Boiler combustion optimization method and system based on multi-parameter adjustment

Abstract

The invention discloses a boiler combustion optimization method and system based on multi-parameter adjustment, and the method comprises the steps: obtaining and extracting combustion data related to the combustion efficiency of a boiler in the historical operation data of the boiler, and extracting the characteristics of the combustion data, constructing a boiler combustion prediction model based on the features and a preset neural network model to perform prediction to obtain combustion prediction data; analyzing the combustion prediction data to determine key combustion parameters influencing the combustion state of the boiler, and determining data characteristics of the key combustion parameters; evaluating the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, and determining the weight of each control parameter based on the boiler state evaluation value; and determining an adjustment coefficient corresponding to each control parameter based on the weight so as to optimize and adjust each control parameter. The advanced data analysis technology is combined with real-time monitoring data and model prediction, multiple control parameters in the boiler combustion process are intelligently adjusted and optimized, the boiler combustion efficiency is improved, energy consumption is reduced, emission is reduced, and the stability of the boiler is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and particularly to a boiler combustion optimization method and system based on multi-parameter regulation. Background Art

[0002] With the continuous improvement of industrial production requirements for energy efficiency and environmental protection, boilers, as important energy equipment, their combustion processes are crucial for production efficiency and environmental protection. Traditional boiler combustion control methods often rely on empirical regulation, suffering from problems such as low regulation accuracy, low energy efficiency, and large environmental impact. To solve these problems, a boiler combustion optimization method based on multi-parameter regulation has emerged. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a boiler combustion optimization method and system based on multi-parameter regulation, including: Obtain the historical operation data of the boiler, and extract combustion data related to the boiler combustion efficiency from the historical operation data; Extract the characteristics of the combustion data, construct a boiler combustion prediction model based on the characteristics and a preset neural network model, and perform prediction based on the boiler combustion prediction model to obtain combustion prediction data; Analyze the combustion prediction data, determine the key combustion parameters affecting the boiler combustion state, and determine the data characteristics of the key combustion parameters; Evaluate the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, and determine the weights of each control parameter based on the boiler combustion efficiency evaluation value; Determine the adjustment coefficients corresponding to each control parameter based on the weights of each control parameter, and optimize and adjust each control parameter according to the adjustment coefficients to perform boiler combustion control.

[0004] Further, the obtaining the historical operation data of the boiler and extracting the combustion data related to the boiler combustion efficiency from the historical operation data includes: Obtain the historical operation data of the boiler, and determine the boiler combustion efficiency in the historical operation data; Divide the remaining data in the historical operation data into several groups of operation parameter data according to the parameter type, and calculate the correlation degree between each group of operation parameter data and the boiler combustion efficiency; Preset a preset threshold, and extract the groups of operation parameter data whose correlation degree exceeds the preset threshold, and determine them as the combustion data related to the boiler combustion efficiency.

[0005] Further, the extracting the characteristics of the combustion data, constructing a boiler combustion prediction model based on the characteristics and a preset neural network model, and performing prediction based on the boiler combustion prediction model to obtain combustion prediction data includes: Extract the features of each combustion data, and correspond each combustion data with the features corresponding to the combustion data, and construct data sets respectively; Input each data set into the preset neural network model corresponding to each combustion data to construct an initial combustion prediction model corresponding to each combustion data; Divide each data set into a training set and a test set according to a preset ratio, and input the training set and the test set into each initial combustion prediction model; Train and test each initial combustion prediction model until each initial combustion prediction model meets the preset convergence condition to obtain the corresponding combustion prediction model; Fuse each combustion prediction model to obtain a boiler combustion prediction model, obtain the current combustion data of the boiler, and input the current combustion data into the boiler combustion prediction model for prediction to obtain combustion prediction data.

[0006] Further, analyzing the combustion prediction data to determine the key combustion parameters affecting the boiler combustion state, and determining the data characteristics of the key combustion parameters, including: Divide the combustion prediction data into several key combustion parameter data groups according to the parameter type, and construct combustion parameter change curves based on each key combustion parameter data group respectively; Determine the standard combustion curve of each combustion parameter in the stable combustion state in each combustion parameter change curve, and determine the similarity between each combustion parameter change curve and the corresponding standard combustion curve; Preset a preset similarity, and determine the parameters corresponding to the combustion parameter change curves with similarity less than the preset similarity as the key combustion parameters affecting the boiler combustion state; Determine the combustion parameter change curves corresponding to each key combustion parameter, and determine the data variance value and change amplitude value of the combustion parameter change curves corresponding to each key combustion parameter; Determine the data variance value and change amplitude value corresponding to each key combustion parameter as the data characteristics of each key combustion parameter.

[0007] Further, evaluating the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, including: Evaluate and obtain the data variance estimate and change amplitude estimate for the data variance value and change amplitude value corresponding to each key combustion parameter respectively, and add the data variance estimate and change amplitude estimate to calculate the data change evaluation value of each key combustion parameter; Preset the preset weight of each key combustion parameter in advance, and perform weighted addition calculation on the data change evaluation value of each key combustion parameter and the corresponding preset weight to obtain the boiler state evaluation value.

[0008] Further, determining the weights of the control parameters based on the boiler combustion efficiency evaluation value includes: Preset the preset values of the control parameters, calculate the ratio of the boiler combustion efficiency evaluation value to the preset values of the control parameters to obtain the numerical ratios of the control parameters, and determine the numerical ratios of the control parameters as the weights of the control parameters.

[0009] Further, determining the adjustment coefficients corresponding to the control parameters based on the weights of the control parameters, and optimizing and adjusting the control parameters according to the adjustment coefficients to perform boiler combustion control includes: Preset the corresponding relationship between the adjustment coefficient - weight interval of the control parameters. Among them, for each weight interval in the corresponding relationship between the adjustment coefficient - weight interval, a corresponding adjustment coefficient is associated; Obtain the weights of the control parameters, and select the adjustment coefficient corresponding to the weight interval as the corresponding adjustment coefficient of each control parameter based on the mapping relationship of the weight interval to which the weight belongs in the corresponding relationship between the adjustment coefficient - weight interval; Optimize and adjust the control parameters according to the adjustment coefficients, and perform boiler combustion control according to the optimized and adjusted control parameters.

[0010] The present invention also provides a boiler combustion optimization system based on multi - parameter adjustment, including: An acquisition module, configured to acquire the historical operation data of the boiler and extract the combustion data related to the boiler combustion efficiency from the historical operation data; A prediction module, configured to extract the features of the combustion data, construct a boiler combustion prediction model based on the features and a preset neural network model, and perform prediction based on the boiler combustion prediction model to obtain combustion prediction data; An analysis module, configured to analyze the combustion prediction data, determine the key combustion parameters affecting the boiler combustion state, and determine the data features of the key combustion parameters; A determination module, configured to evaluate the boiler combustion state based on the data features to obtain a boiler state evaluation value, and determine the weights of the control parameters based on the boiler combustion efficiency evaluation value; An optimization module, configured to determine the adjustment coefficients corresponding to the control parameters based on the weights of the control parameters, and optimize and adjust the control parameters according to the adjustment coefficients to perform boiler combustion control.

[0011] Compared with the prior art, the beneficial effects of the boiler combustion optimization method and system based on multi - parameter adjustment in the embodiments of the present invention are as follows: By establishing a combustion prediction model and a control parameter optimization system, the present invention can realize the prediction and optimization of the boiler combustion efficiency, improve the energy utilization efficiency and reduce the energy consumption; Based on the analysis of historical data and model establishment, the present invention can provide data-driven decision support for boiler operation management, helping operation and maintenance personnel better understand and control the combustion process; The prediction model and optimization system established by the present invention can realize real-time monitoring and adjustment of the boiler combustion state, timely discover problems and perform optimization control; By optimizing control parameters and improving combustion efficiency, the present invention can effectively reduce the operation cost of the boiler, reduce energy waste and maintenance costs; The entire process of the present invention utilizes advanced technologies such as data analysis, machine learning, and neural networks to realize the intelligent management of the boiler combustion process and improve the automation level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a schematic flow structure diagram of the boiler combustion optimization method based on multi-parameter adjustment in the embodiment of the present invention; Figure 2 is a schematic composition diagram of the boiler combustion optimization system based on multi-parameter adjustment in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] The following combines the drawings and embodiments to further describe in detail the specific embodiments of the present application. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0014] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0015] The terms "", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined by "", "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0016] In the description of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0017] As Figure 1 shown, in an embodiment of the present application, a boiler combustion optimization method based on multi-parameter adjustment is provided, including: S100: Obtain the historical operation data of the boiler, and extract the combustion data related to the boiler combustion efficiency from the historical operation data; S200: Extract the characteristics of the combustion data, construct a boiler combustion prediction model based on the characteristics and a preset neural network model, and perform prediction based on the boiler combustion prediction model to obtain combustion prediction data; S300: Analyze the combustion prediction data, determine the key combustion parameters affecting the boiler combustion state, and determine the data characteristics of the key combustion parameters; S400: Evaluate the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, and determine the weights of the control parameters based on the boiler combustion efficiency evaluation value; S500: Determine the adjustment coefficients corresponding to the control parameters based on the weights of the control parameters, and optimize and adjust the control parameters according to the adjustment coefficients to perform boiler combustion control.

[0018] Furthermore, by establishing a combustion prediction model and a control parameter optimization system, the present invention can realize the prediction and optimization of the boiler combustion efficiency, improve the energy utilization efficiency and reduce the energy consumption; based on the analysis of historical data and model establishment, the present invention can provide data-driven decision-making support for boiler operation management, helping operation and maintenance personnel better understand and control the combustion process; the prediction model and optimization system established by the present invention can realize the real-time monitoring and adjustment of the boiler combustion state, timely discover problems and perform optimization control; by optimizing the control parameters and improving the combustion efficiency, the present invention can effectively reduce the operation cost of the boiler, reduce energy waste and maintenance costs; the entire process of the present invention utilizes advanced technologies such as data analysis, machine learning, and neural networks to realize the intelligent management of the boiler combustion process and improve the automation level of the system.

[0019] In an embodiment of the present application, a boiler combustion optimization method based on multi-parameter adjustment is provided. The method includes obtaining the historical operation data of the boiler and extracting combustion data related to the boiler combustion efficiency from the historical operation data, including: obtaining the historical operation data of the boiler and determining the boiler combustion efficiency in the historical operation data; dividing the remaining data in the historical operation data into several groups of operation parameter data according to the parameter type, and calculating the correlation degree between each group of operation parameter data and the boiler combustion efficiency; presetting a preset threshold, and extracting the groups of operation parameter data whose correlation degree exceeds the preset threshold, and determining them as combustion data related to the boiler combustion efficiency.

[0020] Specifically, collect the historical operation data of the boiler, including parameters such as temperature, pressure, fuel supply amount, air supply amount, etc.; divide the remaining historical operation data into different groups according to the parameter type, such as temperature group, pressure group, fuel supply amount group, air supply amount group, etc.; for each parameter group, calculate the correlation degree between it and the boiler combustion efficiency, and the method used is the Pearson correlation coefficient method; preset a threshold to screen the parameter groups related to the combustion efficiency, and extract the groups of operation parameter data whose correlation degree exceeds the preset threshold, and determine them as combustion data related to the boiler combustion efficiency. Through this correlation degree calculation step, the parameters related to the boiler combustion efficiency can be accurately determined, helping to find the key influencing factors in complex data; based on the analysis of historical data, it can provide data support for the boiler combustion efficiency, helping decision-makers better understand and optimize the combustion process. In summary, through this process, the key parameters related to the boiler combustion efficiency can be extracted from historical data, providing an important reference for subsequent combustion efficiency optimization and control, so as to achieve more effective boiler management and operation optimization.

[0021] In an embodiment of the present application, a boiler combustion optimization method based on multi-parameter adjustment is provided. The method includes extracting the features of the combustion data, constructing a boiler combustion prediction model based on the features and a preset neural network model, and performing prediction based on the boiler combustion prediction model to obtain combustion prediction data, including: extracting the features of each combustion data, and corresponding each combustion data to its corresponding feature to respectively construct a data set; inputting each data set into the preset neural network model corresponding to each combustion data to construct an initial combustion prediction model corresponding to each combustion data; dividing each data set into a training set and a test set according to a preset ratio, and inputting the training set and the test set into each initial combustion prediction model; training and testing each initial combustion prediction model until each initial combustion prediction model meets the preset convergence condition to obtain the corresponding combustion prediction model; fusing each combustion prediction model to obtain a boiler combustion prediction model, and obtaining the current combustion data of the boiler, and inputting the current combustion data into the boiler combustion prediction model for prediction to obtain combustion prediction data.

[0022] Specifically, extract features from each combustion data as the input of the neural network model; correspond each combustion data with the corresponding features to construct a data set, where each sample contains combustion data and corresponding features; input the data set into a preset neural network model to construct an initial combustion prediction model corresponding to each combustion data; divide the data set into a training set and a test set according to a preset ratio, and input the training set and the test set into each initial combustion prediction model respectively; train and test each initial combustion prediction model until the model meets the preset convergence condition, and train to obtain the corresponding combustion prediction model; fuse each combustion prediction model to obtain an overall boiler combustion prediction model, obtain the current boiler combustion data, input it into the boiler combustion prediction model for prediction, and obtain combustion prediction data. This step of constructing the combustion prediction model can help accurately predict the boiler combustion data, discover problems in advance and make adjustments; based on the prediction of the neural network model, it can provide data support for boiler operation management and help decision-makers formulate more effective operation strategies; through model training and testing, the combustion prediction model can be optimized to improve the prediction accuracy, thereby optimizing the boiler operation efficiency; the constructed combustion prediction model can realize real-time monitoring and prediction of the boiler combustion state, and timely adjust parameters to maintain system stability. In summary, construct an efficient boiler combustion prediction model to achieve accurate prediction and optimal control of the boiler combustion efficiency, improve energy utilization efficiency, reduce operating costs, and ensure the stability and reliability of the boiler system.

[0023] In an embodiment of the present application, a boiler combustion optimization method based on multi-parameter adjustment is provided. Analyze the combustion prediction data to determine the key combustion parameters affecting the boiler combustion state, and determine the data characteristics of the key combustion parameters, including: divide the combustion prediction data into several key combustion parameter data groups according to the parameter type, and construct combustion parameter change curves based on each key combustion parameter data group respectively; determine the standard combustion curves of each combustion parameter in the stable combustion state in each combustion parameter change curve, and determine the similarity between each combustion parameter change curve and the corresponding standard combustion curve; preset a preset similarity, and determine the parameters corresponding to the combustion parameter change curves with similarity less than the preset similarity as the key combustion parameters affecting the boiler combustion state; determine the combustion parameter change curves corresponding to each key combustion parameter, and determine the data variance value and change amplitude value of each key combustion parameter corresponding combustion parameter change curve; determine the data variance value and change amplitude value corresponding to each key combustion parameter as the data characteristics of each key combustion parameter.

[0024] Specifically, the combustion prediction data is divided into several key combustion parameter data groups according to the parameter type; for each key combustion parameter data group, a corresponding combustion parameter change curve is constructed; in the combustion parameter change curve, the standard combustion curve of each combustion parameter in the stable combustion state is determined; the similarity between each combustion parameter change curve and the corresponding standard combustion curve is calculated, and the method used is curve similarity comparison; a similarity threshold is preset in advance, and the parameters corresponding to the combustion parameter change curves with similarity less than the preset similarity are determined as the key combustion parameters affecting the boiler combustion state; for the combustion parameter change curve corresponding to each key combustion parameter, the data variance value and the change amplitude value are calculated as the data characteristics of the parameter. This step accurately identifies the key combustion parameters affecting the boiler combustion state through curve similarity calculation and threshold setting; by calculating the data variance value and the change amplitude value, the data characteristics of each key combustion parameter can be quantified, providing a basis for further analysis and optimization; identifying the parameters with large data variance values and change amplitude values helps to discover abnormal situations or potential problems and make timely adjustments and treatments; based on the data characteristics of the key parameters, the control strategy of the boiler combustion state can be optimized, improving the combustion efficiency and system stability. In summary, the characteristics and change laws of each key combustion parameter can be deeply understood, helping to better monitor and control the boiler combustion state, timely discover problems and make adjustments, thereby improving the operation efficiency and reliability of the boiler.

[0025] In an embodiment of the present application, a boiler combustion optimization method based on multi-parameter adjustment is provided. The boiler combustion state is evaluated based on data characteristics to obtain a boiler state evaluation value, including: respectively evaluating and obtaining the data variance estimate and the change amplitude estimate for the data variance value and the change amplitude value corresponding to each key combustion parameter, and adding the data variance estimate and the change amplitude estimate to calculate the data change evaluation value of each key combustion parameter; presetting the preset weight of each key combustion parameter, and performing weighted addition calculation on the data change evaluation value of each key combustion parameter and the corresponding preset weight to obtain the boiler state evaluation value.

[0026] Specifically, the data variance values and change amplitude values of each key combustion parameter are evaluated and taken to obtain the data variance estimate and change amplitude estimate; the data variance estimate and change amplitude estimate are added together to obtain the data change evaluation value of each key combustion parameter; preset weights for each key combustion parameter are set in advance to reflect the importance of each parameter in the state evaluation; the data change evaluation values of each key combustion parameter are weighted and added together with the corresponding preset weights to obtain the boiler state evaluation value. This step can obtain a more comprehensive boiler state evaluation value by comprehensively considering the data variance and change amplitude of each key combustion parameter, as well as the preset weights; by setting the preset weights, the importance of each parameter in the state evaluation can be adjusted according to the actual situation, making the evaluation more in line with the actual requirements; the boiler state evaluation value can be used to monitor the operating state of the boiler in real time, promptly detect abnormal situations or potential problems, and improve the safety and stability of the system; based on the state evaluation value, intelligent decision-making and optimized control can be supported, helping the operation and maintenance personnel to respond and adjust quickly, and improving production efficiency and equipment utilization rate. In summary, the state of the boiler can be comprehensively evaluated using the data change evaluation value and the preset weights, helping to better understand the operation of the boiler, promptly take measures to ensure the normal operation of the equipment, and improve production efficiency and equipment reliability.

[0027] In an embodiment of the present application, a boiler combustion optimization method based on multi-parameter adjustment is provided. Determining the weights of each control parameter based on the boiler combustion efficiency evaluation value includes: presetting the preset values of each control parameter, calculating the ratio of the boiler combustion efficiency evaluation value to the preset values of each control parameter to obtain the numerical ratio of each control parameter, and determining the numerical ratio of each control parameter as the weight of each control parameter.

[0028] Specifically, preset the preset values of each control parameter, compare and calculate the boiler combustion efficiency evaluation value with the preset values of each control parameter to obtain the numerical ratio of each control parameter; determine the weight of each control parameter as the numerical ratio of each control parameter, that is, the larger the numerical ratio, the greater the influence of the parameter on the combustion efficiency, and then determine its importance. This step automatically determines the weight of each control parameter by comparing with the combustion efficiency evaluation value, without manual intervention, saving time and being more objective; the control parameter with a larger numerical ratio is given a higher weight, which makes the system pay more attention to the parameters with a greater impact on the combustion efficiency and helps to optimize the control strategy; since the weight is calculated based on the real-time combustion efficiency evaluation value, it can be adjusted in real time with the change of the combustion state to maintain the accuracy of the weight; based on the weight of the control parameter, more refined control can be achieved, optimizing and adjusting according to the importance of different parameters to improve the combustion efficiency and system stability. In summary, the weight of each control parameter can be automatically determined according to the real-time combustion efficiency evaluation value, so as to achieve a more accurate control strategy, improve the combustion efficiency and performance of the boiler, and reduce energy consumption and operating costs at the same time.

[0029] In an embodiment of the present application, a boiler combustion optimization method based on multi-parameter adjustment is provided. The adjustment coefficient corresponding to each control parameter is determined based on the weight of each control parameter, and each control parameter is optimized and adjusted according to the adjustment coefficient to perform boiler combustion control, including: presetting the corresponding relationship between the adjustment coefficient-weight interval of each control parameter, wherein for each weight interval of the adjustment coefficient-weight interval corresponding relationship, a corresponding adjustment coefficient is associated; obtaining the weight of each control parameter, and selecting the adjustment coefficient corresponding to the weight interval as the corresponding adjustment coefficient of each control parameter based on the mapping relationship of the weight interval to which the weight belongs in the adjustment coefficient-weight interval corresponding relationship; optimizing and adjusting each control parameter according to the adjustment coefficient, and performing boiler combustion control according to the optimized and adjusted control parameters.

[0030] Specifically, preset the correspondence between the adjustment coefficient - weight interval of each control parameter to ensure that each weight interval has a corresponding adjustment coefficient; determine the weights of each control parameter within the corresponding weight interval, and select the corresponding adjustment coefficient as the adjustment coefficient of the control parameter according to the mapping relationship within the interval; obtain the weights of each control parameter, and select the corresponding adjustment coefficient as the adjustment coefficient of each control parameter according to the mapping relationship of the weight interval to which the weight belongs in the adjustment coefficient - weight interval correspondence; optimize and adjust each control parameter according to the obtained adjustment coefficient to make it better meet the requirements of combustion efficiency; based on the numerical values of each control parameter after optimization and adjustment, conduct boiler combustion control to ensure the normal operation and efficient combustion of the boiler. This step determines the adjustment coefficient of each control parameter according to the weight, realizes personalized adjustment of each parameter, and better adapts to the actual situation; through the selection of the mapping relationship and adjustment coefficient, realizes intelligent parameter optimization and adjustment, improves the stability and efficiency of the system; conducts combustion control according to the numerical values of the control parameters after optimization and adjustment, realizes precise control of the boiler combustion process, improves combustion efficiency and reduces energy consumption; through continuous optimization and adjustment, the system can perform feedback and adjustment according to real - time data and maintain the best combustion state. To sum up, the adjustment coefficient of each control parameter can be determined according to the weight, realizing intelligent and personalized optimization and adjustment, improving the boiler combustion efficiency and system performance, while reducing energy consumption and operating costs.

[0031] As Figure 2 shown, in the embodiment of the present application, a boiler combustion optimization system based on multi - parameter adjustment is provided, including: an acquisition module, used to acquire the historical operation data of the boiler and extract the combustion data related to the boiler combustion efficiency from the historical operation data; a prediction module, used to extract the characteristics of the combustion data, construct a boiler combustion prediction model based on the characteristics and a preset neural network model, and conduct prediction based on the boiler combustion prediction model to obtain combustion prediction data; an analysis module, used to analyze the combustion prediction data, determine the key combustion parameters affecting the boiler combustion state, and determine the data characteristics of the key combustion parameters; a determination module, used to evaluate the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, and determine the weights of each control parameter based on the boiler combustion efficiency evaluation value; an optimization module, used to determine the adjustment coefficients corresponding to each control parameter based on the weights of each control parameter, and optimize and adjust each control parameter according to the adjustment coefficients to conduct boiler combustion control.

[0032] In summary, the embodiment of the present invention provides a boiler combustion optimization method and system based on multi-parameter adjustment, which includes: acquiring and extracting combustion data related to the boiler combustion efficiency from the historical operation data of the boiler, and extracting its features, constructing a boiler combustion prediction model based on the features and a preset neural network model to perform prediction to obtain combustion prediction data; analyzing the combustion prediction data to determine the key combustion parameters affecting the boiler combustion state, and determining their data features; evaluating the boiler combustion state based on the data features to obtain a boiler state evaluation value, and determining the weights of each control parameter based on it; determining the adjustment coefficients corresponding to each control parameter based on the weights to optimize and adjust each control parameter. The present invention uses advanced data analysis technology combined with real-time monitoring data and model prediction to intelligently adjust and optimize multiple control parameters in the boiler combustion process, improving the boiler combustion efficiency, reducing energy consumption, reducing emissions, and improving the stability of the boiler.

[0033] Finally, it should be noted that: Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

[0034] The above is only one embodiment of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the protection scope of the present invention and being restricted. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related descriptions of the above-described platform can refer to the corresponding process in the foregoing platform embodiment, and will not be repeated here.

[0035] The term "including" or any other similar term is intended to cover non-exclusive inclusion, so that a process, platform, article, or device / platform including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in these processes, platforms, articles, or devices / platforms.

[0036] So far, the technical solution of the present invention has been described in combination with the further embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0037] The above is only the preferred embodiment of the present invention, and is not used to limit the protection scope of the present invention.

Claims

1. A boiler combustion optimization method based on multi-parameter adjustment, characterized in that: include: Acquire historical operation data of the boiler, and extract combustion data related to boiler combustion efficiency from the historical operation data; Extract the features of the combustion data, build a boiler combustion prediction model based on the features and a preset neural network model, and make predictions based on the boiler combustion prediction model to obtain combustion prediction data; Analyze the combustion prediction data, determine the key combustion parameters that affect the boiler combustion state, and determine the data characteristics of the key combustion parameters; Evaluate the boiler combustion state based on the data characteristics to obtain a boiler state evaluation value, and determine the weight of each control parameter based on the boiler combustion efficiency evaluation value; Based on the weight of each control parameter, the adjustment coefficient corresponding to each control parameter is determined, and each control parameter is optimized and adjusted according to the adjustment coefficient to perform boiler combustion control.

2. A boiler combustion optimization method based on multi-parameter adjustment according to claim 1, characterized in that: The obtaining of historical operation data of the boiler and extracting combustion data related to the combustion efficiency of the boiler from the historical operation data includes: Obtain historical operating data of the boiler and determine the boiler combustion efficiency in the historical operating data; Divide the remaining data in the historical operation data into several groups of operation parameter data according to parameter types, and calculate the correlation between each operation parameter data group and the boiler combustion efficiency; A preset threshold is set in advance, and the operating parameter data group whose correlation exceeds the preset threshold is extracted and determined as combustion data related to the boiler combustion efficiency.

3. A boiler combustion optimization method based on multi-parameter adjustment according to claim condition 2, characterized in that: The extraction of combustion data features, building a boiler combustion prediction model based on the features and a preset neural network model, and performing prediction based on the boiler combustion prediction model to obtain combustion prediction data include: Extract the features of each combustion data, and correspond each combustion data with the features corresponding to each combustion data to construct data sets respectively; Input each data set into a preset neural network model corresponding to each combustion data, and construct an initial combustion prediction model corresponding to each combustion data; Divide each data set into a training set and a test set according to a preset ratio, and input the training set and the test set into each combustion prediction initial model; Training and testing each combustion prediction initial model until each combustion prediction initial model meets a preset convergence condition to obtain a corresponding combustion prediction model; The combustion prediction models are integrated to obtain a boiler combustion prediction model, and the current combustion data of the boiler is obtained. The current combustion data is input into the boiler combustion prediction model for prediction to obtain combustion prediction data.

4. A boiler combustion optimization method based on multi-parameter adjustment according to claim condition 3, characterized in that: The combustion prediction data is analyzed to determine the key combustion parameters that affect the combustion state of the boiler, and the data characteristics of the key combustion parameters are determined, including: Dividing the combustion prediction data into several key combustion parameter data groups according to parameter types, and constructing combustion parameter variation curves based on each key combustion parameter data group; Determining a standard combustion curve of each combustion parameter under a stable combustion state in each combustion parameter variation curve, and determining a similarity between each combustion parameter variation curve and the corresponding standard combustion curve; A preset similarity is pre-set, and the parameters corresponding to the combustion parameter variation curves whose similarity is less than the preset similarity are determined as key combustion parameters affecting the combustion state of the boiler; Determine the combustion parameter variation curve corresponding to each key combustion parameter, and determine the data variance value and variation amplitude value of the combustion parameter variation curve corresponding to each key combustion parameter; The data variance value and the variation amplitude value corresponding to each key combustion parameter are determined as the data characteristics of each key combustion parameter.

5. A boiler combustion optimization method based on multi-parameter adjustment according to claim condition 4, characterized in that: The boiler combustion state is evaluated based on the data characteristics to obtain a boiler state evaluation value, including: Evaluate and obtain the data variance value and the variation amplitude value corresponding to each key combustion parameter respectively, obtain the data variance estimation value and the variation amplitude estimation value, and add the data variance estimation value and the variation amplitude estimation value to obtain the data variation estimation value of each key combustion parameter; The preset weights of the key combustion parameters are preset, and the data change evaluation values ​​of the key combustion parameters are weighted and added together with the corresponding preset weights to obtain the boiler state evaluation value.

6. A boiler combustion optimization method based on multi-parameter adjustment according to claim 5, characterized in that: The step of determining the weight of each control parameter based on the boiler combustion efficiency evaluation value includes: The preset value of each control parameter is set in advance, the boiler combustion efficiency evaluation value is compared with the preset value of each control parameter to obtain the numerical ratio of each control parameter, and the numerical ratio of each control parameter is determined as the weight of each control parameter.

7. A boiler combustion optimization method based on multi-parameter adjustment according to claim 6, characterized in that: The method of determining the adjustment coefficient corresponding to each control parameter based on the weight of each control parameter, and optimizing and adjusting each control parameter according to the adjustment coefficient to perform boiler combustion control includes: Presetting the corresponding relationship between the adjustment coefficient and the weight interval of each control parameter, wherein the corresponding relationship between the adjustment coefficient and the weight interval is associated with a corresponding adjustment coefficient for each weight interval; Obtaining the weight of each control parameter, and based on the mapping relationship between the weight interval to which the weight belongs and the corresponding relationship between the adjustment coefficient and the weight interval, selecting the adjustment coefficient corresponding to the weight interval as the corresponding adjustment coefficient of each control parameter; The various control parameters are optimized and adjusted according to the adjustment coefficient, and the boiler combustion control is performed according to the optimized and adjusted control parameters.

8. A boiler combustion optimization system based on multi-parameter adjustment, characterized in that: include: An acquisition module, used for acquiring historical operation data of the boiler and extracting combustion data related to the combustion efficiency of the boiler from the historical operation data; A prediction module is used to extract the features of the combustion data, build a boiler combustion prediction model based on the features and a preset neural network model, and make predictions based on the boiler combustion prediction model to obtain combustion prediction data; An analysis module is used to analyze the combustion prediction data, determine the key combustion parameters that affect the boiler combustion state, and determine the data characteristics of the key combustion parameters; A determination module is used to evaluate the boiler combustion state based on the data characteristics, obtain a boiler state evaluation value, and determine the weight of each control parameter based on the boiler combustion efficiency evaluation value; The optimization module is used to determine the adjustment coefficient corresponding to each control parameter based on the weight of each control parameter, and optimize and adjust each control parameter according to the adjustment coefficient to perform boiler combustion control.

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