基于QSCSO-BLS和集成学习的锅炉NOx排放预估

doi:10.3969/j.issn.1000-1565.2025.06.012

河北大学学报(自然科学版) ›› 2025, Vol. 45 ›› Issue (6): 661-672.DOI: 10.3969/j.issn.1000-1565.2025.06.012

• • 上一篇

基于QSCSO-BLS和集成学习的锅炉NOx排放预估

杨振勇^1,2,邢智炜^1,2,刘磊^1,2,康静秋^1,2

收稿日期:2024-12-23 发布日期:2025-11-19
作者简介:杨振勇(1977—),男,国网冀北电力有限公司电力科学研究院正高级工程师,主要从事火电机组调频调峰智能控制方向研究.E-mail:13810286796@163.com
基金资助:
华北电力科学研究院科技项目(KJZ2023022)

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

YANG Zhenyong^1,2, XING Zhiwei^1,2, LIU Lei^1,2, KANG Jingqiu^1,2

1. State Grid Jibei Electric Power Research Institute, Beijing 100045, China; 2. North China Electric Power Research Institute Co., Ltd., Beijing 100045, China

Received:2024-12-23 Published:2025-11-19

摘要/Abstract

摘要： 锅炉选择性催化还原(selective catalytic reduction,SCR)系统NOx排放测量存在实时性差、吹扫时测量异常的问题,精准的锅炉NOx排放预估可以提高测量的实时性和准确性.为此,提出了一种基于QSCSO-BLS和集成学习的锅炉NOx排放预估方法:在沙丘猫群算法(SCSO)中融合Lévy飞行策略和量子策略提出了改进沙丘猫群算法(QSCSO),对宽度学习系统(BLS)的权重、偏置进行优化,建立了QSCSO-BLS模型;采用QSCSO-BLS构造不同工况下的个体学习器,并使用参数回归方法将个体学习器输出和工况隶属度作为输入对结合器进行训练,得到BLS全工况NOx排放预估的集成学习模型.以某660 MW火电机组SCR系统运行数据为算例进行验证,结果表明,所提方法可以提高NOx预估的精度,为锅炉NOx排放预估提供了新方法.

关键词: NOx预估, 宽度学习系统, 集成学习, 沙丘猫群算法

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

中图分类号:

TP271

杨振勇,邢智炜,刘磊,康静秋. 基于QSCSO-BLS和集成学习的锅炉NOx排放预估[J]. 河北大学学报(自然科学版), 2025, 45(6): 661-672.

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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基于QSCSO-BLS和集成学习的锅炉NOx排放预估

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

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基于QSCSO-BLS和集成学习的锅炉NOx排放预估

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

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