Integrated modeling of coking flue gas indices based on mechanism model and improved neural network

doi:10.1177/0142331218754621

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 工业智能技术与系统

	Integrated modeling of coking flue gas indices based on mechanism model and improved neural network
	Li, Yaning1,2 ; Wang, Xuelei1 ; Tan, Jie1
发表期刊	TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
ISSN	0142-3312
	2019
卷号	41 期号:1 页码:85-96
通讯作者	Wang, Xuelei(Xlwang98@sina.com)
摘要	Focusing on the first domestic coking flue gas desulfurization and denitration integrated unit in China, the current condition of inlet flue gas indices cannot be determined timely owing to the large detection lag and complex upstream coking process, which is extremely unfavorable for the optimal control of desulfurization and denitration process. In order to solve this problem, an intelligent integrated modeling method of flue gas SO2 concentration, O-2 content and NOx concentration is proposed. Firstly, the gas flow diagram in combustion process is built, the mechanism models of SO2, NOx concentration and O-2 content are established according to the principle of material balance and reaction kinetics, respectively. Then the RBF neural network is adopted to compensate the prediction error, an improved training algorithm combining optimal stopping principle and dual momentum adaptive learning rate is proposed to improve the training speed and generalization ability of neural network. Based on the practical data of two 55-hole and 6-meter top charging coke ovens in the coking group, the effectiveness and superiority of proposed model and method are verified by simulation via comparison of various methods.
关键词	Coking process control neural network (NN) mechanism model integrated modeling
DOI	10.1177/0142331218754621
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1701262] ; Ministry of Industry and Information Technology of China[2016ZXFM06005] ; National Natural Science Foundation of China[U1701262] ; Ministry of Industry and Information Technology of China[2016ZXFM06005]
项目资助者	National Natural Science Foundation of China ; Ministry of Industry and Information Technology of China
WOS研究方向	Automation & Control Systems ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Instruments & Instrumentation
WOS记录号	WOS:000457923800009
出版者	SAGE PUBLICATIONS LTD
七大方向——子方向分类	智能控制
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25294
专题	中科院工业视觉智能装备工程实验室_工业智能技术与系统
通讯作者	Wang, Xuelei
作者单位	1.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Li, Yaning,Wang, Xuelei,Tan, Jie. Integrated modeling of coking flue gas indices based on mechanism model and improved neural network[J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,2019,41(1):85-96.
APA	Li, Yaning,Wang, Xuelei,&Tan, Jie.(2019).Integrated modeling of coking flue gas indices based on mechanism model and improved neural network.TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL,41(1),85-96.
MLA	Li, Yaning,et al."Integrated modeling of coking flue gas indices based on mechanism model and improved neural network".TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 41.1(2019):85-96.