Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling

Yuanhao Shi, Mengwei Li, Jie Wen, Yanru Yang, Fangshu Cui and Jianchao Zeng
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Yuanhao Shi: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Mengwei Li: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Jie Wen: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Yanru Yang: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Fangshu Cui: School of Data Science and Technology, North University of China, Taiyuan 030051, China
Jianchao Zeng: School of Data Science and Technology, North University of China, Taiyuan 030051, China

Energies, 2021, vol. 14, issue 13, 1-19

Abstract: Ash fouling has been an important factor in reducing the heat transfer efficiency and safety of the coal-fired power plant boilers. Scientific and accurate prediction of ash fouling of heat transfer surfaces is the basis of formulating a reasonable soot blowing strategy to improve energy efficiency. This study presented a comprehensive approach of dynamic prediction of the ash fouling of heat transfer surfaces in coal-fired power plant boilers. At first, the cleanliness factor is used to reflect the fouling level of the heat transfer surfaces. Then, a dynamic model is proposed to predict ash deposits in the coal-fired boilers by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and nonlinear autoregressive neural networks (NARNN). To construct a reasonable network model, the minimum information criterion and trial-and-error method are used to determine the delay orders and hidden layers. Finally, the experimental object is established on the 300 MV economizer clearness factor dataset of the power station, and the root mean square error and mean absolute percentage error of the proposed method are the smallest. In addition, the experimental results show that this multiscale prediction model is more competitive than the Elman model.

Keywords: coal-fired power plant boiler; ash fouling; heat transfer efficiency; CEEMDAN; NARNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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