CASIA OpenIR
Integrated modeling of coking flue gas indices based on mechanism model and improved neural network
Li, Yaning1,2; Wang, Xuelei1; Tan, Jie1
Source PublicationTRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
ISSN0142-3312
2019
Volume41Issue:1Pages:85-96
Corresponding AuthorWang, Xuelei(Xlwang98@sina.com)
AbstractFocusing 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.
KeywordCoking process control neural network (NN) mechanism model integrated modeling
DOI10.1177/0142331218754621
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[U1701262] ; Ministry of Industry and Information Technology of China[2016ZXFM06005]
Funding OrganizationNational Natural Science Foundation of China ; Ministry of Industry and Information Technology of China
WOS Research AreaAutomation & Control Systems ; Instruments & Instrumentation
WOS SubjectAutomation & Control Systems ; Instruments & Instrumentation
WOS IDWOS:000457923800009
PublisherSAGE PUBLICATIONS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25294
Collection中国科学院自动化研究所
综合信息系统研究中心
Corresponding AuthorWang, Xuelei
Affiliation1.Chinese Acad Sci, Inst Automat, 95 East Zhongguancun Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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