ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Parsimonious Batch Data Analysis

3 September 2018, 15:10 – 15:30

Abstract

Submitted by
Marco P. Seabra dos Reis
Authors
Marco P. Seabra dos Reis (Department of Chemical Engineering, University of Coimbra)
Abstract
Batch processes play an important role in modern industrial sectors such as chemical, pharmaceutical, semiconductor, among others. Batch quality is typically assessed at the end of each batch run, through complex analytical procedures whose outcomes only became available with a considerable delay. Both measurements taken during batch operation and batch-end parameters are routinely stored in large databases, providing the basis for developing data-driven models for quality prediction.

The current standard method to deal with these datasets requires synchronization (e.g. using dynamic time warping) and batch-wise unfolding (BWU) of the 3-way array into a 2-way array (I×(J×K)) [1, 2]. Synchronization is not a trivial task and the unfolding step usually originates a wide matrix with hundreds or thousands of pseudo-variables, leading to over-parametrized models where the potential for overfitting is high.

The aforementioned approaches for batch data analysis rank high in terms of complexity, both in terms of “estimation complexity” as well as of “implementation complexity”. These two dimensions have consequences on the performance of the methods (accuracy, robustness) and on the tangibility of their impact in industry. Recently, alternative parsimonious formulations for batch data analysis have been developed, presenting lower complexity features in both dimensions. Examples include the class of feature-oriented approaches and multiresolution methodologies. This talk summarizes these new developments on batch data analysis, and demonstrate their benefits with several illustrative examples.

References
1. Nomikos, P. and J.F. MacGregor, Monitoring batch processes using multiway principal component analysis. AIChE Journal, 1994. 40(8): p. 1361-1375.
2. González-Martínez, J.M., et al., Effect of Synchronization on Bilinear Batch Process Modeling. Industrial & Engineering Chemistry Research, 2014. 53: p. 4339-4351.

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