Dynamic model-based fault diagnosis for (bio)chemical batch processes

To ensure constant and satisfactory product quality, close monitoring of batch processes is an absolute requirement in the (bio)chemical industry. Principal Component Analysis (PCA)-based techniques exploit historical databases for fault detection and diagnosis. In this paper, the fault detection and diagnosis performance of Batch Dynamic PCA (BDPCA) and Auto-Regressive PCA (ARPCA) is compared with Multi-way PCA (MPCA). Although these methods have been studied before, the performance is often compared based on few validation batches. Additionally, the focus is on fast fault detection, while correct fault identification is often considered of lesser importance. In this paper, MPCA, BDPCA, and ARPCA are benchmarked on an extensive dataset of a simulated penicillin fermentation. Both the detection speed, false alarm rate and correctness of the fault diagnosis are taken into account. The results indicate increased detection speed when using ARPCA as opposed to MPCA and BDPCA at the cost of fault classification accuracy.

► Fault detection and diagnosis performance comparison of PCA-based techniques.
► Batch Dynamic PCA (BDPCA), Auto-regressive PCA (ARPCA) and Multi-way PCA (MPCA).
► Benchmarked on extensive dataset of simulated industrial scale penicillin fermentor.
► ARPCA has higher detection speed but lower diagnosis accuracy than BDPCA and MPCA.
► A multi-model approach could combine fast detection with accurate diagnosis.