ENDBOSS: Industrial endpoint detection using batch-specific control spaces of spectroscopic data

Abstract (Bio)chemical industrial batch reactions have to be terminated timely, to prevent waste of resources and decreased production quality due to prolonging the production when the (primary) reaction has already finished. Approaches for detecting endpoints on off-line and/or on-line analysis exist, but may be inaccurate for productions with high batch-to-batch variations. In this study, we present a novel multi-step strategy for endpoint detection named ENDBOSS (ENdpoint Detection using Batch-specific cOntrol Spaces of Spectroscopic data). This strategy is designed to have higher robustness against batch-to-batch variation than endpoint detection methods reported in literature and to be implemented for on-line monitoring. We demonstrate ENDBOSS on three industrially relevant reactions with high batch variations. A method for optimizing the settings of ENDBOSS for a given production process is proposed and demonstrated. The correlations between detected and reference endpoint were validated to be 0.96, 0.80 and 0.18 for the three demonstrator reactions. ENDBOSS has high performance for two reactions. For the third reaction, the size of the dataset was too limited, indicating that ENDBOSS does benefit from quantitative integration with strategic data collection. ENDBOSS is furthermore shown to outperform endpoint detection methods currently reported in literature for all demonstrator reactions.