An assumption-free model is developed for the monitoring of batch processes. The model is based on variable-wise unfolded multy-way principal component analysis (MPCA) and avoids the problem of batch alignment, which is necessary in the case of a batch-wise unfolded MPCA. The assumption-free model and a model based on batch-wise unfolded MPCA are developed and tested on different batch processes datasets in order to evaluate their performances on process monitoring and fault detection.

An assumption-free model is developed for the monitoring of batch processes. The model is based on variable-wise unfolded multy-way principal component analysis (MPCA) and avoids the problem of batch alignment, which is necessary in the case of a batch-wise unfolded MPCA. The assumption-free model and a model based on batch-wise unfolded MPCA are developed and tested on different batch processes datasets in order to evaluate their performances on process monitoring and fault detection.

Batch process monitoring using an assumption-free modeling methodology

FRACASSETTO, ALICE
2021/2022

Abstract

An assumption-free model is developed for the monitoring of batch processes. The model is based on variable-wise unfolded multy-way principal component analysis (MPCA) and avoids the problem of batch alignment, which is necessary in the case of a batch-wise unfolded MPCA. The assumption-free model and a model based on batch-wise unfolded MPCA are developed and tested on different batch processes datasets in order to evaluate their performances on process monitoring and fault detection.
2021
Batch process monitoring using an assumption-free modeling methodology
An assumption-free model is developed for the monitoring of batch processes. The model is based on variable-wise unfolded multy-way principal component analysis (MPCA) and avoids the problem of batch alignment, which is necessary in the case of a batch-wise unfolded MPCA. The assumption-free model and a model based on batch-wise unfolded MPCA are developed and tested on different batch processes datasets in order to evaluate their performances on process monitoring and fault detection.
batch process
process monitoring
data-driven models
data analytics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/37066