Temporal Double Graph Convolutional Network for CO and CO<sub>2</sub> Prediction in Blast Furnace Gas

doi:10.1109/TIM.2023.3341110

	Temporal Double Graph Convolutional Network for CO and CO₂ Prediction in Blast Furnace Gas
	Zhang, Tingkun 1; Liu, Chengbao2 ; Liu, Zhenjie2 ; Tan, Jie2 ; Ahmat, Mutellip 1
发表期刊	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN	0018-9456
	2024
卷号	73 页码:13
通讯作者	Liu, Chengbao(liuchengbao2016@ia.ac.cn) ; Ahmat, Mutellip(mtlp@xju.edu.cn)
摘要	Accurately predicting CO and CO2 content in blast furnace gas (BFG) holds immense significance, ensuring stable furnace operation and improving energy utilization. However, due to the variable operating conditions of blast furnace (BF) ironmaking and complex chemical reactions in the BF, it is difficult to accurately predict the changing trend of CO and CO2 content in BFG. To solve this problem, this study proposes a temporal double graph convolutional network (TDGCN) model for CO and CO2 content prediction. It consists of three parts: graph convolution, hypergraph convolution, and TimesNet. Specifically, we constructed a BF ironmaking feature graph in the face of the complex coupling relationship between BF ironmaking features. The graph convolutional network (GCN) is used to extract the topology on the feature graph and to update the feature variables. To further extract feature correlations and relevant information, we employ a hypergraph convolutional network to explore high-order correlations within the hypergraph. Subsequently, we utilize this information to update the feature graph, endowing the TDGCN model with dynamic adaptive capabilities under varying operating conditions. Finally, we introduce TimesNet to model long-term dependencies in BF ironmaking data. Through a series of experiments, the results show that the prediction effect of the TDGCN model is better than that of the traditional methods.
关键词	Blast furnace gas (BFG) CO and CO2 content graph convolutional network (GCN) hypergraph convolutional network prediction
DOI	10.1109/TIM.2023.3341110
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2020YFB1711101] ; National Nature Science Foundation of China[62003344]
项目资助者	National Key Research and Development Program of China ; National Nature Science Foundation of China
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:001132683400234
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55444
专题	中科院工业视觉智能装备工程实验室
通讯作者	Liu, Chengbao; Ahmat, Mutellip
作者单位	1.Xinjiang Univ, Sch Elect, Urumqi 830046, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
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	Zhang, Tingkun,Liu, Chengbao,Liu, Zhenjie,et al. Temporal Double Graph Convolutional Network for CO and CO₂ Prediction in Blast Furnace Gas[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2024,73:13.
APA	Zhang, Tingkun,Liu, Chengbao,Liu, Zhenjie,Tan, Jie,&Ahmat, Mutellip.(2024).Temporal Double Graph Convolutional Network for CO and CO₂ Prediction in Blast Furnace Gas.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,73,13.
MLA	Zhang, Tingkun,et al."Temporal Double Graph Convolutional Network for CO and CO₂ Prediction in Blast Furnace Gas".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73(2024):13.