Multi-modal framework to model wet milling through numerical simulations and artificial intelligence (part 1)

• Main Authors: Böttcher, A.-C (Author), Kwade, A. (Author), Möhlen, F. (Author), Schilde, C. (Author), Thon, C. (Author), Yu, M. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: CFD-DEM simulation; Magnetic particle tracking; Mechanistic modelling; Parameter and geometry impact; Wet stirred media mills
• Online Access: View Fulltext in Publisher

 Stirred media mills are used in many industries from mechanochemistry, to ore crushing, battery production and pharmaceutics. Investigation and modelling can be a costly, difficult and time consuming endeavour, limiting the investigable parameter space with some aspects only being accessible via numerical methods such as simulations or artificial intelligence (AI). Simulations allow investigations of inner mechanisms and generate large quantities of data while being computationally demanding. AI requires large data quantities yet is applicable with limited computing power for large parameter spaces. For this reason, the intelligent integration of different methods holds great promise to achieve maximum benefits at minimum cost by synergistically combining respective advantages. Here, a multi-modal framework is demonstrated, combining experiments, simulations and AI. In this first paper direct magnetic measurements via a tracer particle are compared with simulations of wet mills. With two way coupled CFD-DEM simulations inner mechanisms like the impact of disc geometry, tip speed and grinding bead size are investigated. Differences in translational and relative velocity distributions are investigated, showing a consistent displacement of a factor three toward higher energies throughout the complete distribution for the hole disc in comparison to the full disc, and less consistent deviations for translational velocity, specifically for the higher energy collisions, being larger by a factor of 8. Also comparisons with the existing mechanistic model of Kwade are performed. An additional indirect influence of diameter on tip speed could be detected. In a second publication AI modelling will be performed on the basis of the simulations. © 2022 The Authors