Title :

A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties

Author(s) :

Strani, L. [Auteur]
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Tanzilli, D. [Auteur]
Bonacini, F. [Auteur]
Perolo, A. [Auteur]
Mantovani, E. [Auteur]
Ferrando, A. [Auteur]
Cocchi, M. [Auteur]

Journal title :

Sensors

Abbreviated title :

Sensors

Volume number :

22

Pages :

-

Publication date :

2022-07-08

ISSN :

1424-8220

English keyword(s) :

response-oriented sequential alternation (ROSA)
real-time monitoring
quality prediction
polymer production
multivariate statistical process control
multiblock-partial least squares (MB-PLS)
low-level data fusion
Acrylonitrile-Butadiene-Styrene

HAL domain(s) :

Chimie/Chimie théorique et/ou physique

English abstract : [en]

Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses ...
Show more >Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters’ prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS

Collections :

• Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Submission date :

2024-02-28T22:31:47Z
2024-03-20T09:07:46Z