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Grey box modelling of decanter centrifuges by coupling a numerical process model with a neural network

Menesklou, Philipp ORCID iD icon 1; Sinn, Tabea 1; Nirschl, Hermann 1; Gleiss, Marco 1
1 Institut für Mechanische Verfahrenstechnik und Mechanik (MVM), Karlsruher Institut für Technologie (KIT)

Abstract:

Continuously operating decanter centrifuges are often applied for solid-liquid separation in the chemical and mining industries. Simulation tools can assist in the configuration and optimisation of separation processes by, e.g., controlling the quality characteristics of the product. Increasing computation power has led to a renewed interest in hybrid models (subsequently named grey box model), which combine parametric and non-paramteric models. In this article, a grey box model for the simulation of the mechanical dewatering of a finely dispersed product in decanter centrifuges is discussed. Here, the grey box model consists of a mechanistic model (as white box model) presented in a previous research article and a neural network (as black box model). Experimentally determined data is used to train the neural network in the area of application. The mechanistic approach considers the settling behaviour, the sediment consolidation, and the sediment transport. In conclusion, the settings of the neural network and the results of the grey box model and white box model are compared and discussed. Now, the overall grey box model is able to increase the accuracy of the simulation and physical effects that are not modelled yet are integrated by training of a neural network using experimental data.


Verlagsausgabe §
DOI: 10.5445/IR/1000136101
Veröffentlicht am 08.08.2021
Originalveröffentlichung
DOI: 10.3390/min11070755
Scopus
Zitationen: 2
Web of Science
Zitationen: 2
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mechanische Verfahrenstechnik und Mechanik (MVM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2075-163X
KITopen-ID: 1000136101
Erschienen in Minerals
Verlag MDPI
Band 11
Heft 7
Seiten 755
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagwörter solid-liquid separation; decanter centrifuge; grey box model; machine learning; neural network; mineral processing
Nachgewiesen in Dimensions
Scopus
Web of Science
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