Discriminating between critical and non-critical disturbances in (bio-)chemical batch processes using multi-model fault detection and end-quality prediction

Abstract

This paper proposes a novel multimodel methodology for discriminating between critical and noncritical process disturbances in (bio)chemical batch processes, in addition to providing online prediction of batch-end quality. A multivariate multiway partial least squares (MPLS) or multiway principal component analysis (MPCA) model monitoring all available measurements is coupled with an MPLS or MPCA model monitoring only those measurements influencing the final product quality. Hence, process disturbances are labeled critical or noncritical, depending on whether they impact final quality and require immediate attention. This avoids unnecessary control actions or even early batch terminations for noncritical disturbances. The presented approach is illustrated on a simulated industrial-scale penicillin production process. On the basis of extensive simulation results, it is concluded that the proposed methodology discriminates between critical (according to a hypothesis test with 0.05 significance level) and noncritical disturbances. In addition, accurate online estimations of the batch-end product quality are provided.

An extensive reference dataset for fault detection and identification in batch processes

“…This enables unrealistically tight control of the process around its temperature and pH set points, greatly facilitating SPM. Many authors already recognized this problem and manually added measurement noise to the simulation data (e.g., [2][3][4]6,14,32,35,37,50,79,87,93]). In addition, Pensim only simulates a limited set of process upsets.…”

Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment

“…Standard PCA-based fault detection is employed in this work because it is the standard benchmark for data-driven FDI [6]. It and its many variants are widely studied (e.g., [1,2,8,[26][27][28][29][30][31][32][33][34]). Detailed overviews of research and applications can be found in, e.g., Qin [6] or Ge et al [35].…”