DEM simulations : mixing of dry and wet granular material with different contact angles

In solid mixing the raw materials typically differ at least in one material property, such as particle size, solid density and wetting properties, which in turn influence particle mobility. For example, smaller particles can percolate through the voids of larger ones under the influence of strain and gravity. This may produce fine particle accumulation at the bottom of the mixing vessel which results in undesired, inhomogeneous final products. When wet particles with different wetting properties need to be mixed, heteroagglomeration may occur as another segregation mechanism. We present a new capillary bridge force model to study segregation in moist cohesive mixing processes using DEM. New analytical equations of best fit are derived by solving the Young–Laplace equation and performing a regression analysis, in order to investigate discontinuous mixing processes of dry and moist materials with different particle sizes and different contact angles. Compared to a dry mixing process, mixing efficiency is improved by the addition of a small amount of liquid. While percolating segregation is reduced, heteroagglomerates occur in the wet mixing process

Digitalization Platform for Sustainable Battery Cell Production: Coupling of Process, Production, and Product Models

Lithium-ion batteries are used in a wide range of applications, with the electromobility sector being the main contributor to the increasing demand predicted for the next decade. Although batteries play an important role in decarbonizing the transportation sector, their production includes energy-intensive processes that hinder a more sustainable production. Moreover, the production processes are characterized by a manifold of parameters leading to complex cause–effect relations along the process chain which influences the battery cell quality. Therefore, a sustainable future for battery production and the electromobility sector depends on the environmentally and economically efficient production of high-performance batteries. Against this background, this work presents a digitalization platform based on the coupling of mechanistic models to digitally reproduce the battery cell production and provide a deeper understanding of the interdependencies on the process, production, and product levels. In addition to a description of the individual models contained in the platform, this work demonstrates their coupling on a use case to study the effects of different solids contents of the coating suspension. Besides providing a multilevel assessment of the parameter interdependencies, considering quality, environmental and economic aspects, the presented framework contributes to knowledge-based decision support and improvement of production and battery cell performance

DEM simulations: mixing of dry and wet granular material with different contact angles

In solid mixing the raw materials typically differ at least in one material property, such as particle size, solid density and wetting properties, which in turn influence particle mobility. For example, smaller particles can percolate through the voids of larger ones under the influence of strain and gravity. This may produce fine particle accumulation at the bottom of the mixing vessel which results in undesired, inhomogeneous final products. When wet particles with different wetting properties need to be mixed, heteroagglomeration may occur as another segregation mechanism. We present a new capillary bridge force model to study segregation in moist cohesive mixing processes using DEM. New analytical equations of best fit are derived by solving the Young–Laplace equation and performing a regression analysis, in order to investigate discontinuous mixing processes of dry and moist materials with different particle sizes and different contact angles. Compared to a dry mixing process, mixing efficiency is improved by the addition of a small amount of liquid. While percolating segregation is reduced, heteroagglomerates occur in the wet mixing process