region of the control target are designated as critical temperature points, and the real temperature points in the axially trisected section of the control target are designated as matching temperature points. The set of coordinates for each real temperature point is recorded. ,in This represents the actual number of temperature points;

[0019] The virtual matching temperature point and the matching temperature point are overlapped to align the control target with the virtual temperature model;

[0020] The temperature gradient is defined as the temperature difference between a real temperature point and a virtual temperature point. Including the basic temperature gradient Key temperature gradient Matching temperature gradients 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 points is: ;

[0021] The temperature difference between the virtual base temperature point and the base temperature point is: The temperature difference between the virtual critical temperature point and the critical 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 actual temperature points ,definition and The ratio is the temperature point missing rate. .

[0022] Preferably, the index definition module comprises: obtaining the thermodynamic parameters of the standard reactor, including material thermal conductivity, specific heat capacity, reaction activation energy, target working temperature range, maximum temperature rise rate limit; defining the preset temperature index according to the thermodynamic parameters, including preset temperature gradient threshold, preset temperature point absence rate threshold, preset temperature rise rate threshold, preset thermal equilibrium deviation threshold.

[0023] Preferably, the control execution module comprises:

[0024] Performing steady-state regulation, dynamic response regulation and thermal equilibrium regulation on the control target positioned in the reference control framework;

[0025] The steps of steady-state regulation are: obtaining the coordinates and number of real temperature points and virtual temperature points, calculating the temperature gradient And the temperature point absence rate If And The preset temperature gradient threshold and the preset temperature point absence rate threshold are met at the same time, it is determined that the control target passes the steady-state regulation;

[0026] Building a dynamic response regulation and thermal equilibrium regulation environment for the control target that passes the steady-state regulation, performing temperature rise rate regulation, temperature drop rate regulation and thermal distribution uniformity regulation, and outputting the regulation results in quantitative data;

[0027] Performing compound regulation on the control target that passes the dynamic response regulation and thermal equilibrium regulation, simulating the temperature response behavior under variable working condition.

[0028] Preferably, the steps of building a dynamic response regulation and thermal equilibrium regulation environment, performing regulation and outputting quantitative data are:

[0029] Defining the dynamic response factor of the control target Including the temperature rise response factor , the temperature drop response factor , and the thermal equilibrium factor ;

[0030] The temperature rise response factor includes the key area temperature rise factor and the non-key area temperature rise factor, the temperature drop response factor includes the key area temperature drop factor and the non-key area temperature drop factor, and the thermal equilibrium factor includes the axial thermal equilibrium factor and the radial thermal equilibrium factor.

[0031] Building a dynamic response regulation environment, including applying step temperature rise instructions, step temperature drop instructions, and sinusoidal temperature disturbance instructions to the control target;

[0032] Defining the temperature rise response test result as the key area temperature rise rate non-critical area heating rate The cooling response test results show the cooling rate of the critical area. Cooling rate in non-critical areas The thermal balance test result is the axial temperature difference. and radial temperature difference ;

[0033] like , , , , , If both the preset temperature rise rate threshold and the preset thermal balance deviation threshold are met, then the control target is determined to be controlled through dynamic response regulation and thermal balance regulation.

[0034] Preferably, the step of performing composite regulation is as follows:

[0035] The comprehensive regulation stability coefficient of the control target is calculated as follows:

[0036]

[0037] in, To comprehensively regulate the stability coefficient, To increase the number of variable working conditions, and These are the weighting coefficients. Indicates the first Temperature response factor under varying operating conditions Indicates the first Thermal balance factor under varying operating conditions.

[0038] Preferably, the analysis and diagnostic module includes:

[0039] Obtain control data for control targets that fail to meet dynamic response control and thermal balance control, and calculate the temperature gradient direction vector. ,in For virtual temperature point coordinate vectors, The coordinate vector of the actual temperature point;

[0040] based on Analyze the conduction path of abnormal temperature differences to locate heat flow stagnation 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 temperature point density of the segment, and verify the material thermal conductivity of segments that exceed the preset threshold.

[0041] Preferably, the system further includes:

[0042] The data acquisition module is connected with the temperature field modeling module and the sensor configuration module, periodically acquires a virtual temperature point coordinate set and a real temperature point coordinate set , constructs a temperature difference distribution matrix, and stores the temperature difference distribution matrix to a local database, wherein the data acquisition period is synchronized with the execution period of dynamic response regulation, the dimension of the temperature difference distribution matrix is consistent with the number of virtual temperature points .

[0043] Preferably, the system further comprises:

[0044] The communication interface module is connected with an external distributed control system, encapsulates the regulation results of the control execution module and the diagnosis report of the analysis and diagnosis module into an industrial protocol data packet, and transmits the industrial protocol data packet to the distributed control system through Ethernet.

[0045] Compared with the prior art, the present application has the following advantages:

[0046] 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, establishes a spatial coordinate system with a geometric center, and generates uniform virtual temperature points, wherein the catalyst bed area is set as a virtual key temperature point, and the axial three-equal-section is set as a virtual matching temperature point, thereby realizing fine modeling of the internal temperature distribution of the reactor. The sensor configuration module aligns the controlled reactor with the virtual temperature model, arranges real temperature points corresponding to the virtual temperature points, and acquires real-time data such as temperature gradient and temperature point missing rate. This precise modeling and point arrangement method based on the spatial coordinate system can comprehensively and real-timely reflect the temperature state of each region in the reactor, especially the temperature change of the key region, thereby solving the problem that the traditional single-point or limited-point monitoring cannot comprehensively perceive the temperature field distribution, and providing a reliable data basis for precise control.

[0047] In terms of multi-dimensional control strategy, the control execution module realizes the organic combination of steady-state regulation, dynamic response regulation, thermal balance regulation and composite regulation. The steady-state regulation ensures the temperature control accuracy of the reactor under stable working conditions by judging whether the temperature gradient and the temperature point missing rate meet the preset threshold. The dynamic response regulation ensures that the reactor can quickly and stably respond to temperature changes under dynamic working conditions by monitoring the temperature rising and falling rates of key and non-key regions and the axial and radial temperature differences, and effectively suppressing temperature fluctuations. The thermal balance regulation ensures uniform heat distribution in the reactor by accurately calculating and regulating the axial and radial thermal balance factors, thereby improving the uniformity and conversion rate of the reaction. The composite regulation calculates the comprehensive regulation stability coefficient by simulating the temperature response behavior under variable working conditions, so that the system can adapt to changes in various complex working conditions, thereby significantly improving the self-adaptive ability and robustness of the system.

[0048] In terms of fault diagnosis and control strategy optimization, the analysis and diagnosis module can quickly locate the heat flow blockage area or local overheating area by calculating the temperature gradient direction vector and analyzing the conduction path of abnormal temperature difference. At the same time, by dividing the reactor into equal thickness sections in the axial direction, counting the temperature point density and checking the material thermal conductivity, the accuracy and efficiency of fault diagnosis are further improved. The diagnostic report generated according to the diagnosis result can timely adjust the control strategy, realize the closed-loop management from fault detection to diagnosis to control strategy optimization, shorten the fault handling time, reduce the risk of production interruption, and improve the reliability and stability of the system.

[0049] The setting of data acquisition module and communication interface module further enhances the data management and interaction ability of the system. The data acquisition module periodically acquires the virtual and real temperature point coordinate set, constructs the temperature difference distribution matrix and stores it to the local database, providing support for historical data tracing and analysis of the system. The communication interface module encapsulates the regulation and control results and the diagnostic report into industrial protocol data packets, transmits them to the distributed control system through Ethernet, realizes seamless connection with external distributed control system, improves the automation and intelligent level of production process, and facilitates production management personnel to master the running state of the reactor in real time and conduct global production scheduling and management.

[0050] The present application improves the accuracy, real-time performance and self-adaptive ability of the temperature control of the catalytic cracking reactor through innovative temperature field modeling method, multi-dimensional control strategy, intelligent fault diagnosis mechanism and perfect data management and interaction ability, which has important significance for improving the chemical production efficiency, ensuring the product quality, reducing the production cost and safety risk. BRIEF DESCRIPTION OF DRAWINGS

[0051] Fig. 1 The working principle diagram of the step-by-step catalytic cracking reactor temperature intelligent control system is shown in the figure;

[0052] Fig. 2 The working flowchart of the control execution module is shown in the figure;

[0053] Fig. 3 The flowchart of dynamic response regulation and thermal balance regulation is shown in the figure. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0055] Referring to Figs. 1-3 The present application relates to a step catalytic cracking reactor temperature intelligent control system, the system includes a temperature field modeling module, a sensor configuration module, an index definition module, a control execution module and an analysis diagnosis module, each module cooperates to realize intelligent control of the reactor temperature, as follows:

[0056] The temperature field modeling module: a standard temperature field distribution model is obtained by scanning the internal structure of a standard reactor. A spatial coordinate system is constructed with the geometric center of the model as a reference control framework, and the temperature field distribution model is used as a virtual temperature model. Virtual temperature points are created in the virtual temperature model at a predetermined interval, including virtual basic temperature points, virtual key temperature points and virtual matching temperature points. The virtual key temperature points are used to represent the temperature characteristics of the catalyst bed region of the reactor, and the virtual matching temperature points are set at the three-equal-section axial sections of the reactor.

[0057] The sensor configuration module: connected with the temperature field modeling module, the controlled reactor is defined as a control target and placed in the reference control framework. Real temperature points are arranged on the control target at a predetermined interval, including basic 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 three-equal-section axial sections of the control target. After aligning the virtual temperature model with the control target, the temperature difference between the real temperature points and the virtual temperature points is defined as the temperature gradient.

[0058] The index definition module: connected with the sensor configuration module, the thermodynamic parameters of the standard reactor are obtained, such as material thermal conductivity, specific heat capacity, reaction activation energy, target working temperature range, maximum temperature rise rate limit, etc., and the predetermined temperature index is defined based on these parameters.

[0059] The control execution module: connected with the index definition module, the control target is temperature regulated, and the regulation methods include steady-state regulation, dynamic response regulation, thermal balance regulation and composite regulation. The dynamic response regulation includes temperature rise rate regulation, temperature drop rate regulation and temperature fluctuation suppression regulation.

[0060] The analysis diagnosis module: connected with the control execution module, the control target that does not pass the dynamic response regulation and the thermal balance regulation is traced to the source of the fault, a diagnosis report is generated and the control strategy is adjusted.

[0061] The present application will be further described in conjunction with Examples 1 to 5:

[0062] In the specific implementation of the temperature field modeling module, the internal structure of a standard reactor is scanned by infrared thermal imaging technology to construct a standard temperature field distribution model. The infrared thermal imaging technology can non-contact obtain the temperature distribution information of the object surface. By collecting and processing the temperature data of different regions inside the reactor, an intuitive temperature field distribution image is formed, which can clearly reflect the temperature difference and distribution law of each part inside the reactor.

[0063] A space coordinate system is established with the geometric center of the standard temperature field distribution model as the origin. The space coordinate system adopts a three-dimensional rectangular coordinate system, with the axial direction of the reactor as the Z axis and the radial direction as the X and Y axes. The geometric center is the origin (0, 0, 0) of the coordinate system. By establishing the space coordinate system, a unified reference framework is provided for subsequent analysis of the temperature field distribution model and positioning of the virtual temperature points.

[0064] In the established space coordinate system, virtual temperature points are generated in the virtual temperature model at a preset interval. These virtual temperature points uniformly cover the entire temperature field distribution model, and the interval of adjacent virtual temperature points is equal. The value of the preset interval is determined according to the size of the reactor and the temperature control accuracy requirement. For example, for a larger reactor, the preset interval can be appropriately increased, but it is necessary to ensure that the virtual temperature points can fully reflect the distribution characteristics of the temperature field; for a reactor with higher temperature control accuracy requirement, the preset interval needs to be reduced to improve the accuracy of temperature monitoring and control.

[0065] The virtual temperature points include virtual basic temperature points, virtual key temperature points, and virtual matching temperature points. The virtual temperature points in the catalyst bed region of the reactor are virtual key temperature points. The catalyst bed is the core region of the catalytic cracking reaction, and its temperature distribution directly affects the reaction rate and product distribution, so the temperature of this region needs to be monitored and controlled. By densely setting virtual key temperature points in the catalyst bed region, the temperature information of this region can be more accurately obtained, providing more detailed basis for subsequent temperature regulation.

[0066] The virtual temperature points in the axially three-equal-sections of the reactor are virtual matching temperature points. The reactor is equally divided into three sections along the axial direction (i.e. Z axis direction), and a cross section is taken at the middle position of each section, i.e. the axially three-equal-sections. Virtual matching temperature points are set on these cross sections, which are convenient for subsequent alignment with the matching temperature points of the controlled reactor, ensuring the consistency of the virtual temperature model and the controlled reactor in spatial position, so as to accurately compare and analyze the temperature difference between the two.

[0067] The coordinate set of all virtual temperature points is recorded as wherein is the number of virtual temperature points. The coordinates of each virtual temperature point are determined by its X, Y, Z axis coordinate values in the spatial coordinate system, for example, the coordinates of a virtual temperature point 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.

[0068] After the generation of virtual temperature points is completed, the temperature field distribution model is taken as the virtual temperature model, which represents the temperature distribution of the standard reactor under ideal working conditions and serves as a reference benchmark for subsequent temperature control of the controlled reactor. The spatial coordinate system serves as a reference control framework, providing a unified spatial reference for the positioning of the controlled reactor and the layout of the temperature points.

[0069] In the process of generating virtual temperature points, it is necessary to ensure that the virtual temperature points uniformly cover the entire temperature field distribution model. The purpose of uniform coverage is to avoid blind spots in temperature monitoring and ensure that the distribution characteristics of the temperature field can be fully and accurately reflected. By reasonably setting the preset interval and the number of virtual temperature points, the distribution density of virtual temperature points in various directions such as the axial, radial, and circumferential directions of the reactor is basically consistent, thereby ensuring the accuracy and reliability of the temperature field modeling.

[0070] For the setting of virtual key temperature points, in addition to selecting the catalyst bed area, the structure and reaction characteristics of the catalyst bed also need to be considered. For example, if the catalyst bed is divided into different regions such as the inlet region, reaction region, and outlet region, virtual key temperature points may need to be set in each sub-region to more meticulously monitor the temperature changes in different regions. At the same time, the density of virtual key temperature points can be adjusted according to the intensity of the reaction in the catalyst bed, with denser virtual key temperature points set in regions with intense reactions to improve the accuracy of temperature monitoring.

[0071] Virtual matching temperature points are set at axial three-equal-sections, because axial three-equal-sections can uniformly divide the reactor into multiple segments along the axial direction, facilitating the alignment of the matching temperature points of the controlled reactor with the virtual matching temperature points in the subsequent sensor configuration module, thereby achieving the spatial alignment of the virtual temperature model and the controlled reactor. On each axial three-equal-section, the distribution of virtual matching temperature points also needs to be uniform, for example, virtual matching temperature points can be set at certain angular intervals on the section to form a uniform distribution on a circumference.

[0072] In constructing the standard temperature field distribution model, the boundary conditions and heat conduction characteristics of the reactor also need to be considered. For example, the reactor shell may have heat dissipation phenomenon, resulting in lower temperature in the area close to the shell, so when generating virtual temperature points, the density of virtual temperature points in the area close to the shell needs to be appropriately increased to more accurately reflect the temperature change in this area. At the same time, for the area near the heating element or cooling element inside the reactor, due to the large temperature gradient, the density of virtual temperature points also needs to be increased.

[0073] When recording the virtual temperature point coordinate set, accurate coordinate measurement and recording methods need to be used to ensure the accuracy of the coordinate values. The coordinate information of the virtual temperature points can be automatically obtained through the combination of infrared thermal imaging equipment and computer software, and stored in the database for subsequent calling and analysis. At the same time, in order to facilitate the management and identification of different types of virtual temperature points, the virtual basic temperature points, virtual key temperature points and virtual matching temperature points can be classified and identified in the database, for example, by adding different labels or attribute fields to distinguish them.

[0074] The implementation of the sensor configuration module is as follows: the reactor to be controlled is defined as the control target, and it is precisely placed in the reference control framework constructed by the temperature field modeling module through three-dimensional spatial positioning technology. The reference control framework adopts a three-dimensional rectangular coordinate system, with the geometric center of the standard reactor as the origin, the axial direction as the Z axis, and the radial direction as the X and Y axes. The positioning of the reactor to be controlled needs to ensure that its geometric center coincides with the origin of the reference control framework, and the axial and radial directions are completely aligned with the coordinate system, so as to eliminate the influence of spatial position deviation on temperature control.

[0075] After the positioning of the control target is completed, real temperature points are arranged on the control target at a preset interval. The preset interval is consistent with the preset interval of the virtual temperature points in the temperature field modeling module, ensuring that they have comparability in spatial resolution. The real temperature points are also divided into three categories: basic temperature points, key temperature points and matching temperature points. The catalyst bed area of the control target is the core area of temperature control, and the real temperature points in this area are used as key temperature points to monitor the temperature change of the reaction core area in real time. The real temperature points on the three-equal-section cross sections of the control target (i.e. the middle cross sections of each segment after the reactor is equally divided along the axial direction) are used as matching temperature points to realize spatial alignment with the virtual matching temperature points in the virtual temperature model.

[0076] The arrangement of real temperature points needs to follow the principle of uniform coverage, that is, all real temperature points are uniformly 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, temperature points are arranged at a preset interval from the top to the bottom of the reactor; in the radial direction, temperature points are uniformly arranged on the circumference at different radius positions with the center axis of the reactor as the reference, and the circumferential interval angle can be set according to the control accuracy requirement (such as 30°, 45°, etc.), to ensure that the temperature points in each cross section can fully reflect the temperature distribution characteristics of the cross section. The coordinate set of the real temperature points is recorded as wherein is the number of real temperature points, and the coordinates of each real temperature point correspond to its spatial position in the reference control framework, which can be obtained through a high-precision sensor positioning system or three-dimensional modeling software combined with physical measurement.

[0077] The alignment of the virtual temperature model and the control target is realized by the coincidence of the virtual matching temperature points and the matching temperature points. The specific operation is as follows: first, extract the virtual matching temperature point coordinate set on the axial three-equal-section of the virtual temperature model, and obtain the matching temperature point coordinate set on the corresponding axial three-equal-section of the control target; then adjust the attitude of the control target in the reference control framework through coordinate transformation algorithm (such as translation, rotation, etc.), so that the matching temperature points of the control target and the virtual matching temperature points coincide in space. This alignment process needs to be completed with the help of computer-aided algorithm, which minimizes the coordinate error between the matching temperature points through iterative calculation, to ensure that the virtual temperature model and the control target are completely aligned in space.

[0078] The definition of temperature gradient is based on the temperature difference between real temperature points and virtual temperature points, which can be divided into three categories: the temperature difference between virtual basic temperature points and corresponding basic temperature points is the basic temperature point gradient , the temperature difference between virtual key temperature points and corresponding key temperature points is the key temperature point gradient , and the temperature difference between virtual matching temperature points and corresponding matching temperature points is the matching temperature point gradient . Since the basic temperature points and the matching temperature points belong to non-key temperature points, their temperature gradients are uniformly denoted as , which is used to distinguish the temperature difference between key areas and non-key areas. The calculation of temperature gradient needs to be carried out for each real temperature point and the corresponding virtual temperature point, and the temperature gradient of each point is obtained by subtracting the preset temperature value (or the model temperature value obtained by interpolation algorithm) of the virtual temperature point in the standard model from the temperature value of the real temperature point collected by the sensor in real time.

[0079] The calculation of temperature point missing rate involves the number of virtual temperature points and real temperature point quantity , the calculation formula is This parameter is used to measure the integrity of the real temperature point layout on the control target: when , , it means that the real temperature point quantity is completely consistent with the virtual temperature point quantity; when , , it reflects that there is a temperature point missing on the control target, which may be caused by factors such as sensor failure, limited installation location, or reactor structure difference. The introduction of temperature point missing rate helps to evaluate the effectiveness of the temperature monitoring system. When exceeds the preset threshold, the system can trigger an early warning and prompt to increase sensor layout or check equipment status.

[0080] When laying real temperature points, the type and installation method of the sensor need to be considered. Key temperature points, located in the catalyst bed area, may require special sensors (such as armored thermocouples) that are resistant to high temperatures and corrosion, and through customized installation brackets to ensure that the sensor probe penetrates deep into the bed layer to obtain accurate temperature data. Non-key temperature points (such as base temperature points and matching temperature points) can use conventional temperature sensors installed in the reactor shell or the reserved detection hole of the axial section. The installation process needs to ensure the sealing of the sensor and the reactor wall to avoid heat leakage affecting the measurement accuracy.

[0081] For the layout of matching temperature points on the axial three-equal-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 evenly distributed in two circles with a radius of , ( ) on an axial three-equal-section, and each circle is equipped with 12 temperature points (30° interval), then the corresponding section of the control target needs to be equipped with the same number of temperature points at the same radius position with the same interval, to ensure that the spatial distribution of the matching temperature points completely matches the virtual model, avoiding alignment errors caused by distribution differences.

[0082] In the coordinate recording process, a one-to-one correspondence between real temperature points and virtual temperature points needs to be established. For key temperature points, the correspondence is achieved through geometric position matching (such as specific region coordinates in the catalyst bed); for matching temperature points, the correspondence is achieved through axial section position and radial radius matching; and for base temperature points, the correspondence is achieved through uniform spatial coordinates in non-key regions. The establishment of this correspondence relationship needs the help of a database management system, which marks the corresponding virtual temperature point number for each real temperature point, facilitating the calculation of temperature gradient and missing rate in the future.

[0083] In addition, the sensor configuration module needs to have a dynamic adjustment function. When the structure of the reactor to be controlled differs from the standard reactor (such as different diameters, changes in catalyst bed height, etc.), the preset spacing or temperature point distribution pattern can be modified to reposition the real temperature points, ensuring that the system can adapt to different models or modified reactors. At the same time, the system needs to regularly check the effectiveness of the real temperature points, comparing the temperature values of adjacent temperature points, analyzing the reasonableness of the temperature gradient, and other ways to detect whether the sensor has failed or drifted.

[0084] In Example 3, the implementation of the index definition module is as follows: First, the thermodynamic parameters of the standard reactor need to be comprehensively obtained, which are the basis for defining the preset temperature index, including material thermal conductivity, specific heat capacity, reaction activation energy, target operating temperature range, maximum temperature rise rate limit, etc. The material thermal conductivity reflects the ability of the reactor shell and internal components to conduct heat, and its value is determined by standard heat conduction experiments, such as using the flat plate method or hot wire method, according to the type of material (such as stainless steel, ceramic, etc.) and specifications to select the corresponding test standards 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, which is determined by calorimetry, combined with the chemical composition and physical state of the material (such as solid, liquid) to determine the specific value.

[0085] The reaction activation energy is a key kinetic parameter of the catalytic cracking reaction, and its value is obtained through catalytic reaction kinetics experiments, usually using the Arrhenius equation to fit the reaction rate constant at different temperatures. The target operating temperature range is determined according to the process requirements of the catalytic cracking reaction, with the lower limit being the minimum temperature to ensure the start of the reaction and the upper limit being the maximum temperature to avoid catalyst deactivation or the intensification of side reactions. This range needs to be set comprehensively based on the process manual and historical operation data. The maximum temperature rise rate limit is a parameter set to prevent the reactor temperature from rising suddenly to cause safety risks or affect product quality, and its value takes into account the heat capacity of the reactor, the power of the heating equipment, and the response ability of the control system.

[0086] After obtaining the above thermodynamic parameters, the preset temperature indicators need to be defined based on these parameters, including preset temperature gradient threshold, preset temperature point absence rate threshold, preset temperature rise rate threshold and preset thermal equilibrium deviation threshold. The preset temperature gradient threshold is used to limit the allowed temperature difference range between the real temperature point and the virtual temperature point, which needs to be set considering the control accuracy requirement and thermodynamic characteristics of the reactor. For key temperature points (such as catalyst bed area), since temperature change has a significant impact on the reaction, the preset temperature gradient threshold needs to be set to a small value; for non-key temperature points (such as basic temperature points and matching temperature points), the threshold can be appropriately relaxed. The determination process of this threshold needs to refer to the temperature distribution data of the standard reactor in the stable running state, and the historical temperature difference fluctuation range of each temperature point is analyzed by statistical analysis, and the process safety margin is adjusted.

[0087] The preset temperature point absence rate threshold is used to measure the completeness of the real temperature point layout, which is related to the monitoring accuracy requirement of the reactor. When the temperature point absence 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 absence rate threshold can be set to 10%, that is, the difference between the number of real temperature points and the number of virtual temperature points is not more than 10% of the total number of virtual temperature points; for small and precise reactors, the threshold can be tightened to 5%. The setting of the threshold needs to consider the actual feasibility of sensor installation, avoiding excessive pursuit of completeness and increasing hardware cost and installation complexity.

[0088] The preset temperature rise rate threshold is used to limit the rate of the reactor during the heating process, which is directly related to the maximum temperature rise rate limit value, and is usually set to 80%-90% of the maximum temperature rise rate limit value to reserve a certain control buffer space. For example, if the maximum temperature rise rate limit value is 5℃ / min, the preset temperature rise rate threshold can be set to 4.5℃ / min. The determination of this threshold needs to consider the reaction kinetics characteristics, avoiding the situation that the reactants pass through the catalyst bed before being fully reacted due to too fast temperature rise rate, affecting the product yield and selectivity. At the same time, the adjustment accuracy of the heating equipment and the response delay of the control system need to be considered.

[0089] The preset thermal equilibrium deviation threshold is used to evaluate the temperature uniformity of the reactor in the thermal equilibrium state, including axial thermal equilibrium deviation and radial thermal equilibrium deviation. The axial thermal equilibrium deviation is measured by the average temperature difference of each cross section along the reactor axis, and the radial thermal equilibrium deviation is measured by the temperature difference between different radius positions in the same cross section. For example, the axial thermal equilibrium deviation threshold can be set to ±3℃, that is, the average temperature difference of adjacent cross sections in the axial direction is required to be not more than 3℃; the radial thermal equilibrium deviation threshold is ±2℃, that is, the temperature difference between any two points in the same cross section is not more than 2℃. The setting of these thresholds needs to refer to the thermal equilibrium data of the standard reactor in the stable running state, and the heat conduction and convection characteristics in the reactor are analyzed based on the principles of heat transfer.

[0090] In defining the preset temperature indicators, the correlation between parameters needs to be established. For example, the preset temperature gradient threshold is related to the thermal conductivity of the material. When the reactor uses a material with high thermal conductivity, heat transfer is faster, and the temperature gradient may be smaller, so the threshold can be appropriately tightened; conversely, if the thermal conductivity of the material is low, the temperature gradient may be large, 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 heat capacity of the reactor. The reactor with larger specific heat capacity needs more heat when heating up, and the temperature rise rate is slower, so a higher threshold can be set; the reactor with smaller specific heat capacity has a faster temperature rise rate, and a lower threshold needs to be set to avoid temperature overshoot.

[0091] In addition, the preset temperature indicators need to be adjustable to adapt to different process conditions or changes in reactor state. For example, when replacing the catalyst type causes the reaction activation energy to change, the target working temperature range and the temperature rise rate threshold need to be re-evaluated; after the reactor is maintained or modified, if the shell material or internal structure changes, the thermal conductivity and specific heat capacity of the material need to be re-determined, and the temperature gradient and heat balance deviation threshold need to be adjusted accordingly. The system can provide a modification interface for the preset temperature indicators through a human-computer interaction interface, allowing the operator to adjust the parameters according to the actual situation, and recording the adjustment history for traceability.

[0092] To ensure the rationality of the preset temperature indicators, multiple rounds of verification and optimization are needed. First, based on the historical running data of the standard reactor, the temperature changes under different working conditions are simulated to check whether each preset indicator can effectively distinguish between normal operation state and abnormal state; second, the thermal model of the reactor is established through simulation software, different thermodynamic parameters and disturbance conditions are input, and the influence of the preset indicators on the performance of the control system is analyzed; finally, the empty load and load tests are carried out on the actual reactor, the actual performance of each indicator in the temperature control process is observed, and the preset indicators are fine-tuned according to the test results.

[0093] In the embodiment, the control execution module is implemented as follows: first, steady-state control is performed for the control target positioned in the reference control framework. This process needs to obtain the coordinates and number of real temperature points and virtual temperature points, and judge whether the control target meets the preset conditions by calculating the temperature gradient and the temperature point missing rate . The temperature gradient is the temperature difference between the real temperature point and the corresponding virtual temperature point, reflecting the difference between the temperature distribution of the control target and the standard model; the temperature point missing rate is calculated by the formula , where is the number of virtual temperature points, is the number of real temperature points, and is used to measure the integrity of the temperature point layout. If and If the preset temperature gradient threshold and the preset temperature point missing rate threshold are met, it is determined that the control target passes the steady-state regulation, and enters a subsequent dynamic response regulation and thermal balance regulation stage; if not, it returns to the sensor configuration module to check the temperature point layout or re-performs model alignment.

[0094] For the control target passing the steady-state regulation, a dynamic response regulation and thermal balance regulation environment needs to be constructed to perform temperature rise rate regulation, temperature drop rate regulation, and thermal distribution uniformity regulation. In this process, first, define the dynamic response factors of the control target , including temperature rise response factors , temperature drop response factors , and thermal balance factors . Among them, the temperature rise response factors are subdivided into key area temperature rise factors and non-key area temperature rise factors , corresponding to the temperature rise rate of the catalyst bed area and other non-key areas, respectively; the temperature drop response factors are subdivided into key area temperature drop factors and non-key area temperature drop factors , used to represent the temperature drop rates of different areas; the thermal balance factors include axial thermal balance factors and radial thermal balance factors , respectively measured by the average temperature difference between adjacent sections in the reactor axis direction and the maximum temperature difference at different radius positions in the same section.

[0095] The dynamic response regulation environment simulates temperature changes in actual working conditions by applying step temperature rise instructions, step temperature drop instructions, and sinusoidal temperature disturbance instructions. The step temperature rise instruction gradually increases the temperature of the control target 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, which is used to test the response capability of the reactor in the linear temperature rise process; the step temperature drop instruction reduces the temperature at a constant rate, such as from 500°C to 300°C at a rate of 2°C / min, which is used to investigate the stability of the temperature drop process; the sinusoidal temperature disturbance instruction simulates temperature fluctuations under variable working conditions by inputting a periodic temperature fluctuation signal (such as a sinusoidal wave with a frequency of 0.1 Hz and an amplitude of ±10°C), and tests the system's ability to suppress temperature oscillation.

[0096] During the application of the above instructions, real-time collection of temperature rise response test results (key area temperature rise rate , non-key area temperature rise rate ), temperature drop response test results (key area temperature drop rate , non-key area temperature drop rate ), and thermal balance test results (axial temperature difference , radial temperature difference ). For example, the critical zone temperature rise rate The non-critical zone temperature rise rate is calculated by the temperature change rate of the critical temperature point in the catalyst bed zone Take the average change rate of the non-critical temperature point; axial temperature difference The average temperature difference of the three equal sections adjacent to the axis is the radial temperature difference The temperature difference between the maximum radius in the same section and the central axis. If , , , All do not exceed the preset temperature rise rate threshold (such as the critical zone limit is 3 ℃ / min, and the non-critical zone limit is 5 ℃ / min), and , Meet the preset thermal equilibrium deviation threshold (such as axial temperature difference ≤4 ℃, radial temperature difference ≤3 ℃), it is determined that the control target passes the dynamic response regulation and thermal equilibrium regulation; if not, trigger the analysis and diagnosis module to trace the fault.

[0097] For the control target passing the first two regulations, further composite regulation is performed to simulate the temperature response behavior under variable working condition. The composite regulation applies different temperature disturbance modes in turn through a plurality of preset variable working condition scenes (such as raw material composition change, feed flow fluctuation, catalyst activity attenuation, etc.), for example, superimposing random temperature fluctuation in the stepwise heating process, or introducing stepwise load change in the constant temperature stage. The system evaluates the stability of the control target under multiple working conditions by calculating the comprehensive regulation stability coefficient , the calculation formula is:

[0098]

[0099] Among them, The comprehensive regulation stability coefficient, the value range reflects the temperature control stability of the system under variable working conditions; The number of variable working condition scenes, set according to actual process requirements (such as containing 5-10 typical working conditions); And The weight coefficients respectively represent the importance of the temperature response factor and the thermal equilibrium factor (such as , , which can be determined by process expert experience or analytic hierarchy process); Indicates the temperature response factor under the Variable working condition scene, which comprehensively reflects the temperature rise / decrease rate deviation under this working condition; Indicates the thermal equilibrium factor under the Variable working condition scene, which reflects the temperature uniformity deviation under this working condition. By calculating The system can quantitatively evaluate the overall performance of the control target under complex working conditions.

[0100] During the regulation process, all test results are output in the form of quantitative data, including real-time temperature values, temperature gradients, heating / cooling rates, temperature difference data, and stability coefficients at each temperature point. These data are stored in real time by the data acquisition module to the local database, forming a historical record for tracing and analysis. The control execution module also needs to work with the communication interface module to encapsulate the regulation results and abnormal state information as industrial protocol data packets, and transmit them to the external distributed control system through Ethernet to realize remote monitoring and linkage control.

[0101] In addition, the control execution module has an adaptive adjustment function. When the temperature response of the control target exceeds the preset index, the system automatically adjusts the regulation parameters, such as increasing the heating / cooling power, changing the temperature instruction waveform, or adjusting the weight coefficient 、 to optimize the regulation effect. For control targets that have failed multiple times in regulation, the system automatically triggers the analysis and diagnosis process, locates the abnormal heat flow area based on temperature gradient direction vectors and section temperature point density data, and generates regulation strategy adjustment suggestions.

[0102] The implementation of the analysis and diagnosis module is as follows: when the control target fails to pass the dynamic response regulation and thermal balance regulation, the system automatically triggers the analysis and diagnosis process. First, obtain the regulation data of the control target, including the real temperature point coordinate set , the virtual temperature point coordinate set , the real-time temperature value of each temperature point, the temperature gradient , and the dynamic response factor , etc. Based on these data, calculate the temperature gradient direction vector , the formula is , where is the virtual temperature point coordinate vector (such as ), is the real temperature point coordinate vector (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 abnormal temperature difference.

[0103] Taking a catalytic cracking reactor as an example, suppose that during the dynamic response regulation, it is found that the key temperature point gradient of the catalyst bed area continues to be high (such as exceeding the preset threshold ±2℃), by calculating the temperature gradient direction vector , it is found that the real temperature point coordinate vector of this area deviates from the virtual temperature point coordinate vector in the axial (Z-axis) and radial (X-axis) directions., indicating that abnormal temperature difference may conduct along the axial and radial directions at the same time. Further analysis of the vector components, if the Z-axis component is positive (i.e. the real temperature point is located above the virtual temperature point), and the X-axis component is positive (biased to the right side of the reactor), it can be preliminarily located that the heat flow resistance area is possibly above the right side of the catalyst bed, which may be caused by the high catalyst bulk density or uneven distribution of heat transfer elements in this area.

[0104] To further verify, the control target is divided into a finite number of equal thickness sections along the axial direction (for example, the axial length of the reactor is divided into 10 sections, each with a thickness of ), and a preset temperature point density threshold is defined (for example, 5 temperature points per cubic meter). The number of temperature points in each section is counted, and the section temperature point density (temperature point number / section volume) is calculated. If the temperature point density of a certain section (for example, the 3rd section) is 8 per cubic meter, which exceeds the preset threshold of 5 per cubic meter, it indicates that the temperature point distribution in this section is too dense, which may cause sensor redundancy or complex temperature changes in this section. At this time, the material thermal conductivity verification is carried out for this section, by comparing the measured thermal conductivity of the reactor shell material in this section with the standard value (for example, the standard thermal conductivity of stainless steel material is 16 W / (m·K)), if the measured value is 12 W / (m·K), which is significantly lower than the standard value, it can be judged that the material in this section has a decreased thermal conductivity due to corrosion or fouling, which in turn causes local temperature abnormalities.

[0105] In another scenario, if the axial temperature difference of the control target in the thermal balance regulation exceeds the preset threshold (for example, greater than 4°C), through temperature gradient direction vector analysis, it is found that the temperature gradient direction vectors of the middle section and the top section in the Z-axis direction are negatively offset (the real temperature point is lower than the virtual temperature point), indicating that the temperature of the middle section is low. After dividing the reactor into 5 sections, it is found that the temperature point density of the middle section is 4 per cubic meter, which is lower than the preset threshold of 5 per cubic meter, which may cause insufficient temperature monitoring in this section. At this time, although the material thermal conductivity verification is not triggered, it is necessary to prompt to increase the temperature point distribution in this section to improve the monitoring accuracy.

[0106] The analysis and diagnosis module also needs to make comprehensive judgments in combination with historical data and regulation instructions. For example, when a step-up heating instruction is applied, the heating rate of a non-critical area suddenly increases, and the temperature gradient direction vector shows that the real temperature point of this area is offset towards the heating element, which may be caused by abnormal heating element power or control algorithm parameter drift. By retrieving the power output data of the heating element and comparing it with the preset heating rate curve, the control parameter deviation can be quickly located and the PID controller parameters can be adjusted.

[0107] In the fault tracing process, the system adopts hierarchical diagnostic logic: first, determine the spatial distribution of abnormal temperature difference and the conduction direction through the temperature gradient direction vector, preliminarily locate the heat flow blockage or overheating area; Then exclude the monitoring blind area or sensor layout problem through the section temperature point density analysis; Finally, check the material thermal conductivity of the high-risk section or check the equipment status, such as checking whether the catalyst bed layer is blocked, whether the heat transfer pipeline is blocked, etc. This multi-dimensional diagnostic method can gradually narrow down the fault range and improve the diagnostic efficiency.

[0108] After diagnosis, the system generates a detailed diagnostic report, including abnormal temperature difference area coordinates, temperature gradient direction vector components, section temperature point density statistical results, material thermal conductivity verification data, and fault cause speculation (such as "heat flow blockage in the upper right of the catalyst bed layer, which may be caused by catalyst accumulation" "material thermal conductivity decreases in the axial middle section, suggest cleaning scale" etc.). The diagnostic report also proposes control strategy adjustment suggestions, such as increasing local cooling air volume for heat flow blockage area, adjusting heating element power distribution, or arranging equipment maintenance for material thermal conductivity reduction problem.

[0109] The analysis and diagnosis module is linked with the data acquisition module, and all data generated during the diagnosis process (such as temperature gradient direction vector, section temperature point density, material thermal conductivity measured value, etc.) are stored in the local database to form a fault case library. This case library can be used for machine learning model training, and through the analysis of historical fault data, the system's ability to identify new abnormalities is improved, and the diagnostic strategy is continuously optimized.

[0110] The communication interface module is responsible for packaging the diagnostic report into industrial protocol data packets during this process, and transmitting it to the external distributed control system (DCS) or operation station through Ethernet, so that engineers can obtain detailed information about the reactor temperature anomaly in real time, and remotely execute control strategy adjustment. For example, when the diagnostic report indicates that the material thermal conductivity of a certain section needs to be cleaned, the DCS can automatically switch to the standby reactor operation, or trigger the online cleaning program.

[0111] Through temperature gradient direction vector analysis, section temperature point density statistics, and material thermal conductivity verification, combined with the spatial coordinate offset and data anomaly in specific cases, the analysis and diagnosis module realizes accurate fault tracing of control targets that do not pass through regulation and control. This module not only can locate physical faults such as heat flow blockage and local overheating, but also can identify sensor layout defects and control parameter deviations, providing reliable fault handling support for temperature intelligent control systems through hierarchical diagnostic logic and data-driven diagnostic methods.

[0112] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and illustrative figures, it should be apparent that the scope of the present application is not limited to these specific embodiments.

[0113] While the embodiments of the application have been shown and described herein, it is to be understood that the scope of the application, jointly pointed out in the appended claims, is not to be limited to the above-described embodiments but can be otherwise variously changed, modified, replaced, and altered within the principles and spirit of the present application.

Claims

1. A temperature intelligent control system for a step catalytic cracking reactor, characterized in that, The system comprises: a temperature field modeling module: a standard temperature field distribution model is obtained by scanning the internal structure of a standard reactor, a spatial coordinate system is constructed at the geometric center of the temperature field distribution model, the temperature field distribution model is taken as a virtual temperature model, the spatial coordinate system is taken as a reference control framework, virtual temperature points of the virtual temperature model are created at a preset interval, and the virtual temperature points include virtual basic temperature points, virtual key temperature points, and virtual matching temperature points; a sensor configuration module: connected with the temperature field modeling module, a controlled reactor is defined as a control target, the control target is placed in the reference control framework, real temperature points of the control target are arranged at a preset interval, and the real temperature points include basic temperature points, key temperature points, and matching temperature points, the virtual temperature model is aligned with the control target, and a temperature gradient between the real temperature points and the virtual temperature points is defined; an index definition module: connected with the sensor configuration module, thermodynamic parameters of the standard reactor are obtained, and preset temperature indexes are defined based on the thermodynamic parameters; a control execution module: connected with the index definition module, temperature regulation is performed on the control target, including steady-state regulation, dynamic response regulation, thermal balance regulation, and composite regulation, and the dynamic response regulation includes temperature rise rate regulation, temperature drop rate regulation, and temperature fluctuation suppression regulation; an analysis and diagnosis module: connected with the control execution module, fault tracing is performed on the control target that does not pass the dynamic response regulation and the thermal balance regulation, a diagnosis report is generated, and the control strategy is adjusted.

2. The temperature intelligent control system for a catalytic cracking reactor of claim 1, wherein, The temperature field modeling module comprises: The standard reactor internal structure is scanned by infrared thermal imaging, and a standard temperature field distribution model is constructed; a space coordinate system is established based on the geometric center of the temperature field distribution model, virtual temperature points including virtual basic temperature points, virtual key temperature points and virtual matching temperature points are generated, the virtual temperature points uniformly cover the entire temperature field distribution model and the adjacent virtual temperature points have equal spacing, wherein the virtual temperature points in the catalyst bed area of the reactor are virtual key temperature points, the virtual temperature points in the three-equal-sections of the reactor are virtual matching temperature points, and the virtual temperature point coordinate set is recorded wherein is the number of virtual temperature points; the temperature field distribution model is taken as the virtual temperature model, and the spatial coordinate system is taken as the reference control framework.

3. The temperature intelligent control system for a catalytic cracking reactor of claim 1, wherein, The sensor configuration module comprises: The defined reactor to be controlled is a control target, the control target is positioned in a reference control framework, real temperature points including basic temperature points, key temperature points and matching temperature points are arranged, the real temperature points uniformly cover the entire control target and the adjacent real temperature points have equal spacing, wherein the real temperature points of the catalyst bed area of the control target are key temperature points, the real temperature points of the axial three-equal-sections of the control target are matching temperature points, and a real temperature point coordinate set is recorded wherein is the number of real temperature points the virtual matching temperature points and the matching temperature points are overlapped to align the control target with the virtual temperature model; The temperature gradient between the real temperature point and the virtual temperature point is defined as a temperature gradient , including a basic temperature point gradient , a key temperature point gradient , a matching temperature point gradient , the basic temperature point and the matching temperature point are defined as non-key temperature points, and the temperature gradient of the non-key temperature point is ; wherein 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 , and the temperature difference between the virtual matching temperature point and the matching temperature point is ; the number of virtual temperature points and the number of real temperature points are obtained, and the ratio of and is defined as the temperature point missing rate .

4. The temperature intelligent control system for a catalytic cracking reactor of claim 1, wherein, The index definition module comprises: thermodynamic parameters of the standard reactor are obtained, including material thermal conductivity, specific heat capacity, reaction activation energy, target working temperature range, and maximum temperature rise rate limit value; preset temperature indexes are defined based on the thermodynamic parameters, including a preset temperature gradient threshold value, a preset temperature point absence rate threshold value, a preset temperature rise rate threshold value, and a preset thermal balance deviation threshold value.

5. The temperature intelligent control system for a catalytic cracking reactor of claim 3, wherein, The control execution module comprises: steady-state regulation, dynamic response regulation, and thermal balance regulation are performed on the control target positioned in the reference control framework; Wherein the step of steady-state regulation is: obtaining coordinates and quantity of real temperature points and virtual temperature points, calculating temperature gradient And temperature point missing rate If And The preset temperature gradient threshold and the preset temperature point missing rate threshold are satisfied at the same time, it is determined that the control target passes the steady-state regulation; a dynamic response regulation and thermal balance regulation environment is constructed for the control target that passes the steady-state regulation, temperature rise rate regulation, temperature drop rate regulation, and thermal distribution uniformity regulation are performed, and the regulation results are output in the form of quantitative data; composite regulation is performed on the control target that passes the dynamic response regulation and the thermal balance regulation, and temperature response behavior under variable working condition is simulated.

6. The temperature intelligent control system for a catalytic cracking reactor of claim 5, wherein, The steps of constructing the dynamic response regulation and thermal balance regulation environment, performing regulation, and outputting quantitative data are: Dynamic response factors defining control objectives Including a warm-up response factor , a cool-down response factor , a thermal balance factor ; wherein the temperature rise response factor including a critical zone temperature rise factor and a non-critical zone temperature rise factor, a temperature drop response factor including a critical zone temperature drop factor and a non-critical zone temperature drop factor, a thermal balance factor including an axial thermal balance factor and a radial thermal balance factor; a dynamic response regulation environment is constructed, including step-up temperature instructions, step-down temperature instructions, and sinusoidal temperature disturbance instructions applied to the control target; defining the temperature rise response test result as a critical zone temperature rise rate and a non-critical zone temperature rise rate defining the temperature drop response test result as a critical zone temperature drop rate and a non-critical zone temperature drop rate defining the thermal equilibrium test result as an axial temperature difference and a radial temperature difference ; If , , , , , all satisfy the corresponding preset temperature rise rate threshold and preset thermal equilibrium deviation threshold, it is determined that the control target passes the dynamic response regulation and the thermal equilibrium regulation.

7. The temperature intelligent control system for a catalytic cracking reactor of claim 5, wherein, The step of performing composite regulation is: a comprehensive regulation stability coefficient of the control target is calculated as: wherein, is a comprehensive stability coefficient, is the number of variable working condition scenarios, and is a weight coefficient, represents the temperature response factor under the th variable working condition scenario, represents the thermal balance factor under the th variable working condition scenario.

8. The temperature intelligent control system for a catalytic cracking reactor of claim 1, wherein, The analysis and diagnosis module comprises: Obtaining the regulation data of the control target which does not pass the dynamic response regulation and the heat balance regulation, calculating a temperature gradient direction vector wherein is a virtual temperature point coordinate vector, is a real temperature point coordinate vector; Based on Conducting path of abnormal temperature difference is analyzed, and a heat flow blocking area or a local overheating area is located; a control target is divided into a limited number of equal thickness sections along an axial direction, a preset temperature point density threshold is defined, a number of temperature points of each section is counted, a section temperature point density is calculated, and a material thermal conductivity check is performed on a section exceeding the preset threshold.

9. The temperature intelligent control system for a catalytic cracking reactor of claim 3, wherein, The system further comprises: The data acquisition module is connected with the temperature field modeling module and the sensor configuration module, periodically acquires a virtual temperature point coordinate set and a real temperature point coordinate set , constructs a temperature difference distribution matrix, stores the temperature difference distribution matrix to a local database, wherein the data acquisition period is synchronized with the execution period of the dynamic response regulation, and the dimension of the temperature difference distribution matrix is consistent with the number of virtual temperature points .

10. The temperature intelligent control system for a catalytic cracking reactor of claim 5, wherein, The system further comprises: The communication interface module is connected with an external distributed control system, encapsulates the regulation and control result of the control execution module and the diagnosis report of the analysis and diagnosis module into an industrial protocol data packet, and transmits the industrial protocol data packet to the distributed control system through Ethernet.

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