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Intelligent integrated coking flue gas indices prediction
Li YN(李亚宁)1,2; Wang XL1; Tan J1; Liu CB1,2; Bai XW1,2
2017-06
Conference Name18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Pages39-45
Conference Date26-28 June 2017
Conference PlaceKanazawa
AbstractFocus on the first China domestic coking flue gas desulfurization and denitriation integrated device, in order to solve the problem that the entrance parameters fluctuate and a detection lag exists due to the upstream coking workshop, which is extremely unfavorable to the optimal control of desulfurization and denitriation process. An intelligent integrated prediction model of flue gas SO2 concentration, O2 content and NOx concentration was proposed: the mechanism models of SO2, NOx concentration and O2 content were established according to the principle of material balance and reaction kinetics, respectively. For the prediction error, raw data was pretreated and the auxiliary variables were determined by principal component analysis, in order to improve the training speed and generalization ability of neural network, an improved RBFNN combining optimal stopping principle and dual momentum adaptive learning rate was proposed and used to compensate the error. 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 were verified by simulation via comparison of various models.
KeywordCoking Flue Gas Mechanism Model Neural Networks Integrated Modeling
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21182
Collection综合信息系统研究中心
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li YN,Wang XL,Tan J,et al. Intelligent integrated coking flue gas indices prediction[C],2017:39-45.
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