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

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.