Ammonia spraying control method and device of denitration system, electronic equipment and storage medium

Abstract

The invention discloses an ammonia spraying control method and device of a denitration system, electronic equipment and a storage medium. The performance monitoring module is used for collecting disturbance factor data such as boiler load size, load change direction, load change rate, mill operation mode, mill start-stop sequence, primary air and secondary air ratio and action sequence; based on the disturbance factor data, a target ammonia injection amount is predicted through an ammonia demand amount prediction module, and a feedforward control signal is generated through an intelligent feedforward control module; and adjusting the total ammonia spraying amount based on the feed-forward control signal. Compared with the prior art, by introducing a nonlinear control algorithm and a rapid adjusting loop, system overshoot and oscillation are effectively restrained, the control stability is improved, and particularly, the performance is excellent under disturbance working conditions such as sudden load change or mill start and stop; and by accurately controlling the ammonia spraying amount, unnecessary ammonia consumption is reduced, and the operation economy of the denitration system is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of denitration control, and particularly relates to a method and device for ammonia injection control of a denitration system, an electronic device and a storage medium. BACKGROUND

[0002] As a core component of the environmental protection system of coal-fired power plants, denitration control technology is widely used in the field of flue gas denitration, aiming to effectively control the emission of nitrogen oxides (NOx). In related technologies, a whole-process control system from data acquisition, state prediction to ammonia injection adjustment is constructed through the collaborative work of DCS system integrated control, NOx measurement instruments and control algorithms.

[0003] However, in the existing denitration control method, directly using traditional extracted NOx measurement instruments and single PID feedback control strategies may cause problems such as NOx measurement lag, untimely control response, uneven ammonia injection distribution, or system overshoot and oscillation under disturbances such as load fluctuation and mill start-stop, thereby affecting the stability and emission compliance ability of the denitration system. SUMMARY

[0004] The present disclosure provides a method and device for ammonia injection control of a denitration system, an electronic device and a storage medium. The main purpose is to solve the problems of NOx measurement lag, untimely control response, uneven ammonia injection distribution, or system overshoot and oscillation under disturbances such as load fluctuation and mill start-stop, thereby affecting the stability and emission compliance ability of the denitration system.

[0005] According to a first aspect of the present disclosure, a method for ammonia injection control of a denitration system is provided, comprising:

[0006] In response to changes in the operating state of the boiler, the performance monitoring module collects disturbance factor data such as boiler load size, load change direction, load change rate, mill operating mode, mill start-stop sequence, primary air and secondary air ratio and action sequence, etc.

[0007] Based on the disturbance factor data, the ammonia demand prediction module predicts the target ammonia injection amount, and the intelligent feedforward control module generates a feedforward control signal;

[0008] Based on the feedforward control signal, the adjustment of the total amount of ammonia injection is performed.

[0009] Optionally, the adjustment of the total amount of ammonia injection based on the feedforward control signal comprises:

[0010] When the chimney inlet NOx concentration approaches or exceeds the emission standard, the fast adjustment module adjusts the opening of the ammonia injection regulating valve according to the feedforward control signal and the real-time feedback signal to suppress system overshoot and oscillation;

[0011] The data fusion module integrates real-time data collected by the DCS system with multi-point measurement data of the ammonia injection grid, and corrects the total ammonia injection amount based on the fused data.

[0012] Based on the corrected total ammonia injection amount, the three-level adjustment control module sequentially performs total coarse adjustment, zone optimization fine adjustment, and branch pipe fine adjustment to achieve dynamic optimization control of the ammonia injection amount.

[0013] Optionally, the step of performing coarse adjustment of the total ammonia injection, fine adjustment of the zone optimization, and fine adjustment of each branch pipe in sequence through a three-level adjustment control module based on the corrected total ammonia injection amount further includes:

[0014] The adaptive nozzle adjustment module automatically adjusts the nozzle opening based on the local flue gas velocity corresponding to each branch pipe to achieve local instantaneous response of ammonia injection volume.

[0015] Optionally, the performance monitoring module collects boiler operating status data in real time through the DCS system and transmits the data to the ammonia demand prediction module for processing.

[0016] Optionally, the ammonia demand prediction module predicts the target ammonia injection amount based on a correlation model between historical operating data and disturbance factors, using machine learning algorithms or regression models.

[0017] Optionally, the intelligent feedforward control module generates a feedforward control signal based on the prediction result and combines it with the real-time feedback signal for adjusting the opening of the ammonia injection regulating valve.

[0018] According to a second aspect of this disclosure, an ammonia injection control device for a denitrification system is provided, comprising:

[0019] The data acquisition unit is used to respond to changes in the boiler's operating status by collecting data on disturbance factors such as boiler load size, load change direction, load change rate, mill operating mode, mill start-up and shutdown sequence, primary air and secondary air ratio, and action sequence through the performance monitoring module.

[0020] The generation unit is used to predict the target ammonia injection amount through the ammonia demand prediction module based on the disturbance factor data, and to generate a feedforward control signal through the intelligent feedforward control module.

[0021] The adjustment unit is used to adjust the total amount of ammonia injected based on the feedforward control signal.

[0022] Optionally, the adjustment unit is further configured to:

[0023] When the NOx concentration at the chimney inlet approaches or exceeds the emission standard, the opening of the ammonia injection regulating valve is adjusted by the rapid adjustment module according to the feedforward control signal and the real-time feedback signal to suppress system overshoot and oscillation.

[0024] The data fusion module integrates real-time data collected by the DCS system with multi-point measurement data of the ammonia injection grid, and corrects the total ammonia injection amount based on the fused data.

[0025] Based on the corrected total ammonia injection amount, the three-level adjustment control module sequentially performs total coarse adjustment, zone optimization fine adjustment, and branch pipe fine adjustment to achieve dynamic optimization control of the ammonia injection amount.

[0026] Optionally, the adjustment unit is further configured to:

[0027] The adaptive nozzle adjustment module automatically adjusts the nozzle opening based on the local flue gas velocity corresponding to each branch pipe to achieve local instantaneous response of ammonia injection volume.

[0028] Optionally, the performance monitoring module collects boiler operating status data in real time through the DCS system and transmits the data to the ammonia demand prediction module for processing.

[0029] Optionally, the ammonia demand prediction module predicts the target ammonia injection amount based on a correlation model between historical operating data and disturbance factors, using machine learning algorithms or regression models.

[0030] Optionally, the intelligent feedforward control module generates a feedforward control signal based on the prediction result and combines it with the real-time feedback signal for adjusting the opening of the ammonia injection regulating valve.

[0031] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0032] At least one processor; and

[0033] A memory communicatively connected to the at least one processor; wherein,

[0034] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0035] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0036] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0037] The ammonia injection control method, device, electronic equipment, and storage medium for the denitrification system disclosed in this disclosure mainly include the following technical solutions: Responding to changes in boiler operating status, a performance monitoring module collects data on disturbance factors such as boiler load magnitude, load change direction, load change rate, mill operating mode, mill start-up and shutdown sequence, primary and secondary air ratio, and action sequence; based on the disturbance factor data, a target ammonia injection quantity is predicted by an ammonia demand prediction module, and a feedforward control signal is generated by an intelligent feedforward control module; and adjustments to the total ammonia injection quantity are executed based on the feedforward control signal. Compared with related technologies, the embodiments of this application effectively suppress system overshoot and oscillation by introducing a nonlinear control algorithm and a fast adjustment loop, improving control stability, especially under disturbance conditions such as sudden load changes or mill start-up and shutdown; and by precisely controlling the ammonia injection quantity, unnecessary ammonia consumption is reduced, improving the operating economy of the denitrification system.

[0038] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0039] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0040] Figure 1 This is a schematic flowchart of an ammonia injection control method for a denitrification system provided in an embodiment of this disclosure;

[0041] Figure 2 This is a schematic diagram of the structure of an ammonia injection control device for a denitrification system provided in an embodiment of this disclosure;

[0042] Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0043] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0044] The following description, with reference to the accompanying drawings, outlines an ammonia injection control method, apparatus, electronic device, and storage medium for a denitrification system according to embodiments of the present disclosure.

[0045] Figure 1 This is a schematic flowchart of an ammonia injection control method for a denitrification system provided in an embodiment of this disclosure.

[0046] like Figure 1 As shown, the method includes the following steps:

[0047] Step 101: In response to changes in boiler operating status, the performance monitoring module collects data on disturbance factors such as boiler load size, load change direction, load change rate, mill operating mode, mill start-up and shutdown sequence, primary air and secondary air ratio, and action sequence.

[0048] In low-delay ammonia injection systems, changes in boiler operating status directly affect the flue gas parameters of the denitrification system, leading to fluctuations in NOx concentration. To achieve precise ammonia injection control, it is necessary to promptly acquire key disturbance factors that cause NOx changes. The performance monitoring module plays a crucial role in this process. When boiler operating status changes, this module can respond quickly and collect data on a series of disturbance factors that significantly affect NOx concentration in real time. Specifically, these disturbance factors include: boiler load magnitude, which directly relates to the intensity and scale of combustion, thus affecting NOx formation; load change direction, i.e., whether the load increases or decreases, reflecting the trend of operating condition changes; load change rate, reflecting the speed of load change, the magnitude of which affects the severity of NOx concentration fluctuations; mill operating mode, different operating modes will change the fuel grinding effect and supply status, thus affecting the combustion process; mill start-up and shutdown sequence, different of which will lead to phased changes in fuel supply, thus affecting combustion; primary air to secondary air ratio, which determines the oxygen supply and mixing during combustion, directly related to NOx formation; and the sequence of primary air and secondary air operation, the differences of which will affect the dynamic process of combustion, also affecting NOx concentration. By collecting data on these disturbance factors through the performance monitoring module, accurate and timely basic information can be provided for subsequent ammonia injection control decisions, to better cope with frequent changes in boiler operating conditions.

[0049] Step 102: Based on the disturbance factor data, predict the target ammonia injection amount through the ammonia demand prediction module, and generate a feedforward control signal through the intelligent feedforward control module.

[0050] In low-delay ammonia injection systems, after the performance monitoring module collects data on disturbance factors such as boiler load magnitude, load change direction, load change rate, mill operation mode, mill start-up and shutdown sequence, primary and secondary air ratio, and action sequence, the ammonia demand prediction module predicts the target ammonia injection amount based on this data. Since these disturbance factors directly affect boiler combustion conditions, leading to changes in NOx concentration in the denitrification system, the ammonia demand prediction module analyzes the correlation between disturbance factors accumulated from numerous experiments and NOx generation and required ammonia amount. Combined with real-time collected disturbance data, it can predict in advance the total ammonia injection amount required to control NOx within the target range. Simultaneously, the intelligent feedforward control module generates corresponding feedforward control signals based on the target ammonia injection amount obtained from the ammonia demand prediction module. Based on the changing trends of the aforementioned disturbance factors, the intelligent feedforward control module adopts cause-based state control logic to issue control signals in advance before the NOx concentration shows significant fluctuations. This allows for rapid response to changes in boiler operating conditions, reducing delays in ammonia injection adjustment caused by factors such as measurement lag, and providing timely instruction support for subsequent precise ammonia injection control.

[0051] Step 103: Adjust the total amount of ammonia injected based on the feedforward control signal.

[0052] Adjusting the total ammonia injection based on the aforementioned feedforward control signal is a key step in achieving low-delay ammonia injection control. This feedforward control signal originates from the intelligent feedforward control module's analysis and calculation of various disturbances during boiler operation (such as load magnitude, load change direction, load change rate, mill operation mode, mill start-up and shutdown sequence, primary and secondary air ratios and action sequences, etc.), enabling it to anticipate potential changes in NOx concentration in the denitrification system. During adjustment, the system relies on real-time monitoring data provided by the integrated DCS design and semiconductor in-situ NOx / O2 analyzers, combined with advanced control methods such as nonlinear control, to convert the feedforward control signal into specific ammonia injection adjustment commands. This is achieved by controlling the ammonia injection-related equipment to change the total ammonia injection. This feedforward control signal-based adjustment method effectively reduces the lag in NOx measurement, allowing the total ammonia injection to respond quickly to changes in boiler operating conditions. While ensuring the total ammonia injection meets denitrification requirements, it avoids excessive NOx emissions or ammonia waste due to untimely adjustments, thereby improving the overall control system's adjustment quality and providing strong support for achieving ammonia injection below the red line. When encountering situations with large NOx fluctuations or when NOx at the chimney inlet is about to exceed emission standards, this adjustment can also be combined with a rapid adjustment loop to curb system overshoot and oscillations by timely control of the ammonia injection valve, ensuring that the total amount of ammonia injected is always within a precise and controllable range.

[0053] In some embodiments, adjusting the total ammonia injection amount based on the feedforward control signal includes:

[0054] When the NOx concentration at the chimney inlet approaches or exceeds the emission standard, the opening of the ammonia injection regulating valve is adjusted by the rapid adjustment module according to the feedforward control signal and the real-time feedback signal to suppress system overshoot and oscillation.

[0055] The data fusion module integrates real-time data collected by the DCS system with multi-point measurement data of the ammonia injection grid, and corrects the total ammonia injection amount based on the fused data.

[0056] Based on the corrected total ammonia injection amount, the three-level adjustment control module sequentially performs total coarse adjustment, zone optimization fine adjustment, and branch pipe fine adjustment to achieve dynamic optimization control of the ammonia injection amount.

[0057] When the NOx concentration at the chimney inlet approaches or exceeds emission standards, the rapid adjustment module comes into play. It receives the feedforward control signal generated by the intelligent feedforward control module and, combined with feedback signals such as real-time NOx concentration and flue gas velocity monitored in the denitrification system, quickly calculates the required opening change of the ammonia injection regulating valve through preset control logic. This drives the valve to adjust accordingly, thus curbing further increases or fluctuations in NOx concentration within a short period. This effectively suppresses overshoot and oscillations that may occur due to sudden changes in operating conditions, ensuring that NOx emissions remain within a controllable range. Simultaneously, the data fusion module integrates the overall denitrification system operating data (such as boiler load and flue gas flow) collected in real-time by the DCS system with local NOx concentration and flue gas velocity data obtained from multi-point measurements of the ammonia injection grid. By eliminating data errors and supplementing data details, it obtains more accurate and comprehensive operating data. Based on this fused data, the total ammonia injection is then corrected to better match the actual denitrification requirements. After the total ammonia injection is corrected, the three-level regulation and control module will perform regulation operations in sequence: First, a coarse adjustment of the total amount is performed to quickly adjust the total ammonia injection to a range that roughly matches the current operating conditions; then, a fine adjustment of the zone optimization is performed, and the ammonia injection amount of each zone is adjusted in detail according to the differences in flue gas characteristics in different areas of the denitrification system, so that the ammonia-nitrogen ratio in each zone is more reasonable; finally, a fine adjustment of each ammonia injection branch is performed to precisely control the ammonia injection amount of each branch, ensuring that the ammonia injection at each local location can be adapted to the flue gas conditions at that location, thereby achieving continuous optimization control of the ammonia injection amount under dynamic operating conditions, ensuring both low latency in total response and precise local ammonia injection.

[0058] In some embodiments, the step of performing coarse adjustment of the total amount of ammonia injection, fine adjustment of the zonal optimization, and fine adjustment of each branch pipe in sequence through a three-level adjustment control module based on the corrected total ammonia injection amount further includes:

[0059] The adaptive nozzle adjustment module automatically adjusts the nozzle opening based on the local flue gas velocity corresponding to each branch pipe to achieve local instantaneous response of ammonia injection volume.

[0060] Based on the corrected total ammonia injection, the adaptive nozzle adjustment module plays a role during the fine-tuning of each branch pipe by the three-level adjustment control module. This module acquires the flue gas velocity at the corresponding position of each branch pipe of the ammonia injection grid in real time. Since, under conditions where the concentration deviation is not large, the local flue gas velocity is high, the total amount of nitrogen oxides in the flue gas is greater, and more ammonia injection is required in the local area, while the local velocity is low, and less ammonia injection is required in the local area, the adaptive nozzle adjustment module automatically adjusts the opening of the nozzles on the corresponding branch pipe according to these real-time local flue gas velocities. Through such local velocity adaptive adjustment, the ammonia injection quantity at each local position can be matched with the flue gas conditions at that location in real time under stable and fluctuating operating conditions, realizing local real-time response of ammonia injection quantity, thereby achieving precise ammonia injection control in conjunction with the adjustment of the total ammonia injection.

[0061] In some embodiments, the performance monitoring module collects boiler operating status data in real time through the DCS system and transmits the data to the ammonia demand prediction module for processing.

[0062] During operation, the performance monitoring module establishes a close data interaction relationship with the DCS system. Leveraging the DCS system's powerful real-time data acquisition and processing capabilities, it continuously monitors and acquires boiler operating status data. This data specifically includes key disturbance factors such as boiler load magnitude, load change direction, load change rate, mill operating mode, start-up and shutdown sequence, and the ratio and sequence of primary and secondary air. The performance monitoring module receives this data from the DCS system in real time, performs initial integration and filtering to ensure accuracy and timeliness, and then promptly transmits this processed boiler operating status data to the ammonia demand prediction module. This provides reliable raw data support for the ammonia demand prediction module to predict the target ammonia injection amount based on the disturbance factor data, ensuring that the ammonia demand prediction closely matches the boiler's real-time operating conditions.

[0063] In some embodiments, the ammonia demand prediction module predicts the target ammonia injection amount based on a correlation model between historical operating data and disturbance factors, using machine learning algorithms or regression models.

[0064] When predicting the target ammonia injection rate, the ammonia demand prediction module constructs a correlation model between disturbance factors and the required ammonia quantity based on historical operating data. This historical operating data includes information on various disturbance factors under different boiler operating conditions in the past, such as load magnitude, load change rate, mill operating mode, primary and secondary air ratios and action sequences, as well as the actual ammonia injection rate and NOx control effect under the corresponding operating conditions. By analyzing and summarizing this historical data, the intrinsic relationship between changes in disturbance factors and the required ammonia quantity can be identified, thus establishing a correlation model. Based on this, the ammonia demand prediction module uses machine learning algorithms or regression models to input the disturbance factor data collected in real time by the performance monitoring module into the aforementioned correlation model. By utilizing the algorithm to process the data and match the model, it calculates and predicts the target ammonia injection rate adapted to the current boiler operating state, thereby providing an accurate predictive basis for ammonia injection control.

[0065] In some embodiments, the intelligent feedforward control module generates a feedforward control signal based on the prediction result and combines it with a real-time feedback signal for adjusting the opening of the ammonia injection regulating valve.

[0066] When generating the feedforward control signal, the intelligent feedforward control module uses the target ammonia injection quantity prediction result obtained by the ammonia demand prediction module as a basis, and combines it with the real-time changing trends of various disturbance factors during boiler operation (such as load changes, mill operating status, air ratio, etc.) to generate a feedforward control signal that can respond to fluctuations in operating conditions in advance. This signal can issue adjustment commands before significant fluctuations in NOx concentration occur. Simultaneously, to further improve adjustment accuracy, the module combines the generated feedforward control signal with real-time feedback signals. These real-time feedback signals include data such as NOx concentration, flue gas velocity, and local parameters measured at multiple points on the ammonia injection grid, all collected in real-time by the DCS system. By combining the advanced adjustment capability of the feedforward control signal with the current actual operating conditions reflected in the real-time feedback signal, the intelligent feedforward control module can accurately calculate the required opening change of the ammonia injection regulating valve, and then drive the valve to make corresponding adjustments. This not only quickly responds to changes in operating conditions to reduce lag, but also corrects the adjustment range based on actual feedback, effectively avoiding over- or under-adjustment. Thus, while suppressing system overshoot and oscillation, it achieves precise and timely control of the ammonia injection regulating valve opening.

[0067] Corresponding to the ammonia injection control method for the denitrification system described above, this invention also proposes an ammonia injection control device for the denitrification system. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0068] Figure 2 This is a schematic diagram of the structure of an ammonia injection control device for a denitrification system provided in an embodiment of this disclosure, as shown below. Figure 2As shown, it includes:

[0069] The data acquisition unit 21 is used to respond to changes in the boiler operating status by collecting data on disturbance factors such as boiler load size, load change direction, load change rate, mill operating mode, mill start-up and shutdown sequence, primary air and secondary air ratio and action sequence through the performance monitoring module.

[0070] The generation unit 22 is used to predict the target ammonia injection amount through the ammonia demand prediction module based on the disturbance factor data, and to generate a feedforward control signal through the intelligent feedforward control module.

[0071] Adjustment unit 23 is used to adjust the total amount of ammonia injected based on the feedforward control signal.

[0072] Furthermore, in one possible implementation of this disclosure, the adjustment unit 23 is further configured to:

[0073] When the NOx concentration at the chimney inlet approaches or exceeds the emission standard, the opening of the ammonia injection regulating valve is adjusted by the rapid adjustment module according to the feedforward control signal and the real-time feedback signal to suppress system overshoot and oscillation.

[0074] The data fusion module integrates real-time data collected by the DCS system with multi-point measurement data of the ammonia injection grid, and corrects the total ammonia injection amount based on the fused data.

[0075] Based on the corrected total ammonia injection amount, the three-level adjustment control module sequentially performs total coarse adjustment, zone optimization fine adjustment, and branch pipe fine adjustment to achieve dynamic optimization control of the ammonia injection amount.

[0076] Furthermore, in one possible implementation of this disclosure, the adjustment unit 23 is further configured to:

[0077] The adaptive nozzle adjustment module automatically adjusts the nozzle opening based on the local flue gas velocity corresponding to each branch pipe to achieve local instantaneous response of ammonia injection volume.

[0078] Furthermore, in one possible implementation of this disclosure embodiment, the performance monitoring module collects boiler operating status data in real time through the DCS system and transmits the data to the ammonia demand prediction module for processing.

[0079] Furthermore, in one possible implementation of this disclosure embodiment, the ammonia demand prediction module predicts the target ammonia injection amount based on a correlation model between historical operating data and disturbance factors, using machine learning algorithms or regression models.

[0080] Furthermore, in one possible implementation of this disclosure embodiment, the intelligent feedforward control module generates a feedforward control signal based on the prediction result and combines it with a real-time feedback signal for adjusting the opening of the ammonia injection regulating valve.

[0081] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0082] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0083] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0084] like Figure 3 As shown, device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. RAM 303 can also store various programs and data required for the operation of device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O (Input / Output) interface 305 is also connected to bus 304.

[0085] Multiple components in device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0086] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the ammonia injection control method for a denitrification system. For example, in some embodiments, the ammonia injection control method for a denitrification system can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 may be configured by any other suitable means (e.g., by means of firmware) to perform the aforementioned ammonia injection control method for the denitrification system.

[0087] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0088] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0089] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0090] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0091] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0092] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0093] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0094] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0095] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for controlling ammonia injection in a denitrification system, characterized in that, include: In response to changes in boiler operating status, the performance monitoring module collects data on disturbance factors such as boiler load magnitude, load change direction, load change rate, mill operating mode, mill start-up and shutdown sequence, primary air and secondary air ratio, and action sequence. Based on the disturbance factor data, the target ammonia injection amount is predicted by the ammonia demand prediction module, and a feedforward control signal is generated by the intelligent feedforward control module. The total amount of ammonia injected is adjusted based on the feedforward control signal.

2. The method according to claim 1, characterized in that, The adjustment of the total ammonia injection amount based on the feedforward control signal includes: When the NOx concentration at the chimney inlet approaches or exceeds the emission standard, the opening of the ammonia injection regulating valve is adjusted by the rapid adjustment module according to the feedforward control signal and the real-time feedback signal to suppress system overshoot and oscillation. The data fusion module integrates real-time data collected by the DCS system with multi-point measurement data of the ammonia injection grid, and corrects the total ammonia injection amount based on the fused data. Based on the corrected total ammonia injection amount, the three-level adjustment control module sequentially performs total coarse adjustment, zone optimization fine adjustment, and branch pipe fine adjustment to achieve dynamic optimization control of the ammonia injection amount.

3. The method according to claim 3, characterized in that, The process of dynamically optimizing and controlling the ammonia injection volume by sequentially performing coarse adjustment of the total volume, fine adjustment of the zone optimization, and fine adjustment of each branch pipe through a three-level adjustment control module based on the corrected total ammonia injection volume also includes: The adaptive nozzle adjustment module automatically adjusts the nozzle opening based on the local flue gas velocity corresponding to each branch pipe to achieve local instantaneous response of ammonia injection volume.

4. The method according to claim 1, characterized in that, The performance monitoring module collects boiler operating status data in real time through the DCS system and transmits the data to the ammonia demand prediction module for processing.

5. The method according to claim 4, characterized in that, The ammonia demand prediction module predicts the target ammonia injection amount based on the correlation model between historical operating data and disturbance factors, using machine learning algorithms or regression models.

6. The ammonia injection control method according to claim 3, characterized in that, The intelligent feedforward control module generates a feedforward control signal based on the prediction results and combines it with the real-time feedback signal for adjusting the opening of the ammonia injection regulating valve.

7. An ammonia injection control device for a denitrification system, characterized in that, include: The data acquisition unit is used to respond to changes in the boiler's operating status by collecting data on disturbance factors such as boiler load size, load change direction, load change rate, mill operating mode, mill start-up and shutdown sequence, primary air and secondary air ratio, and action sequence through the performance monitoring module. The generation unit is used to predict the target ammonia injection amount through the ammonia demand prediction module based on the disturbance factor data, and to generate a feedforward control signal through the intelligent feedforward control module. The adjustment unit is used to adjust the total amount of ammonia injected based on the feedforward control signal.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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