Data Alignment Via Dynamic Time Warping as a Prerequisite for Batch-End Quality Prediction
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 4065)
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.
KeywordsBatch Process Dynamic Time Warping Data Alignment Batch Phase Warping Path
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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