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Enhancing decanter centrifuge process design with data‐driven material parameters in multi‐compartment modeling

Zhai, Ouwen 1; Ehret, Niklas 2; Rhein, Frank ORCID iD icon 1; Gleiß, Marco 1
1 Institut für Mechanische Verfahrenstechnik und Mechanik (MVM), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)

Abstract:

Predicting the separation performance of decanter centrifuges is challenging due to dynamic events within the apparatus. Current methods for designing decanter centrifuges rely on simplified models, often leading to inaccuracies. Consequently, manufacturers must perform time-intensive pilot scale experiments to derive their own correction factors. Growing computing power sparks interest in alternative modeling strategies. Grey box models (GBM) combine mechanistic white box models (WBM) and data-driven black box models (BBM), with the optimal structure (parallel or serial) varying by application. For modeling decanter centrifuges, we propose a serial GBM that comprises an artificial neural network that outputs unknown material parameters into a first-principle multi-compartment model. Comparing this approach to alternative data-driven modeling strategies (pure BBM, parallel GBM), we conclude that the serial GBM excels in terms of extrapolation, prediction ability, and transparency while also enabling a better comprehension of the separation process.


Verlagsausgabe §
DOI: 10.5445/IR/1000171546
Veröffentlicht am 13.06.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mechanische Verfahrenstechnik und Mechanik (MVM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 2637-403X
KITopen-ID: 1000171546
Erschienen in Journal of Advanced Manufacturing and Processing
Verlag John Wiley and Sons
Band 6
Heft 3
Seiten Art.-Nr.: e10179
Vorab online veröffentlicht am 03.06.2024
Schlagwörter artificial neural network, decanter centrifuge, grey box modeling, machine learning, solid–liquid separation
Nachgewiesen in Dimensions
Scopus
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