Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

doi:10.1109/JAS.2022.105464

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net
	Zhaolin Yuan, Xiaorui Li, Di Wu, Xiaojuan Ban, Nai-Qi Wu, Hong-Ning Dai, Hao Wang
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2022
卷号	9 期号:4 页码:686-698
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.
关键词	Industrial 24 paste thickener,ordinary differential equation (ODE)-net,recurrent neural network,time series prediction
DOI	10.1109/JAS.2022.105464
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47226
专题	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.

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