Novel Two-Stream Deep Slow and Nonstationary Fast Feature Extraction for Chemical Process Soft Sensing Application

Chemical processes involve complex physical and chemical mechanisms that exhibit slow and fast-varying features and nonstationary characteristics, making it difficult for single model-based methods to satisfactorily extract both slow and nonstationary fast-varying features for soft sensing. To address this issue, we propose a two-stream slow and nonstationary fast feature (TS-SNFF) model. This model includes a slow feature stream (SF-stream) and a nonstationary fast feature stream (NFF-streama). In the SF-stream, an encoder-decoder based Siamese network and a linear mapping layer are used for slow feature extraction. It employs long-short term memory (LSTM) networks as encoder and decoder units. Meanwhile, the NFF-stream utilizes the LSTM, differential LSTM (D-LSTM), and linear mapping layers for nonstationary fast feature extraction. The D-LSTM unit is established by embedding differential operations into the LSTM cell to obtain the nonstationary information. Then, the obtained features are fused using the merging layer, followed by a multilayer perceptron as the regressor. The proposed TS-SNFF model is utilized to address the slow and fast-varying dynamics in nonstationary conditions and nonlinearity problem in chemical processes. The effectiveness and superiority of the proposed method are demonstrated in a numerical example and an industrial process case