Abstract
In this work, a 4-phase dynamic time warping is implemented to align measurement profiles from an existing chemical batch reactor process, making all batch measurement profiles equal in length, while also matching the major events occurring during each batch run.
This data alignment is the first step towards constructing an inferential batch-end quality sensor, capable of predicting 3 quality variables before batch run completion using a multivariate statistical partial least squares model. This inferential sensor provides on-line quality predictions, allowing corrective actions to be performed when the quality of the polymerization product does not meet the specifications, saving valuable production time and reducing operation cost.
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© 2006 Springer-Verlag Berlin Heidelberg
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Gins, G., Espinosa, J., Smets, I.Y., Van Brempt, W., Van Impe, J.F.M. (2006). Data Alignment Via Dynamic Time Warping as a Prerequisite for Batch-End Quality Prediction. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790853_39
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DOI: https://doi.org/10.1007/11790853_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36036-0
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