PCA-Based Modeling and On-line Monitoring Strategy for Uneven-Length Batch Processes

Abstract

This paper extends the stage-based sub-PCA modeling method originally proposed by the authors to the monitoring of batch processes with durations of uneven lengths. Two models for each stage are developed, one for the stage division and the other for process monitoring. The purposes of the stage division are two-fold, to enhance process understanding and to provide stage-division information necessary for the development of PCA monitoring models. With the proposed method, batch processes with durations of uneven lengths can be effectively monitored for fault detection and diagnosis.

Bilinear modelling of batch processes. Part I: theoretical discussion

“…Autocorrelations and cross-correlations are obtained independently of the precise sampling time. This model structure is very useful for regulation and can be used to relax the necessity of alignment of the batches [5]. Nonetheless, as it happens for variable-wise unfolded models, modelling with a reduced number of LMVs assumes a constant correlation structure during the batch.…”

DE based economic control chart design and application for a typical petrochemical process

“…From these data, many data-based process monitoring and fault detection methods have been proposed in recent years. Statistical process control methods, such as principal component analysis (Lu et al, 2004), partial least squares (Wang and Shi, 2014;Hu et al, 2013), and independent component analysis (Lee et al, 2004), are well-known fault detection methods. Most of these methods use T 2 and SPE statistic as control limits to detect the faults of the process.…”

Quality-related process monitoring for dynamic non-Gaussian batch process with multi-phase using a new data-driven method

“…Thus one common method, simply synchronizing the whole batch length, cannot achieve a desired monitoring effect [52][53][54][55][56]. Literature [57][58][59][60][61] raise different ideas of phase division.…”