Temperature adjusting system for last-stage reheater of boiler

Abstract

The invention relates to the field of boiler systems, and discloses a temperature adjusting system for a last-stage reheater of a boiler, which is used for improving the operation safety and reliability of the last-stage reheater of the boiler. Comprising the steps of collecting real-time temperature data of key parts of a boiler by connecting a power plant SIS or DCS system, and additionally installing a thermocouple enhancement data set to generate a temperature data set containing spatial position information. And calculating to obtain the three-dimensional temperature field distribution of the pipeline by using a model considering the dynamic change of the oxide skin. And based on the distribution, monitoring overtemperature points in real time, and generating temperature state evaluation and overtemperature early warning information. According to the invention, an equipment life prediction model is established, the damage accumulation degree of the pipeline material is evaluated, the residual life is predicted, a preventive maintenance strategy is made, and a maintenance plan suggestion is generated and integrated to a power plant maintenance plan system to guide equipment maintenance management.

Description

Technical Field

[0001] This invention relates to the field of boiler systems, and more particularly to a temperature control system for a boiler final stage reheater. Background Technology

[0002] The boiler's final-stage reheater is a critical component of a thermal power generating unit, and its temperature status directly affects the unit's operational safety and economy. In actual operation, the reheater pipes are constantly exposed to high-temperature and high-pressure environments. Localized overheating can accelerate material degradation and even lead to tube rupture accidents. Therefore, accurate monitoring and assessment of the temperature field are crucial.

[0003] Currently, boiler temperature monitoring mainly relies on a limited number of temperature measurement points deployed within the power plant's DCS or SIS systems. This monitoring method has significant shortcomings: The existing measurement points are sparsely distributed in space, resulting in blind spots in temperature monitoring and making it difficult to fully reflect the true distribution of the three-dimensional temperature field of the reheater. Traditional temperature assessment methods fail to fully consider the oxide film growth characteristics of different materials at high temperatures. The formation of oxide scale can significantly change the heat transfer characteristics of the pipe wall, resulting in bias in the model that extrapolates the actual pipe wall temperature based on surface temperature measurement. Existing technologies lack the ability to conduct refined assessments of overheating risks, and mostly rely on fixed thresholds for alarms, making it impossible to dynamically assess the severity and development trend of overheating areas.

[0004] Therefore, we propose a boiler final stage reheater temperature control system to solve the above problems. Summary of the Invention

[0005] This invention provides a boiler final stage reheater temperature control system to improve the safety and reliability of boiler final stage reheater operation.

[0006] The first aspect of this invention provides a boiler final-stage reheater temperature control system, comprising: a measurement module, used to collect real-time temperature data from existing measuring points of the boiler water-cooled wall, final-stage superheater, and reheater by connecting to a power plant SIS or DCS system, and to perform temperature measurement by installing thermocouples at strain measurement points in the superheater and reheater outlet headers, generating an enhanced temperature dataset product; a processing module, used to input the enhanced temperature dataset product into a temperature field calculation model, and obtain a three-dimensional temperature field distribution data product of the superheater and reheater pipes based on the growth characteristics of the inner wall oxide film of different materials at different temperatures; an evaluation module, used to generate a temperature state evaluation product by real-time monitoring and recording of over-temperature point data and locations based on the three-dimensional temperature field distribution data product; a setting module, used to input the temperature state evaluation product into a temperature control algorithm, and generate a temperature control command product by combining boiler operating condition parameters; and a display module, used to operate through the DCS system according to the temperature control command product, and simultaneously dynamically display the temperature distribution, over-temperature warning area, and control effect in a three-dimensional visualization platform, forming a closed-loop system product for temperature monitoring and early warning.

[0007] Optionally, in a first implementation of the first aspect of the present invention, the method includes: establishing a standard data interface for a DCS / SIS system and constructing a data communication protocol product according to the power plant database acquisition specifications; based on the data communication protocol product, collecting real-time temperature data from existing measuring points of the boiler water-cooled wall, the final superheater, and the reheater to form a basic temperature monitoring dataset product; deploying a distributed thermocouple array at the strain measurement point locations of the superheater and reheater outlet headers, acquiring high-frequency temperature signals through a dedicated signal conditioning circuit, and generating an additional measuring point temperature dataset product; processing the basic temperature monitoring dataset product and the additional measuring point temperature dataset product to generate an enhanced temperature dataset product; and establishing a three-dimensional spatial coordinate mapping relationship for each measuring point in the enhanced temperature dataset product to form a spatial location information product.

[0008] Optionally, in a second implementation of the first aspect of the present invention, the method includes: generating material oxidation characteristic parameter products based on experimental data of oxide film growth of different steels at high temperatures; generating dynamic oxide scale thickness distribution products by driving an oxide film growth model with real-time historical temperature data based on the enhanced temperature dataset product and the material oxidation characteristic parameter products; establishing a heat conduction correction model considering the influence of oxide scale thermal resistance based on the dynamic oxide scale thickness distribution products and combined with pipeline geometric parameters, and generating heat resistance correction parameter products; inputting the enhanced temperature dataset product and the heat resistance correction parameter products into a three-dimensional temperature field reconstruction algorithm, and generating pipeline wall temperature distribution data products through spatial interpolation and heat conduction calculation; and performing overheating region identification and temperature gradient analysis on the pipeline wall temperature distribution data products to generate three-dimensional temperature field distribution data products.

[0009] Optionally, in the third implementation of the first aspect of the present invention, the method includes: generating a multi-level over-temperature criterion library product based on the temperature resistance limit of the pipeline material and operational safety requirements; performing real-time comparison and analysis between the three-dimensional temperature field distribution data product and the multi-level over-temperature criterion library product to identify temperature regions exceeding various threshold limits and generating an over-temperature region identification result product; assessing the severity and development trend of the over-temperature region based on the over-temperature region identification result product and combined with the dynamic oxide scale thickness distribution product, and generating an over-temperature risk level assessment product; performing spatial cluster analysis on the over-temperature region identification result product to identify key hotspot regions and their distribution characteristics, and generating a hotspot region distribution map product; and integrating the over-temperature risk level assessment product and the hotspot region distribution map product to generate a temperature state assessment product.

[0010] Optionally, in a fourth implementation of the first aspect of the present invention, based on the overheating region identification result product and combined with the dynamic oxide scale thickness distribution product, the severity and development trend of the overheating region are assessed, and the overheating risk index at that point is set as R: Where k is an empirical coefficient.

[0011] Optionally, in the fifth implementation of the first aspect of the present invention, the method includes: generating a coordinated control rule product based on the boiler operating characteristics and the coupling relationship of the thermal system; performing correlation analysis between the temperature state assessment product and real-time boiler operating condition parameters to identify the causal relationship between temperature anomalies and operating parameters, and generating a temperature anomaly root cause analysis product; calculating the dynamic response characteristics of each regulating variable based on the temperature anomaly root cause analysis product and the coordinated control rule product, and generating a multivariate coordinated regulation scheme product; performing safety boundary verification on the multivariate coordinated regulation scheme product, and generating an optimized regulation instruction product based on equipment operating limitations and process constraints; and arranging and prioritizing the optimized regulation instruction product according to the DCS system execution cycle to generate a temperature regulation instruction product.

[0012] Optionally, in the sixth implementation of the first aspect of the present invention, the method includes: establishing a DCS command conversion interface to convert the temperature regulation command product into a control signal format recognizable by the DCS system, generating a DCS executable control command product; driving the boiler combustion system and the desuperheating water regulation system through the DCS executable control command product to generate a boiler operating parameter adjustment product; collecting temperature response data after the boiler operating parameter adjustment product in real time, and combining it with the enhanced temperature dataset product to generate a regulation effect feedback dataset product; performing fusion analysis on the regulation effect feedback dataset product and the three-dimensional temperature field distribution data product, updating the temperature field distribution display in real time through a three-dimensional visualization engine to generate a three-dimensional temperature field visualization product; and evaluating the degree of conformity between the temperature regulation effect and the expected target based on the regulation effect feedback dataset product and the three-dimensional temperature field visualization product to generate a temperature monitoring and early warning closed-loop system product.

[0013] Optionally, in the seventh implementation of the first aspect of the present invention, a prevention module is further included: establishing an equipment life prediction model based on oxide scale growth kinetics; generating oxide scale growth prediction data products using the dynamic oxide scale thickness distribution product and historical overtemperature record data; matching and analyzing the oxide scale growth prediction data products with a pipeline material performance database to assess the degree of damage accumulation in the pipeline material and generating a material damage accumulation assessment result product; calculating the remaining service life of key pipe sections based on the material damage accumulation assessment result product and pipeline operation stress analysis data, and generating a pipeline life prediction result product; formulating preventive maintenance strategies and time schedules based on the pipeline life prediction result product, and generating an equipment maintenance plan recommendation product; and integrating the equipment maintenance plan recommendation product with a power plant overhaul planning system to generate a preventive maintenance scheme product.

[0014] Optionally, in the eighth implementation of the first aspect of the present invention, the oxide scale thickness growth of the pipe section is predicted under future operating conditions, and the oxide scale growth prediction data is as follows: : ; in, It was measured on-site; This is the running data; k and n are material property constants; It is the set prediction duration.

[0015] The mechanism of this invention is as follows: By establishing a space-enhanced temperature monitoring network, the traditional DCS system measuring points are spatiotemporally aligned and fused with the added thermocouple array, which solves the problem of spatial discontinuity in temperature monitoring, realizes high-precision reconstruction and early warning of the temperature field of the final stage reheater, and significantly improves the safety and economy of boiler operation. Beneficial effects: Establishing a standard data interface for the DCS / SIS system and building a data communication protocol enables secure data exchange with the power plant control system, ensuring the stability and security of data acquisition and avoiding signal interference and data loss problems that may occur in traditional data acquisition. A database of oxidation kinetics for pipeline materials was established. Based on experimental data of oxide film growth of different steels at high temperatures, material oxidation characteristic parameters were generated, providing a scientific basis for accurately simulating oxide scale growth. The influence of material properties on the oxidation process was considered, making the calculation of oxide scale thickness evolution more accurate. A multi-level over-temperature criterion database, including warning thresholds, alarm thresholds, and protective action thresholds, has been established, providing clear standards for the identification of over-temperature zones. Compared with traditional single-threshold judgments, multi-level criteria can more finely classify the degree of over-temperature, promptly issue warning information at different levels, and improve safety and reliability. A prediction model for equipment life based on oxide scale growth kinetics was established. Using dynamic oxide scale thickness distribution products and historical overtemperature records, oxide scale growth prediction data was generated. The model takes into account the impact of oxide scale growth and overtemperature on equipment life, and can more accurately predict the remaining service life of pipelines. Integrating equipment maintenance plan recommendations with the power plant overhaul planning system generates preventative maintenance plans that include overhaul time windows and maintenance content. This guides the power plant's equipment maintenance management, helps improve equipment reliability and availability, and reduces maintenance costs and downtime. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of one embodiment of the boiler final stage reheater temperature control system in this invention. Figure 2 This is a schematic diagram of data acquisition and fusion for the boiler final stage reheater temperature control system in an embodiment of the present invention; Figure 3 This is a schematic diagram of the three-dimensional temperature field of the boiler final stage reheater temperature control system in an embodiment of the present invention; Figure 4 This is a schematic diagram of another embodiment of the boiler final stage reheater temperature control system in this invention; Figure 5 This is a schematic diagram of one embodiment of the boiler final stage reheater temperature regulation device in this invention. Detailed Implementation

[0017] This invention provides a boiler final-stage reheater temperature control system to improve the safety and reliability of boiler final-stage reheater operation. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the boiler final stage reheater temperature control system of the present invention includes: 101. Measurement module, used to collect real-time temperature data of existing measuring points of boiler water-cooled wall, final superheater and reheater by connecting to the power plant SIS or DCS system in accordance with the database acquisition specifications. At the same time, thermocouples are installed at the strain measurement points of the superheater and reheater outlet headers to measure temperature and generate an enhanced temperature dataset product containing spatial location information. It is understood that the executing entity of this invention can be a boiler final-stage reheater temperature control device, or it can be a terminal or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0019] It should be noted that this example uses a 600MW supercritical coal-fired boiler. A secure connection is established with the power plant's SIS (System-In-Service) and DCS (Distributed Control System) via industrial Ethernet. The SIS system is responsible for historical data storage, while the DCS system provides real-time monitoring. During connection, the database acquisition specifications conform to the IEC 61850 standard to ensure consistent data communication protocols. The server uses the OPC UA protocol to sample temperature data from the DCS system once per second and extracts historical data specifications from the SIS database via SQL queries. The data format is floating-point numbers with timestamp precision in milliseconds.

[0020] The boiler currently has fixed thermocouples at the water-cooled walls, the final superheater, and the reheater. These measuring points are pre-installed in key locations: 10 measuring points on the water-cooled walls, distributed at the four corners of the furnace, numbered W1 to W10, with their spatial locations represented by three-dimensional coordinates (W1 is located at a height of X=5.2 m, Y=3.1 m, Z=15.0 m). 5 measuring points on the final superheater, numbered S1 to S5, are located on the outlet pipe, with S1 at X=7.0 m, Y=2.5 m, Z=25.0 m. 5 measuring points on the reheater, numbered R1 to R5, with R1 at X=6.8 m, Y=2.8 m, Z=22.0 m.

[0021] Temperature data from these measuring points is collected in real time. At any given moment, the collected data includes: W1 temperature 385℃, W2 temperature 390℃; S1 temperature 580℃, S2 temperature 575℃; R1 temperature 540℃, R2 temperature 545℃. Each data point is accompanied by a timestamp (2023-10-01 10:00:00) and a location tag.

[0022] To enhance data, type K thermocouples were installed at strain measurement points in the superheater and reheater outlet headers. These points, originally used for strain monitoring, were now fitted with thermocouples to measure temperature: three thermocouples, numbered SA1 to SA3, were installed in the superheater outlet header, with SA1 located in the middle of the header (X=7.2 m, Y=2.6 m, Z=26.0 m). Three thermocouples, numbered RA1 to RA3, were installed in the reheater outlet header, with RA1 located at the lower end of the header (X=6.9 m, Y=2.9 m, Z=23.0 m). The installation process was performed by the maintenance team: the system was shut down first, the thermocouple probes were installed, connected to the backup analog input module of the DCS, and the accuracy was calibrated (error ±1℃). After installation, the server collected data from these new measurement points in real time; SA1 temperature was 585℃, and RA1 temperature was 550℃.

[0023] Existing measurement point data and data from newly added thermocouples are integrated to generate an enhanced temperature dataset. The dataset is stored in tabular form, with each record containing the measurement point ID, spatial coordinates (X, Y, Z), temperature value, unit (°C), and timestamp. Some data is shown below: Measurement point ID: W1, Location: (5.2, 3.1, 15.0), Temperature: 385°C, Time: 2023-10-01 10:00:00; Measurement point ID: SA1, Location: (7.2, 2.6, 26.0), Temperature: 585°C, Time: 2023-10-01 10:00:00; The dataset outputs are quality-assured through data validation rules (range check: temperature 200-650℃) and anomalies are marked.

[0024] 102. Processing module, which is used to input the enhanced temperature dataset product into the temperature field calculation model that considers the dynamic change of oxide scale thickness. Based on the growth characteristics of the inner wall oxide film of different materials at different temperatures, it calculates and obtains the three-dimensional temperature field distribution data product of the superheater and reheater pipes. The product contains the spatial distribution information of the pipe wall temperature. It should be noted that the following explanation will continue to use a 600MW supercritical coal-fired boiler as an example.

[0025] The enhanced temperature dataset generated in step 101 is invoked. This dataset contains real-time temperatures and their three-dimensional coordinates from 21 measuring points (15 originally, 6 newly added) across the water-cooled walls, superheater, and reheater. Input data at a given moment includes: a temperature of 585℃ at measuring point SA1 (X=7.2m, Y=2.6m, Z=26.0m) at the final superheater outlet header, and a temperature of 545℃ at measuring point R1 (X=6.8m, Y=2.8m, Z=22.0m) at the reheater outlet. Simultaneously, the server retrieves key operating parameters such as the current boiler load (600MW) and main steam pressure (25.4MPa) from the boiler operation database as boundary conditions for the model.

[0026] The model pre-stores oxide film growth characteristics parameters for different materials used in the superheater and reheater pipes. The high-temperature section pipes of the final superheater are made of T91 steel, and the model parameters for its oxide scale growth rate are: after operating at 580℃ for 1000 hours, the expected oxide scale thickness increase is approximately 0.05 mm. The pipes in the reheater are made of TP347H, and after operating at 540℃ for 1000 hours, the oxide scale thickness increase is approximately 0.03 mm.

[0027] The model dynamically calculates the actual oxide scale thickness on the inner wall of each pipe at the current moment based on the historical operating temperature profile of each pipe (provided by the SIS system; the average operating temperature of a T91 pipe over the past year was 575°C) and the cumulative operating time (the pipe has been running for 35,000 hours). The calculation shows that the current oxide scale thickness of the superheater pipe numbered SH-25 is 0.18 mm. The presence of oxide scale increases the thermal resistance of the pipe wall and is a key factor in calculating the actual metal wall temperature.

[0028] Based on the principles of heat transfer, enhanced temperature data is used as known point temperatures. Combined with a three-dimensional mesh model of the boiler structure (discrete the superheater and reheater piping systems into hundreds of thousands of tiny elements), and the dynamic oxide scale thickness values ​​of each piping element obtained in the previous step are substituted, a three-dimensional temperature field iterative calculation is performed. The calculation process comprehensively considers the effects of flue gas convection heat transfer, pipe wall heat conduction, and oxide scale thermal resistance.

[0029] After calculation, the model outputs a three-dimensional temperature field distribution data product. This product is a pipe wall temperature dataset containing spatial location information. The data product explicitly indicates that the superheater pipe unit located at coordinates (X=7.15m, Y=2.55m, Z=25.8m) has a calculated outer wall temperature of 592℃ and an inner wall temperature (considering the effect of oxide scale) of 598℃; while the reheater pipe unit located at (X=6.85m, Y=2.85m, Z=22.2m) has an outer wall temperature of 552℃ and an inner wall temperature of 557℃. The entire temperature field data accurately reflects the actual metal temperature of each pipe at different locations, rather than just the temperature at the measuring point.

[0030] 103. Evaluation module, which is used to generate a temperature status evaluation product containing over-temperature early warning information based on the three-dimensional temperature field distribution data product by real-time monitoring and recording of over-temperature point data and locations. This product identifies temperature anomaly areas that need to be monitored. It should be noted that, based on the three-dimensional temperature field distribution data calculated in step 102, the over-temperature points are monitored and recorded in real time, and a temperature status assessment product containing early warning information is generated to identify the temperature anomaly areas that need to be monitored.

[0031] The system retrieves the maximum allowable wall temperature limits for pipes made of different materials in the final stage reheater and superheater from the boiler design code database. For the final stage reheater pipes made of T91 material, the maximum allowable metal temperature during long-term operation is 580℃; for the final stage superheater pipes made of TP347H material, the limit is 605℃. The system sets two warning thresholds: Level 1 warning is triggered when the temperature reaches 95% of the limit (95% of 580℃ for the reheater is 551℃), used for early warning; Level 2 warning is triggered when the temperature reaches or exceeds the limit (≥580℃), requiring immediate intervention.

[0032] The three-dimensional temperature field distribution data from step 102 is received in real time. This data contains the calculated wall temperatures of tens of thousands of discrete elements in the superheater and reheater piping system. The data stream includes: a superheater piping element located at coordinates (X=7.15m, Y=2.55m, Z=25.8m) with a calculated inner wall temperature of 598℃; and a reheater piping element located at coordinates (X=6.90m, Y=2.80m, Z=22.1m) with a calculated inner wall temperature of 583℃.

[0033] The temperature value of each pipe unit received is compared with a preset threshold. When the temperature of a unit exceeds the threshold, it is immediately marked as an over-temperature point, and detailed information is recorded.

[0034] During a certain monitoring period, the system detected the following: Severe Over-Temperature Point (Level 2 Warning): The temperature of the reheater piping unit (coordinates: X=6.90m, Y=2.80m, Z=22.1m) reached 583℃, exceeding the limit of 580℃. The system recorded the precise spatial location, over-temperature value (583℃), over-temperature range (3℃), and timestamp of this point. Mild Over-Temperature Point (Level 1 Warning): The temperature of the superheater piping unit (coordinates: X=7.18m, Y=2.52m, Z=25.9m) reached 602℃. Although this did not exceed the limit of TP347H (605℃), it exceeded the Level 1 warning threshold (605℃ * 95% = 574.75℃). The system also recorded its location and temperature information. The monitoring process continues, and the system tracks the duration of the over-temperature point. If the over-temperature point (583°C) of the reheater lasts for more than 5 minutes, its warning level will be upgraded and marked as "continuous over-temperature", which is a higher risk level.

[0035] Based on the monitoring results, the server generates a temperature status assessment product. This product is a structured report, the main contents of which include: Over-temperature warning summary: Statistics on the total number of over-temperature points at the current time: "Currently, 3 secondary warning points and 15 primary warning points have been detected."

[0036] List of Key Overheating Areas: Detailed information for each overheating point: Area ID: RH-Alert-001; Warning Level: Level 2; Spatial Location: (X=6.90m, Y=2.80m, Z=22.1m) surrounding area (within a radius of 0.5 meters); Maximum Wall Temperature: 583℃; Overheating Range: 3℃; Affected Component: Last Stage Reheater, 15th Panel, 8th Tube; Temperature Anomaly Area Distribution Map: The product includes a map associated with three-dimensional coordinates, clearly marking the spatial location of all Level 1 and Level 2 warning points. It visually displays the temperature anomaly areas that operators need to focus on monitoring. The evaluation product will indicate that the over-temperature points are mainly concentrated in the area east of the reheater outlet section.

[0037] 104. Setting module, used to input the temperature status evaluation product into the temperature regulation control algorithm, and combine it with the boiler operating condition parameters to generate temperature regulation command product for the last stage reheater. The command includes control strategies such as desuperheating water volume adjustment and combustion air distribution optimization. It should be noted that by combining the temperature status assessment product with the real-time operating conditions of the boiler, specific and executable temperature regulation commands are generated through control algorithms to eliminate or mitigate the overheating problem of the final stage reheater.

[0038] The temperature status assessment product from step 103 is received. This product clearly indicates the existence of an overheating area: the area near the 15th screen and the 8th tube of the final reheater (coordinates X=6.90m, Y=2.80m, Z=22.1m) is a severely overheating point, with a current wall temperature of 583℃, exceeding the limit by 3℃. Simultaneously, the algorithm reads boiler operating parameters from the DCS real-time database, including: current unit load 600MW, main steam flow 1800 tons / hour, total furnace air volume 2500 tons / hour, burnout damper opening 60%, current opening of the final reheater desuperheating water regulating valve 30% (corresponding to a flow rate of approximately 15 tons / hour), burner operation mode for each stage, and boiler efficiency parameters.

[0039] The temperature regulation control algorithm, based on a built-in expert rule base and model prediction function, comprehensively analyzes the input data and generates regulation commands. Its core logic is to balance heat input and output, optimize the heat distribution on the flue gas side and the steam side, and reduce the pipe wall temperature in specific areas.

[0040] The algorithm determined that the overheating point was located in the reheater region, primarily due to excessive heat absorption from the flue gas side. The algorithm first assessed the feasibility of adjusting the desuperheating water flow. Currently, the reheater desuperheating water flow rate is 15 tons / hour. Based on a thermodynamic calculation model, the algorithm predicts that increasing the desuperheating water flow rate to 17.5 tons / hour (i.e., increasing the regulating valve opening to 35%) would directly absorb some heat, potentially lowering the overheating point temperature by approximately 8°C, bringing it within the safe range of 575°C. The overheating point was identified as being located on the right side of the furnace (based on coordinate Y=2.80m), indicating potentially stronger combustion on the right side, leading to higher flue gas temperatures. Therefore, the algorithm initiated a combustion air distribution optimization strategy. It calculated that the current deviation between the left and right side dampers is 0%, meaning symmetrical air supply. The algorithm decides to implement "biased combustion" adjustment and generates instructions to close the right wind box damper by 3% and open the left wind box damper by 3% to weaken the combustion intensity on the right and enhance the combustion on the left, thereby making the flue gas temperature field more uniformly distributed in the furnace width direction. It is expected to reduce the flue gas temperature in the right reheater area by 10-15℃.

[0041] In addition, the algorithm takes into account that the total air volume of the boiler is already at an optimized value of 2500 tons / hour and the load is stable, so it does not recommend adjusting the total air volume to avoid affecting the boiler efficiency.

[0042] Based on the above analysis, the algorithm generates a structured temperature regulation command product. This product contains a list of specific commands that can be directly recognized and executed by the DCS system: Command ID: TC-20231001-1045; Target equipment: Final stage reheater; Control strategy details; Desuperheating water flow adjustment: Execution object: Final stage reheater emergency desuperheating water regulating valve (DCS tag number: FV-7102); Command content: Linearly increase the valve opening from the current 30% to 35%, and increase the expected flow rate from 15 tons / hour to 17.5 tons / hour. Execution rate: The adjustment is completed smoothly within 5 minutes. Combustion air distribution optimization: Execution object: Right side bellows damper (DCS tag number: DV-5201R), left side bellows damper (DCS tag number: DV-5201L); Command content: Reduce the right baffle command by 3% (from 50% off to 47%), and simultaneously increase the left baffle command by 3% (from 50% on to 53%). Execution rate: Complete the bias setting within 3 minutes.

[0043] The instruction deliverables also include a description of the expected effects: "After implementation, the over-temperature point RH-Alert-001 temperature is expected to drop below 575℃," and mark the instruction's validity period as before the next significant change in operating conditions. The algorithm will re-execute every 30 seconds, evaluating the deliverables and operating parameters based on the latest temperature status, and dynamically updating or maintaining these instructions.

[0044] 105. Display module, which is used to execute temperature adjustment commands through the DCS system and dynamically display temperature distribution, over-temperature warning area and adjustment effect in a three-dimensional visualization platform, forming a complete temperature monitoring and early warning closed-loop system product.

[0045] It should be noted that the adjustment instructions are translated into actual actions and the effects are displayed in real time, completing a full closed loop from monitoring and decision-making to execution.

[0046] The temperature regulation command generated in step 104 is sent to the power plant's DCS system via a secure communication interface (OPC DA protocol). After receiving and verifying the command, the DCS system automatically executes the following specific operations: Controlling the desuperheating water regulating valve of the final reheater: Based on the command "increase the opening of valve FV-7102 from 30% to 35%", the DCS system sends an analog control signal to the positioner of the regulating valve. The valve operates smoothly within 5 minutes, with the opening indication gradually increasing from 30% to 35%. The corresponding desuperheating water flow rate is monitored in real time by the DCS flow meter FI-7102, showing an increase from 15.0 tons / hour to 17.5 tons / hour. Adjusting the combustion air distribution: Based on the command "close the right-side damper DV-5201R to 47% and open the left-side damper DV-5201L to 53%", the DCS system sends control signals to the two actuators respectively. The dampers are adjusted to their positions within 3 minutes. The DCS screen showed that the air volume measurement points of the left and right air boxes changed accordingly. The air volume on the right side decreased slightly from 650 tons / hour to 630 tons / hour, while the air volume on the left side increased from 650 tons / hour to 670 tons / hour, achieving the expected offset combustion effect.

[0047] Simultaneously, the 3D visualization platform is dynamically updated, providing operators with an intuitive monitoring interface. Initial status display: Based on the 3D temperature field data from step 102, the platform uses the 3D model of the boiler equipment as a background and renders the pipe wall temperature with color. Overheating points (coordinates X=6.90m, Y=2.80m, Z=22.1m) and their surrounding areas are displayed in striking red (representing 583℃), while other normal temperature areas are displayed in green (below 550℃) or yellow (550-570℃). Command execution status prompt: When the DCS begins executing a command, a prompt message pops up on the platform interface: "Executing adjustment command TC-20231001-1045: Desuperheating water volume increased, combustion air distribution offset adjusted...". Real-time display of adjustment effects: The platform continuously acquires the latest temperature data from the data streams of steps 101 and 102. After the adjustment measures take effect (approximately 5-8 minutes), the visualization screen begins to change dynamically: Temperature distribution changes: The reheater overheating area, initially displayed in red, gradually changed color from red to orange, then to yellow, and finally stabilized in green. Data labels on the platform updated in real time, showing that the temperature at that point had decreased from 583℃ to 575℃. Overheating warning area update: The previously marked "RH-Alert-001" severe overheating warning label automatically disappeared or changed to a "normal" state. Temperature status assessment products were refreshed in real time, showing that the overheating point had been eliminated. Overlay display of adjustment parameters: Next to the 3D model, a trend curve window simultaneously displayed key parameters: one curve showed the process of the overheating point temperature decreasing from 583℃ to 575℃; another curve showed the change in desuperheating water flow rate from 15 tons / hour to 17.5 tons / hour, intuitively demonstrating the causal relationship between the adjustment action and the temperature effect.

[0048] Through the reliable execution of the DCS and the dynamic feedback of the visualization platform, a complete "monitoring-evaluation-decision-execution-feedback" closed loop is achieved. The system generates a closed-loop record, including the instruction content, execution time, comparison of key parameters before and after execution (the over-temperature point temperature drops from 583℃ to 575℃), and a status conclusion (the over-temperature warning has been lifted). This record proves that the closed-loop cycle is complete, and the system then enters the next monitoring phase, thereby achieving continuous automatic early warning and optimization adjustment.

[0049] In this embodiment of the invention, real-time temperature data of existing measuring points of the boiler water-cooled wall, final superheater and reheater are collected by connecting to the power plant SIS or DCS system. At the same time, thermocouples are installed at key locations to generate an enhanced temperature dataset containing spatial location information, which comprehensively covers the key parts of the boiler. The data is accompanied by timestamps and location tags, providing an accurate and detailed basis for subsequent analysis. Compared with the traditional single data acquisition method, it can more accurately reflect the actual operating temperature of the boiler. An enhanced temperature dataset is input into a temperature field calculation model that considers the dynamic changes in oxide scale thickness. Based on the growth characteristics of the inner wall oxide film of different materials at different temperatures, and combined with a three-dimensional mesh model of the boiler structure, a three-dimensional temperature field iterative calculation is performed. This model not only considers real-time temperature data, but also dynamically calculates the impact of oxide scale thickness on pipe wall thermal resistance, as well as various effects such as flue gas convection heat transfer and pipe wall heat conduction. Compared with traditional static temperature field calculations, it can more accurately reflect the spatial distribution information of pipe wall temperature, providing a reliable basis for subsequent temperature regulation. Based on three-dimensional temperature field distribution data, the system monitors and records over-temperature points and their locations in real time. By comparing these data with preset two-level warning thresholds, a temperature status assessment product containing over-temperature warning information is generated. This assessment product not only identifies temperature anomaly areas requiring key monitoring but also counts the total number of over-temperature points, lists detailed information for each over-temperature point, and generates a temperature anomaly area distribution map. This enables operators to quickly and accurately grasp the boiler temperature anomalies and take timely measures to prevent equipment damage and safety accidents. By combining temperature status assessment results with real-time boiler operating conditions, a temperature regulation control algorithm generates temperature regulation commands for the final-stage reheater. Based on a built-in expert rule base and model prediction capabilities, the algorithm comprehensively considers various control strategies, such as desuperheating water flow adjustment and combustion air distribution optimization. According to the specific conditions of the overheating point and boiler operating parameters, it intelligently generates specific and executable regulation commands. For the reheater overheating point, by increasing the desuperheating water flow and implementing offset combustion regulation, it balances heat input and output, optimizes the heat distribution on the flue gas and steam sides, and effectively reduces tube wall temperature. Compared to traditional experience-based regulation, this approach is more scientific and targeted.

[0050] Please see Figure 2-4 Another embodiment of the boiler final stage reheater temperature control system in this invention includes: 201. Measurement module, used to collect real-time temperature data of existing measuring points of boiler water-cooled wall, final stage superheater and reheater by connecting to the power plant SIS or DCS system in accordance with the database acquisition specifications. At the same time, thermocouples are installed at the strain measurement points of the superheater and reheater outlet headers to measure temperature and generate an enhanced temperature dataset product containing spatial location information. Specifically, a standard data interface for the DCS / SIS system is established, and a data communication protocol product is constructed according to the power plant database acquisition specifications. This product enables secure data exchange with the power plant control system. Based on the data communication protocol product, real-time temperature data from existing measuring points of the boiler water-cooled wall, the final superheater, and the reheater are collected to form a basic temperature monitoring dataset product, which contains time-series temperature measurements. Distributed thermocouple arrays are deployed at strain measurement points in the superheater and reheater outlet headers, and high-frequency temperature signals are acquired through dedicated signal conditioning circuits to generate an additional measuring point temperature dataset product. The basic temperature monitoring dataset product and the additional measuring point temperature dataset product are spatiotemporally aligned and fused to generate an enhanced temperature dataset product with a unified timestamp and spatial coordinates. A three-dimensional spatial coordinate mapping relationship is established for each measuring point in the enhanced temperature dataset product to form a spatial location information product containing the topology of the measuring point.

[0051] It should be noted that the final stage reheater system of a 600MW supercritical boiler in a power plant requires temperature monitoring. First, a standard data interface with the power plant's DCS system needs to be established. The OPC UA protocol is adopted as the communication standard, and a data communication protocol is constructed. This protocol defines the data exchange format (sampling frequency 1Hz, data accuracy 0.1℃) and configures a security authentication mechanism (IP whitelist and encrypted transmission) to ensure a secure connection with the DCS system.

[0052] Based on this protocol, real-time temperature data from existing monitoring points on the boiler are collected. The water-cooled wall has three monitoring points (WL01, WL02, and WL03), and the temperatures collected at timestamp 2023-10-01 10:00:00 were 305℃, 310℃, and 308℃, respectively. The final stage superheater has two monitoring points (GR01 and GR02) with temperatures of 485℃ and 490℃; the reheater has two monitoring points (ZR01 and ZR02) with temperatures of 460℃ and 465℃. This data forms the basic temperature monitoring dataset, stored as a time series, and updated every second.

[0053] Distributed thermocouple arrays were installed at strain measurement points in the superheater and reheater outlet headers. Four K-type thermocouples (measurement points JR01 to JR04) were deployed in the superheater outlet header, and three thermocouples (measurement points JR05 to JR07) were deployed in the reheater outlet header. High-frequency temperature signals (sampling frequency 10Hz) were acquired through a dedicated signal conditioning circuit. At the same time stamp, 10 consecutive temperature values ​​(482.1℃, 482.3℃, etc.) were collected at measurement point JR01, generating a dataset of temperature data from the installed measurement points.

[0054] The basic dataset (1Hz sampling) and the added dataset (10Hz sampling) were aligned to a unified timestamp using a time synchronization algorithm (averaging the values ​​of the added data segments at 1-second intervals). At 10:00:00, the average temperature of measuring point JR01 was 482.2℃. Simultaneously, spatial coordinates were assigned to each measuring point: based on the boiler's 3D design drawings, the water-cooled wall measuring point WL01 was mapped to coordinates (2.1, 5.0, 10.0) meters, the superheater measuring point GR01 to (8.5, 3.2, 15.0) meters, and the added thermocouple JR01 to (12.0, 4.5, 18.0) meters. The resulting fused dataset generated an enhanced temperature dataset containing the temperature values, timestamps, and coordinates of all measuring points.

[0055] Based on the pipeline layout, the measuring points are connected according to their spatial adjacency to form a grid topology, identifying the relative positions of the reheater outlet header measuring point group (JR05-JR07) and the superheater measuring point group (GR01-GR02). The final output includes an enhanced dataset and spatial location information for subsequent temperature field calculations.

[0056] 202. Processing module, which is used to input the enhanced temperature dataset product into the temperature field calculation model that considers the dynamic change of oxide scale thickness. Based on the growth characteristics of the inner wall oxide film of different materials at different temperatures, it calculates and obtains the three-dimensional temperature field distribution data product of the superheater and reheater pipes. The product contains the spatial distribution information of the pipe wall temperature. Specifically, a pipeline material oxidation kinetics database is established. Based on experimental data of oxide film growth of different steels at high temperatures, material oxidation characteristic parameter products are generated, which include oxide film growth rate and thermal resistance characteristics at different temperatures. The enhanced temperature dataset product and material oxidation characteristic parameter product are input into the oxide scale thickness evolution calculation module. The oxide film growth model is driven by real-time historical temperature data to generate dynamic oxide scale thickness distribution products. Based on the dynamic oxide scale thickness distribution products and combined with pipeline geometric parameters, a heat conduction correction model considering the influence of oxide scale thermal resistance is established to generate thermal resistance correction parameter products. The enhanced temperature dataset product and thermal resistance correction parameter products are input into a three-dimensional temperature field reconstruction algorithm. Through spatial interpolation and heat conduction calculation, pipeline wall temperature distribution data products containing the influence of oxide scale are generated. The pipeline wall temperature distribution data products are subjected to overheating region identification and temperature gradient analysis to generate three-dimensional temperature field distribution data products containing hotspot locations and temperature anomaly regions.

[0057] It should be noted that we have obtained the enhanced temperature dataset product at a certain time (10:05:00 on October 1, 2023) from step 201. This product contains temperature data from 20 measuring points of the final stage reheater. Among them, measuring point ZR05 and the added measuring point JR06 in a certain area of ​​the outlet header show higher temperatures, at 578℃ and 581℃ respectively.

[0058] The pre-defined pipeline material oxidation kinetics database was invoked. The material of the final reheater tube section was identified as T91 steel. The database stores experimental data for T91 steel: at a continuous operating temperature of 580°C, the growth rate of the oxide film on its inner wall is approximately 0.1 mm / kWh. The thermal conductivity of the oxide film is much lower than that of the metal substrate, set at 2.0 W / (m·K). These parameters are encapsulated as material oxidation characteristic parameter products and used as one of the inputs for the calculation.

[0059] The oxide scale thickness evolution calculation module calls upon historical temperature data from the past 30 days for measuring points ZR05 and JR06, finding that the average operating temperature in this area is around 575℃. Based on material oxidation characteristic parameters, the model calculates the predicted oxide scale thickness for this pipe section after 6000 hours of operation. At a sustained temperature of 575℃, the oxide scale thickness is approximately 0.072 mm. For the currently monitored hotspot area at 581℃, the model accelerates the oxide scale growth calculation, predicting that its local thickness may have already reached 0.075 mm. This generates a dynamic oxide scale thickness distribution product corresponding to the spatial location of the pipeline, showing the differences in oxide scale thickness in different pipe sections due to varying temperature histories.

[0060] The heat conduction correction model quantifies the additional thermal resistance caused by the oxide scale based on the pipe geometry (wall thickness of 4.5 mm) and the calculated dynamic oxide scale thickness distribution. For a region with an oxide scale thickness of 0.075 mm, the additional thermal resistance is calculated to be equivalent to a decrease of approximately 3% in the effective thermal conductivity of the pipe wall. This "thermal resistance correction parameter product" will be used to correct the temperature calculation model.

[0061] The 3D temperature field reconstruction algorithm begins operation. Based on the enhanced temperature dataset generated in the previous step (i.e., the measured temperatures at 20 measuring points), it uses a spatial interpolation algorithm (Kriging interpolation) to estimate the initial outer wall temperature distribution of the entire reheater tube bank. Then, the algorithm introduces a thermal resistance correction parameter: considering the thermal resistance effect of oxide scale, heat is more difficult to transfer from the flue gas to the steam inside the pipes, resulting in a higher actual temperature of the metal tube walls (especially near the inner wall) than without oxide scale. After correction, the model outputs a more realistic pipe wall temperature distribution. At measuring point JR06, although the thermocouple measures an outer wall temperature of 581°C, the model calculates that the actual temperature of the metal near the inner wall may have reached 592°C. The entire 3D temperature field of the reheater is reconstructed with high resolution.

[0062] The calculated wall temperature distribution was used for overheating zone identification and gradient analysis. The alarm threshold for T91 steel was set at 595℃. Analysis revealed multiple areas with wall temperatures exceeding 590℃ within a 1.5 square meter region centered on measuring point JR06, with the highest point being the calculated 592℃, designated as a "Level 1 Warning" hotspot. Simultaneously, the large temperature gradient in this area indicated uneven heat load. The final generated three-dimensional temperature field distribution data not only included the temperature value of each virtual grid point but also clearly marked the location and extent of this hotspot region, providing a precise basis for subsequent early warning and adjustment.

[0063] 203. Evaluation module, which is used to generate a temperature status evaluation product containing over-temperature early warning information based on the three-dimensional temperature field distribution data product by real-time monitoring and recording of over-temperature point data and locations. This product identifies temperature anomaly areas that need to be monitored. Specifically, a multi-level over-temperature criterion standard is established. Based on the temperature resistance limit of pipeline materials and operational safety requirements, a multi-level over-temperature criterion library product is generated, including early warning thresholds, alarm thresholds, and protection action thresholds. The three-dimensional temperature field distribution data product is compared and analyzed in real time with the multi-level over-temperature criterion library product to identify temperature regions exceeding each threshold limit, generating an over-temperature region identification result product. Based on the over-temperature region identification result product and combined with the dynamic oxide scale thickness distribution product, the severity and development trend of the over-temperature region are assessed, generating an over-temperature risk level assessment product. Spatial cluster analysis is performed on the over-temperature region identification result product to identify key hotspot areas and their distribution characteristics, generating a hotspot area distribution map product. The over-temperature risk level assessment product and the hotspot area distribution map product are integrated to generate a temperature state assessment product that includes the location, severity, risk level, and trend of over-temperature.

[0064] It should be noted that the three-dimensional temperature field distribution data obtained from step 202 shows that there is a high-temperature zone in the outlet header area of ​​the final stage reheater at timestamp 2023-10-01 10:05:00.

[0065] Multiple over-temperature criteria were pre-set for the T91 steel piping material of the final reheater. The warning threshold is 585℃ (remind to pay attention), the alarm threshold is 595℃ (intervention and adjustment required), and the protection action threshold is 610℃ (emergency intervention and load reduction required).

[0066] The system compares the 3D temperature field data with a criterion database in real time. It identifies five consecutive grid points within a region centered at coordinates (X=12.5m, Y=4.2m, Z=18.1m) where the wall temperature exceeds the warning threshold. The highest temperature point is located at grid G-205, with a temperature of 592℃, exceeding the warning threshold but not reaching the alarm threshold. The system generates an over-temperature area identification result, marking the area as 0.8 square meters, the highest temperature as 592℃, and recording its 3D spatial boundary.

[0067] An evaluation was conducted using dynamic oxide scale thickness distribution data. It was found that the historical operating temperature of this overheated region (grid G-205) was higher than normal, and the current predicted oxide scale thickness has reached 0.078 mm. The system calculated the overheating risk index R for this point using the formula: (Where k is an empirical coefficient, taken as 0.5). The calculated risk index R for this point is 0.85, classifying it as "medium risk". Simultaneously, the system indicates that the temperature in this area has risen by 3°C in the past 5 minutes, showing a "rapid increase" trend, thus increasing the urgency of the risk.

[0068] Spatial cluster analysis was performed on all identified hyperthermia points. In addition to the main hotspot region (cluster A), another smaller, isolated hyperthermia point (cluster B) was found 3 meters downstream, with a temperature of 587°C. Cluster A was identified as the "core hotspot region," with an elliptical shape and its major axis along the flue gas flow direction, indicating a possible flue gas deviation. Cluster B was marked as a "secondary concern."

[0069] Integrating the above information forms a complete temperature status assessment product. This product clearly indicates: Overheat location: The core hotspot area is located southeast of the outlet header of the final stage reheater (providing specific three-dimensional coordinates). Severity: The current highest temperature is 592℃, at the warning level. Risk level: Medium risk (risk index R=0.85), and the trend is rapidly increasing. Distribution characteristics: There is one main hotspot cluster A and one secondary cluster B; it is recommended to prioritize addressing cluster A.

[0070] 204. Setting module, used to input the temperature status evaluation product into the temperature regulation control algorithm, and combine it with the boiler operating condition parameters to generate temperature regulation command product for the last stage reheater. The command includes control strategies such as desuperheating water volume adjustment and combustion air distribution optimization. Specifically, a multivariate coordinated control strategy library is established. Based on boiler operating characteristics and the coupling relationship of the thermal system, coordinated control rule products including desuperheating water regulation, combustion optimization, and air distribution adjustment are generated. The temperature state assessment product is correlated with real-time boiler operating parameters to identify the causal relationship between temperature anomalies and operating parameters, generating a temperature anomaly root cause analysis product. Based on the temperature anomaly root cause analysis product and combined with the coordinated control rule product, the dynamic response characteristics of each regulation variable are calculated using a fractional differential inclusion algorithm to generate a multivariate coordinated regulation scheme product. The multivariate coordinated regulation scheme product undergoes safety boundary verification. Combined with equipment operating limitations and process constraints, an optimized regulation instruction product under safety constraints is generated. The optimized regulation instruction product is then time-sequentially arranged and prioritized according to the DCS system execution cycle to generate a temperature regulation instruction product with execution time sequence and priority markers.

[0071] It should be noted that the temperature status assessment product obtained from step 203 indicates that there is a core hot spot area on the southeast side of the outlet header of the final stage reheater (around coordinates X=12.5m, Y=4.2m, Z=18.1m), with a maximum temperature of 592℃, a risk level of "medium" and a rapidly increasing trend.

[0072] The assessment results were correlated with real-time boiler operating parameters. The current unit load is 580MW, and the total air volume is 2800t / h. However, the flue gas temperature deviation between the two sides of the boiler reaches 25℃ (the left side is higher than the right side), and the secondary air damper opening of the C-layer burner corresponding to the hot spot area is only 40% (lower than the average opening of 50% for other burners in the same layer). The system determined that the root cause of this temperature anomaly is "uneven combustion air distribution within the furnace leading to flue gas bias, resulting in excessively high local heat load in this area," rather than insufficient desuperheating water on the steam side.

[0073] Based on the above root cause analysis, the system selects appropriate rules from the multivariate coordinated control strategy library. The rules specify that for localized overheating caused by flue gas-related factors, combustion air distribution should be adjusted first, supplemented by fine-tuning of the desuperheating water. The system employs an algorithm that considers dynamic response characteristics to calculate the specific adjustment amount, using a proportional control formula based on deviation: The target temperature is set below 585℃, and Kp and Kd are specific coefficients for the damper and desuperheating water damper. For the secondary damper of the burner on the right side of layer C, calculations show that the opening needs to be increased by 15% (from 40% to 55%) to correct the flue gas deviation. As an auxiliary measure, the desuperheating water damper of the final stage reheater is fine-tuned, with the opening increased by 3% to absorb some heat.

[0074] The coordination plan underwent safety verification. Verification rules included: a single damper adjustment must not exceed 20%, and the total opening of the desuperheating water damper must not exceed 85% to prevent water carryover. The above plan (damper adjustment of 15%, desuperheating water adjustment of 3%) was within the safety boundaries; therefore, the plan was approved as an optimized adjustment instruction.

[0075] Based on the control logic priority and the DCS's 2-second execution cycle, the instructions are time-sequenced. The final instruction output is as follows: Timing 1 (T+0 seconds): Execute the instruction with "high" priority, increasing the opening of the secondary air damper of the right burner on layer C from 40% to 55%. Timing 2 (T+4 seconds, after the air volume change has initially stabilized): Execute the instruction with "medium" priority, slightly increasing the opening of the desuperheating water damper of the final reheater from the current 45% to 48%.

[0076] The temperature regulation command output clearly defines the regulation object, target value, execution sequence and priority, and can be directly sent to the DCS system for execution, aiming to smoothly eliminate overheated areas through coordinated control.

[0077] 205. Display module, which is used to execute temperature adjustment commands through the DCS system and dynamically display temperature distribution, over-temperature warning area and adjustment effect in a three-dimensional visualization platform, forming a complete closed-loop system product for temperature monitoring and early warning.

[0078] Specifically, a DCS command conversion interface is established to convert temperature regulation command products into a control signal format recognizable by the DCS system, generating DCS executable control command products. These DCS executable control command products drive the boiler combustion system and desuperheating water regulation system, generating boiler operating parameter adjustment products, which include the actual changes in fuel quantity, air volume, and desuperheating water flow rate. Temperature response data after the boiler operating parameter adjustment products are collected in real time and combined with enhanced temperature dataset products to generate regulation effect feedback dataset products. The regulation effect feedback dataset products are then fused and analyzed with three-dimensional temperature field distribution data products. A three-dimensional visualization engine updates the temperature field distribution display in real time, generating dynamically updated three-dimensional temperature field visualization products. Based on the regulation effect feedback dataset products and the dynamically updated three-dimensional temperature field visualization products, the degree of conformity between the temperature regulation effect and the expected target is evaluated, generating closed-loop system performance evaluation products.

[0079] It should be noted that the temperature regulation command product generated from step 204 includes two specific commands: 1. Increase the opening of the secondary air damper of the burner on the right side of layer C from 40% to 55%; 2. Slightly increase the opening of the desuperheating water damper of the final stage reheater from 45% to 48%. The commands have been assigned execution timing and priority.

[0080] The DCS command conversion interface converts the aforementioned high-level commands into standard control signals that the DCS system can directly recognize. For example, it converts "increase damper opening to 55%" into a 4-20mA analog output signal sent to the corresponding actuator, with the address corresponding to AIC-205B. This DCS-executable control command product is sent to the DCS controller. The DCS system then drives the actuator. Actual feedback shows that the opening of the secondary damper on the right side of layer C smoothly increases from 40% to 54.8% within 15 seconds, and the opening of the desuperheating water damper in the final reheater simultaneously increases to 47.9%. This process generates boiler operating parameter adjustment products, recording the actual changes in air volume and desuperheating water flow rate.

[0081] Within two minutes of command execution, the system continuously collects temperature response data using the enhanced temperature dataset (the measuring points established in step 201). The temperature change at the key measuring point JR06 (located in the original hotspot area) is highlighted: the temperature was 592℃ at 10:07:00; it dropped to 589℃ at 10:08:00; and further decreased to 586℃ by 10:09:00. Simultaneously, the flue gas temperature deviation on both sides of the boiler decreased from 25℃ to 12℃. These real-time data, along with the corresponding operating parameters (actual damper opening and desuperheating water flow rate), constitute the regulation effect feedback dataset.

[0082] Upon receiving new feedback data on the adjustment effect, the 3D visualization platform immediately triggers a rapid update calculation of the temperature field model. On the platform screen, the hot spot area (592℃) originally highlighted in orange on the 3D tube bank model representing the final stage reheater gradually changes to light yellow (586℃), indicating a significant temperature drop. Simultaneously, a pop-up notification appears stating, "Adjustment command executed; hot spot temperature showing a clear downward trend." This dynamically updated screen is the product of 3D temperature field visualization, providing operators with an intuitive demonstration of the effect.

[0083] The system automatically compared the adjustment effect with the expected target (keeping the temperature below 585℃). The assessment concluded that the adjustment reduced the over-temperature point from 592℃ to 586℃ within 3 minutes, a decrease of 6℃, without causing abnormal temperature fluctuations in other areas. The response was rapid and the effect was good. The assessment conclusion was "The adjustment command was effective, and the closed-loop system performance met the standards," and this was recorded and archived. If the temperature did not decrease as expected, the system would mark it as "Ineffective adjustment."

[0084] 206. Prevention Module: This module is used to establish an equipment life prediction model based on oxide scale growth kinetics. It generates oxide scale growth prediction data using dynamic oxide scale thickness distribution products and historical overtemperature records. The module then matches these prediction data with a pipeline material performance database to assess the degree of damage accumulation in pipeline materials, generating a material damage accumulation assessment result. Based on this assessment result and pipeline operating stress analysis data, it calculates the remaining service life of key pipe sections, generating a pipeline life prediction result. Based on the pipeline life prediction result, it formulates preventative maintenance strategies and timelines, generating an equipment maintenance plan recommendation. Finally, it integrates this recommendation with the power plant overhaul planning system to generate a preventative maintenance scheme that includes overhaul time windows and maintenance content. This scheme guides the power plant's equipment maintenance management.

[0085] It should be noted that the system has been running continuously for a year, accumulating a large amount of dynamic oxide scale thickness distribution products and historical over-temperature records.

[0086] Historical data for tube segment G-205, located in the hotspot region of the final reheater (approximately coordinates X=12.5m, Y=4.2m, Z=18.1m), was retrieved. This tube segment is made of T91 steel, and its average operating temperature over the past year was 575℃, with three brief overheating events reaching 592℃. Based on an oxide scale growth kinetic model, the system predicts the oxide scale thickness growth of this tube segment under future operating conditions. The prediction formula is as follows: Where k and n are material property constants. Calculations show that, maintaining the current operating level, the oxide scale thickness of this pipe section will increase from the current 0.078 mm to a critical threshold of 0.15 mm within the next 18 months.

[0087] The predicted oxide scale growth data (up to 0.15 mm thick) was matched with the material property database of T91 steel. Database information indicates that once the oxide scale thickness exceeds 0.12 mm, the risk of peeling increases significantly, and creep damage to the base metal accelerates. A systematic assessment determined that the cumulative material damage (or life loss fraction) caused by the combined effects of sustained high temperature and oxidation in this pipe section has reached 35%.

[0088] Based on a remaining service life fraction of 65% (i.e., 100% - 35%), and combined with the operating stress data of this pipe section under design pressure, the remaining service life is calculated using a service life calculation model (modified Larson-Miller parameter method). The model calculates that the predicted remaining service life of this critical pipe section under current operating conditions is approximately 45,000 hours (approximately 5.1 years).

[0089] Based on a projected remaining service life of 5.1 years, the system develops a preventative maintenance strategy. Considering that power plants typically undergo major overhauls every 4 to 6 years, it is recommended that a detailed inspection (endoscopic examination) and preventative replacement of this pipe section be scheduled for the next major overhaul approximately 4 years from now. The generated recommendations clearly outline the maintenance content: inspect the inner and outer walls of pipe section G-205 and the three adjacent pipes, measure the actual thickness of the oxide scale, and determine whether replacement is necessary based on the inspection results.

[0090] The maintenance plan recommendation (target time window: October 2027, corresponding to the next planned overhaul) was integrated with the power plant's maintenance planning system. A formal preventative maintenance plan was generated, which included: "For pipe section G-205 in the hot spot area on the southeast side of the final stage reheater, a special inspection and assessment is planned to be carried out during the unit's overhaul in October 2027, with an estimated duration of 3 days, requiring 20 meters of T91 steel pipe as spare parts." This plan provides the power plant with precise data-driven decision-making support for equipment management.

[0091] In this embodiment of the invention, not only is temperature data from existing measuring points collected, but thermocouples are also installed at key locations to generate an enhanced temperature dataset containing spatial location information, overcoming the shortcomings of incomplete data collection in traditional methods. A temperature field calculation model considering the dynamic changes in oxide scale thickness is established. Based on the growth characteristics of the inner wall oxide film of different materials at different temperatures, the three-dimensional temperature field distribution of the pipeline is calculated, taking into account the influence of oxide scale on heat conduction. A multi-level over-temperature criterion standard is established, and real-time comparative analysis of over-temperature areas is performed to identify temperature areas exceeding various threshold limits. The severity and development trend of over-temperature areas are assessed in conjunction with the dynamic oxide scale thickness distribution, generating an over-temperature risk level assessment. A multi-variable coordinated control strategy library is established, and correlation analysis is performed between temperature state assessment and real-time boiler operating parameters to identify the causal relationship between temperature anomalies and operating parameters. Fractional differential algorithms calculate the dynamic response characteristics of each regulating variable, generating a multi-variable coordinated regulation scheme. Temperature distribution, over-temperature warning zones, and regulation effects are dynamically displayed on a 3D visualization platform, forming a complete closed-loop temperature monitoring and early warning system. Real-time temperature response data after boiler operating parameter adjustments is collected to assess the degree of conformity between the temperature regulation effect and the expected target. An equipment life prediction model based on oxide scale growth kinetics is established. Using dynamic oxide scale thickness distribution and historical over-temperature records, the cumulative damage to pipeline materials is assessed, the remaining service life of key pipe sections is calculated, and preventative maintenance strategies and timelines are developed. This allows for early prediction of equipment life and potential failures, enabling reasonable maintenance planning, preventing equipment failures due to overuse, extending equipment lifespan, and reducing maintenance costs and downtime losses.

[0092] Figure 5 This is a schematic diagram of a boiler final-stage reheater temperature control device 300 provided in an embodiment of the present invention. The boiler final-stage reheater temperature control device 300 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the boiler final-stage reheater temperature control device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the boiler final-stage reheater temperature control device 300.

[0093] The boiler final stage reheater temperature control device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 5 The illustrated structure of the boiler final stage reheater temperature control device does not constitute a limitation on the boiler final stage reheater temperature control device, which may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0094] The present invention also provides a boiler final stage reheater temperature regulation device, the boiler final stage reheater temperature regulation device includes a memory and a processor, the memory stores computer-readable instructions, when the computer-readable instructions are executed by the processor, the processor performs the steps of the boiler final stage reheater temperature regulation system in the above embodiments.

[0095] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the boiler final stage reheater temperature control system.

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0097] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0098] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A temperature control system for a boiler's final-stage reheater, characterized in that, include: The measurement module is used to collect real-time temperature data from existing measuring points of the boiler water-cooled wall, final superheater and reheater by connecting to the power plant SIS or DCS system, and to install thermocouples at the strain measurement points of the superheater and reheater outlet headers to measure the temperature and generate an enhanced temperature dataset. The processing module is used to input the enhanced temperature dataset product into the temperature field calculation model, and obtain the three-dimensional temperature field distribution data product of the superheater and reheater pipes based on the growth characteristics of the inner wall oxide film of different materials at different temperatures. The evaluation module is used to generate a temperature status evaluation product based on the three-dimensional temperature field distribution data product by real-time monitoring and recording of over-temperature point data and locations; The setting module is used to input the temperature state evaluation product into the temperature regulation control algorithm, and generate temperature regulation command product in combination with boiler operating condition parameters. The display module is used to operate through the DCS system according to the temperature adjustment command, and simultaneously dynamically display the temperature distribution, over-temperature warning area and adjustment effect in the three-dimensional visualization platform, forming a closed-loop system product for temperature monitoring and early warning.

2. The boiler final stage reheater temperature control system according to claim 1, characterized in that, include: Establish a standard data interface for the DCS / SIS system and construct a data communication protocol product according to the power plant database acquisition specifications; Based on the data communication protocol product, real-time temperature data of existing measuring points of boiler water-cooled wall, final stage superheater and reheater are collected to form basic temperature monitoring dataset product. Distributed thermocouple arrays are deployed at the strain measurement points of the superheater and reheater outlet headers. High-frequency temperature signals are collected through a dedicated signal conditioning circuit to generate a dataset of temperature data at the added measurement points. The basic temperature monitoring dataset and the temperature dataset from the added measuring points are processed to generate an enhanced temperature dataset. A three-dimensional spatial coordinate mapping relationship is established for each measuring point in the enhanced temperature dataset product to form a spatial location information product.

3. The boiler final stage reheater temperature control system according to claim 2, characterized in that, include: Based on experimental data of oxide film growth of different steels at high temperatures, material oxidation characteristic parameters were generated. Based on the enhanced temperature dataset product and the material oxidation characteristic parameter product, the oxide film growth model is driven by real-time temperature historical data to generate a dynamic oxide scale thickness distribution product. Based on the dynamic oxide scale thickness distribution product, and combined with the pipeline geometry parameters, a heat conduction correction model considering the influence of oxide scale thermal resistance is established, and a thermal resistance correction parameter product is generated. The enhanced temperature dataset product and the thermal resistance correction parameter product are input into the three-dimensional temperature field reconstruction algorithm, and the pipe wall temperature distribution data product is generated through spatial interpolation and heat conduction calculation. The pipe wall temperature distribution data product is subjected to overheating region identification and temperature gradient analysis to generate a three-dimensional temperature field distribution data product.

4. The boiler final stage reheater temperature control system according to claim 3, characterized in that, include: Based on the temperature resistance limit of pipeline materials and operational safety requirements, a multi-level over-temperature criterion library product is generated. The three-dimensional temperature field distribution data product is compared and analyzed in real time with the product of the multi-level over-temperature criterion library to identify temperature regions that exceed each threshold limit and generate over-temperature region identification result product. Based on the overheating region identification results, combined with the dynamic oxide scale thickness distribution product, the severity and development trend of the overheating region are assessed, and an overheating risk level assessment product is generated. Spatial clustering analysis is performed on the results of the overheated region identification to identify key hotspot regions and their distribution characteristics, and to generate hotspot region distribution map products. The above-mentioned over-temperature risk level assessment product and hotspot area distribution map product are integrated to generate a temperature status assessment product.

5. The boiler final-stage reheater temperature control system according to claim 4, characterized in that, Based on the identified overheating region results and the dynamic oxide scale thickness distribution, the severity and development trend of the overheating region are assessed, and the overheating risk index for this point is set as R: Where k is an empirical coefficient.

6. The boiler final-stage reheater temperature control system according to claim 4, characterized in that, include: Based on the boiler operating characteristics and the coupling relationship of the thermal system, a coordinated control rule product is generated. The temperature status assessment product is correlated with the real-time boiler operating parameters to identify the causal relationship between temperature anomalies and operating parameters, and to generate a temperature anomaly root cause analysis product. Based on the temperature anomaly root cause analysis product and combined with the coordinated control rule product, the dynamic response characteristics of each regulation variable are calculated to generate a multivariate coordinated control scheme product. The safety boundary of the multivariate coordinated adjustment scheme product is verified, and optimized adjustment instruction product is generated by combining equipment operation limitations and process constraints. The optimized adjustment command products are arranged in a time sequence and prioritized according to the DCS system execution cycle to generate temperature adjustment command products.

7. The boiler final-stage reheater temperature control system according to claim 6, characterized in that, include: Establish a DCS command conversion interface to convert the temperature regulation command product into a control signal format that the DCS system can recognize, and generate DCS executable control command product. The DCS executes control commands to drive the boiler combustion system and desuperheating water regulation system, generating boiler operating parameter adjustment products. The temperature response data after the boiler operating parameters are adjusted is collected in real time, and combined with the enhanced temperature dataset product to generate the adjustment effect feedback dataset product. The adjustment effect feedback dataset product is fused and analyzed with the three-dimensional temperature field distribution data product. The temperature field distribution display is updated in real time through the three-dimensional visualization engine to generate a three-dimensional temperature field visualization product. Based on the feedback dataset of the regulation effect and the three-dimensional temperature field visualization product, the degree of conformity between the temperature regulation effect and the expected target is evaluated, and the closed-loop system product of temperature monitoring and early warning is generated.

8. The boiler final stage reheater temperature control system according to claim 1, characterized in that, It also includes a prevention module: A device life prediction model based on oxide scale growth kinetics is established, and oxide scale growth prediction data products are generated using the dynamic oxide scale thickness distribution product and historical overtemperature record data. The oxide scale growth prediction data product is matched and analyzed with the pipeline material performance database to assess the degree of damage accumulation of pipeline materials and generate material damage accumulation assessment result product. Based on the material damage accumulation assessment results, combined with pipeline operation stress analysis data, the remaining service life of key pipe sections is calculated, and pipeline life prediction results are generated. Based on the pipeline life prediction results, preventive maintenance strategies and timelines are developed, and equipment maintenance plan recommendations are generated. The equipment maintenance plan recommendations are integrated with the power plant overhaul plan system to generate preventative maintenance solutions.

9. The boiler final stage reheater temperature control system according to claim 8, characterized in that, The predicted growth rate of oxide scale thickness in this pipe section under future operating conditions is as follows: : ； in, It was measured on-site; This is the running data; k and n are material property constants; It is the set prediction duration.

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