Boiler NOx prediction method based on MI-LSTM

A prediction method and boiler technology, applied in neural learning methods, chemical process analysis/design, biological neural network models, etc., can solve problems such as inaccurate models, affecting model prediction accuracy, and dimensional disasters, and reduce model complexity. The effect of improving model accuracy and generalization ability

Inactive Publication Date: 2020-01-10
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

However, in the actual modeling process, NO x The generation mechanism is complex and affected by multiple variables, the selection of auxiliary variables will directly affect the prediction accuracy of the model
Multiple selection of auxiliary variables will take more time for feature analysis and model training, and may also lead to the "curse of dimensionality", making the model overly complex and reducing its generalization ability; while few or missing variables will make the model inaccurate

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  • Boiler NOx prediction method based on MI-LSTM
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  • Boiler NOx prediction method based on MI-LSTM

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Embodiment Construction

[0033] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0034] Power Station BoilerNO x Emission is affected by multiple thermal variables, and the variables have correlations and the time series characteristics of on-site data. x The emission is predicted dynamically. In this example, the data of a 660MW unit of a coal-fired power plant in Henan is used as an example. The combustion method of the boiler is the front and rear wall hedging combustion. There are 6 layers of burners (3 layers in the front and rear walls), and each layer has 6 swirl burners. The wall is layer D, E, and F from bottom to top, and each medium-speed coal mill provides air-pulverized mixture for 6 pulverized coal burners on the same layer. At the same time, one layer of exhaust air is arranged above the swirling pulverized coal burners on the front and rear walls, and each layer has 6 exhaust air (AAP) nozzles and 2 side exh...

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Abstract

The invention discloses a boiler NOx prediction method based on MI-LSTM. The method comprises: ranking the importance of auxiliary variables and selecting variables by using mutual information (MI) "minimum redundancy maximum correlation" as a criterion; using a selected auxiliary variable set as an input of a long short term memory (LSTM) model; establishing an MI-LSTM model for NOx emission prediction, and searching for an optimal parameter for the LSTM model by using multilayer grids. In the process of variable selection, a sequence forward selection method is used to determine the optimalinput feature set and the optimal model parameter with the model prediction accuracy as a goal, thereby effectively reducing the information redundancy between the input variables, reducing the modelcomplexity, and improving the prediction accuracy and generalization capability of the model.

Description

technical field [0001] The invention belongs to the technical field of combustion control, and in particular relates to a MI-LSTM-based boiler NO x method of prediction. Background technique [0002] NO produced during coal combustion x It is an important source of air pollutants and seriously affects human health and air quality. In 2011, the Ministry of Environmental Protection promulgated new pollutant emission standards, requiring coal-fired boilers to x Emission does not exceed 100mg / m 3 . Restrictions on nitrogen oxide emissions will become more and more stringent. In order to meet emission standards, thermal power plants need to control NOx x Emissions are monitored and controlled in real time. However, the existing measurement equipment cannot achieve real-time measurement of NO x requirements, and monitoring is difficult. To this end, establish an effective NO x Emission prediction model, for NO x Efficient and rapid monitoring of concentration is necessar...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16C20/70G16C20/10G06N3/04G06N3/08
CPCG16C20/70G16C20/10G06N3/08G06N3/044G06N3/045
Inventor 杨国田王英男李新利刘禾王孝伟
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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