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A deep-learning model for predicting spatiotemporal evolution in reactive fluidized bed reactor

Author

Listed:
  • Hu, Chenshu
  • Guo, Xiaolin
  • Dai, Yuyang
  • Zhu, Jian
  • Cheng, Wen
  • Xu, Hongbo
  • Zeng, Lingfang

Abstract

Detailed information of flow fields is of great significance for designing and optimizing multiphase flow systems. However, predicting spatiotemporal evolution of gas-solid flows using numerical simulation often requires a significant amount of computation and time. In this study, we proposed a 3D convolutional neural network for predicting reactive dense gas-solid flows. We first explored the design of model architecture and extensively evaluated the performance in terms of efficiency, accuracy, long-term prediction stability and generalizability for a non-reactive fluidized bed. Then we extended the method to a biomass fast pyrolysis process. The proposed model achieves real-time prediction, 3–4 orders of magnitude faster than CFD-DEM simulations. The surrogate model reasonably captures bubble-driven flow behaviors and effects of bubble on fast pyrolysis reactions. The predicted bubble characteristics, and time-averaged and RMS flow fields match well with the simulation results. Our approach exhibits excellent long-term stability and has good generalization capability to unseen fluidization velocities. To the best of our knowledge, this is the first time a neural network has been successfully applied to learn spatiotemporal evolution of reactive dense gas-solid flows.

Suggested Citation

  • Hu, Chenshu & Guo, Xiaolin & Dai, Yuyang & Zhu, Jian & Cheng, Wen & Xu, Hongbo & Zeng, Lingfang, 2024. "A deep-learning model for predicting spatiotemporal evolution in reactive fluidized bed reactor," Renewable Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:renene:v:225:y:2024:i:c:s0960148124003100
    DOI: 10.1016/j.renene.2024.120245
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    References listed on IDEAS

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    1. Xie, Xinyu & Wang, Xiaofang & Zhao, Pu & Hao, Yichen & Xie, Rong & Liu, Haitao, 2023. "Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning," Energy, Elsevier, vol. 263(PD).
    2. Zhong, Hanbin & Xiong, Qingang & Yin, Lina & Zhang, Juntao & Zhu, Yuqin & Liang, Shengrong & Niu, Ben & Zhang, Xinyu, 2020. "CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network," Renewable Energy, Elsevier, vol. 152(C), pages 613-626.
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