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.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/37066