Manifold Learning and Clustering for Automated Phase Identification and Alignment in Data Driven Modeling of Batch Processes

Manifold Learning and Clustering for Automated Phase Identification and Alignment in Data Driven Modeling of Batch Processes

Processing data that originates from uneven, multi-phase batches is a challenge in data-driven modeling. Training predictive and monitoring models requires the data to be in the right shape to be informative. Only then can a model learn meaningful features that describe the deterministic variability...

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Bibliographic Details
Main Authors: Carlos André Muñoz López, Satyajeet Bhonsale, Kristin Peeters, Jan F. M. Van Impe
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-11-01
Series:Frontiers in Chemical Engineering
Subjects:
manifold learning
clustering
t-distributed stochastic neighbor embedding
support vector machines
phase identification and alignment
batch processes
Online Access:https://www.frontiersin.org/articles/10.3389/fceng.2020.582126/full

Internet

https://www.frontiersin.org/articles/10.3389/fceng.2020.582126/full

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