Modeling nonlinear heat exchanger dynamics with convolutional recurrent networks
Chandrachur Bhattacharya, Ankush Chakrabarty, Christopher R. Laughman, Hongtao Qiao
Abstract
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.