CASIA OpenIR  > 综合信息系统研究中心
Modeling of Integrated Processes for Coking Flue Gas Desulfurization and Denitrification Based on RBFNN
Li YN(李亚宁); Wang XL; Tan J
2016-06
Conference Name15th IEEE/ACIS International Conference on Computer and Information Science
Pages71-76
Conference Date June 26-29, 2016
Conference PlaceOkayama
AbstractThis paper proposes an efficient modeling method based on the history running data of a coking chemical company flue gas desulfurization and denitrification integration device: construct data set according to the technology principle and corresponding data preprocessing method; make division of working conditions and reduce the sample set by means of K-Means clustering method; realize static modeling for each of the conditions based on RBF neural network. The simulation results show the effectiveness of the method and the artificial neural network model.
KeywordDesulfurization And Denitrification Kmeans Clustering Rbfnn
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21180
Collection综合信息系统研究中心
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li YN,Wang XL,Tan J. Modeling of Integrated Processes for Coking Flue Gas Desulfurization and Denitrification Based on RBFNN[C],2016:71-76.
Files in This Item: Download All
File Name/Size DocType Version Access License
Modeling of Integrat(537KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li YN(李亚宁)]'s Articles
[Wang XL]'s Articles
[Tan J]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li YN(李亚宁)]'s Articles
[Wang XL]'s Articles
[Tan J]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li YN(李亚宁)]'s Articles
[Wang XL]'s Articles
[Tan J]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Modeling of Integrated Processes for Coking Flue Gas Desulfurization and Denitrification Based on RBFNN.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.