Recent advances in time-series analysis methods for identifying fluid flow characteristics in stirred tank reactors

doi:10.1016/j.cjche.2024.09.028

中国化学工程学报 ›› 2025, Vol. 77 ›› Issue (1): 310-327.DOI: 10.1016/j.cjche.2024.09.028

• • 上一篇

Recent advances in time-series analysis methods for identifying fluid flow characteristics in stirred tank reactors

Xiaoyu Tang^1,2, Facheng Qiu³, Peiqiao Liu¹, Yundong Wang⁴, Hong Li⁵, Zuohua Liu¹

1. Department of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China;
2. Department of Industrial Chemistry ‘Toso Montanari', University of Bologna, via Terracini 34, Bologna 40131, Italy;
3. Department of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China;
4. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
5. Graduate School, Chongqing University, Chongqing 400044, China

收稿日期:2024-05-17 修回日期:2024-09-12 接受日期:2024-09-13 出版日期:2025-01-28 发布日期:2024-11-22
通讯作者: Zuohua Liu,E-mail:liuzuohua@cqu.edu.cn
基金资助:
The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (22078030), the National Key Research and Development Project (2019YFC1905802, 2022YFB3504305), the Joint Funds of the National Natural Science Foundation of China (U1802255, CSTB2022NSCQ-LZX0014), the Key Project of Independent Research Project of State Key Laboratory of Coal Mine Disaster Dynamics and Control (2011DA105287-zd201902). The Visiting Ph.D. Scholarship Awarded to Xiaoyu Tang (State Scholarship Fund, CSC, China) is also acknowledged.

Recent advances in time-series analysis methods for identifying fluid flow characteristics in stirred tank reactors

Xiaoyu Tang^1,2, Facheng Qiu³, Peiqiao Liu¹, Yundong Wang⁴, Hong Li⁵, Zuohua Liu¹

1. Department of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China;
2. Department of Industrial Chemistry ‘Toso Montanari', University of Bologna, via Terracini 34, Bologna 40131, Italy;
3. Department of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China;
4. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
5. Graduate School, Chongqing University, Chongqing 400044, China

Received:2024-05-17 Revised:2024-09-12 Accepted:2024-09-13 Online:2025-01-28 Published:2024-11-22
Contact: Zuohua Liu,E-mail:liuzuohua@cqu.edu.cn
Supported by:
The authors gratefully acknowledge financial support from the National Natural Science Foundation of China (22078030), the National Key Research and Development Project (2019YFC1905802, 2022YFB3504305), the Joint Funds of the National Natural Science Foundation of China (U1802255, CSTB2022NSCQ-LZX0014), the Key Project of Independent Research Project of State Key Laboratory of Coal Mine Disaster Dynamics and Control (2011DA105287-zd201902). The Visiting Ph.D. Scholarship Awarded to Xiaoyu Tang (State Scholarship Fund, CSC, China) is also acknowledged.

摘要/Abstract

摘要： Leveraging big data signal processing offers a pathway to the development of artificial intelligence-driven equipment. The analysis of fluid flow signals and the characterization of fluid flow behavior are of critical in two-phase flow studies. Significant research efforts have focused on discerning flow regimes using various signal analysis methods. In this review, recent advances in time series signals analysis algorithms for stirred tank reactors have been summarized, and the detailed methodologies are categorized into the frequency domain methods, time-frequency domain methods, and state space methods. The strengths, limitations, and notable findings of each algorithm are highlighted. Additionally, the interrelationships between these methodologies have also been discussed, as well as the present progress achieved in various applications. Future research directions and challenges are also predicted to provide an overview of current research trends in data mining of time series for analyzing flow regimes and chaotic signals. This review offers a comprehensive summary for extracting and characterizing fluid flow behavior and serves as a theoretical reference for optimizing the characterization of chaotic signals in future research endeavors.

关键词: Flow characteristics, Time series analysis, Flow signal, Chaos analysis, Stirred tank reactor

Abstract: Leveraging big data signal processing offers a pathway to the development of artificial intelligence-driven equipment. The analysis of fluid flow signals and the characterization of fluid flow behavior are of critical in two-phase flow studies. Significant research efforts have focused on discerning flow regimes using various signal analysis methods. In this review, recent advances in time series signals analysis algorithms for stirred tank reactors have been summarized, and the detailed methodologies are categorized into the frequency domain methods, time-frequency domain methods, and state space methods. The strengths, limitations, and notable findings of each algorithm are highlighted. Additionally, the interrelationships between these methodologies have also been discussed, as well as the present progress achieved in various applications. Future research directions and challenges are also predicted to provide an overview of current research trends in data mining of time series for analyzing flow regimes and chaotic signals. This review offers a comprehensive summary for extracting and characterizing fluid flow behavior and serves as a theoretical reference for optimizing the characterization of chaotic signals in future research endeavors.

Key words: Flow characteristics, Time series analysis, Flow signal, Chaos analysis, Stirred tank reactor

Xiaoyu Tang, Facheng Qiu, Peiqiao Liu, Yundong Wang, Hong Li, Zuohua Liu. Recent advances in time-series analysis methods for identifying fluid flow characteristics in stirred tank reactors[J]. 中国化学工程学报, 2025, 77(1): 310-327.

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Recent advances in time-series analysis methods for identifying fluid flow characteristics in stirred tank reactors

Recent advances in time-series analysis methods for identifying fluid flow characteristics in stirred tank reactors

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