侯儒伟,田放,蔡浩,马华,刘伯阳

• 1:华能南京热电有限公司

摘要(Abstract)：

针对传统飞灰含碳量预测模型存在局部最优解陷阱和泛化能力不足的问题，在锅炉热态多工况试验的基础上，经数据采集、处理以及变量Pearson相关性分析和重要度排序筛选出28个关键特征参数，采用麻雀搜索算法（sparrow search algorithm,SSA）确定随机森林（random forest,RF）模型最优超参数，构建SSA-RF预测模型。模型验证结果表明：SSARF模型在训练集和测试集的均方根误差分别降至0.010 8和0.019 1，决定系数R~2提升至0.999 7和0.998 1，显示模型优异的预测准确性和泛化能力。进一步提出ISSA-RF-SSA算法，融合多种策略改进SSA，实现燃烧参数的全局极值寻优。工程验证显示，ISSA-RF-SSA算法预测飞灰含碳量与实际值误差为0.03百分点，该算法优化后锅炉实际飞灰含碳量由2.500%降至1.345%。研究结果表明，通过多策略改进的ISSA-RF-SSA方法显著提升了算法的寻优性能，为燃煤机组燃烧优化提供了新思路。

关键词(KeyWords)： 燃煤锅炉;飞灰含碳量;燃烧优化;麻雀搜索算法;随机森林

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 侯儒伟,田放,蔡浩,马华,刘伯阳

DOI: 10.19666/j.rlfd.202503038

参考文献(References)：

• [1]李欣宇,唐德东,张淑敏,等.发电厂锅炉燃烧状态监测技术综述[J].中国特种设备安全, 2024, 40(1):28-33.LI Xinyu, TANG Dedong, ZHANG Shumin, et al.Overview of combustion status monitoring technology for power plant boilers[J]. China Special Equipment Safety,2024, 40(1):28-33.
• [2]王芳,马素霞,王河.基于随机森林变量选择的飞灰含碳量预测模型[J].热力发电, 2018, 47(11):89-95.WANG Fang, MA Suxia, WANG He. Prediction model of carbon content in fly ash using random forest variable selection method[J]. Thermal Power Generation, 2018,47(11):89-95.
• [3]朱竞东.基于神经网络的火电厂飞灰含碳量测量[D].保定:华北电力大学, 2012:1.ZHU Jingdong. Measurement of the carbon content in fly ash of thermal power plants based on neural network[D].Baoding:North China Electric Power University, 2012:1.
• [4]朱琎琦,牛晓凡,肖显斌.基于改良BP神经网络的生物质锅炉飞灰含碳量预测模型研究[J].可再生能源,2020, 38(2):150-157.ZHU Jinqi, NIU Xiaofan, XIAO Xianbin. Prediction models of the carbon content of fly ash in abiomass boiler based on improved BP neural networks[J]. Renewable Energy Resources, 2020, 38(2):150-157.
• [5]李琳.基于SVM的火电厂锅炉飞灰含碳量软测量方法研究[D].保定:华北电力大学, 2013:1.LI Lin. Research on soft sensing method of unburned carbon content in fly ash based on support vector machine[D]. Baoding:North China Electric Power University, 2013:1.
• [6]李路.电站锅炉燃烧优化在线控制策略研究[D].保定:华北电力大学, 2014:1.LI Lu. Research on strategy of combustion optimization online control of utility boilers[D]. Baoding:North China Electric Power University, 2014:1.
• [7]周昊,朱洪波,曾庭华,等.基于人工神经网络的大型电厂锅炉飞灰含碳量建模[J].中国电机工程学报,2002, 22(6):97-101.ZHOU Hao, ZHU Hongbo, ZENG Tinghua, et al.Artificial neural network modelling on the unburned carbon in fly ash from utility boilers[J]. Proceedings of the CSEE, 2002, 22(6):97-101.
• [8]陈波,曹歌瀚,黄亚继,等.基于机器学习的燃煤锅炉燃烧效率在线计算[J].洁净煤技术, 2021, 27(4):174-179.CHEN Bo, CAO Gehan, HUANG Yaji, et al. Online calculation of coal-fired boiler combustion efficiency based on machine learning[J]. Clean Coal Technology,2021, 27(4):174-179.
• [9]陈植元,谭厚章,成思扬,等.基于机器学习的飞灰含碳量预测模型比较研究[J].热力发电, 2023, 52(7):64-73.CHEN Zhiyuan, TAN Houzhang, CHENG Siyang, et al.Comparison of prediction models of carbon content of fly ash based on machine learning[J]. Thermal Power Generation, 2023, 52(7):64-73.
• [10]骆海瑞.基于机器学习的飞灰含碳量软测量模型研究[D].武汉:武汉纺织大学, 2023:1.LUO Hairui. Research on soft sensing model of fly ash carbon content based on machine learning[D]. Wuhan:Wuhan Textile University, 2023:1.
• [11]陈浩.基于降低飞灰含碳量的火电厂锅炉燃烧过程运行优化研究[J].化工管理, 2024(36):120-123.CHEN Hao. Research on operational optimization of combustion process of thermal power plant boiler based on reducing carbon content of fly ash[J]. Chemical Engineering Management, 2024(36):120-123.
• [12]史红金.基于狼群算法优化极限学习机的北斗卫星钟差预报[J].测绘与空间地理信息, 2025, 48(2):73-76.SHI Hongjin. Optimization of extreme learning machine based on wolf optimization algorithm for predicting Beidou satellite clock errors[J]. Geomatics&Spatial Information Technology, 2025, 48(2):73-76.
• [13]秦秋霞,梁仲月,徐毅.基于Logistic-Tent混沌映射和位平面的图像加密算法[J].大连民族大学报, 2022,24(3):245-252.QIN Qiuxia, LIANG Zhongyue, XU Yi. Image encryption algorithm based on logistic-tent chaotic mapping and bit plane[J]. Journal of Dalian Minzu University, 2022, 24(3):245-252.
• [14]滕志军,付雨珊,谷梁岑,等.融合动态权重系数与Levy飞行的黏菌优化算法[J].陕西科技大学报, 2024,42(4):191-198.TENG Zhijun, FU Yushan, GU Liangcen, et al. An optimized slime mould algorithm combining dynamic weight coefficient and levy flight[J]. Journal of Shaanxi University of Science&Technology, 2024, 42(4):191-198.
• [15]李楠,薛建凯,舒慧生.基于自适应t分布变异麻雀搜索算法的无人机航迹规划[J].东华大学学报(自然科学版), 2022, 48(3):69-74.LI Nan, XUE Jiankai, SHU Huisheng. A sparrow search algorithm with adaptive t distribution mutation-based path planning of unmanned aerial vehicles[J]. Journal of Donghua University(Natural Science), 2022, 48(3):69-74.