NOx emission prediction method of thermal power plant based on generalized mutual entropy auto-encoder

A self-encoder and prediction method technology, applied in prediction, neural learning methods, instruments, etc., can solve the problem of poor robustness of the loss function, and achieve the effect of improving robustness, accurate and reliable prediction, and high practical engineering application value
CN110782013AActive Publication Date: 2020-02-11TAIYUAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
TAIYUAN UNIV OF TECH
Publication Date
2020-02-11

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Abstract

The invention relates to a NOx emission prediction method of a thermal power plant based on a generalized mutual entropy auto-encoder, in particular to a thermal power plant NOx emission prediction method based on a generalized mutual entropy gated stacked target related auto-encoder. The method comprises the following steps: acquiring thermal power plant data including NOx emission and related influence factors, preprocessing all data, taking the related factors influencing NOx emission as input, performing nonlinear dimensionality reduction and feature extraction through an auto-encoder, and establishing a model between the NOx emission influence factors and the NOx emission. The NOx emission prediction method is accurate and reliable in NOx emission prediction and has high practical engineering application value.
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Description

technical field

[0001] The present invention relates to NO x A method for predicting emissions, specifically a generalized cross-entropy autoencoder-based NO x Emissions Forecasting Methods. Background technique

[0002] Facing the increasingly severe environmental problems in China, there is an urgent need for a method that can reduce energy consumption and pollutant emissions of power plant boilers. Boiler combustion optimization technology has always been an effective means to improve boiler efficiency and reduce pollutant emissions. It is of great significance to establish an accurate power plant boiler combustion model and optimize the input parameters of the model. However, how to establish a power plant boiler combustion model to achieve efficient, automatic, fast and accurate prediction of data is particularly important.

[0003] Generally speaking, there are three types of soft sensor modeling methods, which are mechanism-based models, knowledge-based models, an...

Claims

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