Modeling nonlinear heat exchanger dynamics with convolutional recurrent networks

    •  Bhattacharya, C., Chakrabarty, A., Laughman, C.R., Qiao, H., "", Modeling, Estimation and Control Conference, December 2022.
      BibTeX TR2022-170 PDF
      • @inproceedings{Bhattacharya2022dec,
      • author = {Bhattacharya, Chandrachur and Chakrabarty, Ankush and Laughman, Christopher R. and Qiao, Hongtao},
      • title = {},
      • booktitle = {Modeling, Estimation and Control Conference},
      • year = 2022,
      • month = dec,
      • url = {}
      • }
  • MERL Contacts:
  • Research Areas:

    Machine Learning, Multi-Physical Modeling


Deep learning for system identification has enabled fast and accurate predictions in applications where physics-informed models are either absent or are too complex to be used efficiently for analysis and control. In this paper, we propose a deep state-space modeling framework that combines the feature extraction capabilities of convolutional neural networks (CNNs) with the efficient sequence prediction properties of gated recurrent units (GRUs); we refer to the neural state-space model as CNN-GRU SSM. We compare this model to other state-of-the-art deep state-space modeling tools and demonstrate that our proposed method often outperforms contemporary algorithms on benchmark dynamical system data. We validate the CNN-GRU SSM on a real-world application of predicting multi-input, multi-output, coupled, and nonlinear heat-exchanger dynamics observed in vapor compression cycles.