Boiler fast ramping control system and method based on thermal inertia scheduling and combustion cooperation

Abstract

The application discloses a boiler rapid climbing control system and method based on thermal inertia scheduling and combustion cooperation, comprising a thermal inertia state sensing and estimating module and an intelligent cooperative controller. The sensing and estimating module is used for real-time calculation of the adjustable heat storage of a boiler-turbine system. The intelligent cooperative controller adopts a model predictive control (MPC) algorithm and is configured to generate thermal inertia release control instructions and combustion strengthening control instructions in the process of load increase. First output power is generated based on the thermal inertia release control instructions, second output power is generated based on the combustion strengthening control instructions, the first output power and the second output power are superimposed to track the load instruction, and the system is safely and stably operated. The unit rapid climbing performance is greatly improved, the core parameters are stably ensured in the process of load increase, and the safe operation of the unit is ensured.

Description

Technical Field

[0001] This invention relates to the field of boiler ramp control technology, specifically to a boiler rapid ramp control system and method based on thermal inertial scheduling and combustion coordination. Background Technology

[0002] Thermal power units are one of the core power sources of the power system, but their speed in responding to grid load commands has long been limited by two major inertial factors: the thermal inertia of fuel supply and combustion process, and the thermal inertia of metal heating surfaces and working fluid.

[0003] Traditional control strategies treat these thermal inertias as adverse factors, only passively compensating for the resulting response delays, failing to fully tap the equipment's potential. In recent years, some studies have proposed using boiler thermal storage for rapid frequency regulation; however, these solutions are mostly limited to the rapid action of turbine control valves, with insufficient consideration for the coordinated optimization of boiler-side combustion. This can easily lead to significant fluctuations or even exceedances of key parameters such as main steam pressure and temperature, making continuous and stable operation difficult.

[0004] Existing technologies lack a unified and quantitative model to fully describe the real-time thermal storage status of the entire boiler-turbine system. Furthermore, they lack advanced control algorithms that can globally optimize "thermal inertial scheduling" and "combustion control" as two core components that are both decoupled and require close coordination. This makes it impossible to meet the urgent need for flexibility of thermal power units under a high proportion of new energy power systems. Summary of the Invention

[0005] The purpose of this invention is to propose a boiler rapid ramp control system and method based on thermal inertial scheduling and combustion coordination to solve the problems mentioned in the background art.

[0006] To achieve the above-mentioned objectives, the first technical solution adopted by the present invention is: a boiler rapid ramp control system based on thermal inertial scheduling and combustion coordination, comprising: a thermal inertial state perception and estimation module and an intelligent cooperative controller;

[0007] The sensing and estimation module is used to calculate the schedulable heat storage of the boiler-turbine system in real time. The intelligent collaborative controller employs the Model Predictive Control (MPC) algorithm and is configured to collaboratively generate thermal inertia release control commands and combustion enhancement control commands during the load increase process. The first output power is generated based on the thermal inertia release control command, and the second output power is generated based on the combustion enhancement control command. The first output power and the second output power are superimposed to track the load command, so that the system can operate safely and stably.

[0008] Furthermore, the thermal inertial state perception and estimation module includes a thermal inertial state perception network and a state calculation unit; The thermal inertial state sensing network is used to collect the metal wall temperature of key heating components of the boiler, the pressure, temperature and flow rate parameters of the main steam and feedwater, as well as the inlet and outlet steam and water medium parameters of the heating surface. The state calculation unit is used to calculate the total heat storage of the system, the rapidly dispatchable heat storage, and the time constant and thermal resistance equivalent thermodynamic parameters of each heat storage unit based on the collected real-time parameters.

[0009] Furthermore, the formula for calculating the total heat storage of the system is as follows: Q_stored=Σ(m_metal*cp_metal*T_metal)+Σ(m_fluid*h_fluid); In the formula, Q_stored represents the total heat storage of the system, h_fluid is the specific enthalpy of the working fluid, m_metal represents the mass of the metal component, cp_metal represents the specific heat capacity of the metal at constant pressure, T_metal represents the metal wall temperature, and m_fluid represents the mass of the working fluid.

[0010] Furthermore, the working mode of the intelligent collaborative controller includes a pre-climbing scheduling stage and a coupled control stage during the climbing execution period; The pre-climbing scheduling phase is used to execute the heat storage strategy 3-100 minutes in advance. By increasing the main steam pressure setpoint by 0.5-1.0 MPa or reducing the excess air coefficient, the system can quickly schedule the stored heat. The ramp-up execution period coupling control phase is used to decompose the total load demand increment into instantaneous power increment and steady-state power increment, and simultaneously generate corresponding thermal inertia release control command sequences and combustion enhancement control command sequences.

[0011] Furthermore, it also includes an interface with the high-temperature internal circulation system of fly ash; the combustion enhancement control command sequence includes control commands for the fly ash reinjection system; and / or the heat storage scheduling includes preheating the boiler heating surface by starting the fly ash system in advance.

[0012] Furthermore, the collaborative control executed by the intelligent collaborative controller upon receiving the load increase command includes: Generate a sequence of thermal inertia release control commands for the turbine control valve and / or feedwater system to quickly release the thermal energy stored in the system and generate instantaneous power increments; A sequence of combustion enhancement control commands is generated simultaneously for the coal feed rate and / or air volume system to gradually increase combustion heat release and generate steady-state power increment; The MPC algorithm uses rolling optimization to superimpose the effects of the two instruction sequences over time, while satisfying the prediction constraints on the main steam pressure and temperature parameters.

[0013] Furthermore, the heat storage strategy dynamically adjusts the main steam pressure increase and the excess air coefficient adjustment range based on the unit's current heat storage status and the predicted grid load demand.

[0014] To achieve the aforementioned objectives, the second technical solution adopted by this invention is: a boiler rapid ramp-up control method based on thermal inertial scheduling and combustion coordination, applied to a boiler rapid ramp-up control system based on thermal inertial scheduling and combustion coordination, comprising the following steps: S1: Real-time acquisition of metal wall temperature and working fluid related operating parameters of key heated components of the boiler, and real-time transmission of the acquired parameters to the status calculation unit; S2: Based on the received real-time acquisition parameters, the system calculates the total heat storage, the rapidly dispatchable heat storage, and the equivalent thermodynamic parameters of each heat storage unit online through the built-in thermodynamic model library, and estimates the heat storage state of the system in real time to obtain load increase prediction information. S3: Determine whether load increase prediction information has been received. If so, execute the thermal storage scheduling strategy 3-10 minutes in advance to increase the system's ability to quickly schedule stored heat. If not, remain in standby mode and wait for the rapid load increase command. S4: When the intelligent collaborative controller receives the rapid load increase command, it decomposes the total load demand increment into instantaneous power increment and steady-state power increment based on the MPC algorithm framework, and solves the optimal thermal inertia release control command and combustion enhancement control command within a preset time period. S5: Simultaneously issue and execute two control commands, providing initial rapid power support for load response through thermal inertia release, and providing main power for load increment through combustion enhancement; S6: During the load ramp-up process, the MPC algorithm is used to predict the dynamic change trend of key operating parameters, and the control commands are dynamically adjusted according to the prediction results until the unit load reaches the target value and achieves stable operation.

[0015] Furthermore, the key operating parameters in step S6 include main steam pressure, main steam temperature, reheat steam temperature, metal wall temperature difference, and furnace negative pressure, wherein the main steam pressure fluctuation range is controlled within ±1.2 MPa, and the main steam temperature fluctuation range is controlled within ±5°C.

[0016] Due to the application of the above technical solution, the present invention has the following advantages compared with the prior art: 1. Significantly improves the unit's rapid ramp-up performance, with a response rate reaching industry-leading levels: This invention utilizes a dual-engine collaborative mechanism that provides instantaneous power support through rapid release of thermal inertia and steady-state power increment through enhanced combustion. Combined with the constraint control of rolling optimization using the MPC algorithm, it increases the unit's rapid load ramp-up rate to 5-6% Pe / min, comparable to or even surpassing the load response capability of gas turbines. This effectively solves the technical pain points of traditional units' slow ramp-up rate and inability to meet the grid's rapid peak-shaving requirements, helping thermal power units adapt to the flexible dispatching needs of new power systems.

[0017] 2. Ensuring stable core parameters during ramp-up and guaranteeing safe unit operation: Compared to existing heat storage technologies that rely solely on rapid turbine valve movements, this invention achieves precise timing matching between thermal inertia release and combustion enhancement through an intelligent collaborative controller. Combined with a built-in parameter prediction model and constraint management function, it can effectively suppress fluctuations in core operating parameters such as main steam pressure and main steam temperature, controlling main steam pressure fluctuations within ±1.2 MPa and main steam temperature fluctuations within ±5°C. This avoids risks such as unit tripping and equipment damage caused by parameter exceeding limits. At the same time, it controls parameters such as metal wall temperature difference and furnace negative pressure to meet safety standards, significantly improving the safety and stability of the unit during rapid ramp-up.

[0018] 3. Achieving refined thermal inertia scheduling and improving energy utilization efficiency: This invention constructs a quantitative thermal storage state calculation model through a thermal inertia state perception and estimation module. It can accurately obtain the total heat storage of the system in real time, and quickly schedule the heat storage and equivalent parameters of the heat storage units, breaking the limitations of existing technologies in fuzzy judgment of the heat storage state. At the same time, through the coordinated control of pre-climbing heat storage scheduling and dynamic heat storage release during the climbing process, it maximizes the inherent heat storage potential of the boiler, reduces fuel waste and energy loss, improves the economic efficiency of unit operation, and reduces power generation energy consumption.

[0019] 4. High adaptability, low deployment cost, and easy engineering promotion: The control system of this invention can be independently deployed in the plant-level SIS system of the unit without major modifications to the existing hardware equipment of the unit. The rapid ramp control function can be realized only through software upgrades, which greatly reduces the investment in modification and deployment costs. At the same time, the thermodynamic model library of the state calculation unit can perform offline identification and parameter tuning based on the historical operating data of the unit, adapting to thermal power units of different capacities and boiler types. It can also flexibly cooperate with the high-temperature internal circulation system of fly ash, further expanding the application scenarios and possessing extremely strong engineering promotion value. Attached Figure Description

[0020] Figure 1 The flowchart of the boiler rapid ramp control method based on thermal inertial scheduling and combustion coordination provided by the embodiments of the present invention is shown. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or system 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 systems.

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] This invention provides a boiler rapid ramp control system based on thermal inertial scheduling and combustion coordination, comprising: a thermal inertial state perception and estimation module and an intelligent cooperative controller; The sensing and estimation module is used to calculate the dispatchable heat storage of the boiler-turbine system in real time; The intelligent collaborative controller uses the Model Predictive Control (MPC) algorithm and is configured to collaboratively generate thermal inertia release control commands and combustion enhancement control commands during the load increase process. The first output power is generated based on the thermal inertia release control command, and the second output power is generated based on the combustion enhancement control command. The first output power and the second output power are superimposed to track the load command, so that the system can operate safely and stably.

[0025] It should be noted that the system estimates the dispatchable heat storage of the boiler-turbine system in real time and employs Model Predictive Control (MPC) as an intelligent cooperative controller. During rapid load increases, the MPC synchronously generates and coordinates the execution of two instruction sequences: First, there are commands to rapidly release thermal inertia (such as opening the turbine control valve) to provide an instantaneous power surge; second, there are commands to enhance combustion (such as increasing coal feed) to provide steady-state power growth. MPC ensures that the effects of both are perfectly superimposed, achieving a rapid ramp-up of 5-6% / min while strictly ensuring the safety of key parameters such as main steam pressure and temperature.

[0026] According to an embodiment of the present invention, the thermal inertial state sensing and estimation module includes a thermal inertial state sensing network and a state calculation unit; The thermal inertial state sensing network is used to collect the metal wall temperature of key heating components of the boiler, the pressure, temperature and flow rate parameters of the main steam and feedwater, as well as the inlet and outlet steam and water medium parameters of the heating surface. The state calculation unit is used to calculate the total heat storage of the system based on the collected real-time parameters. The state calculation unit has a built-in thermodynamic model library, which calculates the total heat storage of the system, the heat storage that can be quickly dispatched, and the time constant and thermal resistance equivalent thermodynamic parameters of each heat storage unit online.

[0027] It should be noted that the system perception and modeling layer includes: Thermal inertial state sensing network: Metal thermal storage sensing: Add or utilize existing high-precision temperature sensors (such as armored thermocouples) to key parts of the boiler (such as the upper and lower walls of the steam drum, the outlet header of the water-cooled wall, and the tube walls of the superheater / reheater) to monitor the metal wall temperature in real time.

[0028] Working fluid heat storage sensing: Utilizing existing DCS data, real-time acquisition of main steam pressure, temperature, and flow rate, feedwater pressure, temperature, and flow rate, as well as steam and water parameters at the inlet and outlet of each section of the heating surface.

[0029] State Calculation: Based on the above real-time data, the following key state variables are calculated online using the built-in thermodynamic model library: total system heat storage and rapidly dispatchable heat storage (Q_dispatchable); Rapidly dispatchable thermal storage refers to the portion of heat that can be released or absorbed within a short period (e.g., 1-3 minutes) by changing the operating mode, provided that equipment safety is ensured (stress and temperature difference are within limits) and main parameters (such as main steam temperature) do not exceed limits. This value is dynamically changing and is a key input for control.

[0030] The equivalent parameters of "time constant" and "thermal resistance" of each thermal storage unit are used to predict the dynamic response of thermal inertia release.

[0031] According to an embodiment of the present invention, the formula for calculating the total heat storage of the system is as follows: Q_stored=Σ(m_metal*cp_metal*T_metal)+Σ(m_fluid*h_fluid); In the formula, Q_stored represents the total heat storage of the system, h_fluid is the specific enthalpy of the working fluid, m_metal represents the mass of the metal component, cp_metal represents the specific heat capacity of the metal at constant pressure, T_metal represents the metal wall temperature, and m_fluid represents the mass of the working fluid.

[0032] According to an embodiment of the present invention, the working mode of the intelligent collaborative controller includes a pre-climbing scheduling stage and a coupled control stage during the climbing execution period; The pre-climbing scheduling phase is used to execute the heat storage strategy 3-100 minutes in advance. This is achieved by increasing the main steam pressure setpoint by 0.5-1.0 MPa or reducing the excess air coefficient, thereby increasing the system's ability to quickly schedule stored heat. The ramp-up execution period coupling control phase is used to decompose the total load demand increment into instantaneous power increment and steady-state power increment, and simultaneously generate the corresponding thermal inertia release control command sequence and combustion enhancement control command sequence.

[0033] It should be noted that the intelligent collaborative controller is a dual-engine coupled controller, which is the brain of this invention, and its working mode is divided into two stages: Phase 1: Pre-climb "heat storage" or "preparatory" scheduling (optional, for planned rapid climbs); When the system anticipates a rapid load increase at a future time (e.g., T0) based on market information or scheduling plans, the controller can act in advance (e.g., 3-100 minutes before T0).

[0034] Thermal storage strategy: Under the premise of ensuring safety, the controller issues instructions to slightly and slowly increase the main steam pressure setpoint (e.g., increase by 0.5-1.0 MPa), or appropriately reduce the excess air coefficient. These operations will allow more energy to be stored in the system in the form of pressure energy and high-temperature metal / working fluid internal energy, increasing Q_dispatchable. This process is similar to "storing energy" in a flywheel.

[0035] Phase Two: "Dual-Engine" Coupling Control During the Climbing Execution Period, specifically including: Trigger: At time T0, the rapid load increase command (target load L_target, target ramp rate R_target) is officially received.

[0036] Control objective decomposition: The controller decomposes the total load demand increment ΔL into two parts: 1. Instantaneous power increment (ΔL_thermal_inertia) provided by rapid release of thermal inertia: This part responds extremely quickly (on the order of seconds) and is used to achieve the "initial jump" of load, making up for the initial delay of combustion enhancement.

[0037] 2. Steady-state power increment (ΔL_combustion) provided by combustion system enhancement: This part constitutes the main body of the load increment, but there is a delay and inertia of tens of seconds.

[0038] Dual-engine control command generation: Engine 1 (Thermal Inertia Release) Control Law: Based on the current Q_dispatchable, equipment safety constraints, and the demand for ΔL_thermal_inertia, the controller calculates an optimal thermal inertia release curve. This curve is transformed into a refined, time-sequential sequence of commands for key actuators such as the turbine high-pressure regulating valve (GV) opening and feedwater flow. For example, in the first 30 seconds after the start of the ramp-up, the GV is rapidly but smoothly increased by 5%-10%, utilizing boiler heat storage to generate more electricity in a short period; simultaneously, the feedwater flow may be fine-tuned to balance heat absorption and control steam temperature.

[0039] Engine 2 (Combustion Enhancement) Control Law: The controller synchronously generates a combustion enhancement curve that matches the thermal inertia release curve. This curve command is sent to the fuel main control and airflow control systems. If a fly ash system is coupled, the fly ash system is simultaneously commanded to operate at its maximum capacity. The intensity of combustion enhancement gradually increases over time, and its ramp rate is related to the target ramp rate R_target.

[0040] Coupling and Coordination Algorithms: This is the core algorithm of the invention. It ensures that ΔL_thermal_inertia(t) + ΔL_combustion(t) ≈ total demand ΔL(t), and that all key parameters such as main steam pressure, main / reheat steam temperature, metal wall temperature difference, and furnace negative pressure remain within safe and permissible ranges throughout the ramp-up process.

[0041] The algorithm employs a Model Predictive Control (MPC) framework. In each control cycle (e.g., 1 second), MPC, based on the current system state and a predictive model for the next tens of seconds, performs rolling optimization to find the optimal dual-engine control command sequence for the next few cycles, and then issues the first command. Its optimization objective function is typically: minimizing load tracking error + minimizing critical parameter deviation + penalizing excessively large / rapid control actions.

[0042] Over-limit protection: MPC's built-in model can predict whether continuing to release thermal inertia as it is currently trending will lead to excessively low main steam pressure (affecting subsequent combustion) or excessive steam temperature. Once a risk is predicted, it automatically adjusts the slope of the thermal inertia release curve and the slope of the combustion enhancement curve to achieve a smooth transition by "shaving off peaks and filling valleys".

[0043] According to embodiments of the present invention, it further includes an interface with a high-temperature internal circulation system for fly ash; a combustion enhancement control command sequence includes control commands for the fly ash reinjection system; and / or heat storage scheduling includes preheating the boiler heating surfaces by starting the fly ash system in advance.

[0044] According to an embodiment of the present invention, the collaborative control executed by the intelligent collaborative controller after receiving a load increase command includes: Generate a sequence of thermal inertia release control commands for the turbine control valve and / or feedwater system to quickly release the thermal energy stored in the system and generate instantaneous power increments; A sequence of combustion enhancement control commands is generated simultaneously for the coal feed rate and / or air volume system to gradually increase combustion heat release and generate steady-state power increment; Among them, the MPC algorithm uses rolling optimization to superimpose the effects of the two instruction sequences over time and satisfy the prediction constraints on the main steam pressure and temperature parameters.

[0045] According to an embodiment of the present invention, the thermal storage strategy dynamically adjusts the main steam pressure increase and the excess air coefficient adjustment range based on the current thermal storage status of the unit and the predicted grid load demand.

[0046] like Figure 1 As shown, to achieve the above-mentioned objective, the second technical solution adopted by this invention is: a boiler rapid ramp control method based on thermal inertial scheduling and combustion coordination, applied to a boiler rapid ramp control system based on thermal inertial scheduling and combustion coordination, comprising the following steps: S1: Real-time acquisition of metal wall temperature and working fluid related operating parameters of key heated components of the boiler, and real-time transmission of the acquired parameters to the status calculation unit; S2: Based on the received real-time acquisition parameters, the system calculates the total heat storage, the rapidly dispatchable heat storage, and the equivalent thermodynamic parameters of each heat storage unit online through the built-in thermodynamic model library, and estimates the heat storage state of the system in real time to obtain load increase prediction information. S3: Determine whether load increase prediction information has been received. If so, execute the thermal storage scheduling strategy 3-10 minutes in advance to increase the system's ability to quickly schedule stored heat. If not, remain in standby mode and wait for the rapid load increase command. S4: When the intelligent collaborative controller receives the rapid load increase command, it decomposes the total load demand increment into instantaneous power increment and steady-state power increment based on the MPC algorithm framework, and solves the optimal thermal inertia release control command and combustion enhancement control command within a preset time period. S5: Simultaneously issue and execute two control commands, providing initial rapid power support for load response through thermal inertia release, and providing main power for load increment through combustion enhancement; S6: During the load ramp-up process, the MPC algorithm is used to predict the dynamic change trend of key operating parameters, and the control commands are dynamically adjusted according to the prediction results until the unit load reaches the target value and achieves stable operation.

[0047] According to an embodiment of the present invention, the key operating parameters in step S6 include main steam pressure, main steam temperature, reheat steam temperature, metal wall temperature difference and furnace negative pressure, wherein the main steam pressure fluctuation range is controlled within ±1.2 MPa and the main steam temperature fluctuation range is controlled within ±5°C.

[0048] Due to the application of the above technical solution, the present invention has the following advantages compared with the prior art: 1. Significantly improves the unit's rapid ramp-up performance, with a response rate reaching industry-leading levels: This invention utilizes a dual-engine collaborative mechanism that provides instantaneous power support through rapid release of thermal inertia and steady-state power increment through enhanced combustion. Combined with the constraint control of rolling optimization using the MPC algorithm, it increases the unit's rapid load ramp-up rate to 5-6% Pe / min, comparable to or even surpassing the load response capability of gas turbines. This effectively solves the technical pain points of traditional units' slow ramp-up rate and inability to meet the grid's rapid peak-shaving requirements, helping thermal power units adapt to the flexible dispatching needs of new power systems.

[0049] 2. Ensuring stable core parameters during ramp-up and guaranteeing safe unit operation: Compared to existing heat storage technologies that rely solely on rapid turbine valve movements, this invention achieves precise timing matching between thermal inertia release and combustion enhancement through an intelligent collaborative controller. Combined with a built-in parameter prediction model and constraint management function, it can effectively suppress fluctuations in core operating parameters such as main steam pressure and main steam temperature, controlling main steam pressure fluctuations within ±1.2 MPa and main steam temperature fluctuations within ±5°C. This avoids risks such as unit tripping and equipment damage caused by parameter exceeding limits. At the same time, it controls parameters such as metal wall temperature difference and furnace negative pressure to meet safety standards, significantly improving the safety and stability of the unit during rapid ramp-up.

[0050] 3. Achieving refined thermal inertia scheduling and improving energy utilization efficiency: This invention constructs a quantitative thermal storage state calculation model through a thermal inertia state perception and estimation module. It can accurately obtain the total heat storage of the system in real time, and quickly schedule the heat storage and equivalent parameters of the heat storage units, breaking the limitations of existing technologies in fuzzy judgment of the heat storage state. At the same time, through the coordinated control of pre-climbing heat storage scheduling and dynamic heat storage release during the climbing process, it maximizes the inherent heat storage potential of the boiler, reduces fuel waste and energy loss, improves the economic efficiency of unit operation, and reduces power generation energy consumption.

[0051] 4. High adaptability, low deployment cost, and easy engineering promotion: The control system of this invention can be independently deployed in the plant-level SIS system of the unit without major modifications to the existing hardware equipment of the unit. The rapid ramp control function can be realized only through software upgrades, which greatly reduces the investment in modification and deployment costs. At the same time, the thermodynamic model library of the state calculation unit can perform offline identification and parameter tuning based on the historical operating data of the unit, adapting to thermal power units of different capacities and boiler types. It can also flexibly cooperate with the high-temperature internal circulation system of fly ash, further expanding the application scenarios and possessing extremely strong engineering promotion value.

[0052] Those skilled in the art will understand that, for ease of explanation, the example is provided with one memory and one processor. In actual terminals or servers, multiple processors and memories may exist. Memory can also be referred to as storage medium or storage device, etc., and the embodiments of this application do not limit this.

[0053] It should be understood that in the embodiments of this application, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may also be a general-purpose microprocessor, graphics processing unit (GPU), or one or more integrated circuits to execute relevant programs to achieve the functions required by the embodiments of this application.

[0054] The processor can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of this application can be completed through integrated logic circuits in the processor hardware or instructions in software form. The aforementioned processor can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the functions required by the units included in the methods, systems, and storage media of the embodiments of this application.

[0055] It should also be understood that the memory mentioned in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache.

[0056] By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0057] The memory can also be a Compact Disc Read-Only Memory (CD-ROM) or other optical disc storage, optical disk storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. The memory can exist independently and be connected to the processor via a bus. The memory can also be integrated with the processor. The memory can store programs, and when the program stored in the memory is executed by the processor, the processor performs the various steps of the method determined in the above embodiments of this application.

[0058] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, the memory (storage module) is integrated into the processor. It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0059] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0060] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules within the processor. The software modules can reside in mature storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. Since this storage medium is located in memory, the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method; to avoid repetition, these will not be described in detail here.

[0061] Those skilled in the art will recognize that the various illustrative logical blocks (ILBs) and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0062] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer-programmed program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a processor, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a computer network, or other programmable device.

[0063] This embodiment also provides a computer-readable storage medium storing a computer program that causes a computer to execute in order to implement the above-described method based on multi-stage vortex and intelligent feedforward.

[0064] It should be noted that computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic) or wireless (e.g., infrared, wireless, microwave, etc.) means, or from one website, computer, server, or data center to a mobile phone processor via a wired means. A computer-readable storage medium can be any usable medium that a computer can access, or a data storage system such as a server or data center that integrates one or more usable media. Usable media can be magnetic media (e.g., floppy disks, hard disks), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives), etc.

[0065] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A boiler rapid ramp control system based on thermal inertial scheduling and combustion coordination, characterized in that, include: Thermal inertial state perception and estimation module, intelligent cooperative controller; The sensing and estimation module is used to calculate the schedulable heat storage of the boiler-turbine system in real time. The intelligent collaborative controller employs the Model Predictive Control (MPC) algorithm and is configured to collaboratively generate thermal inertia release control commands and combustion enhancement control commands during the load increase process. The first output power is generated based on the thermal inertia release control command, and the second output power is generated based on the combustion enhancement control command. The first output power and the second output power are superimposed to track the load command, so that the system can operate safely and stably.

2. The boiler rapid ramp control system based on thermal inertia scheduling and combustion coordination as described in claim 1, characterized in that, The thermal inertial state perception and estimation module includes a thermal inertial state perception network and a state calculation unit; The thermal inertial state sensing network is used to collect the metal wall temperature of key heating components of the boiler, the pressure, temperature and flow rate parameters of the main steam and feedwater, as well as the inlet and outlet steam and water medium parameters of the heating surface. The state calculation unit is used to calculate the total heat storage of the system, the rapidly dispatchable heat storage, and the time constant and thermal resistance equivalent thermodynamic parameters of each heat storage unit based on the collected real-time parameters.

3. The boiler rapid ramp control system based on thermal inertia scheduling and combustion coordination as described in claim 2, characterized in that, The formula for calculating the total heat storage of the system is as follows: Q_stored=Σ(m_metal*cp_metal*T_metal)+Σ(m_fluid*h_fluid); In the formula, Q_stored represents the total heat storage of the system, h_fluid is the specific enthalpy of the working fluid, m_metal represents the mass of the metal component, cp_metal represents the specific heat capacity of the metal at constant pressure, T_metal represents the metal wall temperature, and m_fluid represents the mass of the working fluid.

4. The boiler rapid ramp control system based on thermal inertia scheduling and combustion coordination as described in claim 3, characterized in that, The working mode of the intelligent collaborative controller includes a pre-climbing scheduling stage and a coupled control stage during the climbing execution period. The pre-climbing scheduling phase is used to execute the heat storage strategy 3-100 minutes in advance. By increasing the main steam pressure setpoint by 0.5-1.0 MPa or reducing the excess air coefficient, the system can quickly schedule the stored heat. The ramp-up execution period coupling control phase is used to decompose the total load demand increment into instantaneous power increment and steady-state power increment, and simultaneously generate corresponding thermal inertia release control command sequences and combustion enhancement control command sequences.

5. The boiler rapid ramp control system based on thermal inertia scheduling and combustion coordination as described in claim 4, characterized in that, It also includes an interface with the high-temperature internal circulation system of fly ash; the combustion enhancement control command sequence includes control commands for the fly ash reinjection system; and / or the heat storage scheduling includes preheating the boiler heating surface by starting the fly ash system in advance.

6. The boiler rapid ramp control system based on thermal inertia scheduling and combustion coordination as described in claim 5, characterized in that, The collaborative control executed by the intelligent collaborative controller after receiving the load increase command includes: Generate a sequence of thermal inertia release control commands for the turbine control valve and / or feedwater system to quickly release the thermal energy stored in the system and generate instantaneous power increments; A sequence of combustion enhancement control commands is generated simultaneously for the coal feed rate and / or air volume system to gradually increase combustion heat release and generate steady-state power increment; The MPC algorithm uses rolling optimization to superimpose the effects of the two instruction sequences over time, while satisfying the prediction constraints on the main steam pressure and temperature parameters.

7. The boiler rapid ramp control system based on thermal inertia scheduling and combustion coordination as described in claim 6, characterized in that, The heat storage strategy dynamically adjusts the main steam pressure increase and the excess air coefficient adjustment range based on the unit's current heat storage status and the predicted grid load demand.

8. A boiler rapid ramp-up control method based on thermal inertial scheduling and combustion coordination, applied to the boiler rapid ramp-up control system based on thermal inertial scheduling and combustion coordination as described in any one of claims 1-7, characterized in that, Includes the following steps: S1: Real-time acquisition of metal wall temperature and working fluid related operating parameters of key heated components of the boiler, and real-time transmission of the acquired parameters to the status calculation unit; S2: Based on the received real-time acquisition parameters, the system calculates the total heat storage, the rapidly dispatchable heat storage, and the equivalent thermodynamic parameters of each heat storage unit online through the built-in thermodynamic model library, and estimates the heat storage state of the system in real time to obtain load increase prediction information. S3: Determine whether load increase prediction information has been received. If so, execute the thermal storage scheduling strategy 3-10 minutes in advance to increase the system's ability to quickly schedule stored heat. If not, remain in standby mode and wait for the rapid load increase command. S4: When the intelligent collaborative controller receives the rapid load increase command, it decomposes the total load demand increment into instantaneous power increment and steady-state power increment based on the MPC algorithm framework, and solves the optimal thermal inertia release control command and combustion enhancement control command within a preset time period. S5: Simultaneously issue and execute two control commands, providing initial rapid power support for load response through thermal inertia release, and providing main power for load increment through combustion enhancement; S6: During the load ramp-up process, the MPC algorithm is used to predict the dynamic change trend of key operating parameters, and the control commands are dynamically adjusted according to the prediction results until the unit load reaches the target value and achieves stable operation.

9. The boiler rapid ramp control method based on thermal inertia scheduling and combustion coordination as described in claim 8, characterized in that, The key operating parameters in step S6 include main steam pressure, main steam temperature, reheat steam temperature, metal wall temperature difference, and furnace negative pressure. The main steam pressure fluctuation range is controlled within ±1.2 MPa, and the main steam temperature fluctuation range is controlled within ±5°C.

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