Pieter Van den Kerkhof

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.

The RAYMOND simulation package — Generating RAYpresentative MONitoring Data to design advanced process monitoring and control algorithms

Abstract This work presents the RAYMOND simulation package for generating RAYpresentative MONitoring Data. RAYMOND is a free MATLAB package and can simulate a wide range of processes; a number of widely-used benchmark processes are available, but user-defined processes can easily be added. Its modular design results in large flexibility with respect to the simulated processes: input fluctuations resulting from upstream variability can be introduced, sensor properties (measurement noise, resolution, range, etc.) can be freely specified, and various (custom) control strategies can be implemented. Furthermore, process variability (biological variability or non-ideal behavior) can be included, as can process-specific disturbances. In two case studies, the importance of including non-ideal behavior for monitoring and control of batch processes is illustrated. Hence, it should be included in benchmarks to better assess the performance and robustness of advanced process monitoring and control algorithms.

The influence of input and output measurement noise on batch-end quality prediction with partial least squares

In this paper, the influence of measurement noise on batch-end quality prediction by Partial Least Squares (PLS) is discussed. Realistic computer-generated data of an industrial process for penicillin production are used to investigate the influence of both input and output noise on model input and model order selection, and online and offline prediction of the final penicillin concentration. Techniques based on PLS show a large potential in assisting human operators in their decisions, especially for batch processes where close monitoring is required to achieve satisfactory product quality. However, many (bio)chemical companies are still reluctant to implement these monitoring techniques since, among other things, little is known about the influence of measurement noise characteristics on their performance. The results of this study indicate that PLS predictions are only slightly worsened by the presence of measurement noise. Moreover, for the considered case study, model predictions are better than offline quality measurements.