A batch-wise LSTM-encoder decoder network for batch process monitoring - Ren, Jiayang; Ni, Dong (Chemical engineering research & design, 2020)
•Proposed a multi-layer recurrent neural network in the encoder–decoder structure and the corresponding monitoring method for batch process monitoring.•The proposed network can simultaneously capture the between and within batch dynamic features in the nonlinear batch process.•The proposed model outperforms the AutoEncoder and Sub-PCA model in fault detection. Process monitoring is essential to keep quality consistency and operation safety in the batch process. However, the existence of multiphase, nonlinearity and dynamic features in the batch process makes the batch process monitoring a complicated task. In this work, a multi-layer recurrent neural network in the encoder–decoder structure called batch-wise LSTM-encoder decoder network is proposed to solve the difficulties mentioned above in batch process monitoring. The LSTM-encoder extracts the nonlinear dynamic features in both between and within batch direction, then projects the high dimensional input space to a low dimensional hidden state space. The decoder part regenerates the samples from hidden states. Control statistics H2 and SPE are designed for process monitoring, and the corresponding control limits are estimated by kernel density estimation. A case study on an extensive reference penicillin fermentation dataset suggests that the proposed method can detect the fault samples more effectively than previous methods while keeping the same robustness in normal conditions.