Coal-fired boiler combustion optimization control system with cooperation of spectrum detection and intelligent diagnosis

Abstract

The invention relates to the technical field of coal-fired boiler control, in particular to a spectrum detection and intelligent diagnosis collaborative coal-fired boiler combustion optimization control system which comprises a spectrum detection module, and combustion flame spectrum information is collected at preset intervals by means of a fiber optic spectrometer. Collected information is transmitted to a data processing unit, and feature parameters related to the content of key substances are obtained through median filtering, wavelet transform preprocessing and PCA and PLS feature extraction. And the intelligent diagnosis module evaluates the combustion condition in the furnace based on a neural network or a support vector machine model and performs early warning in case of abnormality. And the optimization control module adjusts the air volume of the fan and the rotating speed parameter of the coal feeder according to the diagnosis result to optimize combustion. The display module is controlled to display the data through a graphical interface, and manual operation is supported. According to the system, real-time monitoring, accurate diagnosis and intelligent control are achieved, the combustion efficiency is improved, and pollutant emission is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of coal-fired boiler control, and in particular to a coal-fired boiler combustion optimization control system that collaborates with spectral detection and intelligent diagnosis. Background Art

[0002] In today's industrial development, coal-fired boilers play a crucial and indispensable role in numerous industries, including power generation, chemical engineering, metallurgy, papermaking, and food processing. In the power industry, large coal-fired boilers are core equipment for thermal power generation, responsible for converting the chemical energy of coal into electricity, providing a large and stable supply of electricity. For example, a single million-kilowatt coal-fired power plant can consume thousands of tons of coal daily, continuously supplying electricity to surrounding areas and meeting the electricity needs of industrial production and residents.

[0003] In the chemical industry, coal-fired boilers provide essential heat energy for chemical production processes, supporting various chemical reactions and product manufacturing. For example, the production of synthetic ammonia and urea requires high temperatures and high pressures, and the steam and heat provided by coal-fired boilers ensure smooth production. Furthermore, in the papermaking industry, coal-fired boilers are used for paper drying and steaming, and in the food processing industry, they provide heat for food heating and sterilization. The stable operation of coal-fired boilers is crucial to ensuring the normal production of various industries and the stable development of the social economy.

[0004] Traditional coal-fired boiler combustion control methods primarily rely on conventional parameters such as temperature, pressure, and flow rate. While these parameters can reflect the boiler's operating status to a certain extent, they only indirectly reflect the combustion conditions and have significant limitations. In principle, changes in temperature, pressure, and flow rate parameters are only external manifestations of the combustion process. However, these parameters are difficult to accurately reflect the complex chemical reactions and material changes involved in the combustion process, such as coal cracking, redox reactions, and the formation and conversion of various intermediate products.

[0005] Take temperature, for example. Boiler temperature is affected by numerous factors, including coal quality, combustion air supply, and heat dissipation from the furnace. Even if the temperature remains within a relatively stable range, it doesn't guarantee a completely normal and efficient combustion process. This is because different combustion states can result in identical temperature readings. For example, incomplete and complete combustion can produce similar boiler temperatures but significantly different combustion efficiencies. Similarly, pressure and flow parameters only partially capture the physical phenomena of the combustion process and provide limited insight into the true nature of the combustion reaction.

[0006] Existing coal-fired boiler control methods often lack the ability to monitor and diagnose the combustion process in real time and comprehensively. In actual operation, the combustion process is a dynamic and complex system, influenced by factors such as fluctuating coal quality, equipment wear, and changing environmental conditions. However, traditional control methods are unable to capture these changes in a timely manner and make accurate judgments.

[0007] Due to a lack of real-time monitoring and diagnostic capabilities, control systems struggle to detect abnormalities during the combustion process, such as incomplete combustion or burner failure. By the time these anomalies are detected through conventional parameters, significant losses have already occurred, including wasted coal resources and equipment damage. Furthermore, even when an anomaly is detected, existing control methods struggle to make precise adjustments. Because they lack an accurate understanding of the specific cause and location of the anomaly, they can only make empirical, general adjustments. This not only fails to fundamentally address the problem but can also lead to additional problems.

[0008] Low combustion efficiency and increased pollutant emissions caused by traditional control methods have become significant constraints on the development of coal-fired boilers. Incomplete combustion is a widespread problem. If coal does not fully react during combustion, it results in a significant waste of coal resources. Statistics show that some coal-fired boilers using traditional control methods may only have a coal utilization rate of 60%-70%, meaning a significant portion of the coal is not effectively utilized, resulting in significant energy losses.

[0009] Incomplete combustion also produces a large amount of pollutants, such as carbon monoxide, nitrogen oxides, and sulfur oxides. Carbon monoxide is a toxic gas that binds to hemoglobin in the blood, affecting oxygen transport and supply, and causing serious harm to human health. Nitrogen oxides and sulfur oxides are among the main pollutants that cause acid rain and photochemical smog, causing severe damage to the atmospheric environment and ecosystems. With increasingly stringent environmental protection requirements, pollutant emissions from traditional coal-fired boilers have become a problem that needs to be urgently addressed.

[0010] Furthermore, existing control methods often lack the ability to monitor and diagnose the combustion process in real time and comprehensively, making it difficult to detect combustion anomalies and make precise adjustments. This leads to low combustion efficiency and increased pollutant emissions. For example, incomplete combustion wastes coal resources and produces large amounts of carbon monoxide, nitrogen oxides, and sulfur oxides, which pose a serious environmental threat. Therefore, we have addressed this issue by proposing a coal-fired boiler combustion optimization control system that integrates spectral detection with intelligent diagnosis. Summary of the Invention

[0011] The purpose of the present invention is to address the problems raised by the existing background technology. In order to achieve the above-mentioned invention purpose, the present invention provides the following technical solutions: a coal-fired boiler combustion optimization control system that cooperates with spectral detection and intelligent diagnosis, including a spectral detection module, which is used to collect spectral information of the combustion flame in real time; the spectral detection module is connected to a data processing unit, which is used to preprocess and extract features from the collected spectral information and store the processed data and feature parameters; the data processing unit is connected to the intelligent diagnosis module;

[0012] The intelligent diagnosis module establishes a combustion condition diagnosis model based on the stored data, and the intelligent diagnosis module evaluates the combustion condition and issues fault warnings based on the extracted characteristic parameters; the intelligent diagnosis module is connected to the optimization control module, and the optimization control module formulates and executes the combustion optimization control strategy based on the diagnosis results; the data processing unit, the intelligent diagnosis module and the optimization control module are connected to the control display module, and the control display module is used to display system operation information and provide an operation control interface.

[0013] As a preferred technical solution of the present invention, the spectrum detection module includes a plurality of spectrum sensors, and the spectrum sensors are optical fiber spectrometers that collect spectrum information of the combustion flame through optical fiber probes.

[0014] As a preferred technical solution of the present invention, the preprocessing performed by the data processing unit includes filtering and noise reduction operations, and adopts a median filtering algorithm and a wavelet transform algorithm to remove noise interference in the spectral information.

[0015] As a preferred technical solution of the present invention, the feature extraction performed by the data processing unit adopts principal component analysis PCA and partial least squares PLS to extract characteristic parameters related to the content of oxygen, carbon monoxide, carbon dioxide and nitrogen oxides from the preprocessed spectral information.

[0016] As a preferred technical solution of the present invention, the combustion condition diagnosis model established by the intelligent diagnosis module is a neural network model or a support vector machine model, and the model is trained and optimized through historical data and experimental data.

[0017] As a preferred technical solution of the present invention, the combustion optimization control strategy formulated by the optimization control module includes adjusting the fan air volume, coal feeder speed and burner angle, and adjusting and optimizing the combustion process in real time.

[0018] As a preferred technical solution of the present invention, the control display module displays spectral detection data, combustion condition assessment results and control strategies in a graphical interface, and the operator can manually input control instructions and set system parameters through the interface.

[0019] As a preferred technical solution of the present invention, the optimization control system also includes a database for storing spectral detection data, processed characteristic parameters, combustion condition assessment results and control strategy historical data for subsequent analysis and query.

[0020] As a preferred technical solution of the present invention, the spectrum sensor of the spectrum detection module collects spectrum information according to a preset time interval, and the time interval is adjusted by the control display module.

[0021] As a preferred technical solution of the present invention, when the intelligent diagnosis module determines that the combustion condition is abnormal, it sends a fault warning signal through the sound and light alarm device and the control display module interface to remind the operator to take corresponding measures.

[0022] Compared with existing technologies, this invention offers the following advantages: Conventional coal-fired boiler combustion control technologies often struggle to accurately capture the real-time changes in substances during combustion, leading to incomplete coal combustion and low energy efficiency. This invention, however, utilizes advanced spectral detection technology to accurately and accurately capture detailed information about various substances in the combustion process in real time.

[0023] On this basis, combined with intelligent diagnostic algorithms, a comprehensive, multi-level, and accurate assessment of the combustion conditions is carried out. Based on the data obtained from spectral detection, the problems existing in the current combustion process are quickly analyzed, and targeted optimization suggestions are given. Through this precise assessment and optimized control, the coal in the coal-fired boiler can be burned more fully, allowing each grain of coal to release the maximum energy. This not only improves the utilization rate of coal, but also significantly reduces energy consumption. Against the backdrop of the current global energy shortage, this invention has important practical significance for improving energy utilization efficiency and alleviating energy pressure.

[0024] With environmental protection receiving increasing attention, reducing pollutant emissions is a critical issue that must be addressed in industrial production. Traditional combustion control technologies have limitations in monitoring and controlling pollutant emissions, often making it difficult to promptly detect and address abnormal pollutant generation. However, the present invention, leveraging advanced spectral detection technology, can accurately monitor pollutant generation during combustion. Spectral detection enables real-time analysis of the content and changing trends of carbon monoxide and nitrogen oxide pollutants in combustion products.

[0025] Based on detected pollutant generation, the optimization control module rapidly adjusts combustion parameters, such as fan air volume, coal feeder speed, and burner angle. These fine-tuning adjustments make the combustion process more stable and efficient, thereby reducing emissions of carbon monoxide and nitrogen oxides. This not only complies with national environmental protection requirements and improves environmental quality, but also helps companies avoid significant fines and legal risks associated with exceeding pollutant emission standards.

[0026] Abnormal combustion is a common problem in coal-fired boiler operation, with potentially serious consequences. Traditional monitoring and control methods often fail to detect abnormal combustion in a timely manner. If an abnormality is not addressed promptly, it can lead to further escalation of the problem and even cause a safety accident.

[0027] The intelligent diagnostic module utilizes advanced algorithms and models to quickly identify abnormal signals during the combustion process and issue timely warnings. Once a combustion anomaly is detected, the optimization control module responds swiftly and adjusts the combustion process. Based on the type and severity of the anomaly, the optimization control module automatically adjusts combustion parameters to restore the combustion process to normal. This rapid response and adjustment mechanism prevents further escalation of the fault and significantly improves the stability and reliability of coal-fired boiler operations. This is crucial for ensuring the continuity and safety of industrial production and can help companies reduce production losses caused by equipment failure.

[0028] Traditional coal-fired boiler operating interfaces are often complex and non-intuitive, requiring operators to possess advanced expertise and skills to master them. This not only increases operational difficulty but also increases labor intensity. The control and display module of the present invention, however, provides an intuitive and concise interface. This interface graphically displays spectral detection data, combustion condition assessment results, and key control strategy information.

[0029] This interface allows operators to easily understand the system's real-time operating status without requiring complex data analysis and calculations. Operators can also manually input control commands and set system parameters based on actual conditions. This convenient operation method reduces the need for specialized knowledge, minimizes the likelihood of operational errors, and significantly reduces operational difficulty and labor intensity. Operators can more easily and efficiently complete boiler operation and management tasks, improving work efficiency and production benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The data processing unit processing method and function diagram provided by the present invention;

[0031] Figure 2 This is a diagram of the intelligent diagnosis module model and training method provided by the present invention;

[0032] Figure 3 This is the control strategy diagram of the optimization control module provided by the present invention. DETAILED DESCRIPTION

[0033] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0034] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the invention claimed for protection, but merely represents some embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions in the embodiments can be combined with each other. It should be noted that similar numbers and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0035] Example 1: A coal-fired boiler combustion optimization control system that integrates spectral detection and intelligent diagnosis, including a spectral detection module for collecting spectral information of a combustion flame in real time; a data processing unit connected to the spectral detection module for preprocessing and extracting features from the collected spectral information and storing the processed data and feature parameters; and a data processing unit connected to the intelligent diagnosis module.

[0036] The intelligent diagnosis module establishes a combustion condition diagnosis model based on the stored data. The intelligent diagnosis module evaluates the combustion condition and issues fault warnings based on the extracted characteristic parameters. The intelligent diagnosis module is connected to the optimization control module, which formulates and executes the combustion optimization control strategy based on the diagnosis results. The data processing unit, the intelligent diagnosis module and the optimization control module are connected to the control display module, which is used to display system operation information and provide an operation control interface.

[0037] The spectrum detection module includes multiple spectrum sensors. The spectrum sensor adopts a fiber optic spectrometer to collect the spectrum information of the burning flame through a fiber optic probe.

[0038] The preprocessing performed by the data processing unit includes filtering and noise reduction operations, and adopts median filtering algorithm and wavelet transform algorithm to remove noise interference in spectral information.

[0039] The feature extraction performed by the data processing unit uses principal component analysis PCA and partial least squares PLS to extract characteristic parameters related to the content of oxygen, carbon monoxide, carbon dioxide, and nitrogen oxides from the preprocessed spectral information.

[0040] The combustion condition diagnosis model established by the intelligent diagnosis module is a neural network model or a support vector machine model, and the model is trained and optimized through historical data and experimental data.

[0041] The combustion optimization control strategy formulated by the optimization control module includes adjusting the fan air volume, coal feeder speed and burner angle, and real-time adjustment and optimization of the combustion process.

[0042] The control and display module displays spectral detection data, combustion condition assessment results and control strategies in a graphical interface, through which operators can manually input control instructions and set system parameters.

[0043] The optimization control system also includes a database for storing spectral detection data, processed characteristic parameters, combustion condition assessment results and control strategy historical data for subsequent analysis and query.

[0044] The spectrum sensor of the spectrum detection module collects spectrum information according to a preset time interval, and the time interval is adjusted by the control display module.

[0045] When the intelligent diagnosis module determines that the combustion condition is abnormal, it will send out a fault warning signal through the sound and light alarm device and the control display module interface to remind the operator to take corresponding measures.

[0046] Example 2: A coal-fired boiler combustion optimization control system that integrates spectral detection and intelligent diagnosis. The spectral detection module is configured to install multiple spectral sensors at different locations in the furnace of the coal-fired boiler. These sensors can collect spectral information of the combustion flame in real time.

[0047] Spectral detection module principle: Different substances produce specific spectral characteristics during combustion. By analyzing this spectral information, the types and contents of various substances in the combustion process, such as oxygen, carbon monoxide, carbon dioxide, and nitrogen oxides, can be determined. The spectral sensor transmits the collected spectral data to the data processing unit.

[0048] The data processing unit performs filtering and noise reduction preprocessing operations on the original spectral data transmitted by the spectral sensor to remove interference signals and improve the accuracy and reliability of the data.

[0049] Using spectral analysis algorithms, characteristic parameters related to combustion conditions, such as the location and intensity of characteristic peaks, are extracted from the preprocessed spectral data. The processed data and extracted characteristic parameters are stored in a database for subsequent analysis and query.

[0050] The intelligent diagnosis module establishes intelligent diagnosis models of combustion conditions based on historical data and experimental data, such as neural network models and support vector machine models.

[0051] The characteristic parameters extracted by the data processing unit are input into the intelligent diagnostic model, which evaluates the current combustion status to determine whether combustion is optimal and whether there are any combustion anomalies, such as incomplete combustion or localized overheating. If the intelligent diagnostic model determines that the combustion condition is abnormal, it will promptly issue a fault warning signal, prompting the operator to take appropriate measures.

[0052] The optimization control module formulates a combustion optimization control strategy based on the evaluation results of the intelligent diagnostic module. For example, if the diagnostic results indicate incomplete combustion, the control strategy might include increasing air volume or adjusting the pulverized coal supply. This control strategy is converted into specific control instructions and sent to the coal-fired boiler's actuators, such as the fan and coal feeder, to achieve real-time adjustment and optimization of the combustion process.

[0053] The control display module displays spectrum detection data, combustion status assessment results, and control strategy information in an intuitive interface, allowing operators to understand the operating status of the boiler in real time.

[0054] Operators can manually input control instructions through the human-computer interaction interface to intervene in and adjust the system, and can also set and modify the system parameters.

[0055] Installation and Data Collection of Spectral Detection Modules: Multiple spectral sensors are strategically positioned within the furnace of a coal-fired boiler, based on the characteristics of the combustion process and monitoring requirements. For example, sensors are installed near the burner, in the middle of the furnace, and at the furnace outlet to comprehensively collect spectral information from different areas of the combustion flame. The spectral sensors collect spectral data at intervals of once per second and transmit the data in real time to the data processing unit.

[0056] Data processing in the data processing unit: After receiving the raw spectral data from the spectral sensor, the data processing unit first performs preprocessing. A median filter algorithm is used to remove noise interference from the data, followed by a wavelet transform algorithm for further noise reduction and feature enhancement. Next, spectral analysis algorithms such as principal component analysis (PCA) and partial least squares (PLS) are used to extract characteristic parameters related to combustion conditions from the preprocessed spectral data, such as the characteristic peak intensity corresponding to oxygen content and the characteristic peak position corresponding to carbon monoxide content. Finally, the processed data and extracted characteristic parameters are stored in a database.

[0057] Diagnostic Assessment by the Intelligent Diagnostic Module: The intelligent diagnostic module utilizes a pre-established neural network model to assess combustion conditions. During the model training phase, a large amount of historical data, including spectral characteristic parameters and corresponding combustion condition labels for normal and various abnormal combustion conditions, is collected to train and optimize the neural network model. During actual operation, the characteristic parameters extracted by the data processing unit are input into the trained neural network model, which then outputs an assessment of the current combustion condition, such as normal, incomplete combustion, or localized overheating. If the assessment indicates an abnormal combustion condition, the intelligent diagnostic module promptly issues a fault warning signal.

[0058] The optimization control module executes its control strategy: Based on the evaluation results of the intelligent diagnostic module, the optimization control module formulates a combustion optimization control strategy. For example, if the diagnostic results indicate incomplete combustion, the optimization control module will increase the fan air volume to increase the oxygen supply, adjust the coal feeder speed, and reduce the pulverized coal supply to ensure more complete combustion. Once the control strategy is formulated, the optimization control module sends control instructions to the coal-fired boiler's actuators, which then make corresponding adjustments, achieving real-time optimization of the combustion process.

[0059] Operation and Monitoring of the Control and Display Module: The operator uses the control and display module's interface to view spectral detection data, combustion status assessment results, and control strategy information in real time. If manual intervention is required, the operator can enter control commands on the interface, such as adjusting the fan air volume or coal feeder speed. The operator can also set and modify system parameters, such as the spectral sensor's acquisition frequency and the threshold of the intelligent diagnostic model.

[0060] Coal-fired boiler combustion optimization control system based on spectral detection and intelligent diagnosis. The simulation Python code is as follows:

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] Code Description: Spectrum Detection Module: simulates the acquisition of spectral information of a burning flame, and can set the acquisition time interval. Data Processing Unit: performs preprocessing (here simplified to mean filtering) and feature extraction (PCA and PLS) on the acquired spectral information.

[0069] Intelligent Diagnosis Module: Uses a neural network model (MLPClassifier) to evaluate combustion conditions and issues warnings when abnormalities occur. Optimization Control Module: Executes combustion optimization control strategies based on diagnosis results.

[0070] Control Display Module: Displays system operation information and can set the acquisition time interval of the spectrum detection module. Database: Stores spectrum detection data, characteristic parameters, combustion status assessment results and control strategies.

[0071] The coal-fired boiler combustion optimization control system, which integrates spectral detection and intelligent diagnosis, aims to achieve real-time monitoring, fault warning, and optimized control of the coal-fired boiler combustion process by collecting, processing, analyzing, and diagnosing flame spectral information. The system primarily consists of a spectral detection module, a data processing unit, an intelligent diagnosis module, an optimization control module, a control and display module, and a database. These modules work together to achieve combustion optimization control tasks.

[0072] The spectral detection module serves as the data acquisition frontend for the entire system. Its core components are multiple spectral sensors using fiber optic spectrometers. These sensors penetrate the combustion zone using fiber optic probes and collect spectral information from the combustion flame in real time at preset intervals (adjustable via the control and display module). Different substances emit spectra of specific wavelengths and intensities during combustion. This spectral information contains key information about the content and combustion status of various substances during combustion.

[0073] Data Processing Unit: Preprocessing: The raw spectral information received from the spectral detection module often contains a large amount of noise. To improve the accuracy of subsequent analysis, the data processing unit first preprocesses it. It uses median filtering and wavelet transform algorithms to remove noise from the spectral information. The median filter algorithm effectively removes impulse noise, while the wavelet transform algorithm decomposes and reconstructs the signal at different scales, thereby more accurately removing noise and preserving useful spectral features.

[0074] Feature Extraction: After preprocessing, the spectral data volume remains substantial. To extract key information closely related to combustion conditions, the data processing unit uses principal component analysis (PCA) and partial least squares (PLS) for feature extraction. PCA reduces the dimensionality of high-dimensional spectral data and extracts the primary components. PLS, while considering the correlation between independent and dependent variables, extracts characteristic parameters related to oxygen, carbon monoxide, carbon dioxide, and nitrogen oxide levels from the spectral information. The processed data and characteristic parameters are stored for subsequent use.

[0075] Intelligent Diagnosis Module: Model Building: The intelligent diagnosis module builds a combustion condition diagnosis model based on the data stored in the data processing unit. Neural network models or support vector machine models can be used. These models are trained and optimized using historical and experimental data, enabling them to learn the mapping relationship between spectral characteristic parameters and combustion status under different combustion conditions.

[0076] Combustion status assessment and fault warning: The intelligent diagnostic module uses the trained diagnostic model to assess the current combustion status based on the characteristic parameters extracted by the data processing unit. If the combustion status is abnormal, a fault warning signal is issued through the audio and visual alarm device and the control display module interface, prompting the operator to take appropriate measures.

[0077] The optimization control module is connected to the intelligent diagnostic module and formulates and executes combustion optimization control strategies based on the module's diagnostic results. Specific control strategies include adjusting fan air volume, coal feeder speed, and burner angle. By adjusting these parameters in real time, the combustion process is optimized, combustion efficiency is improved, and pollutant emissions are reduced.

[0078] The control and display module is the interface through which operators interact with the system. It displays spectral detection data, combustion condition assessment results, and control strategy system operation information in a graphical interface. This interface allows operators to manually input control commands and set system parameters, such as adjusting the spectral detection module's acquisition interval, achieving flexible control of the system.

[0079] The database stores spectral detection data, processed characteristic parameters, combustion condition assessment results, and historical control strategy data. This data provides an important basis for subsequent system analysis, troubleshooting, and performance optimization. Operators can query and analyze this data as needed.

[0080] The spectral detection module collects spectral information from the combustion flame in real time and transmits it to the data processing unit. The data processing unit preprocesses and extracts features from the spectral information and stores the results. The intelligent diagnosis module establishes a diagnostic model based on the stored data, assesses the combustion status, and issues fault warnings. The optimization control module formulates and implements combustion optimization control strategies based on the diagnostic results. The control display module displays system operating information and provides an operational control interface. The database stores various data during system operation for subsequent analysis and query. The entire system forms a closed-loop control system, achieving efficient and intelligent optimization control of the coal-fired boiler combustion process.

[0081] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Although this specification has described the present invention in detail with reference to the above embodiments, the present invention is not limited to the above specific implementation methods. Therefore, any modification or replacement of the present invention; and all technical solutions and improvements thereof that do not depart from the spirit and scope of the invention are included in the scope of the claims of the present invention.

Claims

1. A coal-fired boiler combustion optimization control system that combines spectral detection with intelligent diagnosis, characterized in that: It includes a spectrum detection module, which is used to collect spectrum information of the combustion flame in real time; the spectrum detection module is connected to a data processing unit, which is used to pre-process and extract features of the collected spectrum information and store the processed data and feature parameters; the data processing unit is connected to an intelligent diagnosis module; The intelligent diagnosis module establishes a combustion condition diagnosis model based on the stored data, and the intelligent diagnosis module evaluates the combustion condition and issues fault warnings based on the extracted characteristic parameters; the intelligent diagnosis module is connected to the optimization control module, and the optimization control module formulates and executes the combustion optimization control strategy based on the diagnosis results; the data processing unit, the intelligent diagnosis module and the optimization control module are connected to the control display module, and the control display module is used to display system operation information and provide an operation control interface.

2. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The spectrum detection module includes a plurality of spectrum sensors, and the spectrum sensors are optical fiber spectrometers that collect spectrum information of the combustion flame through optical fiber probes.

3. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The preprocessing performed by the data processing unit includes filtering and noise reduction operations, and adopts a median filtering algorithm and a wavelet transform algorithm to remove noise interference in the spectral information.

4. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The feature extraction performed by the data processing unit adopts principal component analysis PCA and partial least squares method PLS to extract characteristic parameters related to the content of oxygen, carbon monoxide, carbon dioxide and nitrogen oxides from the preprocessed spectral information.

5. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The combustion condition diagnosis model established by the intelligent diagnosis module is a neural network model or a support vector machine model, and the model is trained and optimized through historical data and experimental data.

6. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The combustion optimization control strategy formulated by the optimization control module includes adjusting the fan air volume, coal feeder speed and burner angle, and adjusting and optimizing the combustion process in real time.

7. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The control display module displays spectrum detection data, combustion condition assessment results and control strategies in a graphical interface, through which operators manually input control instructions and set system parameters.

8. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The optimization control system also includes a database for storing spectrum detection data, processed characteristic parameters, combustion condition assessment results and control strategy historical data for subsequent analysis and query.

9. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: The spectrum sensor of the spectrum detection module collects spectrum information at preset time intervals, and the time intervals are adjusted by the control display module.

10. The coal-fired boiler combustion optimization control system with coordinated spectrum detection and intelligent diagnosis according to claim 1 is characterized in that: When the intelligent diagnosis module determines that the combustion condition is abnormal, it sends out a fault warning signal through the sound and light alarm device and the control display module interface.

Images