OPLS in batch monitoring – Opens up new opportunities

• Batch process modeling on a chemical hydrogenation process.
• Novel OPLS method was compared to benchmark methods, PCA and PLS.
• OPLS separated process variation correlated to process evolution from other process variation.
• Batch control charts were used to detect problematic batches.
• OPLS allowed improved fault detection and root cause analysis.

In batch statistical process control (BSPC), data from a number of “good” batches are used to model the evolution (trajectory) of the process and they also define model control limits, against which new batches may be compared. The benchmark methods used in BSPC include partial least squares (PLS) and principal component analysis (PCA).In this paper, we have used orthogonal projections to latent structures (OPLS) in BSPC and compared the results with PLS and PCA. The experimental study used was a batch hydrogenation reaction of nitrobenzene to aniline characterized by both UV spectroscopy and process data.The key idea is that OPLS is able to separate the variation in data that is correlated to the process evolution (also known as ‘batch maturity index’) from the variation that is uncorrelated to process evolution. This separation of different types of variations can generate different batch trajectories and hence lead to different established model control limits to detect process deviations.The results demonstrate that OPLS was able to detect all process deviations and provided a good process understanding of the root causes for these deviations. PCA and PLS on the other hand were shown to provide different interpretations for several of these process deviations, or in some cases they were unable to detect actual process deviations. Hence, the use of OPLS in BSPC can lead to better fault detection and root cause analysis as compared to existing benchmark methods and may therefore be used to complement the existing toolbox.

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