A Multi-Scale Approach To Adhesive Mixing

Detta är en avhandling från Chalmers University of Technology

Sammanfattning: Adhesive particle mixing is a fundamental process in many applications, especially in dry powders for inhalation. Despite its importance, a mechanistic process description for designing the structure of interest is still lacking. Due to a wide array of the length and time scales of mechanisms governing adhesive mixing, a multi-scale approach that integrates mathematical models and experiments is a primary choice for achieving insight into such a process. To model a particulate system, balancing between the level of detail of a description and the relevant computational cost is the main challenge, especially in the case of adhesive mixing where microscopic information must be preserved. The proposed strategy to tackle this issue is (i) to isolate mechanisms governing adhesive mixing in order to investigate them independently, (ii) to evaluate their contributions to the mixing process, and (iii) to integrate those mechanisms into a mechanistic description. There are four mechanisms that govern adhesive mixing; (1) random mixing at the macro-scale level, (2) de-agglomeration at the meso-scale level, (3) adhesion, and (4) the redistribution of fines between carrier particles at the micro-scale level. CFD simulations of transient macroscopic flow show that in a high-shear mixer, the timescales of flow development and of random mixing are of the same order of magnitude. DEM simulations were used to study microscopic particle – particle interactions that demonstrate that the relative velocity and interface energies between particles have significant and predictable impacts on the microscopic structure of a mixture. By coupling mathematical models with experimental techniques, it was found that the mesoscopic de-agglomeration is the rate-limiting mechanism to achieve an adhesive mixture. A regime map relating particle properties and processing conditions to the structure of a mixture was constructed using dimensionless numbers, which serves as a predictive tool for designing adhesive mixtures. This research demonstrates that it is feasible to build a mechanistic relationship between particle properties, macroscopic particulate flow, and the microscopic structure of interest to transform the trial-and-error approach in process and product development to a more mechanistic strategy.

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