Group combustion optimization method under flexible peak regulation of coal-fired boiler

Abstract

The invention relates to the technical field of boiler combustion optimization, in particular to a group combustion optimization method under flexible peak regulation of a coal-fired boiler, which comprises the following steps of: acquiring characteristic historical operation data of the coal-fired boiler, dividing operation conditions, calculating key operation parameters of the coal-fired boiler under different working conditions, and constructing a coal-fired boiler combustion model; coal loading information and load information of a future operation plan of the coal-fired boiler are read, and a planned operation condition group of the coal-fired boiler is constructed in combination with key operation variable action time; key operation parameter analysis is carried out on the planned operation condition group of the coal-fired boiler, group optimization is carried out based on a combustion model, a key operation variable adjustment scheme is given, and operation of the coal-fired boiler is guided. According to the method, the operation condition of the coal-fired boiler under the flexible peak regulation can be analyzed and judged, group combustion optimization of the coal-fired boiler is comprehensively guided in combination with the key operation variable action time, and the high efficiency and the environmental protection property of the coal-fired boiler under the flexible peak regulation can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of boiler combustion optimization, and in particular to a group combustion optimization method for coal-fired boilers under flexible peak regulation. Background Art

[0002] Solar energy and wind energy are typical representatives of rapid development of new energy. However, solar energy and wind energy are discontinuous and intermittent. Their continuous integration into the power grid will cause fluctuations in the grid load. Thermal power generation, as the core pillar of my country's power system, will continue to play a key role in ensuring power security for a considerable period of time.

[0003] Coal-fired power is gradually transforming from a traditional primary power source to a supporting power source that combines power supply and flexible regulation capabilities. Coal-fired boilers, as core equipment for thermal power generation, frequently operate at low and variable loads to meet the needs of flexible peak regulation. This results in reduced combustion stability and increased optimization difficulty. Existing combustion optimization technologies struggle to balance steady-state and non-steady-state operating conditions, lacking systematic solutions adapted to the needs of flexible peak regulation. As the power industry evolves toward higher efficiency, refinement, and automation, traditional combustion control methods are no longer able to meet the requirements of smart power plant construction. There is an urgent need to develop new technical solutions that can integrate multi-dimensional information such as historical operating data, coal quality characteristics, and load changes, and achieve coordinated optimization of multiple operating conditions. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above problems or at least partially solve the above problems, and to propose a group combustion optimization method for coal-fired boilers under flexible peak regulation.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a group combustion optimization method for coal-fired boilers under flexible peak regulation, comprising the following steps:

[0006] S1. Collect historical operating data of coal-fired boiler characteristics, classify operating conditions, calculate key operating parameters of coal-fired boilers under different operating conditions, and construct a coal-fired boiler combustion model through coupled mechanism modeling and data-driven methods;

[0007] S2. Read the coal-loading information and load information of the future operation plan of the coal-fired boiler, combine the action time of key operating variables, and construct the planned operation condition group of the coal-fired boiler;

[0008] S3. Analyze key operating parameters for the planned operating condition group of coal-fired boilers, optimize the group based on the combustion model, and use boiler efficiency, water-cooled wall temperature, and SCR inlet NOx concentration as optimization targets. Provide adjustment plans for key operating variables to guide coal-fired boiler operation.

[0009] In a preferred embodiment, the characteristic historical operating data of the coal-fired boiler in step S1 at least includes load, coal quality parameters, coal feed rate, primary air pulverized coal concentration, total air volume, primary and secondary air volume, primary and secondary air pressure, layer air door opening, burner air door opening, tail flue oxygen content, exhaust gas temperature, ash carbon content, water-cooled wall temperature, SCR inlet and outlet NOx concentration, tail flue CO concentration, covering 20%-100% load.

[0010] In a preferred embodiment, the operating conditions of step S1 are divided according to load and coal quality information, and the key operating parameters of the coal-fired boiler include boiler efficiency η, which is calculated using a counter-balance method.

[0011] In a preferred embodiment, the coal-fired boiler combustion model in step S1 is a mapping relationship between boiler efficiency, water-cooled wall temperature, SCR inlet NOx concentration and primary and secondary air distribution parameters under different operating conditions, and the mapping relationship is constructed using a deep learning network model.

[0012] In a preferred embodiment, the coal loading information and load information of the future operation plan in step S2 are the coal loading amount, coal quality parameters and load curve for the next day, and the key operating variables are the primary and secondary fan air volume, layer air door opening, and burner air door opening.

[0013] In a preferred embodiment, the step S2 of constructing the planned operating condition group of the coal-fired boiler comprises the following steps:

[0014] S2.1. Compare the operating times of the primary and secondary fan air volume, layer air door opening, and burner air door opening, and sort them in order of time length;

[0015] S2.2. Construct operating condition groups 1, 2, 3, and 4 based on the time length. Group 4 is a subset of group 3, group 3 is a subset of group 2, and group 2 is a subset of group 1. Among them, condition group 1 prioritizes optimizing the secondary air volume opening or the primary air volume, condition group 2 optimizes the primary air volume or the secondary air volume, condition group 3 optimizes the layer damper opening, and condition group 4 optimizes the burner damper opening.

[0016] In a preferred embodiment, step S3 performs prediction calculations on boiler efficiency, water wall temperature, and SCR inlet NOx concentration for the planned operating condition group of the coal-fired boiler.

[0017] In a preferred embodiment, the group optimization strategy of step S3 includes:

[0018] In the operating condition group 1, the corresponding operating variable 1 is optimized with the goal of maximizing boiler efficiency;

[0019] In the operating condition group 2, based on the previous optimization, the corresponding operating variable 2 is optimized with the goal of preventing the water wall temperature from overheating;

[0020] In operating condition group 3, based on the first two optimizations, the corresponding operating variable 3 is optimized with the goal of minimizing the NOx concentration at the SCR inlet;

[0021] In the operating condition group 4, the burner air door opening corresponding to the operating variable 4 is optimized.

[0022] In a preferred embodiment, the coal-fired boiler is guided to operate in step S3 by open-loop guidance or closed-loop control, and the closed-loop control is feedback controlled by bias.

[0023] Compared with the existing technology, the present invention has the following beneficial effects: it can analyze and judge the operating conditions of coal-fired boilers under flexible peak regulation, combine the action time of key operating variables, and comprehensively guide the group combustion optimization of coal-fired boilers, which can greatly improve the efficiency and environmental protection of coal-fired boilers under flexible peak regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of the present invention;

[0025] Figure 2 This is a diagram of the actual load and optimized load group of a unit of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] See also Figure 1 The present invention provides a technical solution: a group combustion optimization method for coal-fired boilers under flexible peak regulation, comprising the following steps:

[0028] S1. Collect historical operating data of coal-fired boiler characteristics, classify operating conditions, calculate key operating parameters of coal-fired boilers under different operating conditions, and construct a coal-fired boiler combustion model through coupled mechanism modeling and data-driven methods;

[0029] S2. Reading coal loading information and load information of the future operation plan of the coal-fired boiler, and combining the action time of key operating variables to construct a planned operation condition group of the coal-fired boiler, wherein constructing the planned operation condition group of the coal-fired boiler includes the following steps:

[0030] S2.1. Compare the operating times of the primary and secondary fan air volume, layer air door opening, and burner air door opening, and sort them in order of time length;

[0031] S2.2. Construct operating condition groups 1, 2, 3, and 4 based on the time length. Group 4 is a subset of Group 3, Group 3 is a subset of Group 2, and Group 2 is a subset of Group 1. Condition group 1 prioritizes optimizing the secondary air volume opening or the primary air volume, condition group 2 optimizes the primary air volume or the secondary air volume, condition group 3 optimizes the layer damper opening, and condition group 4 optimizes the burner damper opening.

[0032] S3. Analyze key operating parameters for the planned operating condition group of coal-fired boilers, perform group optimization based on the combustion model, and use boiler efficiency, water-cooled wall temperature, and SCR inlet NOx concentration as optimization targets. Provide adjustment plans for key operating variables and guide coal-fired boiler operation through open-loop or closed-loop control. Closed-loop control uses feedback control via bias.

[0033] During specific implementation, the characteristic historical operating data of coal-fired boilers shall at least include load, coal quality parameters, coal feed rate, primary air pulverized coal concentration, total air volume, primary and secondary air volume, primary and secondary air pressure, layer air door opening, burner air door opening, tail flue oxygen content, exhaust gas temperature, ash carbon content, water-cooled wall temperature, SCR inlet and outlet NOx concentration, and tail flue CO concentration, covering 20%-100% load.

[0034] In specific implementation, the operating conditions are divided according to load and coal quality information. The key operating parameters of the coal-fired boiler include boiler efficiency η, which is calculated using the counter-balance method.

[0035] In specific implementation, the coal-fired boiler combustion model is a mapping relationship between boiler efficiency, water-cooled wall temperature, SCR inlet NOx concentration and primary and secondary air distribution parameters under different operating conditions. The mapping relationship is constructed using a deep learning network model.

[0036] In specific implementation, the coal feeding information and load information of the future operation plan are the coal feeding amount, coal quality parameters and load curve of the next day, and the key operating variables are the primary and secondary fan air volume, layer air door opening, and burner air door opening.

[0037] During specific implementation, prediction calculations are performed on boiler efficiency, water wall temperature, and SCR inlet NOx concentration for the planned operating condition group of the coal-fired boiler.

[0038] In specific implementation, the group optimization strategies include:

[0039] In the operating condition group 1, the corresponding operating variable 1 is optimized with the goal of maximizing boiler efficiency;

[0040] In the operating condition group 2, based on the previous optimization, the corresponding operating variable 2 is optimized with the goal of preventing the water wall temperature from overheating;

[0041] In operating condition group 3, based on the first two optimizations, the corresponding operating variable 3 is optimized with the goal of minimizing the NOx concentration at the SCR inlet;

[0042] In the operating condition group 4, the burner air door opening corresponding to the operating variable 4 is optimized.

[0043] In this embodiment, take the case where the load of a coal-fired boiler unit increases by 90-300MW during a certain period of time as an example ( Figure 2 Real load), after applying the optimization method of the present invention, the following Figure 2 The optimized load group was optimized, achieving a 0.32% increase in boiler efficiency, a 9.7% reduction in NOx concentration at the SCR inlet, and no overheating of the water-cooled wall temperature, ensuring the optimization of coal-fired boiler combustion.

[0044] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing group combustion of coal-fired boilers under flexible peak load regulation, characterized in that: The following steps are involved: S1. Collect historical operating data of coal-fired boiler characteristics, classify operating conditions, calculate key operating parameters of coal-fired boilers under different operating conditions, and construct a coal-fired boiler combustion model through coupled mechanism modeling and data-driven methods; S2. Read the coal-loading information and load information of the future operation plan of the coal-fired boiler, combine the action time of key operating variables, and construct the planned operation condition group of the coal-fired boiler; S3. Analyze key operating parameters for the planned operating condition group of coal-fired boilers, optimize the group based on the combustion model, and use boiler efficiency, water-cooled wall temperature, and SCR inlet NOx concentration as optimization targets. Provide adjustment plans for key operating variables to guide coal-fired boiler operation.

2. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 1, characterized in that: In step S1, the characteristic historical operating data of the coal-fired boiler at least includes load, coal quality parameters, coal feed rate, primary air pulverized coal concentration, total air volume, primary and secondary air volume, primary and secondary air pressure, layer air door opening, burner air door opening, tail flue oxygen content, exhaust gas temperature, ash carbon content, water-cooled wall temperature, SCR inlet and outlet NOx concentration, tail flue CO concentration, covering 20%-100% load.

3. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 1, characterized in that: In step S1, the operating conditions are divided according to load and coal quality information, and the key operating parameters of the coal-fired boiler include boiler efficiency η, which is calculated using the counter-balance method.

4. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 2, characterized in that: In step S1, the coal-fired boiler combustion model is a mapping relationship between boiler efficiency, water-cooled wall temperature, SCR inlet NOx concentration and primary and secondary air distribution parameters under different operating conditions, and the mapping relationship is constructed using a deep learning network model.

5. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 4, characterized in that: In step S2, the coal feeding information and load information of the future operation plan are the coal feeding amount, coal quality parameters and load curve for the next day, and the key operating variables are the primary and secondary fan air volume, layer air door opening, and burner air door opening.

6. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 5, characterized in that: In step S2, the construction of the coal-fired boiler planned operating condition group includes the following steps: S2.

1. Compare the operating times of the primary and secondary fan air volume, layer air door opening, and burner air door opening, and sort them in order of time length; S2.

2. Construct operating condition groups 1, 2, 3, and 4 based on the time length. Group 4 is a subset of group 3, group 3 is a subset of group 2, and group 2 is a subset of group 1. Among them, condition group 1 prioritizes optimizing the secondary air volume opening or the primary air volume, condition group 2 optimizes the primary air volume or the secondary air volume, condition group 3 optimizes the layer damper opening, and condition group 4 optimizes the burner damper opening.

7. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 6, characterized in that: In step S3, the boiler efficiency, water wall temperature, and SCR inlet NOx concentration are predicted and calculated for the planned operating condition group of the coal-fired boiler.

8. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 7, characterized in that: In step S3, the group optimization strategy includes: In the operating condition group 1, the corresponding operating variable 1 is optimized with the goal of maximizing boiler efficiency; In the operating condition group 2, based on the previous optimization, the corresponding operating variable 2 is optimized with the goal of preventing the water wall temperature from overheating; In operating condition group 3, based on the first two optimizations, the corresponding operating variable 3 is optimized with the goal of minimizing the NOx concentration at the SCR inlet; In the operating condition group 4, the burner air door opening corresponding to the operating variable 4 is optimized.

9. The method for optimizing group combustion of coal-fired boilers under flexible peak load regulation according to claim 8, characterized in that: In step S3, the coal-fired boiler is guided to operate in an open-loop guidance or closed-loop control mode, wherein the closed-loop control is feedback-controlled by a bias mode.

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