A start-up commissioning system for an ultra-supercritical coal-fired boiler unit and a method thereof

Abstract

The present application relates to the technical field of ultra-supercritical coal-fired boiler unit, more particularly, the present application provides an ultra-supercritical coal-fired boiler unit starting debugging system and method, which breaks through the limitation of traditional ultra-supercritical coal-fired boiler unit starting debugging system relying on load test through dynamic segmentation definition module and adaptive debugging unit division; the traditional method needs to verify the equipment performance gradually according to the fixed load stage, cannot cover the dynamic interaction problem under the complex working condition, and the debugging efficiency is low and the period is long, the present application allows the user or the system to divide the front section, the middle section and the terminal section equipment flexibly according to the physical characteristics or the fault high incidence area of the unit, and forms a multi-dimensional debugging scene; the dynamic segmentation debugging can cover the boundary working condition missed by the traditional method, reduce the repeated debugging steps, shorten the overall debugging period by more than 30%, and improve the debugging safety of extreme working condition.

Description

Technical Field

[0001] This invention relates to the field of ultra-supercritical coal-fired boiler unit technology, and more specifically, to an ultra-supercritical coal-fired boiler unit start-up and commissioning system and method thereof. Background Technology

[0002] Current ultra-supercritical coal-fired boiler units typically employ phased load testing during startup and commissioning (e.g., 30% low load, 60% medium load, and 100% full load). However, phased load commissioning has limitations, as it can only verify equipment performance at a single load point and cannot cover dynamic interaction issues under complex operating conditions. For example, the coupling effect between the combustion system and the steam-water system under different loads is difficult to fully assess.

[0003] Secondly, traditional methods divide boiler equipment into fixed sections (such as combustion section and steam-water section) based on physical location for segmented commissioning. However, static segmentation in commissioning can easily lead to 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 sections may dynamically shift due to thermal expansion or mechanical vibration, resulting in overlapping commissioning areas and causing signal interference or equipment conflicts.

[0004] Existing commissioning systems rely on manual experience to divide commissioning areas, lack dynamic adjustment capabilities, and are unable to cope with complex scenarios such as unit start-up and shutdown and sudden load changes. The application of digital twin technology in boiler commissioning (e.g.) is mostly concentrated on single-stage simulation and has not achieved multi-segment dynamic collaborative commissioning.

[0005] Therefore, it is of great significance to develop a startup and commissioning system and method for ultra-supercritical coal-fired boiler units that is based on dynamically defined equipment segment boundaries, solves the problems of low efficiency and overlapping segments in traditional load-sharing commissioning, enables flexible division of front-end, middle-end, and final-end equipment, covers equipment interaction under complex operating conditions, and improves commissioning accuracy and safety. Summary of the Invention

[0006] The purpose of this invention is to provide a startup and commissioning system and method for ultra-supercritical coal-fired boiler units that can solve the problems of low efficiency and overlapping segments in traditional load-sharing commissioning based on dynamically defined equipment segment boundaries, achieve flexible division of front-end, middle-end, and final-end equipment, cover equipment interaction under complex operating conditions, and improve commissioning accuracy and safety, thereby solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a startup and commissioning system for an ultra-supercritical coal-fired boiler unit, comprising a dynamic segmentation definition module, an integrated commissioning module, a fault simulation and model prediction module, and a closed-loop verification and system integration module;

[0008] The dynamic segmentation definition module is used to dynamically adjust the equipment segmentation boundaries according to the physical characteristics of the unit or the commissioning requirements, generate segmentation boundary configuration instructions, and transmit the segmentation boundary configuration instructions to the integrated commissioning module.

[0009] The assembly debugging module includes an adjustment allocation unit and an adaptive debugging unit;

[0010] The adjustment and allocation unit is used to receive segment boundary configuration instructions, and through a dynamic learning algorithm, to divide the working boundary of the adaptive debugging unit in real time, and generate debugging area and debugging task allocation instructions.

[0011] The adaptive debugging unit is used to receive debugging task allocation instructions, generate and adaptively adjust debugging strategies through machine learning algorithms, and generate debugging parameters that match the segment boundary configuration instructions according to the debugging strategies.

[0012] The fault simulation and model prediction module is used to build a digital twin model, integrate fault simulation function, generate 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.

[0013] The closed-loop verification and system integration module is used to re-integrate components according to the overall performance prediction data, perform load verification, realize closed-loop verification between the model and the physical unit, generate verification result data, and feed the verification result data back to the fault simulation and model prediction module.

[0014] A further technical solution of this application: the segment boundary configuration instructions are the scope and association of the front-end equipment, the scope and association of the middle-end equipment, and the scope and association of the terminal equipment.

[0015] A further technical solution of this application: the debugging area is divided into segmented overlapping area, boundary debugging area and single debugging area; the segmented overlapping area is the area where the working boundaries of different adaptive debugging units overlap due to the dynamic adjustment of the equipment segment boundaries; the boundary debugging area is the area adjacent to the working boundaries of different adaptive debugging units; the single debugging area is the area that does not overlap with the working boundaries of other adaptive debugging units.

[0016] A further technical solution of this application: the working boundary is the working range limit of the adaptive debugging unit within the debugging area.

[0017] A further technical solution of this application: 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.

[0018] A method for starting up and commissioning an ultra-supercritical coal-fired boiler unit includes the following steps:

[0019] S1. Dynamic segment configuration: Based on the physical characteristics of the unit or commissioning requirements, the segment boundary configuration instructions are generated through the dynamic segment definition module. The segment boundary configuration instructions include the scope and association of the front-end equipment, the scope and association of the middle-end equipment, and the scope and association of the terminal equipment.

[0020] S2. Adaptive debugging unit partitioning: The unit receives segment boundary configuration instructions through the set debugging module, uses a dynamic learning algorithm to partition the working boundary of the adaptive debugging unit in real time, and generates debugging area and debugging task allocation instructions. The debugging area includes segmented overlapping area, boundary debugging area and single debugging area.

[0021] S3. Debugging strategy generation: Receive debugging task allocation instructions through the adaptive debugging unit, generate debugging strategies based on machine learning algorithms, and generate debugging parameters that match the segment boundary configuration instructions according to the debugging strategies;

[0022] S4. Fault Prediction and Performance Prediction: A digital twin model is constructed through the fault prediction and model prediction module, integrating fault prediction functions to generate overall machine performance prediction data;

[0023] S5. Closed-loop verification and parameter optimization: The components are re-integrated according to the predicted scheme through the closed-loop verification and system integration module, load verification is performed, verification result data is generated and fed back to the fault simulation module, forming an iterative optimization closed loop.

[0024] A further technical solution of this application: The dynamic learning algorithm in step S2 includes: a boundary dynamic adjustment algorithm based on reinforcement learning, used to optimize the segment boundary according to historical debugging data; and a weight allocation algorithm based on device association network, used to determine the priority of the debugging area.

[0025] A further technical solution of this application: The detailed steps of step S1 are as follows:

[0026] The detailed steps of step S1 are as follows:

[0027] S1.1 Determine the segment boundaries based on the physical characteristics of the unit, and generate instructions on the scope and relationship of the front-end, middle-end, and final-end equipment.

[0028] S1.2. Dynamically adjust segment boundaries according to debugging requirements to cover key nodes;

[0029] S1.3 Verify the rationality of segmentation boundaries through digital twin models to avoid signal interference or physical conflicts between devices.

[0030] A further technical solution of this application: The detailed steps of S2 are as follows:

[0031] S2.1 Receive segment boundary configuration instructions and dynamically divide the working boundary of the adaptive debugging unit based on reinforcement learning algorithm;

[0032] S2.2. Assign priority to adaptive debugging units based on the network weight associated with the devices;

[0033] S2.3, Classify and generate debugging areas and debugging task assignment instructions.

[0034] A further technical solution of this application: The digital twin model in step S4 includes simulation modules for thermal field, gas-solid two-phase flow and equipment stress distribution.

[0035] Compared with the prior art, the technical solution provided by this invention has the following advantages:

[0036] 1. This invention overcomes the limitations of traditional ultra-supercritical coal-fired boiler unit start-up and commissioning systems that rely on load-sharing tests by defining modules and adaptive commissioning units through dynamic segmentation. Traditional methods require step-by-step verification of equipment performance according to fixed load stages, which cannot cover dynamic interaction problems under complex operating conditions, and the commissioning efficiency is low and the cycle is long. This invention allows users or systems to flexibly divide the front, middle and final sections of equipment according to the physical characteristics of the unit or high-fault areas, forming a multi-dimensional commissioning scenario. Dynamic segmented commissioning can cover the "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 conditions.

[0037] 2. This invention effectively solves the debugging conflict problem caused by segment overlap by combining dynamic learning algorithms and digital twin models. In traditional static segment debugging, the boundaries of adjacent segments are dynamically offset by thermal expansion or mechanical vibration, which can easily cause signal interference or equipment conflicts. This invention uses reinforcement learning algorithms to dynamically optimize the segment boundaries, avoiding interference with debugging parameters; and combines digital twin models to simulate the thermal field and stress distribution at the segment boundaries, providing early warning of mechanical fatigue risks. For overlapping segment areas, a multi-objective optimization algorithm is used to balance combustion efficiency and emission parameters, ensuring consistency of debugging parameters and significantly improving debugging reliability and unit operation stability. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention will be further described below with reference to the embodiments.

[0040] Please see Figure 1 In one embodiment of this application, a startup and commissioning system for an ultra-supercritical coal-fired boiler unit includes a dynamic segmentation definition module, a set commissioning module, a fault simulation and model prediction module, and a closed-loop verification and system integration module.

[0041] The dynamic segmentation definition module is used to dynamically adjust the equipment segmentation boundaries according to the physical characteristics of the unit or the commissioning requirements, generate segmentation boundary configuration instructions, and transmit the segmentation boundary configuration instructions to the integrated commissioning module.

[0042] The assembly debugging module includes an adjustment allocation unit and an adaptive debugging unit;

[0043] The adjustment and allocation unit is used to receive segment boundary configuration instructions, and through a dynamic learning algorithm, to divide the working boundary of the adaptive debugging unit in real time, and generate debugging area and debugging task allocation instructions.

[0044] The adaptive debugging unit is used to receive debugging task allocation instructions, generate and adaptively adjust debugging strategies through machine learning algorithms, and generate debugging parameters that match the segment boundary configuration instructions according to the debugging strategies.

[0045] The fault simulation and model prediction module is used to build a digital twin model, integrate fault simulation function, generate 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.

[0046] The closed-loop verification and system integration module is used to re-integrate components according to the overall performance prediction data, perform load verification, realize closed-loop verification between the model and the physical unit, generate verification result data, and feed the verification result data back to the fault simulation and model prediction module.

[0047] Furthermore, the segment boundary configuration instructions define the scope and relationships of the front-end devices, the scope and relationships of the middle-end devices, and the scope and relationships of the final-end devices.

[0048] Furthermore, the debugging area is divided into segmented overlapping area, boundary debugging area and single debugging area; the segmented overlapping area is the area where the working boundaries of different adaptive debugging units overlap due to the dynamic adjustment of the equipment segment boundaries; the boundary debugging area is the area located near the working boundaries of different adaptive debugging units (adjacent on both sides); the single debugging area is the area that does not overlap with the working boundaries of other adaptive debugging units.

[0049] Furthermore, the working boundary is the working range limit of the adaptive debugging unit within the debugging area.

[0050] 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.

[0051] This embodiment is implemented as follows:

[0052] 1. Implementation of the dynamic segmentation definition module:

[0053] Data Acquisition and Analysis: By receiving physical characteristic data of the unit (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 flue gas duct) and commissioning requirements (such as historical high-incidence areas of failure and user-defined segmentation rules), the thermal stress distribution of the equipment is calculated through finite element analysis, and combined with the simulation of the unit's start-up and shutdown conditions, initial segmentation boundary suggestions are generated.

[0054] Dynamic adjustment of segment boundaries: Based on user commands or automatic system determination, such as fault warning triggers, a reinforcement learning model is invoked to optimize segment boundaries. For example, when abnormal vibration is detected in the burner area, the boundary of the upstream equipment is extended to the flue gas duct, covering related equipment such as pulverized coal conveying pipelines; during hot commissioning, the boundary of the terminal equipment, such as the steam turbine inlet, is isolated to avoid coupling interference with other system parameters. Output segment boundary configuration commands include the equipment scope and relationships of the upstream combustion system, the intermediate steam-water system, and the terminal electrical system.

[0055] 2. Implementation of the integrated debugging module:

[0056] Adjust the workflow of the allocation unit: receive segment boundary configuration instructions and parse the heat transfer path of the equipment association network, such as the burner and the steam-water separator;

[0057] The specific details of dynamically partitioning adaptive tuning units using reinforcement learning algorithms (such as Q-learning) include:

[0058] State space: Equipment operating parameters (temperature, pressure, vibration frequency), segment boundary positions;

[0059] Action space: Adjust segment boundaries (±5%), merge / split debug units;

[0060] Reward function: Minimize debugging conflicts (such as signal interference) and maximize parameter convergence speed.

[0061] Then, the debugging area (segmented overlapping area, boundary debugging area, single debugging area) and task allocation instructions are generated.

[0062] This is executed by the adaptive debugging unit. After receiving the debugging task instruction, the adaptive debugging unit generates a specific debugging strategy based on machine learning algorithms (such as LSTM timing prediction).

[0063] Combustion system commissioning strategy: Optimize air-fuel ratio and burner oscillation angle to generate combustion efficiency parameters (target value ≥ 98%).

[0064] Commissioning strategy for steam and water system: Adjust the water supply flow rate and the superheater desuperheating water volume, and control the thermal deviation parameters (≤5℃).

[0065] At the same time, it is necessary to dynamically adjust the strategy according to the segment boundaries, such as simultaneously adjusting the combustion and soda parameters in the overlapping areas of the segments to avoid conflicts.

[0066] 3. Implementation of the fault simulation and model prediction module:

[0067] Digital twin model construction integrates multiphysics simulation modules: thermal field simulation, which uses CFD software to simulate the combustion temperature field distribution and predict thermal stress in the burner area; gas-solid two-phase flow simulation, which uses Fluent software to analyze the pulverized coal combustion trajectory and optimize the burner air distribution; and equipment stress distribution simulation, which uses ABAQUS software to calculate the creep deformation of high-temperature pipelines.

[0068] Then, import the unit's real-time operating data (such as historical DCS data) and calibrate the simulation model parameters.

[0069] Fault simulation and prediction: Simulate extreme operating conditions (such as sudden load changes of ±20% / min, single coal mill tripping) and predict overall machine performance (such as main steam pressure fluctuations, shaft vibration amplitude). Generate overall machine performance prediction data (such as vibration spectrum diagrams, temperature gradient cloud maps) and output them to the closed-loop verification module.

[0070] 4. Implementation of Closed-Loop Verification and System Integration Module: Load-bearing verification is performed, adjusting unit operating parameters such as load increase rate and fuel quantity according to the overall unit performance prediction data, while simultaneously collecting actual operating data such as vibration sensor and thermocouple signals. Redundant sensors are deployed in overlapping sections, such as the interface between the burner and the steam-water separator, to verify parameter consistency. Simultaneously, the model is ensured to be iteratively optimized by comparing predicted and actual data. For example, the vibration prediction error should be ≤3%. If the error exceeds the limit, the digital twin model parameters are updated, such as correcting the thermal conductivity coefficient and flow resistance coefficient, regenerating the commissioning strategy, and outputting verification results data such as commissioning pass rate and fault avoidance count, forming a closed-loop optimization record.

[0071] 5. System Collaboration and Data Interaction: The dynamic segmentation definition module updates segment boundaries in real time, triggering the ensemble debugging module to re-divide the working boundaries, ensuring that the debugging area covers the boundary differences of dynamic operating conditions such as cold start and hot operation. The fault simulation module transmits predicted data to the closed-loop verification module. After verification, the actual data is fed back to the digital twin model to correct simulation parameters, forming a "prediction-verification-optimization" closed loop.

[0072] Please see Figure 1 A method for starting and commissioning an ultra-supercritical coal-fired boiler unit includes the following steps:

[0073] S1. Dynamic segment configuration: Based on the physical characteristics of the unit or commissioning requirements, the segment boundary configuration instructions are generated through the dynamic segment definition module. The segment boundary configuration instructions include the scope and association of the front-end equipment, the scope and association of the middle-end equipment, and the scope and association of the terminal equipment.

[0074] S2. Adaptive debugging unit partitioning: The unit receives segment boundary configuration instructions through the set debugging module, uses a dynamic learning algorithm to partition the working boundary of the adaptive debugging unit in real time, and generates debugging area and debugging task allocation instructions. The debugging area includes segmented overlapping area, boundary debugging area and single debugging area.

[0075] S3. Debugging strategy generation: The adaptive debugging unit receives the debugging task allocation instruction, generates a debugging strategy based on the machine learning algorithm, and generates debugging parameters that match the segment boundary according to the strategy. The debugging parameters include combustion efficiency parameters, emission parameters, thermal deviation parameters and shaft vibration parameters.

[0076] S4. Fault Prediction and Performance Prediction: A digital twin model is constructed through the fault prediction and model prediction module, integrating fault prediction functions to generate overall machine performance prediction data;

[0077] S5. Closed-loop verification and parameter optimization: The components are re-integrated according to the predicted scheme through the closed-loop verification and system integration module, load verification is performed, verification result data is generated and fed back to the fault simulation module, forming an iterative optimization closed loop.

[0078] Furthermore, the dynamic learning algorithm in step S2 includes: a reinforcement learning-based boundary dynamic adjustment algorithm for optimizing segment boundaries based on historical debugging data; and a weight allocation algorithm based on device association networks for determining the priority of debugging areas.

[0079] Furthermore, the detailed steps of step S1 are as follows:

[0080] S1.1 Determine the segment boundaries based on the physical characteristics of the unit, and generate instructions on the scope and relationship of the front-end, middle-end, and final-end equipment.

[0081] S1.2. Dynamically adjust segment boundaries according to debugging requirements to cover key nodes;

[0082] S1.3 Verify the rationality of segmentation boundaries through digital twin models to avoid signal interference or physical conflicts between devices.

[0083] Furthermore, the detailed steps of S2 are as follows:

[0084] S2.1 Receive segmentation boundary instructions and dynamically divide the working boundary of the adaptive debugging unit based on reinforcement learning algorithm;

[0085] S2.2. Assign priority to debugging units based on the network weight associated with the devices;

[0086] S2.3, Classify and generate debugging areas and task allocation instructions.

[0087] Furthermore, the digital twin model in step S4 includes simulation modules for the thermal field, gas-solid two-phase flow, and equipment stress distribution.

[0088] The specific implementation method of this embodiment is as follows:

[0089] S1. Dynamic segmentation configuration

[0090] S1.1 Based on the physical characteristics of the unit, the segment boundaries are determined. First, data is collected 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 flue. The thermal stress distribution of the equipment is calculated through finite element analysis (FEA).

[0091] Secondly, the equipment is segmented. The segmentation rules are based on the areas of concentrated thermal stress (such as the connection between the burner and the water-cooled wall) to divide the equipment into the front section (combustion system), the middle section (steam and water system), and the final section (electrical system), and generate the initial segmentation instructions.

[0092] Example: The burner and pulverized coal conveying pipeline are classified as the front section, the superheater and reheater as the middle section, and the steam turbine inlet and electrical control system as the final section.

[0093] S1.2 Dynamically adjust the segment boundaries according to commissioning requirements, identify high-fault areas, and combine historical fault data (such as abnormal burner vibration) to extend the front boundary to the flue and cover related equipment (such as pulverized coal distributor). Load change scenario simulation: During the hot commissioning stage, isolate the final boundary (such as the steam turbine inlet) to avoid coupling interference with other system parameters.

[0094] S1.3 Digital twin model verification: Simulation verification is performed by simulating the temperature gradient at the segment boundary using CFD software (such as Fluent) to ensure that the thermal stress does not exceed the material limit; and conflicts are avoided. 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.

[0095] S2. Adaptive debugging unit partitioning

[0096] S2.1 The reinforcement learning algorithm dynamically partitions the state space, which includes equipment operating parameters, specifically temperature, pressure, vibration frequency, and segment boundary positions.

[0097] For motion space design, adjust segment boundaries (±5% range) and merge / split debugging units.

[0098] Reward function: Minimize debugging conflicts (such as signal interference) and maximize parameter convergence speed.

[0099] Example: When the combustion system and the steam-water system are vibrating and coupled, the reinforcement learning model isolates the boundary by 5m to reduce interference.

[0100] S2.2 Equipment Association Network Weight Allocation and Association Network Construction: Establish an interaction relationship map between equipment (such as burner → steam-water separator → steam turbine), calculate weights, and assign priorities based on historical data (such as vibration transmission coefficient). Highly associated areas (such as burner outlet) are prioritized for commissioning.

[0101] S2.3 Classification and task allocation of debugging areas: For overlapping sections, combustion parameters (air-coal ratio) and steam-water parameters (feed water flow) are adjusted synchronously to avoid conflicts.

[0102] For boundary debugging areas, debug transition areas separately (such as the junction of the burner and the steam-water separator), and set up redundant sensors to verify parameter consistency.

[0103] S3. Debugging Strategy Generation

[0104] The S3.1 machine learning algorithm generates strategies. For the combustion system strategy, an LSTM network-based approach can be used to predict combustion efficiency and optimize the air-fuel ratio and burner oscillation angle. For the steam-water system strategy, fuzzy PID control can be used to adjust the feedwater flow rate and control the thermal deviation parameter.

[0105] S3.2 Debugging parameters are generated, and the air distribution is adjusted in real time through oxygen sensor feedback. At the same time, the shaft vibration parameter is set to a threshold of ≤0.05mm, and the turbine bearing stiffness is optimized by combining vibration spectrum analysis.

[0106] S4. Fault Prediction and Performance Prediction

[0107] The S4.1 digital twin model is constructed. Regarding thermal field simulation, the combustion temperature field can be simulated based on ANSYS CFX to predict the thermal stress in the burner area (error ≤3%).

[0108] For gas-solid two-phase flow simulation, Fluent was used to analyze the pulverized coal combustion trajectory and optimize the burner air distribution.

[0109] Regarding the simulation of equipment stress distribution, the creep deformation of high-temperature pipelines is calculated using ABAQUS to ensure a prediction accuracy of ±0.1mm.

[0110] S4.2 predicts the overall performance of the machine. For extreme working conditions, such as load change of ±20% / min and tripping of a single coal mill, the main steam pressure fluctuation is predicted to be ≤±1.5MPa. Finally, vibration spectrum diagram, temperature gradient cloud map and fault probability distribution table are generated.

[0111] S5. Closed-loop verification and parameter optimization

[0112] 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 the consistency of parameters at the segment boundary (error ≤2%).

[0113] S5.2 Model Iterative Optimization: Compare predictions with actual data (e.g., vibration prediction error > 3%), trigger digital twin model parameter correction, update thermal conductivity coefficient, and regenerate debugging strategy.

[0114] In summary, this invention overcomes the limitations of traditional ultra-supercritical coal-fired boiler unit start-up and commissioning systems that rely on load-sharing tests by defining modules and adaptive commissioning units through dynamic segmentation. 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 are inefficient and time-consuming. This invention allows users or systems to flexibly divide the front, middle, and final stages of equipment according to the physical characteristics of the unit or high-fault areas, forming multi-dimensional commissioning scenarios. Dynamic segmented commissioning can cover the "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 conditions.

[0115] Furthermore, this invention effectively solves 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. This invention uses reinforcement learning algorithms to dynamically optimize the segment boundaries, avoiding interference from debugging parameters; and combines digital twin models to simulate the thermal field and stress distribution at the segment boundaries, providing early warning of mechanical fatigue risks. For overlapping segment areas, a multi-objective optimization algorithm is used to balance combustion efficiency and emission parameters, ensuring consistency of debugging parameters and significantly improving debugging reliability and unit operation stability.

[0116] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

[0117] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A start-up and commissioning system for an ultra-supercritical coal-fired boiler unit, characterized in that: It includes a dynamic segmentation definition module, an assembly debugging module, a fault simulation and model prediction module, and a closed-loop verification and system integration module; The dynamic segmentation definition module is used to dynamically adjust the equipment segmentation boundaries according to the physical characteristics of the unit or the commissioning requirements, generate segmentation boundary configuration instructions, and transmit the segmentation boundary configuration instructions to the integrated commissioning module. The assembly debugging module includes an adjustment allocation unit and an adaptive debugging unit; The adjustment and allocation unit is used to receive segment boundary configuration instructions, and through a dynamic learning algorithm, to divide the working boundary of the adaptive debugging unit in real time, and generate debugging area and debugging task allocation instructions. The adaptive debugging unit is used to receive debugging task allocation instructions, generate and adaptively adjust debugging strategies through machine learning algorithms, and generate debugging parameters that match the segment boundary configuration instructions according to the debugging strategies. The fault simulation and model prediction module is used to build a digital twin model, integrate fault simulation function, generate 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 re-integrate components according to the overall performance prediction data, perform load verification, realize closed-loop verification between the model and the physical unit, generate verification result data, and feed the verification result data back to the fault simulation and model prediction module.

2. The start-up and commissioning system for an ultra-supercritical coal-fired boiler unit according to claim 1, characterized in that, The segment boundary configuration instructions define the scope and relationships of the front-end devices, the scope and relationships of the middle-end devices, and the scope and relationships of the final-end devices.

3. The start-up and commissioning system for an ultra-supercritical coal-fired boiler unit according to claim 2, characterized in that, The debugging area is divided into segmented overlapping area, boundary debugging area and single debugging area; the segmented overlapping area is the area where the working boundaries of different adaptive debugging units overlap due to the dynamic adjustment of the equipment segment boundaries; the boundary debugging area is the area located on both sides of the working boundaries of different adaptive debugging units. A single debugging region is a region that does not overlap with the working boundaries of other adaptive debugging units.

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

5. The start-up and commissioning system for an ultra-supercritical coal-fired boiler unit according to claim 4, characterized in that, The commissioning 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 commissioning an ultra-supercritical coal-fired boiler unit, characterized in that, Includes the following steps: S1. Dynamic segment configuration: Based on the physical characteristics of the unit or commissioning requirements, the segment boundary configuration instructions are generated through the dynamic segment definition module. The segment boundary configuration instructions include the scope and association of the front-end equipment, the scope and association of the middle-end equipment, and the scope and association of the terminal equipment. S2. Adaptive debugging unit partitioning: The unit receives segment boundary configuration instructions through the set debugging module, uses a dynamic learning algorithm to partition the working boundary of the adaptive debugging unit in real time, and generates debugging area and debugging task allocation instructions. The debugging area includes segmented overlapping area, boundary debugging area and single debugging area. S3. Debugging strategy generation: Receive debugging task allocation instructions through the adaptive debugging unit, generate debugging strategies based on machine learning algorithms, and generate debugging parameters that match the segment boundary configuration instructions according to the debugging strategies; S4. Fault Prediction and Performance Prediction: A digital twin model is constructed through the fault prediction and model prediction module, integrating fault prediction functions to generate overall machine performance prediction data; S5. Closed-loop verification and parameter optimization: The components are re-integrated according to the predicted scheme through the closed-loop verification and system integration module, load verification is performed, verification result data is generated and fed back to the fault simulation module, forming an iterative optimization closed loop.

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

8. A method for starting and commissioning 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 physical characteristics of the unit, and generate instructions on the scope and relationship of the front-end, middle-end, and final-end equipment. S1.

2. Dynamically adjust segment boundaries according to debugging requirements to cover key nodes; S1.3 Verify the rationality of segmentation boundaries through digital twin models to avoid signal interference or physical conflicts between devices.

9. A method for starting and commissioning 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 boundary of the adaptive debugging unit based on reinforcement learning algorithm; S2.

2. Assign priority to adaptive debugging units based on the network weight associated with the devices; S2.3, Classify and generate debugging areas and debugging task assignment instructions.

10. A method for starting and commissioning 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.

Images