Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net
文献类型：期刊论文
| 作者 | Zhaolin Yuan, Xiaorui Li, Di Wu, Xiaojuan Ban, Nai-Qi Wu, Hong-Ning Dai, Hao Wang |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2022 |
| 卷号 | 9期号:4页码:686-698 |
| ISSN号 | 2329-9266 |
| 关键词 | Industrial 24 paste thickener,ordinary differential equation (ODE)-net,recurrent neural network,time series prediction |
| DOI | 10.1109/JAS.2022.105464 |
| 英文摘要 | It is crucial to predict the outputs of a thickening system, including the underflow concentration (UC) and mud pressure, for optimal control of the process. The proliferation of industrial sensors and the availability of thickening-system data make this possible. However, the unique properties of thickening systems, such as the non-linearities, long-time delays, partially observed data, and continuous time evolution pose challenges on building data-driven predictive models. To address the above challenges, we establish an integrated, deep-learning, continuous time network structure that consists of a sequential encoder, a state decoder, and a derivative module to learn the deterministic state space model from thickening systems. Using a case study, we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results. The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories. The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47226]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Zhaolin Yuan, Xiaorui Li, Di Wu, Xiaojuan Ban, Nai-Qi Wu, Hong-Ning Dai, Hao Wang. Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(4):686-698. |
| APA | Zhaolin Yuan, Xiaorui Li, Di Wu, Xiaojuan Ban, Nai-Qi Wu, Hong-Ning Dai, Hao Wang.(2022).Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net.IEEE/CAA Journal of Automatica Sinica,9(4),686-698. |
| MLA | Zhaolin Yuan, Xiaorui Li, Di Wu, Xiaojuan Ban, Nai-Qi Wu, Hong-Ning Dai, Hao Wang."Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net".IEEE/CAA Journal of Automatica Sinica 9.4(2022):686-698. |
入库方式： OAI收割
来源：自动化研究所
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