Temporal Double Graph Convolutional Network for CO and CO2 Prediction in Blast Furnace Gas
文献类型：期刊论文
| 作者 | Zhang, Tingkun2; Liu, Chengbao1 ; Liu, Zhenjie1; Tan, Jie1; Ahmat, Mutellip2
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| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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| 出版日期 | 2024 |
| 卷号 | 73页码:13 |
| 关键词 | Blast furnace gas (BFG) CO and CO2 content graph convolutional network (GCN) hypergraph convolutional network prediction |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2023.3341110 |
| 通讯作者 | Liu, Chengbao(liuchengbao2016@ia.ac.cn) ; Ahmat, Mutellip(mtlp@xju.edu.cn) |
| 英文摘要 | 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. |
| 资助项目 | National Key Research and Development Program of China[2020YFB1711101] ; National Nature Science Foundation of China[62003344] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001132683400234 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Nature Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55443]  |
| 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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Xinjiang Univ, Sch Elect, Urumqi 830046, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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