LAUSR

Data-driven soft sensors have been widely adopted in industrial processes to learn hidden knowledge automatically from process data, then to monitor difficult-to-measure quality variables. However, to extract and utilize useful dynamic latent features accurately for efficient quality estimations remains one of the most important research issues in soft sensor modeling. In this article, a supervised bidirectional long short-term memory (SBiLSTM) is proposed for data-driven dynamic soft sensor modeling. The SBiLSTM incorporates extended quality information with a moving window up to $k$ time steps and enhances learning efficiency by bidirectional architecture. With this novel structure, the SBiLSTM can extract and utilize nonlinear dynamic latent information from both process variables and quality variables, then further improve the prediction performance significantly. The effectiveness of the proposed SBiLSTM network-based soft sensor model is demonstrated through two case studies on a debutanizer column process and an industrial wastewater treatment process. Results show that the SBiLSTM outperforms state-of-the-art and traditional deep learning-based soft sensor models.