A hybrid deep neural network based prediction of 300 MW coal-fired boiler combustion operation condition

In power generation industries, boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation. Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and ma...

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Bibliographic Details
Published in:Science China. Technological sciences Vol. 64; no. 10; pp. 2300 - 2311
Main Authors: Han, ZheZhe, Huang, YiZhi, Li, Jian, Zhang, Biao, Hossain, Md. Moinul, Xu, ChuanLong
Format: Journal Article
Language:English
Published: Beijing Science China Press 01.10.2021
Springer Nature B.V
Subjects:
Artificial neural networks
Boilers
Combustion efficiency
Engineering
Feature extraction
Firing (igniting)
Neural networks
Performance enhancement
Prediction models
Support vector machines
coal-fired power plant
flame image
combustion operation condition prediction
least support vector machine
convolutional sparse autoencoder
ISSN:1674-7321, 1869-1900
Online Access:Get full text
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Summary:In power generation industries, boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation. Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency. However, it is difficult to establish an accurate prediction model based on traditional data-driven methods, which requires prior expert knowledge and a large number of labeled data. To overcome these limitations, a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed. The proposed hybrid model is a combination of convolutional sparse autoencoder (CSAE) and least support vector machine (LSSVM), i.e., CSAE-LSSVM, where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image, and then essential features are input into the least support vector machine for operation condition prediction. A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM. The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images. The prediction accuracy of the proposed hybrid model reaches 98.06%, and its prediction time is 3.06 ms/image. It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.
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ISSN:1674-7321
1869-1900
DOI:10.1007/s11431-020-1796-2