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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year = "2022",
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volume = "450",
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pages = "137947",
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month = "15 " # dec,
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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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ISSN = "1385-8947",
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URL = "https://www.sciencedirect.com/science/article/pii/S1385894722034337",
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DOI = "doi:10.1016/j.cej.2022.137947",
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size = "12 pages",
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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",
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notes = "Institute for Particle Technology (iPAT), Technische
Universitaet Braunschweig, Volkmaroder Str. 5, D-38104
Braunschweig, Germany",
- }
Genetic Programming entries for
Christoph Thon
Ann-Christin Boettcher
Felix-Tom Moehlen
Minghui Yu
Arno Kwade
Carsten Schilde
Citations