A novel unsupervised approach for batch process monitoring using deep learning

•Developed a PLS equivalent deep neural network architecture for batch process monitoring.•Proposed a novel objective function to train deep learning models that explicitly considers fault detection rate.•The proposed method shows superior accuracy compared to other methods.•The use of dynamic control limits result in significant improvements in detection rates. [Display omitted] Process monitoring is an important tool used to ensure safe operation of a process plant and to maintain high quality of end products. The focus of this work is on unsupervised Statistical Process Control (SPC) of batch processes using Deep Learning (DL). A DL architecture referred as Multiway Partial Least Squares Autoencoder (MPLS-AE) is proposed and trained using a genetic optimization algorithm with a novel objective function that directly maximizes the average fault detection rate (FDR¯). The efficacy of the proposed method is demonstrated on an industrial scale Penicillin process. Comparisons of the proposed algorithm with linear Multiway Principal Component Analysis (MPCA) and Multiway Partial Least Squares (MPLS) based fault detection (FD) algorithm, trained with the same objective as used by the DL model, demonstrates the superiority of the deep learning based approach. The use of dynamic control limits significantly improves the detection rates for both the linear and DL models.