Start debugging system and method for ultra-supercritical coal-fired boiler unit

Abstract

The invention relates to the technical field of ultra-supercritical coal-fired boiler units, in particular to an ultra-supercritical coal-fired boiler unit starting debugging system and a method thereof. The limitation that a traditional ultra-supercritical coal-fired boiler unit starting debugging system depends on sub-load testing is broken through; a traditional method needs to verify equipment performance step by step according to a fixed load stage, cannot cover dynamic interaction under complex working conditions, is low in debugging efficiency and long in period, and allows a user or a system to flexibly divide front-section equipment, middle-section equipment and final-section equipment according to unit physical characteristics or fault high-incidence areas to form a multi-dimensional debugging scene; dynamic segmented debugging can cover boundary working conditions missed by a traditional method, repeated debugging steps are reduced, the overall debugging period is shortened by more than 30%, and meanwhile the debugging safety of extreme working conditions is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ultra-supercritical coal-fired boiler units, and more particularly to an ultra-supercritical coal-fired boiler unit startup and debugging system and method thereof. Background Art

[0002] Existing startup and commissioning procedures for ultra-supercritical coal-fired boiler units generally utilize phased load testing (e.g., 30% low load, 60% medium load, and 100% full load). However, phased load testing has limitations, validating equipment performance at only a single load point and failing to account for dynamic interactions under complex operating conditions. For example, the coupling effects between the combustion system and the steam-water system at varying loads are difficult to fully assess.

[0003] Secondly, traditional methods divide boiler equipment into fixed segments (such as the combustion segment and the steam-water segment) based on their physical location for segmented commissioning. However, static segmentation can lead to debugging blind spots or repeated commissioning due to dynamic overlap caused by thermal stress and vibration transmission between equipment during actual operation. In traditional segmented commissioning, the commissioning boundaries of adjacent segments can shift dynamically due to thermal expansion or mechanical vibration, resulting in overlapping commissioning areas and causing signal interference or equipment conflicts.

[0004] The existing debugging system relies on manual experience to divide the debugging area, lacks dynamic adjustment capabilities, and is difficult to cope with complex scenarios such as unit start-up and shutdown, and sudden load changes. The application of digital twin technology in boiler debugging (for example) is mostly concentrated on single-stage simulation, and multi-segment dynamic collaborative debugging has not been realized.

[0005] Therefore, it is of great significance to develop a startup and debugging system and method for ultra-supercritical coal-fired boiler units based on dynamically defined equipment segment boundaries to solve the low efficiency and segment overlap problems of traditional load-sharing debugging, realize the flexible division of front-end, middle-end and terminal equipment, cover equipment interaction under complex working conditions, and improve debugging accuracy and safety. Summary of the Invention

[0006] The purpose of the present invention is to provide a startup and debugging system and method for an ultra-supercritical coal-fired boiler unit that can dynamically define equipment segment boundaries, solve the problems of low efficiency and segment overlap in traditional load-sharing debugging, realize flexible division of front-end, middle-end and terminal equipment, cover equipment interaction under complex working conditions, and improve debugging accuracy and safety, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a startup and debugging system for an ultra-supercritical coal-fired boiler unit, comprising a dynamic segment definition module, a collective debugging module, a fault rehearsal and model prediction module, and a closed-loop verification and system integration module; The dynamic segment definition module is used to dynamically adjust the equipment segment boundaries according to the physical characteristics of the unit or the debugging requirements, generate segment boundary configuration instructions, and transmit the segment boundary configuration instructions to the collective debugging module; The collection debugging module includes an adjustment allocation unit and an adaptive debugging unit; The adjustment allocation unit is used to receive the segment boundary configuration instruction, divide the working boundary of the adaptive debugging unit in real time through a dynamic learning algorithm, and generate the debugging area and debugging task allocation instruction; The adaptive debugging unit is used to receive a debugging task allocation instruction, generate and adaptively adjust a debugging strategy through a machine learning algorithm, and generate debugging parameters that match the segment boundary configuration instruction according to the debugging strategy; The fault rehearsal and model prediction module is used to build a digital twin model, integrate the fault rehearsal function, generate the whole machine performance prediction data according to the debugging parameters, and transmit the whole machine performance prediction data to the closed-loop verification and system integration module; The closed-loop verification and system integration module is used to reintegrate components according to the whole machine performance prediction data, perform load verification, realize closed-loop verification of the model and the physical unit, generate verification result data, and feed the verification result data back to the fault rehearsal and model prediction module.

[0008] A further technical solution of the present application is: the segment boundary configuration instructions are the range and association relationship of the front-end equipment, the range and association relationship of the middle-end equipment and the range and association relationship of the end-end equipment.

[0009] A further technical solution of the present application is: the debugging area is divided into a segmented overlapping area, a boundary debugging area and a single debugging area; the segmented overlapping area is an area where the working boundaries of different adaptive debugging units overlap due to the dynamic adjustment of the device segment boundaries; the boundary debugging area is an area adjacent to both sides of the working boundaries of different adaptive debugging units; the single debugging area is an area that does not overlap with the working boundaries of other adaptive debugging units.

[0010] A further technical solution of the present application is: the working boundary is the limit of the working range of the adaptive debugging unit within the debugging area.

[0011] A further technical solution of the present application is: the debugging parameters include combustion efficiency parameters, emission parameters, thermal deviation parameters, flow efficiency parameters, shaft vibration parameters, steam power parameters, excitation response parameters, electrical performance parameters and system redundancy parameters.

[0012] A method for starting and debugging an ultra-supercritical coal-fired boiler unit comprises the following steps: S1. Dynamic segment configuration: Based on the unit's physical characteristics or commissioning requirements, the dynamic segment definition module generates segment boundary configuration instructions, which include the front-end equipment range and associated relationships, the middle-end equipment range and associated relationships, and the final-end equipment range and associated relationships; S2 adaptive debugging unit division: By collecting the debugging module receives the segment boundary configuration instructions, the use of dynamic learning algorithm in real time to divide the working boundary of the adaptive debugging unit, generate debugging area and debugging task assignment instructions, the debugging area includes segmented overlapping area, boundary debugging area and a single debugging area; S3 debugging strategy generation: receiving debugging task assignment instructions through the adaptive debugging unit, generating a debugging strategy based on a machine learning algorithm, and generating debugging parameters that match the segment boundary configuration instructions according to the debugging strategy; S4. Fault Rehearsal and Performance Prediction: Build a digital twin model through the fault rehearsal and model prediction module, integrate fault rehearsal functions, and generate prediction data for the entire machine's performance. S5. Closed-loop verification and parameter optimization: Through the closed-loop verification and system integration module, components are reintegrated according to the prediction plan, load verification is performed, verification result data is generated and fed back to the fault rehearsal module to form an iterative optimization closed loop.

[0013] A further technical solution of the present application: the dynamic learning algorithm described in step S2 includes: a boundary dynamic adjustment algorithm based on reinforcement learning, which is used to optimize the segmentation boundary according to historical debugging data; and a weight allocation algorithm based on the device association network, which is used to determine the debugging area priority.

[0014] A further technical solution of this application: the detailed steps of step S1 are as follows: The detailed steps of step S1 are as follows: S1.1. Determine the segment boundaries based on the unit's physical characteristics and generate the front, middle, and final segment equipment ranges and associated relationship instructions; S1.2. Dynamically adjust segment boundaries according to debugging requirements to cover key nodes; S1.3. Verify the rationality of segment boundaries through digital twin models to avoid signal interference or physical conflicts between devices.

[0015] A further technical solution of this application: the detailed steps of S2 are as follows: S2.1. Receive segment boundary configuration instructions and dynamically divide the working boundaries of the adaptive debugging unit based on the reinforcement learning algorithm; S2.2. Assigning priority to the adaptive debugging unit based on the device-associated network weight; S2.3. Classify and generate debugging areas and debugging task assignment instructions.

[0016] A further technical solution of the present application: the digital twin model in step S4 includes simulation modules of thermal field, gas-solid two-phase flow and equipment stress distribution.

[0017] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects: 1. The present invention breaks through the limitation of traditional ultra-supercritical coal-fired boiler unit startup and commissioning systems relying on load-sharing tests by dynamically defining modules and adaptively dividing commissioning units. Traditional methods require step-by-step verification of equipment performance according to fixed load stages, which cannot cover dynamic interaction problems under complex working conditions, and the commissioning efficiency is low and the cycle is long. The present invention allows users or systems to flexibly divide front-end, middle-end, and final-end equipment according to the physical characteristics of the unit or high-fault areas, forming a multi-dimensional commissioning scenario. Dynamic segmented commissioning can cover "boundary conditions" missed by traditional methods, reduce repeated commissioning steps, shorten the overall commissioning cycle by more than 30%, and improve the commissioning safety under extreme working conditions.

[0018] 2. This invention effectively resolves the debugging conflict problem caused by segment overlap through the synergistic effect of a dynamic learning algorithm and a digital twin model. In traditional static segment debugging, the boundaries of adjacent segments dynamically shift due to thermal expansion or mechanical vibration, which can easily cause signal interference or equipment conflicts. This invention uses a reinforcement learning algorithm to dynamically optimize segment boundaries to avoid debugging parameter interference. It also combines the digital twin model to simulate the thermal field and stress distribution at the segment boundaries, providing early warning of mechanical fatigue risks. For overlapping segments, a multi-objective optimization algorithm balances combustion efficiency and emission parameters to ensure debugging parameter consistency, significantly improving debugging reliability and unit operational stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. The present invention is further described below in conjunction with the embodiments.

[0021] See also Figure 1 In one embodiment of the present application, a startup and debugging system for an ultra-supercritical coal-fired boiler unit includes a dynamic segment definition module, a set debugging module, a fault rehearsal and model prediction module, and a closed-loop verification and system integration module; The dynamic segment definition module is used to dynamically adjust the equipment segment boundaries according to the physical characteristics of the unit or the debugging requirements, generate segment boundary configuration instructions, and transmit the segment boundary configuration instructions to the collective debugging module; The collection debugging module includes an adjustment allocation unit and an adaptive debugging unit; The adjustment allocation unit is used to receive the segment boundary configuration instruction, divide the working boundary of the adaptive debugging unit in real time through a dynamic learning algorithm, and generate the debugging area and debugging task allocation instruction; The adaptive debugging unit is used to receive a debugging task allocation instruction, generate and adaptively adjust a debugging strategy through a machine learning algorithm, and generate debugging parameters that match the segment boundary configuration instruction according to the debugging strategy; The fault rehearsal and model prediction module is used to build a digital twin model, integrate the fault rehearsal function, generate the whole machine performance prediction data according to the debugging parameters, and transmit the whole machine performance prediction data to the closed-loop verification and system integration module; The closed-loop verification and system integration module is used to reintegrate components according to the whole machine performance prediction data, perform load verification, realize closed-loop verification of the model and the physical unit, generate verification result data, and feed the verification result data back to the fault rehearsal and model prediction module.

[0022] Furthermore, the segment boundary configuration instructions are the range and association relationship of the front-end equipment, the range and association relationship of the middle-end equipment, and the range and association relationship of the end-end equipment.

[0023] Furthermore, the debugging area is divided into a segmented overlapping area, a boundary debugging area and a single debugging area; the segmented overlapping area is an area where the working boundaries of different adaptive debugging units overlap due to the dynamic adjustment of the device segment boundaries; the boundary debugging area is an area located near the working boundaries of different adaptive debugging units (adjacent on both sides); and the single debugging area does not overlap with the working boundaries of other adaptive debugging units.

[0024] Furthermore, the working boundary is a limit of a working range of the adaptive debugging unit within the debugging area.

[0025] Furthermore, the debugging parameters include but are not limited to combustion efficiency parameters, emission parameters, thermal deviation parameters, flow efficiency parameters, shaft vibration parameters, steam power parameters, excitation response parameters, electrical performance parameters and system redundancy parameters.

[0026] This embodiment is achieved as follows: 1. About the implementation of dynamic segment definition module: Data collection and analysis: By receiving the unit's physical property data (such as the thermal expansion coefficient of the boiler water-cooled wall, the turbine shafting stiffness, and the fluid dynamic characteristics of the air and flue duct) and commissioning requirements (such as historical high-failure areas and user-defined segmentation rules), the equipment's thermal stress distribution is calculated through finite element analysis. Combined with the unit's start-up and shutdown condition simulation, initial segmentation boundary recommendations are generated.

[0027] Dynamic segment boundary adjustment: Based on user instructions or automatic system decisions, such as when a fault warning is triggered, a reinforcement learning model is used to optimize segment boundaries. For example, if abnormal vibration is detected in the burner area, the front-end equipment boundary is extended to the air and flue duct to cover associated equipment, such as the pulverized coal conveying pipeline. During the hot commissioning phase, the final equipment boundary, such as the turbine steam inlet, is isolated to prevent interference with other system parameters. Segment boundary configuration instructions are output, including the equipment range and associated relationships for the front-end combustion system, mid-stage steam-water system, and final-stage electrical system.

[0028] 2. Implementation of the collection debugging module: Adjust the distribution unit workflow: receive segment boundary configuration instructions and parse the equipment association network such as the heat transfer path between the burner and the steam-water separator; The specific contents of dynamically dividing the adaptive debugging unit using reinforcement learning algorithms (such as Q-learning) include: State space: equipment operating parameters (temperature, pressure, vibration frequency), segment boundary position; Action space: adjust segment boundaries (±5% range), merge / split debugging units; Reward function: minimize debugging conflicts (such as signal interference) and maximize parameter convergence speed.

[0029] Then the debugging areas (segment overlap areas, boundary debugging areas, single debugging areas) and task allocation instructions are generated.

[0030] The adaptive debugging unit executes the debugging task instruction and generates a specific debugging strategy based on machine learning algorithms (such as LSTM timing prediction): Combustion system debugging strategy: optimize the air-to-coal ratio and burner swing angle to generate combustion efficiency parameters (target value ≥ 98%); Steam-water system debugging strategy: adjust the feed water flow, superheater desuperheating water volume, and control thermal deviation parameters (≤5℃).

[0031] At the same time, it is necessary to dynamically adjust the strategy according to the segment boundaries, for example, synchronously adjust the combustion and steam-water parameters in the segment overlapping area to avoid conflicts.

[0032] 3. Implementation of fault rehearsal and model prediction modules: The digital twin model is constructed, integrating multi-physics field simulation modules: thermal field simulation, simulating the combustion temperature field distribution based on CFD software, and predicting the thermal stress in the burner area; gas-solid two-phase flow simulation, using Fluent software to analyze the coal powder combustion trajectory and optimize the burner air distribution; equipment stress distribution simulation: calculating the creep deformation of high-temperature pipelines through ABAQUS software.

[0033] Then, the real-time operation data of the unit (such as DCS historical data) is imported to calibrate the simulation model parameters.

[0034] Fault rehearsal and prediction: Simulate extreme operating conditions (e.g., sudden load changes of ±20% / min, single coal mill tripping) to predict overall unit performance (e.g., main steam pressure fluctuations, shaft vibration amplitude). Generate overall unit performance prediction data (e.g., vibration spectrum, temperature gradient cloud map) and output it to the closed-loop verification module.

[0035] 4. Implementation of the closed-loop verification and system integration module: Perform verification under load, adjusting unit operating parameters such as load ramp rate and fuel quantity based on predicted unit performance data, while simultaneously collecting actual operating data such as vibration sensor and thermocouple signals. Deploy redundant sensors in overlapping areas of the segments, such as at the junction of the burner and the steam-water separator, to verify parameter consistency. Ensure that the model can be iteratively optimized, comparing predicted data with actual data. Calculate errors, such as vibration prediction errors, to ≤3%. If the error exceeds the limit, trigger a digital twin model parameter update, such as correcting the thermal conductivity coefficient and flow resistance coefficient. Regenerate the debugging strategy, and output verification results such as the debugging pass rate and the number of fault avoidances, to form a closed-loop optimization record.

[0036] 5. System Collaboration and Data Interaction: The dynamic segment definition module updates segment boundaries in real time, triggering the collective debugging module to redefine work boundaries, ensuring that the debugging area covers dynamic operating conditions, such as the boundary differences between cold startup and hot operation. The fault rehearsal module transmits predicted data to the closed-loop verification module. Once verified, the actual data is fed back to the digital twin model to adjust simulation parameters, forming a "prediction-verification-optimization" closed loop.

[0037] See also Figure 1 A method for starting and debugging an ultra-supercritical coal-fired boiler unit comprises the following steps: S1. Dynamic segment configuration: Based on the unit's physical characteristics or commissioning requirements, the dynamic segment definition module generates segment boundary configuration instructions, which include the front-end equipment range and associated relationships, the middle-end equipment range and associated relationships, and the final-end equipment range and associated relationships; S2 adaptive debugging unit division: By collecting the debugging module receives the segment boundary configuration instructions, the use of dynamic learning algorithm in real time to divide the working boundary of the adaptive debugging unit, generate debugging area and debugging task assignment instructions, the debugging area includes segmented overlapping area, boundary debugging area and a single debugging area; S3. Debugging strategy generation: The adaptive debugging unit receives debugging task assignment instructions, generates a debugging strategy based on a machine learning algorithm, and generates debugging parameters that match the segment boundaries based on the strategy. The debugging parameters include combustion efficiency parameters, emission parameters, thermal deviation parameters, and shaft vibration parameters. S4. Fault Rehearsal and Performance Prediction: Build a digital twin model through the fault rehearsal and model prediction module, integrate fault rehearsal functions, and generate prediction data for the entire machine's performance. S5. Closed-loop verification and parameter optimization: Through the closed-loop verification and system integration module, components are reintegrated according to the prediction plan, load verification is performed, verification result data is generated and fed back to the fault rehearsal module to form an iterative optimization closed loop.

[0038] Furthermore, the dynamic learning algorithm in step S2 includes: a boundary dynamic adjustment algorithm based on reinforcement learning, which is used to optimize the segmentation boundary according to historical debugging data; and a weight allocation algorithm based on the device association network, which is used to determine the debugging area priority.

[0039] Furthermore, the detailed steps of step S1 are as follows: S1.1. Determine the segment boundaries based on the unit's physical characteristics and generate the front, middle, and final segment equipment ranges and associated relationship instructions; S1.2. Dynamically adjust segment boundaries according to debugging requirements to cover key nodes; S1.3. Verify the rationality of segment boundaries through digital twin models to avoid signal interference or physical conflicts between devices.

[0040] Furthermore, the detailed steps of S2 are as follows: S2.1. Receive a segment boundary instruction and dynamically divide the working boundary of the adaptive debugging unit based on a reinforcement learning algorithm; S2.2. Assigning debugging unit priorities based on device-associated network weights; S2.3. Classify and generate debugging areas and task allocation instructions.

[0041] Furthermore, the digital twin model in step S4 includes simulation modules for thermal field, gas-solid two-phase flow and equipment stress distribution.

[0042] The specific implementation of this embodiment is as follows: S1. Dynamic segment configuration S1.1 determines the segment boundaries based on the physical characteristics of the unit. First, data collection is performed to obtain physical parameters such as the thermal expansion coefficient of the boiler water-cooled wall, the stiffness of the turbine shaft system, and the fluid dynamic characteristics of the air and flue duct. The thermal stress distribution of the equipment is calculated through finite element analysis (FEA).

[0043] Secondly, segmentation is performed. The segmentation rules divide the equipment range into the front section (combustion system), middle section (steam-water system), and final section (electrical system) according to the thermal stress concentration area (such as the connection between the burner and the water-cooled wall), and generate initial segmentation instructions.

[0044] Example: The burner and pulverized coal conveying pipeline are classified into the front section, the superheater and reheater are classified into the middle section, and the turbine steam inlet and electrical control system are classified into the final section.

[0045] S1.2 dynamically adjusts segment boundaries based on debugging requirements, identifies high-fault areas, and combines historical fault data (such as abnormal burner vibration) to extend the front-end boundary to the air and flue duct, covering associated equipment (such as pulverized coal distributors). Load mutation scenario simulation: During the hot debugging phase, isolate the terminal boundary (such as the turbine steam inlet) to avoid coupling interference with other system parameters.

[0046] S1.3 Digital twin model verification: simulation verification is performed, and the temperature gradient at the segment boundary is simulated using CFD software (such as Fluent) to ensure that thermal stress does not exceed the material limit; and to avoid conflicts. If an abnormal temperature gradient is detected at the boundary between the burner and the steam-water separator, the segment boundary is adjusted to avoid thermal fatigue.

[0047] S2. Adaptive Debug Unit Division S2.1 reinforcement learning algorithm dynamically partitions,the state space definition, including the equipment operating parameters, specifically,,temperature, pressure, vibration frequency and segment boundary positions.

[0048] For action space design, adjust segment boundaries (±5% range), merge / split debugging units.

[0049] Reward function: minimize debugging conflicts (such as signal interference) and maximize parameter convergence speed.

[0050] Example: When the combustion system and the steam-water system are vibrationally coupled, the reinforcement learning model isolates the boundary by 5 meters to reduce interference.

[0051] S2.2 Equipment association network weight allocation, association network construction: establish an interactive relationship map between equipment (such as burner → steam-water separator → turbine), calculate the weight, and assign priorities based on historical data (such as vibration transfer coefficient). High-association areas (such as burner outlet) are debugged first.

[0052] S2.3 Debug area classification and task allocation. For overlapping areas of segments, synchronously adjust the combustion parameters (air-coal ratio) and steam-water parameters (feed water flow) to avoid conflicts.

[0053] For boundary debugging areas, debug the transition area separately (such as the junction between the burner and the steam-water separator), and set up redundant sensors to verify parameter consistency.

[0054] S3. Debugging strategy generation S3.1 Machine learning algorithm generation strategy. For the combustion system strategy, an LSTM network-based prediction of combustion efficiency can be used to optimize the air-to-coal ratio and burner swing angle. For the steam-water system strategy, fuzzy PID control can be used to adjust the water flow rate and control the thermal deviation parameters.

[0055] S3.2 debugging parameters are generated, and the air distribution is adjusted through real-time feedback from the oxygen sensor. At the same time, the threshold of the shaft system vibration parameters is set to ≤0.05mm, and the turbine bearing stiffness is optimized in combination with vibration spectrum analysis.

[0056] S4. Fault Rehearsal and Performance Prediction S4.1 digital twin model construction, regarding thermal field simulation, can simulate the combustion temperature field based on ANSYS CFX and predict the thermal stress in the burner area (error ≤ 3%).

[0057] Regarding gas-solid two-phase flow simulation, Fluent is used to analyze the coal powder combustion trajectory and optimize the burner air distribution.

[0058] Regarding equipment stress distribution simulation, the creep deformation of high-temperature pipelines is calculated using ABAQUS to ensure a prediction accuracy of ±0.1mm.

[0059] S4.2 Whole machine performance prediction: for extreme working condition simulation, load sudden change ±20% / min, single coal mill tripping, main steam pressure fluctuation is predicted to be ≤±1.5MPa, and finally vibration spectrum diagram, temperature gradient cloud diagram and fault probability distribution table are generated.

[0060] S5. Closed-loop verification and parameter optimization S5.1 Load verification: adjust the load increase rate (e.g., 5% / min) and fuel quantity (±1% accuracy) according to the predicted plan; synchronously collect vibration sensor and thermocouple signals to verify parameter consistency at the segment boundaries (error ≤ 2%).

[0061] S5.2 Iteratively optimizes the model, compares the prediction with the actual data (e.g., vibration prediction error > 3%), triggers the correction of digital twin model parameters, updates the heat transfer coefficient, and regenerates the debugging strategy.

[0062] In summary, the present invention overcomes the limitations of traditional ultra-supercritical coal-fired boiler unit startup and commissioning systems, which rely on load-sharing testing, by dynamically defining modules and adaptively dividing commissioning units. Traditional methods require step-by-step verification of equipment performance according to fixed load stages, which cannot cover dynamic interaction issues under complex operating conditions, and have low commissioning efficiency and long commissioning cycles. The present invention allows users or systems to flexibly divide front-end, middle-end, and final-end equipment according to the unit's physical characteristics or high-fault areas, forming a multi-dimensional commissioning scenario. Dynamic segmented commissioning can cover "boundary conditions" missed by traditional methods, reduce repeated commissioning steps, shorten the overall commissioning cycle by more than 30%, and improve commissioning safety under extreme operating conditions. Furthermore, the present invention effectively resolves the debugging conflict problem caused by segment overlap through the synergistic effect of dynamic learning algorithms and digital twin models. In traditional static segment debugging, the boundaries of adjacent segments are dynamically offset due to thermal expansion or mechanical vibration, which can easily cause signal interference or equipment conflicts. The present invention uses a reinforcement learning algorithm to dynamically optimize segment boundaries to avoid debugging parameter interference; it also combines the digital twin model to simulate the thermal field and stress distribution at the segment boundaries, providing early warning of mechanical fatigue risks. For overlapping segments, a multi-objective optimization algorithm is used to balance combustion efficiency and emission parameters, ensuring the consistency of debugging parameters and significantly improving debugging reliability and unit operation stability.

[0063] The above is a schematic description of the present invention and its embodiments, which is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without creatively designing them, they shall fall within the scope of protection of the present invention.

[0064] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A startup and debugging system for an ultra-supercritical coal-fired boiler unit, characterized by: It includes dynamic segment definition module, set debugging module, fault rehearsal and model prediction module, and closed-loop verification and system integration module; The dynamic segment definition module is used to dynamically adjust the equipment segment boundaries according to the physical characteristics of the unit or the debugging requirements, generate segment boundary configuration instructions, and transmit the segment boundary configuration instructions to the collective debugging module; The collection debugging module includes an adjustment allocation unit and an adaptive debugging unit; The adjustment allocation unit is used to receive the segment boundary configuration instruction, divide the working boundary of the adaptive debugging unit in real time through a dynamic learning algorithm, and generate the debugging area and debugging task allocation instruction; The adaptive debugging unit is used to receive a debugging task allocation instruction, generate and adaptively adjust a debugging strategy through a machine learning algorithm, and generate debugging parameters that match the segment boundary configuration instruction according to the debugging strategy; The fault rehearsal and model prediction module is used to build a digital twin model, integrate the fault rehearsal function, generate the whole machine performance prediction data according to the debugging parameters, and transmit the whole machine performance prediction data to the closed-loop verification and system integration module; The closed-loop verification and system integration module is used to reintegrate components according to the whole machine performance prediction data, perform load verification, realize closed-loop verification of the model and the physical unit, generate verification result data, and feed the verification result data back to the fault rehearsal and model prediction module.

2. The startup and debugging system of an ultra-supercritical coal-fired boiler unit according to claim 1, characterized in that: The segment boundary configuration instructions are the range and association relationship of the front-end equipment, the range and association relationship of the middle-end equipment, and the range and association relationship of the end-end equipment.

3. The startup and debugging system of an ultra-supercritical coal-fired boiler unit according to claim 2, characterized in that: The debugging area is divided into a segment overlap area, a boundary debugging area, and a single debugging area; the segment overlap area is an area where the working boundaries of different adaptive debugging units overlap due to dynamic adjustment of the device segment boundaries; the boundary debugging area is an area adjacent to the working boundaries of different adaptive debugging units; The single debugging area is an area that does not overlap with the working boundaries of other adaptive debugging units.

4. The startup and debugging system of an ultra-supercritical coal-fired boiler unit according to claim 3, characterized in that: The working boundary is the limit of the working range of the adaptive debugging unit within the debugging area.

5. The startup and debugging system of an ultra-supercritical coal-fired boiler unit according to claim 4, characterized in that: The debugging parameters include combustion efficiency parameters, emission parameters, thermal deviation parameters, flow efficiency parameters, shaft vibration parameters, steam power parameters, excitation response parameters, electrical performance parameters and system redundancy parameters.

6. A method for starting and debugging an ultra-supercritical coal-fired boiler unit, characterized in that: The following steps are involved: S1. Dynamic segment configuration: Based on the unit's physical characteristics or commissioning requirements, the dynamic segment definition module generates segment boundary configuration instructions, which include the front-end equipment range and associated relationships, the middle-end equipment range and associated relationships, and the final-end equipment range and associated relationships; S2 adaptive debugging unit division: By collecting the debugging module receives the segment boundary configuration instructions, the use of dynamic learning algorithm in real time to divide the working boundary of the adaptive debugging unit, generate debugging area and debugging task assignment instructions, the debugging area includes segmented overlapping area, boundary debugging area and a single debugging area; S3 debugging strategy generation: receiving debugging task assignment instructions through the adaptive debugging unit, generating a debugging strategy based on a machine learning algorithm, and generating debugging parameters that match the segment boundary configuration instructions according to the debugging strategy; S4. Fault Rehearsal and Performance Prediction: Build a digital twin model through the fault rehearsal and model prediction module, integrate fault rehearsal functions, and generate prediction data for the entire machine's performance. S5. Closed-loop verification and parameter optimization: Through the closed-loop verification and system integration module, components are reintegrated according to the prediction plan, load verification is performed, verification result data is generated and fed back to the fault rehearsal module to form an iterative optimization closed loop.

7. The startup and debugging method of an ultra-supercritical coal-fired boiler unit according to claim 6, characterized in that: The dynamic learning algorithm in step S2 includes: a boundary dynamic adjustment algorithm based on reinforcement learning, which is used to optimize segment boundaries according to historical debugging data; and a weight allocation algorithm based on a device association network, which is used to determine the debugging area priority.

8. The startup and debugging method of an ultra-supercritical coal-fired boiler unit according to claim 6, characterized in that: The detailed steps of step S1 are as follows: S1.

1. Determine the segment boundaries based on the unit's physical characteristics and generate the front, middle, and final segment equipment ranges and associated relationship instructions; S1.

2. Dynamically adjust segment boundaries according to debugging requirements to cover key nodes; S1.

3. Verify the rationality of segment boundaries through digital twin models to avoid signal interference or physical conflicts between devices.

9. The startup and debugging method of an ultra-supercritical coal-fired boiler unit according to claim 6, characterized in that: The detailed steps of S2 are as follows: S2.

1. Receive segment boundary configuration instructions and dynamically divide the working boundaries of the adaptive debugging unit based on the reinforcement learning algorithm; S2.

2. Assigning priority to the adaptive debugging unit based on the device-associated network weight; S2.

3. Classify and generate debugging areas and debugging task assignment instructions.

10. The startup and debugging method of an ultra-supercritical coal-fired boiler unit according to claim 6, characterized in that: The digital twin model in step S4 includes simulation modules for thermal field, gas-solid two-phase flow and equipment stress distribution.

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