[1]刘鑫屏,李 波,邓拓宇.一种SSAE+BPNN的变工况飞灰含碳量软测量方法[J].热力发电,2023,52(01):66-73.[doi:10.19666/j.rlfd.202205097]
 LIU Xinping,LI Bo,DENG Tuoyu.A soft measurement method of carbon content in fly ash under variable operating conditions of SSAE+BPNN[J].Thermal Power Generation,2023,52(01):66-73.[doi:10.19666/j.rlfd.202205097]
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一种SSAE+BPNN的变工况飞灰含碳量软测量方法

参考文献/References:

[1] 李霞, 牛培峰, 刘建平, 等. 基于量子迭代混沌的涡流搜索算法预测锅炉飞灰含碳量[J]. 动力工程学报, 2019, 39(7): 531-540.
LI Xia, NIU Peifeng, LIU Jianping, et al. Prediction of carbon content in boiler fly ash based on quantum and iterative chaos vortex search algorithm[J]. Journal of Chinese Society of Power Engineering, 2019, 39(7): 531-540.
[2] 王月兰, 马增益, 尤海辉, 等. 基于自适应神经模糊推理系统的煤粉锅炉飞灰含碳量建模[J]. 热力发电, 2018, 47(1): 26-32.
WANG Yuelan, MA Zengyi, YOU Haihui, et al. Modelling for unburned carbon content in fly ash from coal-fired boilers based on adaptive neuro-fuzzy inference system[J]. Thermal Power Generation, 2018, 47(1): 26-32.
[3] 牛玉广, 任丹彤, 李建军. 自由空间反射法检测飞灰含碳量的实验研究[J]. 仪器仪表学报, 2018, 39(10): 126-133.
NIU Yuguang, REN Dantong, LI Jianjun. Experimental research on measuring carbon content of fly ash by free space reflection method[J]. Chinese Journal of Scientific Instrument, 2018, 39(10): 126-133.
[4] 彭道刚, 李丹阳, 顾立群, 等. 基于ADQPSO-SVR的锅炉飞灰含碳量预测研究[J]. 计算机仿真, 2020, 37(3): 72-77.
PENG Daogang, LI Danyang, GU Liqun, et al. Research on prediction of carbon content in boiler fly ash based on ADQPSO-SVR[J]. Computer Simulation, 2020, 37(3): 72-77.
[5] 田亮, 霍秋宝, 刘鑫屏, 等. 电站锅炉总风量软测 量[J]. 中国电机工程学报, 2014, 34(8): 1261-1267.
TIAN Liang, HUO Qiubao, LIU Xinping, et al. Soft-sensors of the total air volume in utility boilers[J]. Proceedings of the CSEE, 2014, 34(8): 1261-1267.
[6] 田亮, 王桐. 供热机组汽轮机低压缸排汽流量软测 量[J]. 系统仿真学报, 2018, 30(5): 1803-1811.
TIAN Liang, WANG Tong. Soft-sensor method of low pressure cylinder exhaust flow of heating units[J]. Journal of System Simulation, 2018, 30(5): 1803-1811.
[7] 李泽铭, 田亮. 基于卡尔曼滤波器的循环流化床机组燃料发热量软测量[J]. 华北电力大学学报(自然科学版), 2021, 48(2): 89-95.
LI Zeming, TIAN Liang. Soft measurement of fuel calorific value of circulating fluidized bed based on kalman filter[J]. Journal of North China Electric Power University(Natural Science Edition), 2021, 48(2): 89-95.
[8] 赵健, 袁瀚, 梅宁. 燃煤锅炉飞灰含碳量的BP神经网络模型[J]. 热科学与技术, 2016, 15(6): 499-504.
ZHAO Jian, YUAN Han, MEI Ning. BP neural network model of fly ash carbon content of coal-fired boiler[J]. Journal of Thermal Science and Technology, 2016, 15(6): 499-504.
[9] 乔源, 王建峰, 杨永存, 等. 基于神经网络的飞灰含碳量软测量模型及实现[J]. 电力科学与工程, 2019, 35(11): 55-61.
QIAO Yuan, WANG Jianfeng, YANG Yongcun, et al. Soft measurement model and implementation of fly ash carbon content based on neural network[J]. Electric Power Science and Engineering, 2019, 35(11): 55-61.
[10] 王春林, 周昊, 周樟华, 等. 基于支持向量机的大型电厂锅炉飞灰含碳量建模[J]. 中国电机工程学报, 2005, 25(20): 72-76.
WANG Chunlin, ZHOU Hao, ZHOU Zhanghua, et al. Support vector machine modeling on the unburned carbon in fly ash[J]. Proceedings of the CSEE, 2005, 25(20): 72-76.
[11] 卞和营, 方彦军. 基于支持向量回归的飞灰含碳量软测量[J]. 热力发电, 2014, 43(10): 46-50.
BIAN Heying, FANG Yanjun. SVR-based study on soft-sensing measurement of carbon content in fly ash[J]. Thermal Power Generation, 2014, 43(10): 46-50.
[12] 王伟, 常浩, 王宝玉. 飞灰含碳量自适应校正WLSSVM软测量模型[J]. 热力发电, 2013, 42(8): 75-80.
WANG Wei, CHANG Hao, WANG Baoyu. A WLSSVM based self-adaptive correction soft-sensing model for carbon content measurement in fly ash[J]. Thermal Power Generation, 2013, 42(8): 75-80.
[13] 麻红波, 余瑞锋, 倪艳红, 等. 基于GSA-LSSVM的循环流化床锅炉飞灰含碳量预测[J]. 锅炉技术, 2016, 47(2): 53-56.
MA Hongbo, YU Ruifeng, NI Yanhong, et al. The prediction of the carbon content of fly ash of circulating fluidized bed boilers based on GSA-LSSVM[J]. Boiler Technology, 2016, 47(2): 53-56.
[14] 徐志强, 任密蜂, 程兰, 等. 基于时间近邻拉氏正则的多工况软测量回归[J]. 仪器仪表学报, 2021, 42(11): 279-287.
XU Zhiqiang, REN Mifeng, CHENG Lan, et al. Multi-conditions soft sensor regression based on the time-nearest neighbor Laplacian regularization[J]. Chinese Journal of Scientific Instrument, 2021, 42(11): 279-287.
[15] YAO L, GE Z Q. Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application[J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1490-1498.
[16] 潘红光, 裴嘉宝, 苏涛, 等. 基于LSTM的燃煤电厂NOx排量软测量[J]. 西安科技大学学报, 2022, 42(2): 362-370.
PAN Hongguang, PEI Jiabao, SU Tao, et al. LSTM-based soft sensor of NOx emissions from coal-fired power plants[J]. Journal of Xi’an University of Science and Technology, 2022, 42(2): 362-370.
[17] 支恩玮, 闫飞, 任密蜂, 等. 基于迁移变分自编码器-标签映射的湿式球磨机负荷参数软测量[J]. 化工学报, 2019, 70(增刊1): 150-157.
ZHI Enwei, YAN Fei, REN Mifeng, et al. Soft sensor of wet ball mill load parameters based on transfer variational autoencoder-label mapping[J]. CIESC Journal, 2019, 70(Suppl.1): 150-157.
[18] LU C, WANG Z Y, QIN W L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130: 377-388.
[19] YU J, ZHOU X. One-dimensional residual convolutional autoencoder based feature learning for gearbox fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, 16(10): 6347-6358.
[20] 李江坤, 黄海燕. 互信息深度稀疏自编码融合DLSTM预测网络[J]. 计算机工程与应用, 2022, 58(20): 277-285.
LI Jiangkun, HUANG Haiyan. Mutual information deep sparse auto-encoding hybrid DLSTM prediction network[J]. Computer Engineering and Applications, 2022, 58(20): 277-285.
[21] 孙林凯, 金家善, 耿俊豹. 基于修正邓氏灰色关联度的设备费用影响因素分析[J]. 数学的实践与认识, 2012, 42(8): 140-145.
SUN Linkai, JIN Jiashan, GENG Junbao. Analysis of influencing factors of equipment cost based on modified Deng’s grey relational degree[J]. Mathematics in Practice and Theory, 2012, 42(8): 140-145.
[22] 袁非牛, 章琳, 史劲亭, 等. 自编码神经网络理论及应用综述[J]. 计算机学报, 2019, 42(1): 203-230.
YUAN Feiniu, ZHANG Lin, SHI Jinting, et al. Theories and applications of auto-encoder neural networks: a literature survey[J]. Chinese Journal of Computers, 2019, 42(1): 203-230.
[23] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[24] CAI H H, WU Z H, HUANG C, et al. Wind power forecasting based on ensemble empirical mode decomposition with generalized regression neural network based on cross-validated method[J]. Journal of Electrical Engineering & Technology, 2019, 14(5): 1823-1829.
[25] 康英伟, 周昊, 郭为民, 等. 基于模糊关联规则的脱硫系统运行优化[J]. 热能动力工程, 2020, 35(7): 145-151.
KANG Yingwei, ZHOU Hao, GUO Weimin, et al. Operation optimization of desulfurization system based on fuzzy association rules[J]. Journal of Engineering for Thermal Energy and Power, 2020, 35(7): 145-151.
(责任编辑 邓玲惠)

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备注/Memo

刘鑫屏(1976),女,博士,副教授,主要研究方向为大机组智能优化控制、热力发电过程建模与状态参数检测、综合能源系统,lxp@ncepu.edu.cn。

更新日期/Last Update: 2023-01-15