Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 244, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.chemolab.2023.105029
Keywords
Multimode process; Data-driven modeling; Gaussian mixture variational autoencoder; Spatial-temporal attention
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This paper proposes a novel multi-modal hybrid modeling strategy (GMVAE-STA) that can effectively extract deep multi-modal representations and complex spatial and temporal relationships, and applies it to industrial process prediction.
The industrial process is characterized by its multi-modal nature and complex spatial and temporal correlations. Despite the fact that several multi-modal methods have been proposed, few of them can effectively extract deep multi-modal representations and the highly intricate spatial and temporal relationships. In this paper, a novel multi-modal hybrid modeling strategy (GMVAE-STA) is proposed for industrial process prediction. This strategy combines the Gaussian Mixture Variational Autoencoder (GMVAE) and the spatial-temporal attention based Gated Recurrent Unit (STA-GRU). First, the GMVAE maps the raw data to the latent space, which follows a Gaussian Mixture distribution, and the data with the highest probability in each Gaussian are identified as a mode. Then, the STA-GRU captures the complex spatial and temporal relationships within each mode and makes predictions. Experimental results on the Tennessee Eastman process and a real-world fluid catalytic cracking process demonstrate the effectiveness of mode classification and prediction of the proposed method.
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