Decanter centrifuges are frequently used for thickening, dewatering, classification, or degritting in the mining industry and various other sectors. Their use in an industrial process chain requires a sufficiently accurate prediction of the product and the machine behaviour. For this purpose, experiments on a smaller pilot-scale are carried out for scale-up of a decanter centrifuge, which is usually a major challenge. Predicting the process behaviour of decanter centrifuges from laboratory tests is rather difficult. Basically, there are two common ways of scale-up: First, via analytical methods and the law of similarity, which often requires an enormous experimental effort. Second, using numerical models, which demands a mathematically and physically precise description of the multiple processes running simultaneously in such machines. This article provides an overview of both methods for scale-up of a decanter centrifuge. The concept of a previous developed numerical approach is introduced. Pros and cons of both scale-up methods are compared and further discussed. Experiments on lab-scale, pilot-scale, and industrial-scale decanter centrifuges with two different finely dispersed calcium carbonate water suspensions were carried out and simulations were done to investigate and prove the scale-up capability and transferability of the numerical approach.

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.