Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor-liquid-solid fluidized bed evaporator

doi:10.1016/j.cjche.2024.10.010

Chinese Journal of Chemical Engineering ›› 2025, Vol. 78 ›› Issue (2): 67-81.DOI: 10.1016/j.cjche.2024.10.010

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Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor-liquid-solid fluidized bed evaporator

Xiaoping Xu^1,2, Ting Zhang¹, Zhimin Mu¹, Yongli Ma², Mingyan Liu^2,3

1. College of Basic Science, Tianjin Agricultural University, Tianjin 300392, China;
2. School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China;
3. State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300350, China

Received:2024-09-07 Revised:2024-10-11 Accepted:2024-10-13 Online:2024-11-23 Published:2025-02-08
Supported by:
This work was supported by the open foundation of State Key Laboratory of Chemical Engineering (SKL-ChE-22B01) and the Natural Science Foundation of China (22008169).

Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor-liquid-solid fluidized bed evaporator

Xiaoping Xu^1,2, Ting Zhang¹, Zhimin Mu¹, Yongli Ma², Mingyan Liu^2,3

1. College of Basic Science, Tianjin Agricultural University, Tianjin 300392, China;
2. School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China;
3. State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300350, China

通讯作者: Yongli Ma,E-mail:mayl@tju.edu.cn
基金资助:
This work was supported by the open foundation of State Key Laboratory of Chemical Engineering (SKL-ChE-22B01) and the Natural Science Foundation of China (22008169).

Abstract

Abstract: The dynamics of vapor-liquid-solid (V-L-S) flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior, hindering accurate prediction of pressure drop signals. To address this challenge, this study proposes an innovative hybrid approach that integrates wavelet neural network (WNN) with chaos analysis. By leveraging the Cross-Correlation (C-C) method, the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN. Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals, advancing our understanding of the intricate dynamic phenomena occurring with V-L-S fluidized bed evaporators. Moreover, this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.

Key words: Wavelet neural network forecasting, Chaos theory, Phase space reconstruction, Pressure drop forecasting, Fluidized bed evaporator, Multi-phase dynamics

摘要： The dynamics of vapor-liquid-solid (V-L-S) flow boiling in fluidized bed evaporators exhibit inherent complexity and chaotic behavior, hindering accurate prediction of pressure drop signals. To address this challenge, this study proposes an innovative hybrid approach that integrates wavelet neural network (WNN) with chaos analysis. By leveraging the Cross-Correlation (C-C) method, the minimum embedding dimension for phase space reconstruction is systematically calculated and then adopted as the input node configuration for the WNN. Simulation results demonstrate the remarkable effectiveness of this integrated method in predicting pressure drop signals, advancing our understanding of the intricate dynamic phenomena occurring with V-L-S fluidized bed evaporators. Moreover, this study offers a novel perspective on applying advanced data-driven techniques to handle the complexities of multi-phase flow systems and highlights the potential for improved operational prediction and control in industrial settings.

关键词: Wavelet neural network forecasting, Chaos theory, Phase space reconstruction, Pressure drop forecasting, Fluidized bed evaporator, Multi-phase dynamics

Xiaoping Xu, Ting Zhang, Zhimin Mu, Yongli Ma, Mingyan Liu. Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor-liquid-solid fluidized bed evaporator[J]. Chinese Journal of Chemical Engineering, 2025, 78(2): 67-81.

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Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor-liquid-solid fluidized bed evaporator

Application of wavelet neural network with chaos theory for enhanced forecasting of pressure drop signals in vapor-liquid-solid fluidized bed evaporator

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