CN111539615A - Boiler combustion process state monitoring method and system based on deep learning - Google Patents
Boiler combustion process state monitoring method and system based on deep learning Download PDFInfo
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- CN111539615A CN111539615A CN202010314165.1A CN202010314165A CN111539615A CN 111539615 A CN111539615 A CN 111539615A CN 202010314165 A CN202010314165 A CN 202010314165A CN 111539615 A CN111539615 A CN 111539615A
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Abstract
The invention provides a boiler combustion process state monitoring method and system based on deep learning, wherein the monitoring method comprises the following steps: step one, dividing the combustion process state of a boiler from poor to excellent into a plurality of classes; step two, establishing a convolution neural network model by using boiler wall temperature distribution data, and outputting a first characteristic; and step three, establishing a deep confidence network model by using other related data such as unit load and the like, outputting a characteristic two, and step four, combining the output results of the characteristic one and the characteristic two, and performing combustion process state model training by using a BP neural network to realize state monitoring. The boiler combustion process state monitoring method fully considers boiler combustion related variables, combines a deep learning algorithm, adopts the deep learning algorithm to establish the boiler combustion process state monitoring model, improves the reliability and generalization capability of the model, realizes boiler combustion process state monitoring on the basis of safety and reliability, and has important significance in the aspects of operation adjustment for unit operators and energy conservation and emission reduction.
Description
Technical Field
The invention relates to a boiler combustion process state monitoring method and system based on deep learning, and belongs to the technical field of boiler state monitoring.
Background
In the power generation industry, boilers are required to operate under optimized conditions to maintain high efficiency combustion and low emissions. In the combustion process, abnormal combustion states caused by equipment drift or failure, coal quality change and the like not only can cause efficiency reduction and emission increase, but also can cause great negative effects on the operation condition of the system. Therefore, monitoring of the state of the combustion process is of great interest.
The machine learning algorithm is a means for acquiring knowledge from given data by methods such as induction, mining, analogy and the like, and aims to establish a model based on an intelligent algorithm or put forward a certain data processing mode, acquire knowledge by training sample data, and perform characteristic judgment or prediction on output unknown data. At present, a large number of boiler combustion related technologies adopt a data-driven modeling method, utilize algorithms such as a neural network and a support vector machine to establish a model, and provide a plurality of improved algorithms. However, the traditional methods such as the neural network and the support vector machine belong to a shallow layer structure, the efficiency is not high in the process of obtaining the complex nonlinear relation hidden by the equipment operation data, the established model is often limited to fitting of sample data, the generalization capability is poor, and the requirement of real-time judgment cannot be met in the actual use process.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing traditional machine learning algorithm has the problems of local optimization, overfitting and the like in the modeling of the boiler combustion process of the power station.
In order to solve the above problems, the technical solution of the present invention is to provide a boiler combustion process state monitoring method based on deep learning, which is characterized by comprising the following steps:
step one, establishing a boiler combustion state evaluation system based on historical data of a boiler combustion process, and dividing states of the boiler combustion process into a plurality of classes from poor to superior, wherein the classes are represented as K ({ S1，S2，...，SK}) class in which SiIndicating the ith combustion state;
step two, taking the boiler wall temperature distribution data as an input variable, establishing a furnace wall temperature distribution characteristic model by adopting a convolutional neural network, and outputting a characteristic I;
step three, taking boiler operation index data as an input variable, adopting a deep belief network model to establish a boiler combustion state model related to operation indexes, and outputting a characteristic two;
and step four, establishing a BP neural network model for boiler combustion process state model training based on the feature I output by the convolutional neural network model and the feature II output by the deep confidence network model and combining the boiler combustion state evaluation system in the step one, so as to realize boiler combustion process state monitoring.
Preferably, the boiler combustion process history data includes boiler operation efficiency, NOx emissions, and flue gas temperature deviation.
Preferably, the boiler operation index data includes unit load, furnace oxygen amount, primary and secondary air pressure, burner tilt angle, overfire air (SOFA, CCOFA) damper opening, and furnace outlet NOx emission.
The invention provides a boiler combustion process state monitoring system based on deep learning, which is characterized in that: the monitoring and calculating system comprises a database server, a combustion process state monitoring and calculating server, a webpage server and a user side browser, wherein the database server is used for storing historical data of a boiler combustion process and boiler operation index data, the combustion process state monitoring and calculating server is respectively connected with the output end of the database server and the input end of a webpage memory, the combustion process state monitoring and calculating server runs deep learning computer software and outputs a calculation result to the webpage server, and the user side browser and the output end of the webpage server are connected to display the combustion process state of the boiler in real time.
Compared with the prior art, the invention has the beneficial effects that:
the boiler combustion process state monitoring method fully considers boiler combustion related variables, combines a deep learning algorithm, adopts the deep learning algorithm to establish the boiler combustion process state monitoring model, improves the reliability and generalization capability of the model, realizes boiler combustion process state monitoring on the basis of safety and reliability, and has important significance in the aspects of operation adjustment for unit operators and energy conservation and emission reduction.
Drawings
FIG. 1 is a flow chart of a boiler combustion process state monitoring method based on deep learning according to the present invention;
FIG. 2 is a diagram of a boiler combustion process status monitoring system according to the present invention.
Detailed Description
In order to make the invention more comprehensible, preferred embodiments are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a flow chart of a boiler combustion process state monitoring method based on deep learning according to the present invention mainly includes the following four steps:
step one, integrating the experience of operators and historical data of a boiler combustion process, and dividing the boiler combustion process state into a plurality of categories from poor to superior:
(1) based on the indexes of the boiler operation efficiency, the NOx emission and the smoke temperature deviation, a boiler combustion state evaluation system is established, and under the same working condition, the higher the boiler operation efficiency is, the lower the NOx emission is, the smaller the smoke temperature deviation is, and the more excellent the boiler combustion state is.
(2) In order to ensure the reliability of a boiler combustion state evaluation system, single-factor adjustment test analysis is carried out on the boiler oxygen amount, the SOFA wind swing angle, the CCOFA wind swing angle and the primary and secondary wind pressure (air door opening), and the boiler combustion state is divided into K ({ S) by combining operation expert experience and indexes of boiler operation efficiency, NOx emission and smoke temperature deviation1，S2，...，SK}) class in which SiIndicating the ith combustion state.
Step two, establishing a convolutional neural network model by using boiler wall temperature distribution data, and outputting a characteristic I:
(1) due to smoke temperature deviation, the problems that the pipe walls of a superheater and a reheater are overtemperature and even burst and the like can be caused in the combustion process of the boiler, the distribution state of the wall temperature of the boiler can reflect the distribution state of the temperature field in the boiler, the quality degree of the combustion process can be further reflected, and a furnace wall temperature distribution characteristic model is established based on the distribution state of the wall temperature of the boiler.
(2) The convolutional neural network is an excellent network structure and comprises a convolutional layer, a pooling layer, a full-link layer and an output layer, and has important value in sample feature extraction. And (4) establishing a furnace wall temperature distribution characteristic model by adopting a convolutional neural network, and outputting a result according to the state evaluation system established in the step one.
Step three, establishing a deep confidence network model by using other related data such as unit load and the like, and outputting characteristics two:
(1) besides the boiler wall temperature distribution in the step two, the boiler wall temperature distribution can reflect the combustion state, and indexes such as load, furnace oxygen amount, primary and secondary air pressure, burner swing angle, overfire air (SOFA, CCOFA) air door opening degree and furnace outlet NOx discharge amount can also reflect the boiler combustion state, so that a combustion model related to the operation indexes is established on the basis of the indexes.
(2) A deep belief network is widely used as one of representatives of deep learning methods, and the network consists of a plurality of layers of limited boltzmann machines and can sufficiently acquire complex relationships hidden in data. The combustion model is established in a complex modeling process, and in order to better extract the operation characteristics hidden in the complex nonlinear data, the deep belief network is adopted for modeling.
(3) And outputting the output result of the deep confidence network model according to the state evaluation system established in the step one.
And step four, establishing a BP neural network model based on the first characteristic output by the convolutional neural network model and the second characteristic output by the deep confidence network model in combination with a boiler combustion state evaluation system, and performing combustion process state model training by using the BP neural network to realize boiler combustion process state monitoring.
As shown in fig. 2, for the boiler combustion process state monitoring system architecture diagram based on deep learning adopted by the present invention, the boiler combustion process state monitoring system based on deep learning is composed of a combustion process state monitoring calculation server, a database server, a web server and a client browser, wherein the calculation server is connected with the database server and the web server, and the web server is connected with the client browser to display the boiler combustion process state in real time; compiling computer software based on deep learning by adopting C language, running on a state monitoring and calculating server, and applying to state monitoring of the boiler combustion process; the database server stores boiler state quantity data such as boiler wall temperature, load, furnace oxygen amount, primary and secondary air pressure, burner swing angle, overfire air (SOFA, CCOFA) air door opening, furnace outlet NOx and the like.
Claims (4)
1. A boiler combustion process state monitoring method based on deep learning is characterized by comprising the following steps:
step one, establishing a boiler combustion state evaluation system based on historical data of a boiler combustion process, and dividing states of the boiler combustion process into a plurality of classes from poor to superior, wherein the classes are represented as K ({ S1，S2，...，SK}) class in which SiIndicating the ith combustion state;
step two, taking the boiler wall temperature distribution data as an input variable, establishing a furnace wall temperature distribution characteristic model by adopting a convolutional neural network, and outputting a characteristic I;
step three, taking boiler operation index data as an input variable, adopting a deep belief network model to establish a boiler combustion state model related to operation indexes, and outputting a characteristic two;
and step four, establishing a BP neural network model for boiler combustion process state model training based on the feature I output by the convolutional neural network model and the feature II output by the deep confidence network model and combining the boiler combustion state evaluation system in the step one, so as to realize boiler combustion process state monitoring.
2. The boiler combustion process state monitoring method based on deep learning of claim 1, characterized in that: the historical data of the boiler combustion process comprises boiler operation efficiency, NOx emission and smoke temperature deviation.
3. The boiler combustion process state monitoring method based on deep learning of claim 1, characterized in that: the boiler operation index data comprises unit load, furnace oxygen amount, primary and secondary air pressure, burner swing angle, overfire air (SOFA, CCOFA) air door opening and furnace outlet NOx discharge amount.
4. The monitoring system applying the deep learning based boiler combustion process state monitoring method according to claim 1, characterized in that: the monitoring and calculating system comprises a database server, a combustion process state monitoring and calculating server, a webpage server and a user side browser, wherein the database server is used for storing historical data of a boiler combustion process and boiler operation index data, the combustion process state monitoring and calculating server is respectively connected with the output end of the database server and the input end of a webpage memory, the combustion process state monitoring and calculating server runs deep learning computer software and outputs a calculation result to the webpage server, and the user side browser and the output end of the webpage server are connected to display the combustion process state of the boiler in real time.
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