CN111931436A - Burner nozzle air quantity prediction method based on numerical simulation and neural network - Google Patents
Burner nozzle air quantity prediction method based on numerical simulation and neural network Download PDFInfo
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Abstract
A burner nozzle air quantity prediction method based on numerical simulation and a neural network relates to the technical field of burners, aims at the problems of low measurement accuracy and low efficiency of the traditional furnace internal air quantity measurement method, and comprises the following steps: establishing a physical model of the hot secondary air duct of the boiler by using numerical simulation software, and performing simulation operation to generate burner nozzle air quantity simulation data under various working conditions; step two: carrying out data cleaning on the generated burner nozzle air quantity simulation data under each working condition; step three: and training a neural network according to the processed data, and predicting the air quantity of a nozzle of the combustor by using the trained neural network. According to the typical working condition set manually, the neural network is trained as algorithm input, and the air quantity of the burner nozzle under a large number of other working conditions can be predicted through the model. The algorithm is used for replacing a great deal of work such as manual grid drawing, numerical simulation and the like. The prediction time is greatly shortened, the simulation efficiency is improved, and the measurement precision is improved.
Description
Technical Field
The invention relates to the technical field of combustors, in particular to a combustor nozzle air quantity prediction method based on numerical simulation and a neural network.
Background
In order to ensure the stable operation of the boiler, the combustion process of the pulverized coal fuel in the boiler furnace must be in a controllable range, so that the distribution condition of the air quantity entering the furnace needs to be accurately known. The traditional measuring method needs to be improved in measuring precision and efficiency, and is greatly influenced by human factors, and the CFD (computational Fluid dynamics) numerical simulation technology has abundant mathematical calculation models and can accurately reflect the processes of Fluid flow, heat transfer, combustion and the like. However, only CFD prediction is used, and the problems of more operation steps, large workload, low simulation speed, long time consumption in the numerical analysis process, narrow coverage of typical working conditions and the like exist. The neural network simulates human brain through the calculation of a machine, is objective and real, has large information processing amount and high calculation speed, processes complex problems in a simple and quick way, and can accurately predict the future.
Disclosure of Invention
The purpose of the invention is: aiming at the problems of low measurement precision and low efficiency of the traditional furnace internal air quantity measurement method, the burner nozzle air quantity prediction method based on numerical simulation and a neural network is provided.
The technical scheme adopted by the invention to solve the technical problems is as follows:
a burner nozzle air quantity prediction method based on numerical simulation and a neural network comprises the following steps:
the method comprises the following steps: establishing a physical model of the hot secondary air duct of the boiler by using numerical simulation software, and performing simulation operation to generate burner nozzle air quantity simulation data under various working conditions;
step two: carrying out data cleaning on the generated burner nozzle air quantity simulation data under each working condition;
step three: and training a neural network according to the processed data, and predicting the air quantity of a nozzle of the combustor by using the trained neural network.
Further, the specific steps of the first step are as follows:
firstly, carrying out numerical simulation on the air volume of an air box inside a hot secondary air channel of the power station boiler and each nozzle model in fluent software by utilizing real-time or off-line data acquired by the power station boiler, adding boundary condition input behind the off-line data and combining a combustion mechanism, a flow model and a heat transfer model to generate the air volume of a nozzle of a combustor under a typical working condition;
determining influence factors related to the air volume of the burner nozzle, taking the air volume of the air box under each influence factor as an input object, and taking the output object as the air volume data of the burner nozzle of each layer, namely the air volume simulation data of the burner nozzle of each working condition.
Further, the influencing factors comprise air volume of the wind box, opening degree of each sofa air door and opening degree of each layer of combustor air door.
Further, the neural network is a BP neural network, a deep belief neural network DBN or a deep neural network DNN.
Further, the specific step of performing data cleaning on the generated burner nozzle air volume simulation data under each working condition in the step two is as follows:
the first step is as follows: processing by using a K-means algorithm to reduce data dimensionality;
the second step is that: judging whether the data contains a null value, a 0 value or an abnormal value, and if so, filling the data by using a Lagrange interpolation method;
the third step: and carrying out standardization processing on the data.
Further, the normalization processing formula is expressed as:
in the formula
Representing the production parameters of the kth sample under n-dimensional data normalization,
it means that the kth sample arranges n-dimensional metadata in time series, and K is the number of data sets.
Further, the third step comprises the following specific steps:
after the data are processed, the data are divided, seventy percent of the data are randomly selected as a training set, thirty percent of the data are selected as a testing set, air volume of an air box, opening degrees of sofa air doors and opening degrees of the air doors of each layer of combustor are selected as characteristic variables, air volume of a nozzle of each layer of combustor is taken as a target variable, training is carried out by utilizing a neural network, and then a prediction result is output according to the result.
The invention has the beneficial effects that:
1. according to the typical working condition set manually, the neural network is trained as algorithm input, and the air quantity of the burner nozzle under a large number of other working conditions can be predicted through the model. The algorithm is used for replacing a great deal of work such as manual grid drawing, numerical simulation and the like. The prediction time is greatly shortened, the simulation efficiency is improved, and the measurement precision is improved.
2. The method for predicting the air quantity of the nozzle of the burner by utilizing the neural network based on the numerical simulation can save a large number of operation steps of the numerical simulation, accelerate the simulation speed, comprehensively cover various working conditions and reduce the artificial influence.
3. The three neural network algorithms form a neural network algorithm learning machine, and the algorithm prediction time is shortened.
Drawings
FIG. 1 is a BP prediction result graph;
FIG. 2 is a diagram of DBN prediction results;
FIG. 3 is a graph of DNN prediction results;
FIG. 4 is a diagram of a BP neural network prediction structure;
FIG. 5 is a flow chart of the present invention.
Detailed Description
The first embodiment is as follows: referring to fig. 5 to specifically describe the embodiment, the invention provides a burner port air quantity prediction method based on numerical simulation and a neural network, which performs simulation prediction on the flow of hot secondary air in a hot secondary air duct of a thermal power generating unit and the distribution of air quantity of each port, so as to improve the combustion efficiency of a boiler.
And aiming at the acquired off-line data, establishing a physical model of the hot secondary air channel of the boiler by using numerical simulation software, and generating related air quantity, distribution of air doors and corresponding data files. Obtaining a data result to obtain a data result; processing the numerical simulation result by using a data processing method; and finally, forecasting the air quantity of the nozzle of the burner under different working conditions by utilizing neural network forecasting, and providing theoretical and practical basis for realizing the actual operation of the boiler. The method comprises the following steps:
one), numerical simulation. And (3) establishing a physical model of the hot secondary air duct of the boiler by using numerical simulation software, and performing simulation operation.
II), data processing. The numerical simulation results are processed.
Third), algorithm prediction. And (4) predicting the air quantity of a nozzle of the combustor by using four neural networks.
Fourthly), result application. And selecting an optimal effect algorithm from the three neural networks, and predicting the outlet air quantity of the combustor under the specified working condition.
Each step comprises the following specific steps:
one), numerical simulation.
The method is characterized in that real-time or off-line data collected by the power station boiler is utilized, boundary condition input is added behind the off-line data, a combustion mechanism, a flow model and a heat transfer model are combined, numerical simulation is carried out on air volume of an air box inside a hot air channel of the power station boiler and models of nozzles in a fluent state, and related air volume, distribution of air doors and corresponding data files are generated.
Determining the research object as the air quantity of the nozzle of each combustor, determining the influence factors related to the air quantity of the nozzle of the combustor through expert experience, and determining 13 influence factors comprising air quantity of a wind box, the opening degree of each sofa air door, the opening degree of each layer of combustor air door and the like. Namely, the input objects are the air volume of the air box under each working condition, the opening degree of each sofa air door, the opening degree of each layer of combustor air door, and the output objects are the air volume of each layer of combustor nozzle.
II), data processing.
Since CFD-based simulation data have different dimensions and magnitudes. If the original data is directly used as input data for modeling training, the influence of the data with higher numerical value in the modeling process can be highlighted, and the data with lower numerical value level can be ignored. Therefore, in order to ensure the reliability of the result and improve the convergence of the training, the simulation data needs to be normalized before the experiment starts, and modeling training is performed after dimensional differences among different data are eliminated. Preprocessing the required data by adopting data standardization analysis:
in the formula
Representing the production parameters of the kth sample under n-dimensional data normalization,
it means that the kth sample arranges n-dimensional metadata in time series, and K is the number of data sets. Dividing the processed data into training data and verification data.
Third), algorithm prediction.
The programming integrates the BP neural network, the deep belief neural network DBN and the deep neural network DNN into a neural network learning machine, and the air quantity of the nozzle of each layer of combustor is predicted through the neural network learning machine. And training the training data through a neural network learning machine training network to respectively form three different neural networks. And respectively passing the input data of the verification set through the three networks to obtain a prediction result. And selecting the algorithm with the best result as the final prediction algorithm by comparing the fitting degree, the root mean square error and other evaluation indexes.
Fourthly), result application.
And designing single or multiple working conditions to be predicted, wherein the working conditions comprise air volume of an air box, opening degree of each sofa air door and opening degree of each layer of combustor air door as input data, and predicting the air volume of each layer of combustor nozzle by using an optimal neural network.
Fifthly, the invention has the following effects:
and selecting an algorithm with small error and high accuracy after the data set passes through the BP neural network, the deep belief neural network DBN and the deep neural network DNN. The three prediction results are shown in fig. 1, fig. 2 and fig. 3.
The method for predicting the air quantity of the nozzle of the four-corner tangential burner by utilizing the neural network based on the numerical simulation can improve the calculation efficiency and save a large number of numerical simulation operation steps and calculation time. The distribution of the air volume in the actual operation process of the thermal power generating unit can be accurately mastered, and the combustion condition in the boiler furnace is further improved by adjusting the air volume, so that the operation stability of the thermal power generating unit is maintained, and the unit efficiency is further improved. The prediction structure of the BP neural network is shown in FIG. 4: the prediction method flow is shown in fig. 5.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.
Claims (7)
1. A burner nozzle air quantity prediction method based on numerical simulation and a neural network is characterized by comprising the following steps:
the method comprises the following steps: establishing a physical model of the hot secondary air duct of the boiler by using numerical simulation software, and performing simulation operation to generate burner nozzle air quantity simulation data under various working conditions;
step two: carrying out data cleaning on the generated burner nozzle air quantity simulation data under each working condition;
step three: and training a neural network according to the processed data, and predicting the air quantity of a nozzle of the combustor by using the trained neural network.
2. The burner port air quantity prediction method based on numerical simulation and neural network as claimed in claim 1, wherein the specific steps of the first step are as follows:
firstly, carrying out numerical simulation on the air volume of an air box inside a hot secondary air channel of the power station boiler and each nozzle model in fluent software by utilizing real-time or off-line data acquired by the power station boiler and adding boundary condition input behind the real-time or off-line data and combining a combustion mechanism, a flow model and a heat transfer model to generate the air volume of a nozzle of a combustor under a typical working condition;
determining influence factors related to the air volume of the burner nozzle, taking the air volume of the air box under each influence factor as an input object, and taking the output object as the air volume data of the burner nozzle of each layer, namely the air volume simulation data of the burner nozzle of each working condition.
3. The method as claimed in claim 2, wherein the influencing factors include wind box wind volume, opening degree of each sofa wind door and opening degree of each layer of burner wind door.
4. The method of claim 1, wherein the neural network is a BP neural network, a deep belief neural network (DBN), or a Deep Neural Network (DNN).
5. The method for predicting the air quantity of the burner nozzle based on the numerical simulation and the neural network as claimed in claim 1, wherein the specific step of performing data cleaning on the generated simulation data of the air quantity of the burner nozzle under each working condition in the second step is as follows:
the first step is as follows: processing by using a K-means algorithm to reduce data dimensionality;
the second step is that: judging whether the data contains a null value, a 0 value or an abnormal value, and if so, filling the data by using a Lagrange interpolation method;
the third step: and carrying out standardization processing on the data.
6. The method of claim 5, wherein the normalization processing formula is expressed as:
in the formula
Representing the production parameters of the kth sample under n-dimensional data normalization,
it means that the kth sample arranges n-dimensional metadata in time series, and K is the number of data sets.
7. The burner port air quantity prediction method based on numerical simulation and neural network as claimed in claim 6, wherein the concrete steps of the third step are:
after the data are processed, the data are divided, seventy percent of the data are randomly selected as a training set, thirty percent of the data are selected as a testing set, air volume of an air box, opening degrees of sofa air doors and opening degrees of the air doors of each layer of combustor are selected as characteristic variables, air volume of a nozzle of each layer of combustor is taken as a target variable, training is carried out by utilizing a neural network, and then a prediction result is output according to the result.
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| CN113505521A (en) * | 2021-05-20 | 2021-10-15 | 天津大学 | Urban waterlogging rapid forecasting method based on neural network-numerical simulation |
| CN116467817A (en) * | 2023-06-01 | 2023-07-21 | 广东合胜厨电科技有限公司 | Air duct design method based on upper air inlet burner |
| CN117910170A (en) * | 2024-03-19 | 2024-04-19 | 西安慧金科技有限公司 | Method and system for designing upper air duct of industrial furnace |
| CN117910170B (en) * | 2024-03-19 | 2024-06-11 | 西安慧金科技有限公司 | Method and system for designing upper air duct of industrial furnace |
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