Boiler NOx emission estimation based on QSCSO-BLS and ensemble learning

doi:10.3969/j.issn.1000-1565.2025.06.012

Abstract

Abstract: The NOx emission measurement in the selective catalytic reduction(SCR)system of boiler has the problems of poor real-time performance and abnormal measurement during purging. Accurate prediction of boiler NOx emission can improve the real-time performance and accuracy of measurement. Therefore, a boiler NOx emission prediction method based on QSCSO-BLS and ensemble learning is proposed: By combining Lévy flight strategy and quantum strategy in the Dune Cat swarm algorithm(SCSO), an improved Dune Cat swarm algorithm(QSCSO)is proposed. The weight and bias of the width learning system(BLS)are optimized, and the QSCSO-BLS model is established. QSCSO-BLS was used to construct individual learner under different working conditions, and parametric regression method was used to train the combinator with individual learner output and working condition membership as inputs, and an integrated learning model for predicting NOx emission under all working conditions of BLS was obtained. The operation data of SCR system of a 660 MW thermal power unit is taken as an example. The results show that the proposed method can improve the accuracy of NOx prediction and provide a new method for boiler NOx emission prediction.

Key words: NOx estimation, broad learning system, ensemble learning, sand cat swarm algorithm

CLC Number:

TP271

YANG Zhenyong, XING Zhiwei, LIU Lei, KANG Jingqiu. Boiler NO_x emission estimation based on QSCSO-BLS and ensemble learning[J]. Journal of Hebei University(Natural Science Edition), 2025, 45(6): 661-672.

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Boiler NO_x emission estimation based on QSCSO-BLS and ensemble learning

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