de Oliveira, RR, Avila, C, Bourne, R orcid.org/0000-0001-7107-6297 et al. (2 more authors) (2020) Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control. Analytical and Bioanalytical Chemistry, 412. pp. 2151-2163. ISSN 1618-2642
Abstract
Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer-Verlag GmbH Germany, part of Springer Nature 2020. This is an author produced version of a paper published in Analytical and Bioanalytical Chemistry. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Data fusion; Multivariate statistical process control; Near-infrared; Spectroscopic sensors; Chemometrics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Funding Information: | Funder Grant number EU - European Union 637232 |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jan 2020 11:21 |
Last Modified: | 21 Jan 2021 01:38 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s00216-020-02404-2 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155953 |