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.

过程监控对于在批处理过程中保持质量一致性和操作安全至关重要。但是，批处理过程中存在多相，非线性和动态特征，使得批处理过程监视成为一项复杂的任务。在这项工作中，提出了一种编码器-解码器结构中的多层递归神经网络，称为批量LSTM编码器解码器网络，以解决上述在批处理过程监视中遇到的困难。LSTM编码器提取批处理方向之间以及批处理方向之内的非线性动态特征，然后将高维输入空间投影到低维隐藏状态空间。解码器部分从隐藏状态重新生成样本。控制统计量H ²和SPE设计用于过程监控，并且通过内核密度估计来估计相应的控制极限。对大量参考青霉素发酵数据集进行的案例研究表明，所提出的方法可以比以前的方法更有效地检测故障样本，同时在正常条件下保持相同的鲁棒性。