High-efficiency Combustion Control System and Method for Power Plant Boilers

Abstract

The efficient combustion control system and method for a power plant boiler provided by this application relate to the field of combustion control technology. It conducts load forecasting for the power plant and makes combustion control decisions for Q boilers in the power plant to obtain a combustion control plan. It predicts and evaluates the control plan according to the control evaluation channel and weight constraint to obtain an evaluation coefficient, and determines whether this coefficient is less than a predetermined evaluation coefficient. If it is less, a control optimization instruction is generated, and an optimization adjustment is carried out based on the optimization instruction to generate a control optimization strategy. Based on the predetermined future time zone and the predicted power plant load, combustion control is performed on Q boilers according to the control optimization strategy, solving the problems that the combustion control of power plant boilers is difficult to quickly respond to the change of power plant load, with low combustion efficiency and energy waste, resulting in the system stability and safety not being guaranteed, and achieving the effects of improving the operation efficiency of power plant boilers, reducing energy consumption and pollution emissions, and at the same time increasing the stability and safety of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of combustion control, and particularly to an efficient combustion control system and method for a power plant boiler. Background Art

[0002] In the operation management of modern power plant boilers, combustion control is one of the key technologies to ensure safety, improve efficiency, and reduce pollution. Traditional boiler combustion control systems mainly rely on the experience of operators for manual adjustment, or use simple automatic control systems to operate according to fixed parameters. These systems usually rely on static control strategies and are difficult to adapt to the rapid changes in power plant load. In addition, traditional systems lack sufficient intelligence in data processing and decision-making, and cannot perform complex data analysis and long-term performance optimization, resulting in problems such as low operation efficiency and energy waste, and increasing safety and environmental loads.

[0003] In summary, the existing combustion control systems of power plant boilers often have technical problems such as difficulty in quickly responding to the changes in power plant load, resulting in low combustion efficiency and energy waste, and inability to guarantee the stability and safety of the system. Summary of the Invention

[0004] This application provides an efficient combustion control system and method for a power plant boiler, aiming to solve the technical problems existing in the existing combustion control system of power plant boilers, such as difficulty in quickly responding to the changes in power plant load, resulting in low combustion efficiency and energy waste, and inability to guarantee the stability and safety of the system.

[0005] In view of the above problems, this application provides an efficient combustion control system and method for a power plant boiler.

[0006] In the first aspect of this application, an efficient combustion control system for a power plant boiler is provided, and the system includes:

[0007] Power plant load prediction module, which is used to predict the load of the power plant according to a predetermined future time zone to obtain the predicted power plant load; Combustion control decision-making module, which is used to make combustion control decisions for Q boilers of the power plant according to the predicted power plant load to obtain a combustion control plan, where Q is a positive integer greater than 1; Combustion control prediction evaluation module, which is used to predict and evaluate the combustion control plan according to the combustion control evaluation channel and the combustion control evaluation weight constraint to obtain a combustion control evaluation coefficient; Combustion evaluation judgment module, which is used to judge whether the combustion control evaluation coefficient is less than a predetermined combustion control evaluation coefficient. If the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient, a combustion control optimization instruction is generated; Combustion control optimization module, which is used to perform optimization adjustment on the combustion control plan based on the combustion control optimization instruction according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient to generate a combustion control optimization strategy; Combustion control module, which is used to perform combustion control on the Q boilers according to the combustion control optimization strategy based on the predetermined future time zone and the predicted power plant load.

[0008] In the second aspect of the present application, there is provided an efficient combustion control method for a power plant boiler, and the method includes:

[0009] Predict the load of the power plant according to a predetermined future time zone to obtain the predicted power plant load; Make combustion control decisions for Q boilers of the power plant according to the predicted power plant load to obtain a combustion control plan, where Q is a positive integer greater than 1; Predict and evaluate the combustion control plan according to the combustion control evaluation channel and the combustion control evaluation weight constraint to obtain a combustion control evaluation coefficient; Judge whether the combustion control evaluation coefficient is less than a predetermined combustion control evaluation coefficient. If the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient, a combustion control optimization instruction is generated; Based on the combustion control optimization instruction, perform optimization adjustment on the combustion control plan according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient to generate a combustion control optimization strategy; Based on the predetermined future time zone and the predicted power plant load, perform combustion control on the Q boilers according to the combustion control optimization strategy.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] The efficient combustion control system for power plant boilers provided by this application obtains the predicted power plant load by predicting the load of the power plant according to a predetermined future time zone; makes combustion control decisions for Q boilers of the power plant according to the predicted power plant load to obtain a combustion control scheme, where Q is a positive integer greater than 1; performs predictive evaluation on the combustion control scheme according to a combustion control evaluation channel and combustion control evaluation weight constraints to obtain a combustion control evaluation coefficient; determines whether the combustion control evaluation coefficient is less than a predetermined combustion control evaluation coefficient, and if the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient, generates a combustion control optimization instruction; based on the combustion control optimization instruction, performs optimization adjustment on the combustion control scheme according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraints, and the predetermined combustion control evaluation coefficient to generate a combustion control optimization strategy; based on the predetermined future time zone and the predicted power plant load, performs combustion control on the Q boilers according to the combustion control optimization strategy, solving the technical problem that the existing combustion control system of power plant boilers is difficult to quickly respond to the change of power plant load, resulting in low combustion efficiency and energy waste, and the stability and safety of the system cannot be guaranteed, achieving the technical effects of dynamically responding and adjusting the combustion strategy, performing combustion control more accurately and efficiently, improving the operation efficiency of power plant boilers, reducing energy consumption and pollution emissions, and at the same time increasing the stability and safety of the system. Brief Description of the Drawings

[0012] Figure 1 It is a schematic structural diagram of the efficient combustion control system for power plant boilers provided by an embodiment of this application.

[0013] Figure 2 It is a schematic flowchart of the efficient combustion control method for power plant boilers provided by an embodiment of this application.

[0014] Description of the reference numerals: Power plant load prediction module 11, Combustion control decision module 12, Combustion control prediction evaluation module 13, Combustion evaluation judgment module 14, Combustion control optimization module 15, Combustion control module 16. Detailed Description of the Embodiments

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0016] Embodiment 1, as Figure 1 shown, this application provides an efficient combustion control system for power plant boilers, and the system includes:

[0017] Power plant load prediction module 11, which is used to predict the load of the power plant according to a predetermined future time zone and obtain the predicted power plant load.

[0018] The core function of the power plant load prediction module is to accurately predict the operating load of the power plant in the future period to guide the formulation of combustion control decisions. The design of this module is based on in-depth analysis of historical load data and related influencing factors. Through advanced prediction algorithms such as time series analysis, machine learning, or deep learning methods, it realizes the prediction of future power plant load changes. Specifically, the predetermined future time zone refers to the time interval that is expected to affect the operation of the power plant in the future, such as the next 24 hours or longer. The determination of this time interval may be based on various factors including historical load data, seasonal changes, weather conditions, economic activity levels, etc. When performing load prediction, this module collects and analyzes this data and uses statistical or machine learning models to generate a prediction model. For example, if machine learning methods are used, this module may first train a regression model or neural network through historical data and then update the model with recently collected data to ensure the accuracy and real-time nature of the prediction. The output result of the prediction is the predicted power plant load, which is an estimated value indicating the maximum and minimum power demands that the power plant may bear within the predetermined future time zone. This prediction result will be directly used in the subsequent combustion control decision module to provide the basic data for formulating combustion strategies.

[0019] Through the above steps, the power plant load prediction module not only enhances the visualization of the future operating state but also improves the overall efficiency and response ability of the combustion control system, ensuring that the power plant can efficiently respond to market and technological changes while ensuring safety and environmental protection.

[0020] Combustion control decision module 12, which is used to make combustion control decisions for Q boilers of the power plant according to the predicted power plant load and obtain a combustion control scheme, where Q is a positive integer greater than 1.

[0021] The main function of the combustion control decision-making module is to formulate corresponding combustion control strategies for multiple boilers (i.e., Q boilers, where Q is a positive integer greater than 1) in the power plant based on the predicted power plant load data provided by the power plant load prediction module. This module intelligently generates a combustion control plan by combining the operating parameters of the boilers and the environmental conditions to optimize energy utilization efficiency and reduce emissions. During the specific operation process, the combustion control decision-making module first receives data inputs from the power plant load prediction module, and these data reflect the expected operating load of the power plant in a future period. Based on this load prediction, the decision-making module analyzes the current status and potential requirements of each boiler, including but not limited to factors such as fuel type, combustion efficiency, emission level, and safety standards. Then, the module uses a decision support system, such as artificial intelligence or machine learning algorithms, and comprehensively considers the operating history, maintenance records, and performance indicators of each boiler. For example, if a machine learning algorithm is used, the module may train a prediction model by analyzing the relationship between historical combustion data and the load to predict the optimal combustion parameters under different load conditions. After that, the combustion control decision-making module will calculate and generate the best combustion control plan for each boiler according to the prediction model and real-time data, and these plans will specify in detail the adjusted combustion parameters, such as fuel supply quantity, air supply ratio, and combustion temperature, etc., to ensure the highest energy efficiency and the lowest emissions under the predicted load conditions. Finally, these combustion control plans will be sent to the combustion control execution module to implement specific operation adjustments. In this way, the combustion control decision-making module not only improves the operating efficiency of the power plant but also ensures compliance with environmental standards and optimization of economic operation, thus guaranteeing the stable operation of the power plant in a dynamically changing load and technical environment.

[0022] A combustion control prediction and evaluation module 13, and the combustion control prediction and evaluation module is used to perform a prediction and evaluation on the combustion control plan according to a combustion control evaluation channel and a combustion control evaluation weight constraint to obtain a combustion control evaluation coefficient.

[0023] The combustion control prediction and evaluation module is a part used to comprehensively evaluate the combustion control schemes proposed by the combustion control decision-making module. This module implements prediction and evaluation through the combustion control evaluation channels and the combustion control evaluation weight constraint system, and finally generates a quantified combustion control evaluation coefficient, which will be used to guide whether further optimization of the combustion scheme is needed. First, the combustion control prediction and evaluation module collects relevant data through the combustion control evaluation channels, which are a set of predefined evaluation indicators, including but not limited to combustion efficiency, emission levels, fuel consumption rate, and safety standards. These indicators reflect the possible performance of the combustion control scheme under different operating conditions. Then, the module applies the combustion control evaluation weight constraint, that is, the weight system, to determine the relative importance of each evaluation indicator in the overall evaluation. For example, if there are strict emission control requirements in the area where the power plant is located, the weight of the emission level will be set higher. These weights are not only based on environmental standards but may also consider cost-benefit analysis and combustion efficiency. Each prediction and evaluation of the combustion control scheme is based on the results of the simulation model operation. The evaluation module compares the actual operation data with the simulation model prediction results and analyzes the actual performance of each indicator. This process involves advanced data processing technologies, such as machine learning algorithms, to accurately predict the performance of the combustion control scheme in future actual operations. Finally, all evaluation results will be aggregated and converted into a combustion control evaluation coefficient through normalization processing. This coefficient is a comprehensive score that comprehensively considers all key performance indicators and can intuitively reflect the overall quality of the combustion control scheme. If the evaluation coefficient does not reach the predetermined threshold, it indicates that the combustion scheme needs further optimization. This result will provide decision-making support for the combustion control optimization module to ensure the optimal operation of the power plant combustion system.

[0024] The combustion evaluation and judgment module 14, which is used to judge whether the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient. If the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient, a combustion control optimization instruction is generated.

[0025] Furthermore, the combustion evaluation and judgment module is a key component for determining whether the combustion control scheme meets the established standards. This module judges whether the current combustion control scheme needs further optimization by comparing the combustion control evaluation coefficient with the predetermined combustion control evaluation coefficient. First, the combustion evaluation and judgment module receives the combustion control evaluation coefficient from the combustion control prediction and evaluation module. This coefficient is a comprehensive indicator that reflects the performance of the combustion scheme in multiple dimensions such as efficiency, emissions, safety, and cost. The predetermined combustion control evaluation coefficient is a pre-set standard value that represents the minimum performance standard that the combustion control scheme needs to achieve. The working principle of this module is to compare the combustion control evaluation coefficient with the predetermined combustion control evaluation coefficient. If the combustion control evaluation coefficient is lower than the predetermined value, it indicates that the current combustion control scheme fails to meet the operation requirements of the power plant or the environmental protection standards, so further optimization is needed. In this case, the combustion evaluation and judgment module will generate combustion control optimization instructions. These optimization instructions are specific adjustment suggestions, which may include adjusting the combustion temperature, fuel ratio, air supply volume, or other key operation parameters. The generated optimization instructions will be transmitted to the combustion control tuning module to implement these adjustments, thereby improving the performance of the combustion control scheme and ensuring that it reaches or exceeds the predetermined combustion control evaluation coefficient.

[0026] Through the continuous monitoring and real-time feedback of this module, the power plant manager can ensure that the boiler operates in an optimal state, achieve efficient, safe, and environmentally friendly combustion control, while reducing energy consumption and operation costs.

[0027] The combustion control tuning module 15, which is used to optimize and adjust the combustion control scheme based on the combustion control optimization instructions, according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient, and generate a combustion control optimization strategy.

[0028] The combustion control optimization module is used to finely adjust and optimize the combustion control scheme of the power plant boiler based on the combustion control optimization instructions received from the combustion evaluation and judgment module. The goal of this module is to ensure that the boiler operates at its best performance state by adjusting and optimizing combustion parameters, while meeting the requirements of safety, environmental protection, and economic benefits. The combustion control optimization module first analyzes the predicted power plant load, which refers to the future expected operating load of the power plant, and its data comes from the power plant load prediction module. Understanding this load enables the optimization module to predict the performance of the boiler under different operating conditions, thus more accurately adjusting the operating parameters. In addition, the module also refers to the combustion control evaluation channels and combustion control evaluation weight constraints, which are the evaluation indicators and the weights of each indicator defined in the combustion control prediction and evaluation module. The evaluation channels include combustion efficiency, emission levels, fuel utilization rate, etc., and the weight constraints determine the relative importance of each indicator in the final evaluation. After receiving the combustion control optimization instructions, the combustion control optimization module uses this data and instructions to perform optimization adjustment. During the adjustment process, the module explores different combinations of combustion parameters, such as fuel flow rate, air supply volume, combustion temperature, etc., through algorithms to find the optimal operating point. This process involves complex calculations and may use optimization algorithms such as genetic algorithms, simulated annealing, or other modern optimization techniques to ensure finding the optimal solution within the given operating range. Finally, the combustion control optimization module will generate a set of combustion control optimization strategies. This set of strategies not only meets the predetermined combustion control evaluation coefficients but also takes into account long-term sustainable operation and ease of maintenance. These strategies will directly affect the daily operation of the boiler, and the optimized combustion control scheme will be applied to the actual boiler operation to achieve higher energy efficiency, lower emissions, and better economic benefits. In this way, the combustion control optimization module provides a reliable, efficient, and environmentally friendly combustion management solution for the power plant.

[0029] The combustion control module 16, and the combustion control module is used to perform combustion control on the Q boilers according to the combustion control optimization strategy based on the predetermined future time zone and the predicted power plant load.

[0030] The combustion control module is the executive component of the efficient combustion control system. It is responsible for applying the optimization strategy developed by the combustion control tuning module to the Q boilers of the power plant. The module ensures that combustion control is carried out in the most optimized way according to the actual operating needs and predicted load of the power plant, so as to achieve the goals of improving energy efficiency, reducing emissions and ensuring operational safety. The combustion control module first receives the combustion control optimization strategy from the combustion control tuning module. These strategies contain specific operating parameters for each boiler, such as fuel supply rate, air mixing ratio, combustion temperature setting, etc. These parameters are based on the output of the module's previous evaluation and prediction activities, taking into account the power plant load changes and related operating constraints in the predetermined future time zone. In the implementation phase, the combustion control module uses advanced control algorithms and automatic control system technology to accurately control the combustion process of each boiler. For example, the module monitors and adjusts the ratio of combustion air and fuel in real time to cope with real-time load changes and maximize combustion efficiency. At the same time, through the real-time data feedback mechanism, the module can continuously monitor the combustion status of the boiler, such as temperature, pressure and emission levels, and further refine the control strategy based on these data. In addition, the combustion control module also has anomaly detection and emergency response functions, which can quickly adjust or issue alarms when it detects that operating parameters deviate from safety or efficiency standards. This function ensures the high safety and reliability of power plant operation.

[0031] Through the above control measures, the combustion control module ensures that each boiler operates according to the latest optimization strategy and the actual operating needs of the power plant, optimizes energy use, reduces environmental impact, and maintains efficient and safe operation of the system.

[0032] Furthermore, the steps of constructing the combustion control decision module 12 include:

[0033] Combustion control records are retrieved according to the power plant to obtain a historical power plant load set and a historical combustion control scheme set, wherein each historical combustion control scheme in the historical combustion control scheme set includes Q combustion control historical decisions corresponding to the Q boilers.

[0034] The residual neural network is trained according to the historical power plant load set and the historical combustion control scheme set to obtain a combustion control decision network and a combustion control decision loss data set.

[0035] Incremental learning is performed on the combustion control decision network based on the combustion control decision loss data set to generate the combustion control decision module.

[0036] Specifically, the construction of the combustion control decision-making module involves complex data processing and machine learning techniques, aiming to improve the intelligence and accuracy of boiler combustion control. This module first collects and analyzes the historical operation data of the power plant, including load data and combustion control strategies, to establish a decision support system that can predict and optimize combustion performance. First, retrieve the combustion control records from the power plant's data management system, including the historical power plant load set and the historical combustion control scheme set. The historical power plant load set contains the load data of the power plant during past operations, which reflect the energy demands of the power plant at different times and under different conditions; while the historical combustion control scheme set records the combustion control strategies formulated for Q boilers corresponding to these load conditions, and each strategy details the combustion control decisions of each boiler. Next, use these historical data to train the residual neural network. The residual neural network is a deep learning model that can effectively handle nonlinear problems and has excellent learning and generalization abilities, suitable for dealing with complex combustion control problems. During the training process, the network learns how to predict the optimal combustion control decision under given load conditions through the historical power plant load set and the historical combustion control scheme set, and at the same time generates a combustion control decision loss data set, which indicates the performance and error rate of the model during the training process. Finally, perform incremental learning on the combustion control decision network based on the combustion control decision loss data set. Incremental learning is a machine learning strategy that allows the model to update its knowledge when new data is obtained without having to learn from scratch, which enables the combustion control decision-making module to continuously adapt to new operating environments and changing working conditions. In this way, the combustion control decision network is continuously optimized and improved, and finally a stable and effective combustion control decision-making module is formed, which can provide accurate combustion control strategies for the power plant to ensure the maximization of combustion efficiency and safety.

[0037] Furthermore, the combustion control prediction and evaluation module 13 is used to predict and evaluate the combustion control scheme according to the combustion control evaluation channel and the combustion control evaluation weight constraint, and obtain a combustion control evaluation coefficient, including:

[0038] Obtain the basic information of the power plant, and combine it with the predicted power plant load to build a power plant simulation model.

[0039] Based on the combustion control scheme, perform simulated combustion control on the power plant simulation model to obtain simulated combustion condition data.

[0040] Based on the simulated combustion condition data, obtain a combustion control prediction and evaluation result according to the combustion control evaluation channel, where the combustion control evaluation channel includes multi-dimensional combustion control evaluation indicators, and the multi-dimensional combustion control evaluation indicators include combustion efficiency indicators, combustion safety indicators, combustion emission indicators, and combustion energy consumption indicators.

[0041] Normalize the combustion control prediction and evaluation results to obtain the combustion control prediction conversion results.

[0042] Based on the combustion control evaluation weight constraint, perform weighted calculation on the combustion control prediction conversion results to generate the combustion control evaluation coefficient.

[0043] Optionally, the combustion control prediction and evaluation module is a key component for evaluating and optimizing the combustion control strategy of a power plant boiler. It predicts the performance of a predetermined combustion plan in actual operation through simulation and analysis. The workflow of this module includes obtaining basic data, performing simulation evaluation, and finally generating the evaluation coefficient to ensure the optimal effect of the combustion control plan. First, the module collects the basic information of the power plant and combines it with the predicted power plant load data to build a detailed power plant simulation model. This simulation model takes into account the design parameters of the boiler, operating conditions, and dynamic changes related to the power plant load, providing an accurate basic environment for simulating combustion control. Next, the module executes simulated combustion control in the simulation model according to the combustion control plan. This process involves setting combustion parameters such as fuel supply speed, air input volume, and combustion temperature into the model, and then observing the combustion effect of these parameters under the predetermined load. The simulated combustion condition data generated during the simulation process provides detailed combustion process performance, including data in multiple aspects such as efficiency and emissions. Thereafter, the module processes the simulated combustion condition data through the combustion control evaluation channels. These channels include multi-dimensional evaluation indicators such as combustion efficiency indicators, combustion safety indicators, combustion emission indicators, and combustion energy consumption indicators to comprehensively evaluate the performance of the combustion plan. Based on these data, the module normalizes the combustion control prediction and evaluation results and converts them into a unified evaluation result format for further analysis and comparison. The generation of the combustion control evaluation coefficient is completed by combining the normalized evaluation results with the combustion control evaluation weight constraint. The evaluation weight constraint defines the relative importance of different combustion control evaluation indicators in the overall evaluation. By performing weighted calculation, the final combustion control evaluation coefficient is generated. This evaluation coefficient is a quantitative indicator that reflects the overall effectiveness of the combustion plan in meeting the operating requirements of the power plant and environmental standards, providing a scientific basis for the selection and optimization of the combustion control plan. Through this method, the combustion control prediction and evaluation module not only improves the scientificity and accuracy of the combustion plan but also contributes to the economic operation and environmental protection of the power plant.

[0044] Furthermore, the combustion control optimization module 15 is used to perform optimization adjustment on the combustion control plan based on the combustion control optimization instruction, according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient, to generate a combustion control optimization strategy, including:

[0045] Collect the combustion control constraint information of the Q boilers and build a boiler combustion control space.

[0046] Take the predicted power plant load as the combustion control adjustment constraint target, adjust the combustion control scheme according to the boiler combustion control space, and establish a combustion control adjustment scheme space.

[0047] Extract the first combustion control adjustment scheme according to the combustion control adjustment scheme space.

[0048] Based on the combustion control evaluation channel and the combustion control evaluation weight constraint, conduct a prediction evaluation on the first combustion control adjustment scheme to obtain a first adjustment scheme evaluation coefficient.

[0049] Judge whether the first adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient.

[0050] If the first adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient, add the first combustion control adjustment scheme to the combustion control optimization strategy.

[0051] Furthermore, the core task of the combustion control optimization module is to optimize the combustion control scheme of the power plant boiler by comprehensively utilizing various input parameters and constraints, ensuring that it achieves the predetermined efficiency and emission standards during actual operation. Specifically, first, the module collects the combustion control constraint information of each boiler, including the operation parameter limits, safety standards, and environmental emission requirements of the boiler. This step is the basis for constructing the boiler combustion control space, which defines the possible operation range and adjustment limits of the boiler in combustion control. Next, the predicted power plant load is used as the main target for combustion control adjustment. Combining the established boiler combustion control space, the existing combustion control scheme is adjusted. This adjustment process involves analyzing and comparing the performance under different combustion parameter configurations to determine which configurations can best meet the requirements under the predicted load. A combustion control adjustment scheme space is generated during this process, which includes all feasible adjustment schemes. Subsequently, a preliminary adjustment scheme, i.e., the first combustion control adjustment scheme, is extracted from the combustion control adjustment scheme space. This scheme will be sent to the evaluation stage and the combustion control evaluation channel and combustion control evaluation weight constraints are applied to conduct performance prediction and evaluation. The evaluation channel includes indicators such as combustion efficiency, safety, emissions, and energy consumption, while the weight constraints determine the relative importance of these indicators in the overall evaluation. Through the evaluation process, the evaluation coefficient of the first adjustment scheme is calculated. This evaluation coefficient reflects the ability of the scheme to meet the predetermined combustion control evaluation criteria. If this evaluation coefficient reaches or exceeds the predetermined combustion control evaluation coefficient, then this adjustment scheme is considered effective and can be accepted as the final combustion control strategy, and this scheme will be incorporated into the combustion control optimization strategy. If not satisfied, it is necessary to reselect or generate a new adjustment scheme from the adjustment scheme space for evaluation until a combustion control scheme that meets the requirements is found.

[0052] Through the above steps, the combustion control optimization module not only ensures the optimization of boiler operation but also guarantees that the power plant can achieve an efficient, safe, and environmentally friendly operating state under changing operating conditions.

[0053] Furthermore, when the combustion control optimization module 15 executes the step of determining whether the evaluation coefficient of the first adjustment scheme is greater than or equal to the predetermined combustion control evaluation coefficient, it includes:

[0054] If the evaluation coefficient of the first adjustment scheme is less than the predetermined combustion control evaluation coefficient, according to the combustion control adjustment scheme space, a second combustion control adjustment scheme is extracted.

[0055] Based on the combustion control evaluation channel and the combustion control evaluation weight constraints, a prediction and evaluation are conducted on the second combustion control adjustment scheme to obtain the evaluation coefficient of the second adjustment scheme.

[0056] Determine whether the second adjustment plan evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient.

[0057] If the second adjustment plan evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient, add the second combustion control adjustment plan to the combustion control optimization strategy.

[0058] A series of iterative steps are required when evaluating and selecting the best combustion control adjustment plan to ensure that the selected combustion control plan can meet the predetermined performance criteria. The module first evaluates the performance of the proposed first combustion control adjustment plan, which is done by calculating its evaluation coefficient. The evaluation coefficient is calculated based on the combustion control evaluation channels and evaluation weight constraints, covering the performance in multiple dimensions such as combustion efficiency, safety, emissions, and energy consumption. If the evaluation coefficient of the first adjustment plan is less than the predetermined combustion control evaluation coefficient, it means that this plan fails to meet the required performance criteria and further optimization or alternative plans are needed. In the case where the first adjustment plan fails to meet the criteria, the combustion control optimization module extracts a second combustion control adjustment plan from the defined combustion control adjustment plan space. This plan space contains a variety of potential adjustment options, each of which is the predicted result considering different combustion parameters and operating conditions. The selection of the second plan is based on different parameter settings or optimization focuses from the first plan, in the hope of finding a solution that better meets the performance requirements. Subsequently, a predictive evaluation of the second adjustment plan is carried out, and this process is also based on the combustion control evaluation channels and weight constraints. The calculated second adjustment plan evaluation coefficient provides an assessment of the performance of this plan on the predetermined performance criteria. If the evaluation coefficient of the second adjustment plan is greater than or equal to the predetermined combustion control evaluation coefficient, it indicates that this plan has successfully achieved the desired performance criteria and can therefore be incorporated into the final combustion control optimization strategy. If the second plan also fails to meet the predetermined criteria, new plans need to be continuously extracted from the adjustment plan space for evaluation until a combustion control plan that meets all performance criteria is found. Through the above steps, the combustion control optimization module ensures that each adjustment plan can be strictly evaluated and achieve the best combustion efficiency and environmental performance in actual operation.

[0059] Furthermore, the combustion control module 16 is used to perform combustion control on the Q boilers according to the combustion control optimization strategy based on the predetermined future time zone and the predicted power plant load, including:

[0060] Obtain the real-time power plant load corresponding to the predetermined future time zone.

[0061] Based on the real-time power plant load, perform an offset comparison on the predicted power plant load to obtain a load offset comparison result.

[0062] Based on the load offset comparison result, evaluate the impact on boiler combustion to obtain the load offset impact index.

[0063] Determine whether the load offset impact index is greater than or equal to a predetermined load offset impact index.

[0064] If the load offset impact index is greater than or equal to the predetermined load offset impact index, perform combustion control compensation on the Q boilers according to the load offset comparison result.

[0065] The combustion control module obtains the real-time power plant load corresponding to the predetermined future time zone, that is, the operating load that the power plant is expected to face within a certain period in the future. The accurate acquisition of real-time power plant load data is the basis for ensuring the accuracy of combustion control, allowing the module to adjust combustion parameters according to immediate needs to cope with changes in actual operation. Next, the module compares the real-time power plant load with the predicted power plant load for offset. The purpose of this step is to identify the deviation between the actual load and the predicted load, which may be caused by various unforeseen factors, such as weather changes, equipment performance changes, or emergencies. By comparing these deviations, the combustion strategy can be adjusted more accurately to match the actual operating requirements. Subsequently, based on the load offset comparison result, evaluate the impact on boiler combustion, thereby calculating the load offset impact index. This index is a quantitative indicator measuring the degree of impact of load changes on boiler operation, reflecting the potential impact of load fluctuations on combustion efficiency, safety, emissions, and energy consumption. Thereafter, determine whether the load offset impact index reaches or exceeds a predetermined load offset impact index threshold, which is set according to the power plant operation safety and efficiency standards to determine whether measures need to be taken to adjust the combustion control strategy. If the load offset impact index exceeds the predetermined threshold, the module will perform necessary combustion control compensation on all Q boilers according to the load offset comparison result, including adjusting the fuel supply amount, air ratio, combustion temperature, etc., to ensure that the boilers can still operate efficiently and safely under the new load conditions.

[0066] Through the above control steps, the combustion control module not only optimizes the energy utilization of the power plant and reduces environmental impact, but also improves the system's response ability to external changes and the overall operation stability.

[0067] Furthermore, the combustion control module 16 is used to perform combustion control on the Q boilers based on the predetermined future time zone and the predicted power plant load according to the combustion control optimization strategy, including:

[0068] Based on the predetermined future time zone, perform real-time monitoring on the Q boilers to obtain the monitoring data of each boiler.

[0069] Based on the above combustion control optimization strategy, compare the combustion control of the monitored data of each boiler to obtain the combustion control comparison results of each boiler.

[0070] Based on the combustion control comparison results of each boiler, generate Q combustion control correction messages.

[0071] Based on the Q combustion control correction messages, perform combustion control correction on the Q boilers.

[0072] The combustion control module is not limited to the initial setting of combustion parameters, but also includes continuous real-time monitoring and dynamic correction to ensure that the combustion processes of the Q boilers are always in an optimal state. The combustion control module performs real-time monitoring on the Q boilers based on the time period when the power plant conducts combustion control according to the load prediction. During this period, the module collects real-time operation data from each boiler, and these monitored data include core parameters such as the combustion temperature, fuel supply rate, air supply ratio, and emission level of the boiler. This monitoring process is carried out in real time throughout the whole process to ensure that the operation state of the boiler is always within the controllable range. Next, compare the monitored data of each boiler collected based on the previously generated combustion control optimization strategy. The combustion control optimization strategy is the optimal combustion plan based on the power plant load prediction and historical data, and it provides the best operation parameters of the boiler under specific load conditions. By comparing the actual monitored data with the parameters set in the optimization strategy, the module can identify the differences between the current operation state of the boiler and the optimal state, and generate the combustion control comparison results of each boiler. After the comparison results are generated, the combustion control module will generate Q combustion control correction messages based on these comparison results. These correction messages specifically indicate in which aspects each boiler needs to be adjusted, such as excessive fuel supply, too low air ratio, or combustion temperature deviation. The generation of the correction messages ensures that the correction of the boiler can accurately respond to the actual operation deviation. Use the generated correction messages to perform combustion control correction on the Q boilers. This correction process involves real-time adjustment of the key operation parameters of each boiler to ensure that the operation state of the boiler meets the expectations of the optimization strategy. For example, the module may adjust the fuel supply rate, regulate the air flow, or change the combustion temperature setting to restore the operation of the boiler to the optimal state.

[0073] Through the above steps, the combustion control module can ensure that the combustion processes of the Q boilers are always in an optimized state, maximizing the combustion efficiency, reducing emissions, and ensuring safe operation.

[0074] Through the technical solutions of the above embodiments, the efficient combustion control system for power plant boilers provided by the present application solves the technical problems that the existing combustion control systems of power plant boilers are difficult to quickly respond to the changes in power plant loads, resulting in low combustion efficiency and energy waste, and the system stability and safety cannot be guaranteed, and achieves the technical effects of dynamically responding to and adjusting the combustion strategy, performing combustion control more precisely and efficiently, improving the operation efficiency of power plant boilers, reducing energy consumption and pollution emissions, and increasing the stability and safety of the system at the same time.

[0075] Embodiment 2, based on the same inventive concept as the efficient combustion control system for power plant boilers in the foregoing embodiment, as Figure 2 shown, the present application embodiment provides an efficient combustion control method for power plant boilers, and the method includes:

[0076] Performing a load prediction on the power plant according to a predetermined future time zone to obtain a predicted power plant load.

[0077] Making a combustion control decision on Q boilers of the power plant according to the predicted power plant load to obtain a combustion control plan, where Q is a positive integer greater than 1.

[0078] Performing a prediction evaluation on the combustion control plan according to a combustion control evaluation channel and a combustion control evaluation weight constraint to obtain a combustion control evaluation coefficient.

[0079] Judging whether the combustion control evaluation coefficient is less than a predetermined combustion control evaluation coefficient, and if the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient, generating a combustion control optimization instruction.

[0080] Based on the combustion control optimization instruction, performing an optimization adjustment on the combustion control plan according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient to generate a combustion control optimization strategy.

[0081] Based on the predetermined future time zone and the predicted power plant load, performing combustion control on the Q boilers according to the combustion control optimization strategy.

[0082] Further, the method further includes:

[0083] Retrieving the combustion control records of the power plant to obtain a historical power plant load set and a historical combustion control plan set, where in the historical combustion control plan set, each historical combustion control plan includes Q combustion control historical decisions corresponding to the Q boilers.

[0084] Training a residual neural network according to the historical power plant load set and the historical combustion control plan set to obtain a combustion control decision network and a combustion control decision loss data set.

[0085] Perform incremental learning on the combustion control decision-making network based on the combustion control decision-making loss data set to generate a combustion control decision-making module.

[0086] Furthermore, perform predictive evaluation on the combustion control scheme according to the combustion control evaluation channels and the combustion control evaluation weight constraints to obtain a combustion control evaluation coefficient, including:

[0087] Obtain the basic information of the power plant, and combine it with the predicted power plant load to build a power plant simulation model.

[0088] Based on the combustion control scheme, perform simulated combustion control on the power plant simulation model to obtain simulated combustion condition data.

[0089] Based on the simulated combustion condition data, obtain a combustion control prediction evaluation result according to the combustion control evaluation channels, where the combustion control evaluation channels include multi-dimensional combustion control evaluation indicators, and the multi-dimensional combustion control evaluation indicators include combustion efficiency indicators, combustion safety indicators, combustion emission indicators, and combustion energy consumption indicators.

[0090] Perform normalization processing on the combustion control prediction evaluation result to obtain a combustion control prediction conversion result.

[0091] Perform weighted calculation on the combustion control prediction conversion result based on the combustion control evaluation weight constraints to generate the combustion control evaluation coefficient.

[0092] Furthermore, based on the combustion control optimization instruction, perform optimization adjustment on the combustion control scheme according to the predicted power plant load, the combustion control evaluation channels, the combustion control evaluation weight constraints, and the predetermined combustion control evaluation coefficient to generate a combustion control optimization strategy, including:

[0093] Collect the combustion control constraint information of the Q boilers and build a boiler combustion control space.

[0094] Take the predicted power plant load as the combustion control adjustment constraint target, and adjust the combustion control scheme according to the boiler combustion control space to establish a combustion control adjustment scheme space.

[0095] Extract the first combustion control adjustment scheme according to the combustion control adjustment scheme space.

[0096] Based on the combustion control evaluation channels and the combustion control evaluation weight constraints, perform predictive evaluation on the first combustion control adjustment scheme to obtain a first adjustment scheme evaluation coefficient.

[0097] Judge whether the first adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient.

[0098] If the evaluation coefficient of the first adjustment scheme is greater than or equal to the predetermined combustion control evaluation coefficient, add the first combustion control adjustment scheme to the combustion control optimization strategy.

[0099] Further, determining whether the evaluation coefficient of the first adjustment scheme is greater than or equal to the predetermined combustion control evaluation coefficient includes:

[0100] If the evaluation coefficient of the first adjustment scheme is less than the predetermined combustion control evaluation coefficient, extract a second combustion control adjustment scheme according to the combustion control adjustment scheme space.

[0101] Based on the combustion control evaluation channel and the combustion control evaluation weight constraint, perform a prediction evaluation on the second combustion control adjustment scheme to obtain a second adjustment scheme evaluation coefficient.

[0102] Determine whether the evaluation coefficient of the second adjustment scheme is greater than or equal to the predetermined combustion control evaluation coefficient.

[0103] If the evaluation coefficient of the second adjustment scheme is greater than or equal to the predetermined combustion control evaluation coefficient, add the second combustion control adjustment scheme to the combustion control optimization strategy.

[0104] Further, based on the predetermined future time zone and the predicted power plant load, perform combustion control on the Q boilers according to the combustion control optimization strategy, including:

[0105] Obtain the real-time power plant load corresponding to the predetermined future time zone.

[0106] Based on the real-time power plant load, perform an offset comparison on the predicted power plant load to obtain a load offset comparison result.

[0107] Based on the load offset comparison result, perform a boiler combustion impact evaluation to obtain a load offset impact index.

[0108] Determine whether the load offset impact index is greater than or equal to a predetermined load offset impact index.

[0109] If the load offset impact index is greater than or equal to the predetermined load offset impact index, perform combustion control compensation on the Q boilers according to the load offset comparison result.

[0110] Further, based on the predetermined future time zone and the predicted power plant load, performing combustion control on the Q boilers according to the combustion control optimization strategy further includes:

[0111] Based on the predetermined future time zone, perform real-time monitoring on the Q boilers to obtain monitoring data of each boiler.

[0112] Based on the combustion control optimization strategy, perform combustion control comparison on the monitoring data of each boiler to obtain the combustion control comparison results of each boiler.

[0113] Based on the combustion control comparison results of each boiler, generate Q combustion control correction messages.

[0114] Based on the Q combustion control correction messages, perform combustion control correction on the Q boilers.

[0115] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0117] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An efficient combustion control system for a power plant boiler, characterized in that, The system includes: A power plant load prediction module, which is used to predict the load of the power plant according to a predetermined future time zone to obtain the predicted power plant load; A combustion control decision-making module, which is used to make combustion control decisions for Q boilers of the power plant according to the predicted power plant load to obtain a combustion control scheme, where Q is a positive integer greater than 1; A combustion control prediction evaluation module, which is used to predict and evaluate the combustion control scheme according to a combustion control evaluation channel and a combustion control evaluation weight constraint to obtain a combustion control evaluation coefficient; A combustion evaluation judgment module, which is used to judge whether the combustion control evaluation coefficient is less than a predetermined combustion control evaluation coefficient. If the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient, a combustion control optimization instruction is generated; A combustion control optimization module, which is used to perform optimization adjustment on the combustion control scheme based on the combustion control optimization instruction according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient to generate a combustion control optimization strategy; A combustion control module, which is used to perform combustion control on the Q boilers according to the combustion control optimization strategy based on the predetermined future time zone and the predicted power plant load; The construction steps of the combustion control decision-making module include: Retrieving the combustion control records of the power plant to obtain a historical power plant load set and a historical combustion control scheme set. Among the historical combustion control scheme set, each historical combustion control scheme includes Q combustion control historical decisions corresponding to the Q boilers; Training a residual neural network according to the historical power plant load set and the historical combustion control scheme set to obtain a combustion control decision-making network and a combustion control decision-making loss data set; Performing incremental learning on the combustion control decision-making network based on the combustion control decision-making loss data set to generate the combustion control decision-making module.

2. The high-efficiency combustion control system for a power plant boiler according to claim 1, characterized in that, The combustion control prediction evaluation module is used to predict and evaluate the combustion control scheme according to a combustion control evaluation channel and a combustion control evaluation weight constraint to obtain a combustion control evaluation coefficient, including: Obtaining the basic information of the power plant, and combining with the predicted power plant load to build a power plant simulation model; Based on the combustion control scheme, performing simulated combustion control on the power plant simulation model to obtain simulated combustion condition data; Based on the simulated combustion condition data, obtaining a combustion control prediction evaluation result according to the combustion control evaluation channel, where the combustion control evaluation channel includes multi-dimensional combustion control evaluation indicators, and the multi-dimensional combustion control evaluation indicators include a combustion efficiency indicator, a combustion safety indicator, a combustion emission indicator, and a combustion energy consumption indicator; Performing normalization processing on the combustion control prediction evaluation result to obtain a combustion control prediction conversion result; Performing weighted calculation on the combustion control prediction conversion result based on the combustion control evaluation weight constraint to generate the combustion control evaluation coefficient.

3. The high-efficiency combustion control system for a power plant boiler according to claim 1, characterized in that, The combustion control optimization module is used to optimize and adjust the combustion control scheme based on the combustion control optimization instruction, according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient, and generate a combustion control optimization strategy, including: Collect the combustion control constraint information of the Q boilers and build a boiler combustion control space; Take the predicted power plant load as the combustion control adjustment constraint target, adjust the combustion control scheme according to the boiler combustion control space, and establish a combustion control adjustment scheme space; Extract the first combustion control adjustment scheme according to the combustion control adjustment scheme space; Based on the combustion control evaluation channel and the combustion control evaluation weight constraint, conduct a predictive evaluation on the first combustion control adjustment scheme to obtain a first adjustment scheme evaluation coefficient; Judge whether the first adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient; If the first adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient, add the first combustion control adjustment scheme to the combustion control optimization strategy.

4. The high-efficiency combustion control system for a power plant boiler according to claim 3, wherein, Judging whether the first adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient includes: If the first adjustment scheme evaluation coefficient is less than the predetermined combustion control evaluation coefficient, extract the second combustion control adjustment scheme according to the combustion control adjustment scheme space; Based on the combustion control evaluation channel and the combustion control evaluation weight constraint, conduct a predictive evaluation on the second combustion control adjustment scheme to obtain a second adjustment scheme evaluation coefficient; Judge whether the second adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient; If the second adjustment scheme evaluation coefficient is greater than or equal to the predetermined combustion control evaluation coefficient, add the second combustion control adjustment scheme to the combustion control optimization strategy.

5. The high-efficiency combustion control system for a power plant boiler according to claim 1, characterized in that, The combustion control module is used to perform combustion control on the Q boilers based on the predetermined future time zone and the predicted power plant load, according to the combustion control optimization strategy, including: Obtain the real-time power plant load corresponding to the predetermined future time zone; Based on the real-time power plant load, conduct an offset comparison on the predicted power plant load to obtain a load offset comparison result; Conduct a boiler combustion impact evaluation based on the load offset comparison result to obtain a load offset impact index; Judge whether the load offset impact index is greater than or equal to a predetermined load offset impact index; If the load offset impact index is greater than or equal to the predetermined load offset impact index, perform combustion control compensation on the Q boilers according to the load offset comparison result.

6. The high-efficiency combustion control system for a power plant boiler according to claim 1, characterized in that, The combustion control module is used to perform combustion control on the Q boilers based on the predetermined future time zone and the predicted power plant load, according to the combustion control optimization strategy, including: Based on the predetermined future time zone, conduct real-time monitoring on the Q boilers to obtain the monitoring data of each boiler; Based on the combustion control optimization strategy, conduct a combustion control comparison on the monitoring data of each boiler to obtain the combustion control comparison result of each boiler; Generate Q combustion control correction messages based on the comparison results of the combustion controls of the boilers; Perform combustion control correction on the Q boilers based on the Q combustion control correction messages.

7. An efficient combustion control method for a power plant boiler, characterized in that, The method is applied to the high-efficiency combustion control system for power plant boilers according to any one of claims 1-6, and the method includes: Predict the load of the power plant according to a predetermined future time zone to obtain the predicted power plant load; Make combustion control decisions for the Q boilers of the power plant according to the predicted power plant load to obtain a combustion control plan, where Q is a positive integer greater than 1; Perform a predictive evaluation on the combustion control plan according to the combustion control evaluation channel and the combustion control evaluation weight constraint to obtain a combustion control evaluation coefficient; Judge whether the combustion control evaluation coefficient is less than a predetermined combustion control evaluation coefficient. If the combustion control evaluation coefficient is less than the predetermined combustion control evaluation coefficient, generate a combustion control optimization instruction; Based on the combustion control optimization instruction, perform an optimization adjustment on the combustion control plan according to the predicted power plant load, the combustion control evaluation channel, the combustion control evaluation weight constraint, and the predetermined combustion control evaluation coefficient to generate a combustion control optimization strategy; Based on the predetermined future time zone and the predicted power plant load, perform combustion control on the Q boilers according to the combustion control optimization strategy.

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