Predicting product quality and optimising process design using dynamic time warping in batch processes with varying batch times

In this study, the proposed method combines dynamic time warping with a genetic algorithm, enabling the construction of a robust machine learning model tailored for batch process data characterised by diverse batch and sampling times. This comprehensive model incorporates crucial factors, including...

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Hlavní autoři:	Shuto Yamakage, Kazutoshi Terauchi, Fumiya Hamada, Toshinori Yamaji, Hiromasa Kaneko
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2024-06-01
Edice:	Case Studies in Chemical and Environmental Engineering
Témata:	Batch design Batch optimisation Batch profile Endpoint Dynamic time warping Genetic algorithm
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S2666016424000495
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