Abstract
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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This work was supported by the National Natural Science Foundation of China (Grant No. 51976038), the Natural Science Foundation of Jiangsu Province, China for Young Scholars (Grant No. BK20190366), and the China Scholarship Council (Grant No. 202006090164.
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Han, Z., Huang, Y., Li, J. et al. A hybrid deep neural network based prediction of 300 MW coal-fired boiler combustion operation condition. Sci. China Technol. Sci. 64, 2300–2311 (2021). https://doi.org/10.1007/s11431-020-1796-2
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DOI: https://doi.org/10.1007/s11431-020-1796-2