[1]谢锐彪,李新利,王英男,等.基于粒子群优化的双向门控循环神经网络燃煤电厂NOx排放预测[J].热力发电,2021,50(10):87-94.[doi:10.19666/j.rlfd.202103042 ]
 XIE Ruibiao,LI Xinli,WANG Yingnan,et al.NOx emission prediction of coal-fired power plants based on PSO and Bi-GRU[J].Thermal Power Generation,2021,50(10):87-94.[doi:10.19666/j.rlfd.202103042 ]
点击复制

基于粒子群优化的双向门控循环神经网络燃煤电厂NOx排放预测

参考文献/References:

[1] SKALSKA K, MILLER J S, LEDAKOWICZ S. Trends in NOx abatement: a review[J]. Science of the Total Environment, 2010, 408(19): 3976-3989.
[2] 中华人民共和国生态环境部. 燃煤电厂超低排放烟气治理工程技术规范: HJ 2053—2018[S]. 北京: 中国环境科学出版社, 2018: 3.
Ministry of Ecological Environment of the People’s Republic of China. Technical specifications for flue gas ultra-low emission engineering of coal-fired power plant: HJ 2053—2018[S]. Beijing: China Environmental Science Press, 2018: 3.
[3] XIE P, GAO M, ZHANG H, et al. Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network[J]. Energy, 2020, 190: 116482.
[4] SCHOBING J, TSCHAMBER V, BRILHAC J F, et al. Simultaneous soot combustion and NOx reduction over a vanadia-based selective catalytic reduction catalyst[J]. Comptes Rendus Chimie, 2018, 21(3/4): 221-231.
[5] 张晓宇. 600 MW低NOx切圆炉膛燃烧优化分析[J]. 中国电机工程学报, 2016, 36(增刊1): 140-146.
ZHANG Xiaoyu. Analysis of combustion optimization on 600 MW low nitrogen tangentially coal-fired boiler[J]. Proceedings of the CSEE, 2016, 36(Suppl.1): 140-146.
[6] SAFDARNEJAD S M, TUTTLE J F, POWELL K M. Dynamic modeling and optimization of a coal-fired utility boiler to forecast and minimize NOx and CO emissions simultaneously[J]. Computers & Chemical Engineering, 2019, 124: 62-79.
[7] SHI L, FU Z, DUAN X, et al. Influence of combustion system retrofit on NOx formation characteristics in a 300 MW tangentially fired furnace[J]. Applied Thermal Engineering, 2016, 98: 766-777.
[8] TUTTLE J F, VESEL R, ALAGARSAMY S, et al. Sustainable NOx emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization[J]. Control Engineering Practice, 2019, 93: 104167.
[9] SHI Y, ZHONG W, CHEN X, et al. Combustion optimization of ultra supercritical boiler based on artificial intelligence[J]. Energy, 2019, 170: 804-817.
[10] 王东风, 刘千, 韩璞, 等. 基于大数据驱动案例匹配的电站锅炉燃烧优化[J]. 仪器仪表学报, 2016, 37(2): 420-428.
WANG Dongfeng, LIU Qian, HAN Pu, et al. Combustion optimization in power station based on big data-driven case-matching[J]. Chinese Journal of Scientific Instrument, 2016, 37(2): 420-428.
[11] LV Y, YANG T, LIU J. An adaptive least squares support vector machine model with a novel update for NOx emission prediction[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 145: 103-113.
[12] TAN P, ZHANG C, XIA J, et al. NOx emission model for coal-fired boilers using principle component analysis and support vector regression[J]. Journal of Chemical Engineering of Japan, 2018, 49(2): 211-216.
[13] 朱翰超, 马蕊. 考虑需求侧管理的冷热电联供微电网优化配置方法[J]. 电力系统保护与控制, 2019, 47(2): 139-146.
ZHU Hanchao, MA Rui. Optimal configuration method of CCHP microgrid considering demand side manage- ment[J]. Power System Protection and Control, 2019, 47(2): 139-146.
[14] GOODFELLOW I, BENGIO Y, COURVILLE A, et al. Deep learning[M]. Cambridge: MIT Press, 2016: 11.
[15] WANG F, MA S, WANG H, et al. Prediction of NOx emission for coal-fired boilers based on deep belief network[J]. Control Engineering Practice, 2018, 80: 26-35.
[16] TAN P, HE B, ZHANG C, et al. Dynamic modeling of NOx emission in a 660 MW coal-fired boiler with long short-term memory[J]. Energy, 2019, 176: 429-436.
[17] YANG G, WANG Y, LI X. Prediction of the NOx emissions from thermal power plant using long-short term memory neural network[J]. Energy, 2020, 192: 116597.
[18] 王文广, 赵文杰. 基于GRU神经网络的燃煤电站NOx排放预测模型[J]. 华北电力大学学报(自然科学版), 2020, 47(1): 96-103.
WANG Wenguang, ZHAO Wenjie. NOx emission prediction model based on GRU neural network in coal-fired power station[J]. Journal of North China Electric Power University, 2020, 47(1): 96-103.
[19] CHO K, VAN MERRI?NBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder- decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). October 25-29, 2014, Doha, Qatar. 2014: 1724-1734,
[20] 岑可法, 姚强, 骆仲泱, 等. 燃烧理论与污染控制[M]. 北京: 机械工业出版社, 2004: 435-447.
CEN Kefa, YAO Qiang, LUO Zhongyang, et al. Combustion theory and pollution control[M]. Beijing: China Machine Press, 2004: 435-447.
[21] 于磊, 杨国田, 刘禾, 等. 红外测温系统在660 MW电厂锅炉应用研究[J]. 热能动力工程, 2018, 33(9): 138-141.
YU Lei,YANG Guotian,LIU He,et al. Application study of infrared temperature measuring system in 660 MW power plant boiler[J]. Journal of Engineering for Thermal Energy and Power, 2018, 33(9): 138-141.
(责任编辑 杜亚勤)

相似文献/References:

[1]王春昌.几种燃烧器的功能及其局限性研究[J].热力发电,2009,(09):17.
 WANG Chun-chang.STUDY ON FUNCTIONS OF SEVERAL BURNERS AND THEIR LIMITATIONS[J].Thermal Power Generation,2009,(10):17.
[2]李玉然,杨立寨,由长福,等.用于氨和轻烃SCR脱硝的催化剂研究进展[J].热力发电,2009,(04):0.
[3]白旭东,冯兆兴,董建勋,等.常压夹带流气化/燃烧模拟器下超细煤粉燃尽特性试验研究[J].热力发电,2006,(10):0.
[4]王春昌,王顶辉.燃烧器布置方式与锅炉Nox排放研究[J].热力发电,2006,(11):0.
[5]张砺彦,麦永强,张月辉,等.基于神经网络的大型燃煤电厂SO2和NOx的污染物预测模型[J].热力发电,2008,(10):9.
[6]李 忠,徐党旗,文 军.某电厂HG-410/9.8YW15型锅炉采用SCR降低Nox排放的工程方案[J].热力发电,2005,(11):0.
[7]高宏波,谢守明,赵 杰,等.在役电站锅炉导汽管组织状态评估与剩余寿命预测[J].热力发电,2004,(06):0.
[8]翁安心,周 昊,王正华,等.大型四角切圆煤粉炉常用煤种及其混煤燃烧时Nox排放特性研究[J].热力发电,2003,(10):0.
[9]肖 平,蒋敏华.循环流化床锅炉的发展前景[J].热力发电,2004,(01):0.
[10]凌荣华,文 军,齐春松.燃料分级燃烧技术的研究现况和应用前景[J].热力发电,2003,(08):0.

备注/Memo

谢锐彪(1996),男,硕士研究生,主要研究方向为机器学习、电厂节能减排技术,xierb2018@163.com。

更新日期/Last Update: 2021-10-15