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Article

Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

Institute of Mechanical Process Engineering and Mechanics (MVM), Karlsruhe Institute of Technology (KIT), Strasse am Forum 8, 76131 Karlsruhe, Germany
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Academic Editors: Lidia Auret and Kevin Brooks
Minerals 2021, 11(7), 755; https://doi.org/10.3390/min11070755
Received: 28 June 2021 / Revised: 8 July 2021 / Accepted: 10 July 2021 / Published: 13 July 2021
(This article belongs to the Special Issue The Application of Machine Learning in Mineral Processing)
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. View Full-Text
Keywords: solid-liquid separation; decanter centrifuge; grey box model; machine learning; neural network; mineral processing solid-liquid separation; decanter centrifuge; grey box model; machine learning; neural network; mineral processing
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MDPI and ACS Style

Menesklou, P.; Sinn, T.; Nirschl, H.; Gleiss, M. Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network. Minerals 2021, 11, 755. https://doi.org/10.3390/min11070755

AMA Style

Menesklou P, Sinn T, Nirschl H, Gleiss M. Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network. Minerals. 2021; 11(7):755. https://doi.org/10.3390/min11070755

Chicago/Turabian Style

Menesklou, Philipp, Tabea Sinn, Hermann Nirschl, and Marco Gleiss. 2021. "Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network" Minerals 11, no. 7: 755. https://doi.org/10.3390/min11070755

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