Multi-modal framework to model wet milling through numerical simulations and artificial intelligence (part 2)

Artificial Intelligence and Evolutionary Approaches in Particle Technology

“…Simulation-based modeling and breakdown for mechanistic modeling: AI modeling based on simulations is an approach with distinct advantages due to the coupling of two numerical approaches, as they can autonomously interact with one another unsupervised (for instance in active learning (AL), etc.). As an example, on the basis of two-way coupled computer fluid dynamics -discrete element method (CFD-DEM) simulations with Rocky DEM and Ansys Fluent, a representative slice of a wet operated stirred media mill was simulated with varying tip speeds and grinding bead diameters (Thon et al, 2022a;2022b). Heatmaps were extracted, with each voxel representing the averaged values of relative velocities.…”

“…Based on evaluated generalizing models, AI predictions can be obtained in inference for a large parameter space of high resolution whose systematic simulation, if performed completely, can take significantly longer. In the case of the wet mill simulation, systematic AI predictions were generated in the order of seconds, which would have taken time in the range of years with conventional CFD-DEM simulations (Thon et al, 2022a;2022b).…”

“…Specifically, for large AI systems with an abundance of training data, backpropagation is the classically used training approach, although it has a strong tendency toward overfitting in cases with limited data sets. Alternative systems exist, which are focused on training with more limited data, such as genetic reinforcement learning (GRL), which was described in greater detail in Thon et al (2022b) and can be described as the training of AI via a genetic algorithm.…”

“…Reprinted from Ref. (Thon et al, 2022b) under the terms of the CC-BY 4.0 license. Copyright: (2022) The Authors, published by Elsevier.…”

“…Running in parallel, black-box AI models can be used to compensate for the shortcomings of existing white-box models by merely modeling the divergence between white-box model predictions and reality. The parallel AI can, for instance, be broken down again via genetic programming to iteratively extend the white-box model (Thon et al, 2021(Thon et al, , 2022bEvans et al, 2019). In modular hybrid models, an interconnected system of sub-AI models is set up to mirror the known structure of its physical counterpart and facilitate the modeling effort.…”