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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
会议名称18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
页码39-45
会议日期26-28 June 2017
会议地点Kanazawa
摘要Focus 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.
关键词Coking Flue Gas Mechanism Model Neural Networks Integrated Modeling
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/21182
专题综合信息系统研究中心
作者单位1.中国科学院自动化研究所
2.中国科学院大学
推荐引用方式
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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