Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net
Published in IEEE/CAA Journal of Automatica Sinica, 2022
This study develops a deep-learning, continuous-time predictive framework for thickening systems, aiming to forecast critical outputs such as underflow concentration and mud pressure for optimal process control. The proposed model addresses challenges from non-linearities, long delays, partially observed data, and continuous-time dynamics by integrating a sequential encoder, a state decoder, and a derivative module to learn a deterministic state-space representation. Evaluated on a tailing thickener from FLSmidth equipped with extensive sensors, the model outperforms existing discrete-time approaches, effectively mitigating the impact of long delays and accurately predicting both short- and long-term system behavior.
Recommended citation: 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, 2022.
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