Bed temperature optimization control method and system for circulating fluidized bed boiler

Abstract

The invention discloses a circulating fluidized bed boiler bed temperature optimization control method and system, and the method comprises the steps: obtaining and preprocessing the historical operation data of a circulating fluidized bed boiler, analyzing the preprocessed data, and determining the operation characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler; building a bed temperature prediction model based on the operation characteristic parameters and a preset neural network model, and performing prediction according to the bed temperature prediction model to obtain bed temperature prediction data; acquiring a bed temperature requirement for the circulating fluidized bed boiler, and determining a deviation characteristic between the bed temperature prediction data and the bed temperature requirement; analyzing and evaluating the bed temperature difference of the circulating fluidized bed boiler based on the deviation characteristics to obtain a bed temperature difference evaluation value; and determining an operation optimization coefficient according to the bed temperature difference evaluation value, and performing optimization control on the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient. The running efficiency and stability of the circulating fluidized bed boiler can be improved, energy consumption and emission are reduced, and therefore the benefits of energy conservation, emission reduction and cost reduction are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of circulating fluidized bed boiler control, and in particular to a circulating fluidized bed boiler bed temperature optimization control method and system. Background Art

[0002] Circulating fluidized bed boiler is a new type of boiler technology, which uses the characteristics of circulating fluidized bed for combustion. The characteristics of circulating fluidized bed boiler include high combustion efficiency, low combustion temperature, and low environmental pollution during the combustion process. In the circulating fluidized bed boiler, the fuel and combustion air pass through a special device to form a circulating fluidized bed in the boiler. The material in the bed presents a fluid-like state under the action of the airflow, which is conducive to the full combustion of the fuel and can effectively control the combustion temperature. The bed temperature control of the circulating fluidized bed boiler is one of the key issues, which directly affects the thermal efficiency and safe operation of the boiler.

[0003] However, the current bed temperature control method of circulating fluidized bed boilers has limited control accuracy, making it difficult to achieve precise regulation and control of the bed temperature, and it is easy for the bed temperature to fail to meet the set requirements. At the same time, due to the lack of precise bed temperature control, it often leads to low combustion efficiency and low energy utilization efficiency, affecting the overall performance and operating costs of the boiler. In addition, the current bed temperature control system lacks intelligence and adaptability, and cannot be adjusted and optimized in time according to the real-time operating status, resulting in unsatisfactory control effects. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a circulating fluidized bed boiler bed temperature optimization control method and system, comprising:

[0005] Acquire historical operation data of the circulating fluidized bed boiler, and pre-process the historical operation data of the circulating fluidized bed boiler;

[0006] Analyze the pre-processed historical operation data to determine the operation characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler;

[0007] A bed temperature prediction model of a circulating fluidized bed boiler is constructed based on the operating characteristic parameters and a preset neural network model, and prediction is performed according to the bed temperature prediction model to obtain bed temperature prediction data of the circulating fluidized bed boiler;

[0008] Obtain bed temperature requirements for circulating fluidized bed boilers, and determine deviation characteristics between bed temperature prediction data and bed temperature requirements;

[0009] The bed temperature difference of the circulating fluidized bed boiler is analyzed and evaluated based on the deviation characteristics, and the bed temperature difference evaluation value of the circulating fluidized bed boiler is obtained;

[0010] An operation optimization coefficient of the circulating fluidized bed boiler is determined according to the bed temperature difference evaluation value, and the bed temperature of the circulating fluidized bed boiler is optimally controlled according to the operation optimization coefficient.

[0011] Furthermore, the obtaining of historical operation data of the circulating fluidized bed boiler and preprocessing of the historical operation data of the circulating fluidized bed boiler include:

[0012] The historical operation data of the circulating fluidized bed boiler is obtained, and the historical operation data of the circulating fluidized bed boiler is preprocessed, wherein the preprocessing includes removing abnormal data and overloaded data in the historical operation data and filling in missing data.

[0013] Furthermore, the analysis of the pre-processed historical operation data to determine the operation characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler includes:

[0014] Acquire historical operation data of the circulating fluidized bed boiler after pretreatment, and divide the historical operation data into several groups of operation candidate operation parameter data groups according to parameter types;

[0015] Acquire historical bed temperature data of the circulating fluidized bed boiler, and analyze the correlation between each set of candidate operating parameter data and the historical bed temperature data;

[0016] Selecting the candidate operation parameter data groups whose relevance exceeds a preset threshold and using them as candidate operation parameter data groups;

[0017] Determine an i-th candidate operation parameter data group from a plurality of candidate operation parameter data groups, and select historical bed temperature data when other candidate operation parameters are the same;

[0018] The temperature variation of the historical bed temperature data corresponding to the i-th candidate operating parameter data group is analyzed, and the operating parameters corresponding to the i-th candidate operating parameter data group whose temperature variation is greater than a preset temperature variation threshold are determined as operating characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler.

[0019] Furthermore, the bed temperature prediction model of the circulating fluidized bed boiler is constructed based on the operating characteristic parameters and the preset neural network model, and prediction is performed according to the bed temperature prediction model to obtain the bed temperature prediction data of the circulating fluidized bed boiler, including:

[0020] A data set is constructed based on the operating characteristic parameters, and the data set is input into a preset neural network model to construct an initial model for bed temperature prediction;

[0021] The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial bed temperature prediction model;

[0022] The bed temperature prediction initial model is trained and tested until the bed temperature prediction initial model meets the preset convergence condition, thereby obtaining the bed temperature prediction model;

[0023] The real-time operating characteristic parameters of the circulating fluidized bed boiler are obtained, and the real-time operating characteristic parameters are input into a bed temperature prediction model, and the bed temperature prediction model performs prediction to obtain bed temperature prediction data of the circulating fluidized bed boiler.

[0024] Furthermore, the obtaining of the bed temperature requirement for the circulating fluidized bed boiler and determining the deviation characteristics between the bed temperature prediction data and the bed temperature requirement include:

[0025] Obtaining bed temperature requirements for a circulating fluidized bed boiler, and calculating deviation data between bed temperature prediction data and bed temperature requirements;

[0026] The deviation data is plotted into a deviation data curve graph, and from the deviation data curve graph, an ascending curve segment where the data is ascending, a descending curve segment where the data is descending, and a gentle curve segment where the data is gentle;

[0027] The rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment are determined respectively, and the duration and average value of each curve segment are determined as the deviation characteristics between the bed temperature prediction data and the bed temperature requirements.

[0028] Furthermore, the bed temperature difference of the circulating fluidized bed boiler is analyzed and evaluated based on the deviation characteristics to obtain the bed temperature difference evaluation value of the circulating fluidized bed boiler, including:

[0029] Obtaining the duration and average value of each curve segment, and classifying all ascending curve segments as first-category curve segments, all descending curve segments as second-category curve segments, and all gentle curve segments as third-category curve segments;

[0030] Each type of curve segment is evaluated based on the duration and average value of each curve segment to obtain an evaluation value of each type of curve segment, and the evaluation value of each type of curve segment is weightedly added to the preset weight to obtain an evaluation value of the bed temperature difference of the circulating fluidized bed boiler.

[0031] Furthermore, evaluating and valuing each type of curve segment based on the duration and average value of each curve segment to obtain an evaluation value of each type of curve segment includes:

[0032] The rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment are obtained, and each type of curve segment is evaluated and valued based on the rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment, where the calculation formula for the evaluation value of each type of curve segment is:

[0033]

[0034] Among them, S is the evaluation value of each type of curve segment, α is the evaluation coefficient of each type of curve segment, Mi is the average value of the i-th curve segment corresponding to each type of curve segment, and ti is the duration of the i-th curve segment corresponding to each type of curve segment.

[0035] Further, determining the operation optimization coefficient of the circulating fluidized bed boiler according to the bed temperature difference evaluation value, and optimizing the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient, includes:

[0036] The correspondence between the operation optimization coefficient and the bed temperature difference evaluation value interval is preset in advance. For each bed temperature difference evaluation value interval, a corresponding operation optimization coefficient is associated with the corresponding bed temperature difference evaluation value interval.

[0037] Acquire a bed temperature difference evaluation value, and based on a mapping relationship between a bed temperature difference evaluation value interval to which the bed temperature difference evaluation value belongs and an operation optimization coefficient corresponding to a bed temperature difference evaluation value interval corresponding relationship, select an operation optimization coefficient corresponding to the bed temperature difference evaluation value interval;

[0038] The real-time operation characteristic parameters are adjusted according to the operation optimization coefficient, and the circulating fluidized bed boiler is operated according to the adjusted real-time operation characteristic parameters to complete the optimization control of the bed temperature of the circulating fluidized bed boiler.

[0039] The present invention also provides a circulating fluidized bed boiler bed temperature optimization control system, comprising:

[0040] An acquisition module, used for acquiring historical operation data of a circulating fluidized bed boiler and preprocessing the historical operation data of the circulating fluidized bed boiler;

[0041] An analysis module is used to analyze the pre-processed historical operation data and determine the operation characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler;

[0042] A prediction module is used to construct a bed temperature prediction model of a circulating fluidized bed boiler based on operating characteristic parameters and a preset neural network model, and to perform prediction according to the bed temperature prediction model to obtain bed temperature prediction data of the circulating fluidized bed boiler;

[0043] A determination module is used to obtain the bed temperature requirement for the circulating fluidized bed boiler and determine the deviation characteristics between the bed temperature prediction data and the bed temperature requirement;

[0044] An evaluation module, used for analyzing and evaluating the bed temperature difference of the circulating fluidized bed boiler based on the deviation characteristics, and obtaining an evaluation value of the bed temperature difference of the circulating fluidized bed boiler;

[0045] The control module is used to determine the operation optimization coefficient of the circulating fluidized bed boiler according to the bed temperature difference evaluation value, and optimize the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient.

[0046] Compared with the prior art, the circulating fluidized bed boiler bed temperature optimization control method and system according to the embodiment of the present invention have the following beneficial effects:

[0047] The present invention helps to deeply understand the system operation law by determining the operating characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler, and provides a basis for the subsequent construction of a bed temperature prediction model;

[0048] The present invention can more accurately predict the bed temperature change of the circulating fluidized bed boiler by constructing a bed temperature prediction model, which helps to achieve real-time monitoring and control of the system operation status;

[0049] The present invention determines the operation optimization coefficient based on the deviation characteristics and the bed temperature difference evaluation value, and performs corresponding optimization control to ensure that the bed temperature of the circulating fluidized bed boiler can be stabilized within the required range;

[0050] The present invention can utilize fuel more effectively, reduce energy waste, and improve the energy utilization efficiency of the circulating fluidized bed boiler through optimized control;

[0051] In general, the present invention can help improve the operating efficiency and stability of circulating fluidized bed boilers, reduce energy consumption and emissions, and thus achieve multiple benefits such as energy conservation and emission reduction, cost reduction, and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 1 is a schematic diagram of the flow structure of a circulating fluidized bed boiler bed temperature optimization control method according to an embodiment of the present invention;

[0053] Figure 2 It is a schematic diagram of the composition of the circulating fluidized bed boiler bed temperature optimization control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0055] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply 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 understood as a limitation on the present application.

[0056] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0057] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" 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 it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0058] like Figure 1 As shown, in an embodiment of the present application, a circulating fluidized bed boiler bed temperature optimization control method is provided, including: S100: acquiring historical operation data of the circulating fluidized bed boiler, and preprocessing the historical operation data of the circulating fluidized bed boiler; S200: analyzing the preprocessed historical operation data to determine the operation characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler; S300: constructing a bed temperature prediction model of the circulating fluidized bed boiler based on the operation characteristic parameters and a preset neural network model, and predicting according to the bed temperature prediction model to obtain bed temperature prediction data of the circulating fluidized bed boiler; S400: acquiring the bed temperature requirement for the circulating fluidized bed boiler, and determining the deviation characteristics between the bed temperature prediction data and the bed temperature requirement; S500: analyzing and evaluating the bed temperature difference of the circulating fluidized bed boiler based on the deviation characteristics to obtain the bed temperature difference evaluation value of the circulating fluidized bed boiler; S600: determining the operation optimization coefficient of the circulating fluidized bed boiler according to the bed temperature difference evaluation value, and optimizing the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient.

[0059] Furthermore, the present invention helps to deeply understand the system operation rules by determining the operating characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler, and provides a basis for the subsequent construction of a bed temperature prediction model; the present invention can more accurately predict the bed temperature changes of the circulating fluidized bed boiler by constructing a bed temperature prediction model, which helps to achieve real-time monitoring and control of the system operation status; the present invention determines the operation optimization coefficient based on the deviation characteristics and the bed temperature difference evaluation value, and performs corresponding optimization control to ensure that the bed temperature of the circulating fluidized bed boiler can be stabilized within the required range; the present invention can more effectively utilize fuel, reduce energy waste, and improve the energy utilization efficiency of the circulating fluidized bed boiler through optimization control; in general, the present invention can help improve the operating efficiency and stability of the circulating fluidized bed boiler, reduce energy consumption and emissions, thereby achieving multiple benefits such as energy conservation and emission reduction, cost reduction, and environmental protection.

[0060] In an embodiment of the present application, a method for optimizing the bed temperature of a circulating fluidized bed boiler is provided, wherein the historical operating data of the circulating fluidized bed boiler is obtained and the historical operating data of the circulating fluidized bed boiler is preprocessed, including: obtaining the historical operating data of the circulating fluidized bed boiler and preprocessing the historical operating data of the circulating fluidized bed boiler, wherein the preprocessing includes removing abnormal data and overloaded data in the historical operating data and filling in missing data.

[0061] Specifically, the historical operation data of the circulating fluidized bed boiler is the key information collected from the actual equipment, including bed temperature, coal supply, air supply, circulating fan speed, fuel characteristics and other aspects of data. The preprocessing of these data is to ensure the quality and accuracy of the data and provide a reliable basis for subsequent data analysis and modeling. The preprocessing process mainly includes removing abnormal data and duplicate data, and filling missing data. Abnormal data may be caused by equipment failure, sensor failure or data acquisition error, and needs to be identified and eliminated through data analysis and statistical methods; duplicate data may be caused by repeated records of the data acquisition system or other reasons, and needs to be deduplicated to ensure the uniqueness of the data; missing data may be caused by sensor failure, data acquisition interruption or other reasons, and needs to be filled by appropriate methods. Common methods include interpolation, mean value filling, regression filling, etc. The purpose of filling missing data is to ensure the integrity and continuity of the data for subsequent analysis and modeling. By preprocessing the historical operation data, a cleaner and more complete data set can be obtained, providing a reliable basis for subsequent data analysis, modeling and control strategy optimization.

[0062] In an embodiment of the present application, a circulating fluidized bed boiler bed temperature optimization control method is provided, wherein the historical operation data after pretreatment is analyzed to determine the operation characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler, including: obtaining the historical operation data of the circulating fluidized bed boiler after pretreatment, and dividing the historical operation data into several groups of operation candidate operation parameter data groups according to parameter type; obtaining the historical bed temperature data of the circulating fluidized bed boiler, and analyzing the correlation between each group of operation candidate operation parameter data groups and the historical bed temperature data; selecting the operation candidate operation parameter data groups whose correlation exceeds a preset threshold value and using them as candidate operation candidate operation parameter data groups; determining the i-th candidate operation candidate operation parameter data group from several candidate operation candidate operation parameter data groups, and screening the historical bed temperature data when other candidate operation parameters are the same; analyzing the temperature change of the historical bed temperature data corresponding to the i-th candidate operation parameter data group, and determining the operation parameters corresponding to the i-th candidate operation parameter data group whose temperature change is greater than the preset temperature change threshold value as the operation characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler.

[0063] Specifically, based on the historical operating data of the circulating fluidized bed boiler, the data are classified according to parameter types, such as coal feed rate, air feed rate, circulating fan speed, etc., to form different parameter data groups; the historical bed temperature data of the circulating fluidized bed boiler is obtained, and the correlation between each group of operating parameter data groups and the historical bed temperature data is analyzed by a correlation analysis method (such as correlation coefficient, regression analysis, etc.); according to a preset correlation threshold, the operating parameter data groups whose correlation exceeds the threshold are screened out, and these data groups will be regarded as candidate influencing factors; the i-th candidate operating parameter data group is determined one by one from the candidate operating parameter data groups, and the historical bed temperature data corresponding to the parameter group is screened out; the temperature change amount of the historical bed temperature data corresponding to the i-th candidate operating parameter data group is analyzed, and if the temperature change amount is greater than the preset threshold, then the operating parameter data group is determined as an operating characteristic parameter affecting the bed temperature of the circulating fluidized bed boiler.

[0064] In an embodiment of the present application, a method for optimizing the bed temperature of a circulating fluidized bed boiler is provided, wherein a bed temperature prediction model of the circulating fluidized bed boiler is constructed based on operating characteristic parameters and a preset neural network model, and prediction is performed according to the bed temperature prediction model to obtain bed temperature prediction data of the circulating fluidized bed boiler, including: constructing a data set based on the operating characteristic parameters, and inputting the data set into the preset neural network model to construct an initial bed temperature prediction model; dividing the data set into a training set and a test set according to a certain ratio, and inputting the training set and the test set into the initial bed temperature prediction model; training and testing the initial bed temperature prediction model until the initial bed temperature prediction model meets the preset convergence conditions to obtain the bed temperature prediction model; acquiring the real-time operating characteristic parameters of the circulating fluidized bed boiler, and inputting the real-time operating characteristic parameters into the bed temperature prediction model, and predicting by the bed temperature prediction model to obtain the bed temperature prediction data of the circulating fluidized bed boiler.

[0065] Specifically, based on the determined operating characteristic parameters, a data set is constructed, including input characteristics and corresponding bed temperature data, and then the data set is input into a preset neural network model; the data set is divided into a training set and a test set according to a certain ratio, usually 70% of the data is used as the training set and 30% of the data is used as the test set, and the training set is input into the initial model for bed temperature prediction, and the model is trained and tested until the initial model for bed temperature prediction meets the preset convergence conditions, that is, the convergence conditions of the model meet the preset training error and test error; once the initial model for bed temperature prediction meets the convergence conditions, a bed temperature prediction model can be obtained, and the model can predict the bed temperature of the circulating fluidized bed boiler according to the input operating characteristic parameters; the real-time operating characteristic parameters of the circulating fluidized bed boiler are obtained, and these parameters are input into the bed temperature prediction model, and the model performs prediction to obtain the bed temperature prediction data of the circulating fluidized bed boiler.

[0066] In an embodiment of the present application, a method for optimizing the bed temperature of a circulating fluidized bed boiler is provided, wherein the bed temperature requirement for the circulating fluidized bed boiler is obtained and the deviation characteristics between the bed temperature prediction data and the bed temperature requirement are determined, including: obtaining the bed temperature requirement for the circulating fluidized bed boiler and calculating the deviation data between the bed temperature prediction data and the bed temperature requirement; plotting the deviation data into a deviation data curve graph, and determining from the deviation data curve graph an ascending curve segment where the data is ascending, a descending curve segment where the data is descending, and a gentle curve segment where the data is gentle; respectively determining the ascending duration and the ascending average value of each ascending curve segment, the descending duration and the descending average value of each descending curve segment, and the gentle duration and the gentle average value of the gentle curve segment, and determining the duration and the average value of each curve segment as the deviation characteristics between the bed temperature prediction data and the bed temperature requirement.

[0067] Specifically, the deviation data between the bed temperature prediction data and the bed temperature requirement is plotted into a curve graph, with the horizontal axis being time and the vertical axis being the deviation value; from the deviation data curve graph, the rising curve segment where the data is rising, the falling curve segment where the data is falling, and the flat curve segment where the data is flat are determined; for each rising curve segment and falling curve segment, its duration is determined, and the average value of the deviation data is calculated; for the flat curve segment, its duration and average value are also calculated; the duration and average value of each curve segment are used as the deviation characteristics between the bed temperature prediction data and the bed temperature requirement. This step can help determine the deviation characteristics between the bed temperature prediction data and the bed temperature requirement, further understand the difference between the prediction data and the actual requirements, and help to promptly discover the deviation of the bed temperature from the requirements, so as to take corresponding measures for adjustment and optimization.

[0068] In an embodiment of the present application, a method for optimizing the bed temperature of a circulating fluidized bed boiler is provided, wherein the bed temperature difference of the circulating fluidized bed boiler is analyzed and evaluated based on deviation characteristics to obtain an evaluation value of the bed temperature difference of the circulating fluidized bed boiler, including: obtaining the duration and average value of each curve segment, and classifying all rising curve segments as first-category curve segments, classifying all descending curve segments as second-category curve segments, and classifying all gentle curve segments as third-category curve segments; evaluating and valuing each type of curve segment based on the duration and average value of each curve segment to obtain an evaluation value of each type of curve segment, and performing weighted addition calculation on the evaluation value of each type of curve segment and a preset weight to obtain an evaluation value of the bed temperature difference of the circulating fluidized bed boiler.

[0069] Specifically, for each rising curve segment, falling curve segment and gentle curve segment, their duration and average value are calculated, all rising curve segments are classified as first-category curve segments, all falling curve segments are classified as second-category curve segments, and all gentle curve segments are classified as third-category curve segments. Then, for each type of curve segment, an evaluation value is determined based on the duration and average value, and it may be necessary to design an evaluation indicator or scoring standard. For each type of curve segment, a corresponding evaluation value is obtained according to the evaluation value. The evaluation value of each type of curve segment is weightedly added to the preset weight to obtain an evaluation value of the bed temperature difference of the circulating fluidized bed boiler.

[0070] In an embodiment of the present application, a circulating fluidized bed boiler bed temperature optimization control method is provided, wherein each type of curve segment is evaluated and valued based on the duration and average value of each curve segment to obtain an evaluation value of each type of curve segment, including: obtaining the rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment, and evaluating and value each type of curve segment based on the rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment, wherein the calculation formula for the evaluation value of each type of curve segment is:

[0071]

[0072] Among them, S is the evaluation value of each type of curve segment, α is the evaluation coefficient of each type of curve segment, Mi is the average value of the i-th curve segment corresponding to each type of curve segment, and ti is the duration of the i-th curve segment corresponding to each type of curve segment.

[0073] In an embodiment of the present application, a method for optimizing the bed temperature of a circulating fluidized bed boiler is provided, wherein an operation optimization coefficient of the circulating fluidized bed boiler is determined according to a bed temperature difference evaluation value, and the bed temperature of the circulating fluidized bed boiler is optimized and controlled according to the operation optimization coefficient, including: presetting an operation optimization coefficient-bed temperature difference evaluation value interval correspondence relationship, wherein the operation optimization coefficient-bed temperature difference evaluation value interval correspondence relationship is associated with a corresponding operation optimization coefficient for each bed temperature difference evaluation value interval; obtaining a bed temperature difference evaluation value, and selecting an operation optimization coefficient corresponding to the bed temperature difference evaluation value interval based on a mapping relationship between the bed temperature difference evaluation value interval to which the bed temperature difference evaluation value belongs within the operation optimization coefficient-bed temperature difference evaluation value interval correspondence relationship; adjusting a real-time operation characteristic parameter according to the operation optimization coefficient, and having the circulating fluidized bed boiler operate according to the adjusted real-time operation characteristic parameter, thereby completing the optimization control of the bed temperature of the circulating fluidized bed boiler.

[0074] Specifically, different bed temperature difference evaluation value intervals are set, and corresponding operation optimization coefficients are predefined for each interval; the real-time bed temperature difference evaluation value is obtained, and the operation optimization coefficient corresponding to the bed temperature difference evaluation value interval to which the value belongs is mapped, and the operation optimization coefficient corresponding to the interval is selected; the real-time operation characteristic parameters are adjusted using the selected operation optimization coefficient, and the circulating fluidized bed boiler is operated according to the adjusted real-time operation characteristic parameters to achieve optimal control of the bed temperature. Through this step, the operation optimization coefficient can be dynamically adjusted according to the real-time bed temperature difference evaluation value, thereby achieving real-time optimal control of the bed temperature of the circulating fluidized bed boiler to adapt to the bed temperature requirements under different working conditions and improve the operating efficiency and stability of the boiler.

[0075] like Figure 2 As shown, in an embodiment of the present application, a circulating fluidized bed boiler bed temperature optimization control system is provided, including: an acquisition module, used to acquire historical operation data of the circulating fluidized bed boiler and preprocess the historical operation data of the circulating fluidized bed boiler; an analysis module, used to analyze the preprocessed historical operation data and determine the operation characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler; a prediction module, used to construct a bed temperature prediction model of the circulating fluidized bed boiler based on the operation characteristic parameters and a preset neural network model, and predict according to the bed temperature prediction model to obtain bed temperature prediction data of the circulating fluidized bed boiler; a determination module, used to acquire the bed temperature requirement for the circulating fluidized bed boiler and determine the deviation characteristics between the bed temperature prediction data and the bed temperature requirement; an evaluation module, used to analyze and evaluate the bed temperature difference of the circulating fluidized bed boiler based on the deviation characteristics to obtain the bed temperature difference evaluation value of the circulating fluidized bed boiler; a control module, used to determine the operation optimization coefficient of the circulating fluidized bed boiler according to the bed temperature difference evaluation value, and optimize the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient.

[0076] In summary, the embodiment of the present invention provides a circulating fluidized bed boiler bed temperature optimization control method and system, which includes: obtaining and preprocessing the historical operation data of the circulating fluidized bed boiler, and analyzing the preprocessed data to determine the operation characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler; constructing a bed temperature prediction model based on the operation characteristic parameters and a preset neural network model, and predicting the bed temperature prediction data based on it; obtaining the bed temperature requirements for the circulating fluidized bed boiler, and determining the deviation characteristics between the bed temperature prediction data and the bed temperature requirements; analyzing and evaluating the bed temperature difference of the circulating fluidized bed boiler based on the deviation characteristics to obtain a bed temperature difference evaluation value; determining the operation optimization coefficient according to the bed temperature difference evaluation value, and optimizing the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient. The present invention can improve the operation efficiency and stability of the circulating fluidized bed boiler, reduce energy consumption and emissions, and thus achieve the benefits of energy conservation, emission reduction and cost reduction.

[0077] Finally, it should be noted that: Obviously, a person skilled in the art may 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 present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0078] The above is only an example of implementation 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 scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0079] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0080] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person 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, a person skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A circulating fluidized bed boiler bed temperature optimization control method, characterized in that: include: Acquire historical operation data of the circulating fluidized bed boiler, and pre-process the historical operation data of the circulating fluidized bed boiler; Analyze the pre-processed historical operation data to determine the operation characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler; A bed temperature prediction model of a circulating fluidized bed boiler is constructed based on the operating characteristic parameters and a preset neural network model, and prediction is performed according to the bed temperature prediction model to obtain bed temperature prediction data of the circulating fluidized bed boiler; Obtain bed temperature requirements for circulating fluidized bed boilers, and determine deviation characteristics between bed temperature prediction data and bed temperature requirements; The bed temperature difference of the circulating fluidized bed boiler is analyzed and evaluated based on the deviation characteristics, and the bed temperature difference evaluation value of the circulating fluidized bed boiler is obtained; An operation optimization coefficient of the circulating fluidized bed boiler is determined according to the bed temperature difference evaluation value, and the bed temperature of the circulating fluidized bed boiler is optimally controlled according to the operation optimization coefficient.

2. A circulating fluidized bed boiler bed temperature optimization control method according to claim 1, characterized in that: The obtaining of historical operation data of the circulating fluidized bed boiler and preprocessing of the historical operation data of the circulating fluidized bed boiler include: The historical operation data of the circulating fluidized bed boiler is obtained, and the historical operation data of the circulating fluidized bed boiler is preprocessed, wherein the preprocessing includes removing abnormal data and overloaded data in the historical operation data and filling in missing data.

3. A circulating fluidized bed boiler bed temperature optimization control method according to claim condition 2, characterized in that: The analysis of the pre-processed historical operation data to determine the operation characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler includes: Acquire historical operation data of the circulating fluidized bed boiler after pretreatment, and divide the historical operation data into several groups of operation candidate operation parameter data groups according to parameter types; Acquire historical bed temperature data of the circulating fluidized bed boiler, and analyze the correlation between each set of candidate operating parameter data and the historical bed temperature data; Selecting the candidate operation parameter data groups whose relevance exceeds a preset threshold and using them as candidate operation parameter data groups; Determine an i-th candidate operation parameter data group from a plurality of candidate operation parameter data groups, and select historical bed temperature data when other candidate operation parameters are the same; The temperature variation of the historical bed temperature data corresponding to the i-th candidate operating parameter data group is analyzed, and the operating parameters corresponding to the i-th candidate operating parameter data group whose temperature variation is greater than a preset temperature variation threshold are determined as operating characteristic parameters affecting the bed temperature of the circulating fluidized bed boiler.

4. A circulating fluidized bed boiler bed temperature optimization control method according to claim 3, characterized in that: The bed temperature prediction model of the circulating fluidized bed boiler is constructed based on the operation characteristic parameters and the preset neural network model, and prediction is performed according to the bed temperature prediction model to obtain the bed temperature prediction data of the circulating fluidized bed boiler, including: A data set is constructed based on the operating characteristic parameters, and the data set is input into a preset neural network model to construct an initial model for bed temperature prediction; The data set is divided into a training set and a test set according to a certain ratio, and the training set and the test set are input into the initial bed temperature prediction model; The bed temperature prediction initial model is trained and tested until the bed temperature prediction initial model meets the preset convergence condition, thereby obtaining the bed temperature prediction model; The real-time operating characteristic parameters of the circulating fluidized bed boiler are obtained, and the real-time operating characteristic parameters are input into a bed temperature prediction model, and the bed temperature prediction model performs prediction to obtain bed temperature prediction data of the circulating fluidized bed boiler.

5. A circulating fluidized bed boiler bed temperature optimization control method according to claim 4, characterized in that: The obtaining of the bed temperature requirement for the circulating fluidized bed boiler and determining the deviation characteristics between the bed temperature prediction data and the bed temperature requirement include: Obtaining bed temperature requirements for a circulating fluidized bed boiler, and calculating deviation data between bed temperature prediction data and bed temperature requirements; The deviation data is plotted into a deviation data curve graph, and from the deviation data curve graph, an ascending curve segment where the data is ascending, a descending curve segment where the data is descending, and a gentle curve segment where the data is gentle; The rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment are determined respectively, and the duration and average value of each curve segment are determined as the deviation characteristics between the bed temperature prediction data and the bed temperature requirements.

6. A circulating fluidized bed boiler bed temperature optimization control method according to claim 5, characterized in that: The bed temperature difference of the circulating fluidized bed boiler is analyzed and evaluated based on the deviation characteristics to obtain the bed temperature difference evaluation value of the circulating fluidized bed boiler, including: Obtaining the duration and average value of each curve segment, and classifying all ascending curve segments as first-category curve segments, all descending curve segments as second-category curve segments, and all gentle curve segments as third-category curve segments; Each type of curve segment is evaluated based on the duration and average value of each curve segment to obtain an evaluation value of each type of curve segment, and the evaluation value of each type of curve segment is weightedly added to the preset weight to obtain an evaluation value of the bed temperature difference of the circulating fluidized bed boiler.

7. A circulating fluidized bed boiler bed temperature optimization control method according to claim 6, characterized in that: The step of evaluating and valuing each type of curve segment based on the duration and average value of each curve segment to obtain an evaluation value of each type of curve segment includes: The rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment are obtained, and each type of curve segment is evaluated and valued based on the rising duration and rising average value of each rising curve segment, the falling duration and falling average value of each falling curve segment, and the flat duration and flat average value of the flat curve segment, where the calculation formula for the evaluation value of each type of curve segment is: Among them, S is the evaluation value of each type of curve segment, α is the evaluation coefficient of each type of curve segment, Mi is the average value of the i-th curve segment corresponding to each type of curve segment, and ti is the duration of the i-th curve segment corresponding to each type of curve segment.

8. A circulating fluidized bed boiler bed temperature optimization control method according to claim 6, characterized in that: The step of determining the operation optimization coefficient of the circulating fluidized bed boiler according to the bed temperature difference evaluation value, and optimizing the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient, comprises: The correspondence between the operation optimization coefficient and the bed temperature difference evaluation value interval is preset in advance. For each bed temperature difference evaluation value interval, a corresponding operation optimization coefficient is associated with the corresponding bed temperature difference evaluation value interval. Acquire a bed temperature difference evaluation value, and based on a mapping relationship between a bed temperature difference evaluation value interval to which the bed temperature difference evaluation value belongs and an operation optimization coefficient corresponding to a bed temperature difference evaluation value interval corresponding relationship, select an operation optimization coefficient corresponding to the bed temperature difference evaluation value interval; The real-time operation characteristic parameters are adjusted according to the operation optimization coefficient, and the circulating fluidized bed boiler is operated according to the adjusted real-time operation characteristic parameters to complete the optimization control of the bed temperature of the circulating fluidized bed boiler.

9. A circulating fluidized bed boiler bed temperature optimization control system, characterized in that: include: An acquisition module, used for acquiring historical operation data of a circulating fluidized bed boiler and preprocessing the historical operation data of the circulating fluidized bed boiler; An analysis module is used to analyze the pre-processed historical operation data and determine the operation characteristic parameters that affect the bed temperature of the circulating fluidized bed boiler; A prediction module is used to construct a bed temperature prediction model of a circulating fluidized bed boiler based on operating characteristic parameters and a preset neural network model, and to perform prediction according to the bed temperature prediction model to obtain bed temperature prediction data of the circulating fluidized bed boiler; A determination module is used to obtain the bed temperature requirement for the circulating fluidized bed boiler and determine the deviation characteristics between the bed temperature prediction data and the bed temperature requirement; An evaluation module, used for analyzing and evaluating the bed temperature difference of the circulating fluidized bed boiler based on the deviation characteristics, and obtaining an evaluation value of the bed temperature difference of the circulating fluidized bed boiler; The control module is used to determine the operation optimization coefficient of the circulating fluidized bed boiler according to the bed temperature difference evaluation value, and optimize the bed temperature of the circulating fluidized bed boiler according to the operation optimization coefficient.

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