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

doi:10.1109/JAS.2022.105464

Volume 9 Issue 4

Apr. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(4): 686-698

Z. L. Yuan, X. R. Li, D. Wu, X. J. Ban, N.-Q. Wu, H.-N. Dai, and H. Wang, “Continuous-time prediction of industrial paste thickener system with differential ODE-net,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 686–698, Apr. 2022. doi: 10.1109/JAS.2022.105464

Citation:

Z. L. Yuan, X. R. Li, D. Wu, X. J. Ban, N.-Q. Wu, H.-N. Dai, and H. Wang, “Continuous-time prediction of industrial paste thickener system with differential ODE-net,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 686–698, Apr. 2022. doi: 10.1109/JAS.2022.105464

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

doi: 10.1109/JAS.2022.105464

Zhaolin Yuan^1
,,
Xiaorui Li^1
,,
Di Wu^2
,,
Xiaojuan Ban^{1
,
,},
Nai-Qi Wu^3
,,
Hong-Ning Dai^{4
,
,},
Hao Wang^5
,

1.
Beijing Advanced Innovation Center for Materials Genome Engineering, Institute of Artificial Intelligence, Beijing Key Laboratory of Knowledge Engineering for Materials Science, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Department of ICT and Natural Science, Norwegian University of Science and Technology, 6009 Alesund, Norway
3.
Institute of Systems Engineering, and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Macao 999078, China
4.
Department of Computing and Decision Sciences, Lingnan University, Hong Kong, China
5.
Department of Computer Science, Norwegian University of Science and Technology, 2802 Gjøvik, Norway

Funds: This work was supported by National Key Research and Development Program of China (2019YFC0605300), the National Natural Science Foundation of China (61873299, 61902022, 61972028), Scientific and Technological Innovation Foundation of Shunde Graduate School, University of Science and Technology Beijing (BK21BF002), Macao Science and Technology Development Fund under Macao Funding Scheme for Key R&D Projects (0025/2019/AKP), and Macao Science and Technology Development Fund (0015/2020/AMJ)

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Author Bio:
Zhaolin Yuan received the B.S. degree in computer science from University of Science and Technology Beijing, China, in 2017. He is now a Ph.D. candidate at the School of Computer and Communication Engineering, University of Science and Technology Beijing. His current research interests include complex industrial system, time series prediction and modeling, reinforcement learning

Xiaorui Li received the B.E. degree from the Department of Information and Communication Engineering, University of Science and Technology Beijing, China in 2020. Currently he is a Ph.D. candidate at University of Science and Technology Beijing, China. His research interests include time series forecasting, deep learning and process control

Di Wu received the B.S. degree from the Department of Automation, Beijing Institute of Technology, China in 2004, the Ph.D. degree in pattern recognition and intelligent system from the School of Automation, Beijing Institute of Technology in 2010. She worked as Post-doc/Lecturer with the Department of Computer and Communication Engineering, the University of Science and Technology Beijing, China. Currently she is a Ph.D. candidate in data science at Norwegian University of Science and Technology. Her research interests include large scale composable simulation system, artificial life, and data analysis in industrial applications such as water supply systems. She has already published over 35 papers for international journals and conferences

Xiaojuan Ban received the Ph.D. degree from the University of Science and Technology Beijing, Beijing, in 2003. She is currently a Ph.D. Supervisor with the University of Science and Technology Beijing (USTB). Her current research interests include artificial intelligence, natural human-computer interactions, and 3D visualization. She has authored more than 300 articles. She is also the Managing Director of the Chinese Association for Artificial Intelligence (CAAI). She is also a member of the standing committee of the human-computer interaction specialty and the theoretical computer science specialty in the China Computer Society (CCF). She has received the New Century Excellent Talent of the Ministry of Education

Nai-Qi Wu (Follow, IEEE) received the B.S. degree in electrical engineering from Anhui University of Technology, China, in 1982, the M.S. and Ph.D. degrees in systems engineering both from Xi’an Jiaotong University, Xi’an, China in 1985 and 1988, respectively. From 1988 to 1995, he was with Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China, and from 1995 to 1998, with Shantou University, Shantou, China. He moved to Guangdong University of Technology, Guangzhou, China in 1998. He joined Macau University of Science and Technology, Taipa, China in 2013. He was a Visiting Professor at Arizona State University, Tempe, USA, in 1999; New Jersey Institute of Technology, Newark, USA, in 2004; University of Technology of Troyes, Troyes, France, from 2007 to 2009; and Evry University, Evry, France, from 2010 to 2011. He is currently a Chair Professor at the Institute of Systems Engineering, Macau University of Science and Technology, Taipa, China. His research interests include production planning and scheduling, manufacturing system modeling and control, discrete event systems, Petri net theory and applications, intelligent transportation systems, and energy systems. He is the Author or Coauthor of one book, five book chapters, and 180+ peer-reviewed journal papers. He was an Associate Editor of the IEEE Transactions on Systems, Man, & Cybernetics, Part C, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Systems, Man, & Cybernetics: Systems, and Editor in Chief of Industrial Engineering Journal, and is an Associate Editor of Information Sciences

Hong-Ning Dai (Senior Member, IEEE) received the Ph.D. degree in computer science and engineering from Department of Computer Science and Engineering, the Chinese University of Hong Kong. He is currently with Department of Computing and Decision Sciences at Lingnan University, Hong Kong, China as an Associate Professor. His current research interests include the internet of things, big data, and blockchain technology. He has served as Associate Editors/Editors for IEEE Transactions on Industrial Informatics, IEEE Systems Journal, IEEE Access, Ad Hoc Networks, and Connection Science. He is also a Senior Member of Association for Computing Machinery (ACM)

Hao Wang (Senior Member, IEEE) received the B.Eng. and Ph.D. degrees, both in computer science and engineering, from South China University of Technology, China in 2000 and 2006, respectively. He is an Associate Professor in the Department of Computer Science, Norwegian University of Science & Technology, Norway. His research interests include big data analytics, industrial internet of things, high performance computing, and safety-critical systems. He has published 170+ papers in reputable international journals and conferences including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Industrial Electronics, Transactions on Industrial Informatics, Internet of Things Journal, Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on Geoscience and Remote Sensing and ACM Computing Surveys. He serves as the Editorial Board Member and Guest Editors for several international journals. He served as a TPC Co-Chair for IEEE Transactions on Computational Social Systems 2020, IEEE International Conference on Computer and Information Technology 2017, International Conference on Big Data Intelligence and Computing 2015, and Reviewers for many prestigious journals and conferences. He is a Senior Member of IEEE and a Member of ACM. He is the Chair for Sub TC on Healthcare in IEEE IES Technical Committee on Industrial Informatics
Corresponding author: Xiaojuan Ban, e-mail: banxj@ustb.edu.cn; Hong-Ning Dai, e-mail: hndai@ieee.org
Received Date: 2021-08-29
Accepted Date: 2021-10-17

Available Online: 2022-03-03

Abstract

Abstract

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.
- Industrial 24 paste thickener,
- ordinary differential equation (ODE)-net,
- recurrent neural network,
- time series prediction

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Highlights

Networks based on ODE-net perform better in continuous-time system prediction.
Stationary ODE-net outperforms the non-stationary ones in long-term prediction.
The ODE-nets with high-order solvers have better accuracy.
The time-delay in predicting thickening system is about 80min.

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

doi: 10.1109/JAS.2022.105464

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