Multi-modal framework to model wet milling through numerical simulations and artificial intelligence (part 2)
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- @Article{THON:2022:cej,
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author = "Christoph Thon and Ann-Christin Boettcher and
Felix Moehlen and Minghui Yu and Arno Kwade and
Carsten Schilde",
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title = "Multi-modal framework to model wet milling through
numerical simulations and artificial intelligence (part
2)",
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journal = "Chemical Engineering Journal",
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volume = "450",
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pages = "137947",
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year = "2022",
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ISSN = "1385-8947",
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DOI = "
doi:10.1016/j.cej.2022.137947",
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URL = "
https://www.sciencedirect.com/science/article/pii/S1385894722034337",
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keywords = "genetic algorithms, genetic programming, Wet stirred
media mills, Genetic reinforcement learning, CFD-DEM
simulation, Predictive mill models",
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abstract = "Modelling of stirred media mills is crucial because of
their broad use in various industries, ranging from
mechanochemistry and mining to the production of
batteries and pharmaceuticals. Stirred media mills are
responsible for a considerable portion of the global
energy demand. However, requirements exist regarding
highly specific or uniform particle sizes, process
conditions, and reduced wear or abrasion. Multi-modal
modelling, which is the intelligent integration of
different approaches, such as experiments, simulations,
and AI, benefits from respective advantages of each
approach. In the first study, results of an experiment
conducted via magnetic tracking of a tracer bead was
compared with those of simulations, and the inner mill
mechanisms were investigated. The two-way coupled
computer fluid dynamics discrete element method
(CFD-DEM) simulations allowed the investigation of
subsequent modelling through AI methods [1]. A novel AI
training technique called {"}genetic reinforcement
learning{"} (hereinafter, GRL), which combines neural
nets with genetic algorithms, was demonstrated for
cases with limited data. Furthermore, genetic
programming was applied to derive transparent
mathematical equations based on the generated data.
Using these methods and experimentally validated
simulation data, predictive models were trained, and
mathematical equations were derived. Relative velocity
distributions in the entire simulation domain as well
as spatial distributions via heatmaps were predicted
and evaluated for independent cases. Systematic
predictions for the characteristic relative velocity
values were generated instantaneously for varying tip
speeds and bead diameters in a parameter space, which
would have required 1-10 years through simulations.
Finally, a transparent equation was generated via
genetic programming",
- }
Genetic Programming entries for
Christoph Thon
Ann-Christin Boettcher
Felix Moehlen
Minghui Yu
Arno Kwade
Carsten Schilde
Citations