Solid ̶ liquid mixing plays a crucial role in various industrial processes, including crystallization, heterogeneous reactions, and suspension polymerization. Understanding mechanical mixing behavior can be critical for either maintaining uniform mixing ("effective agitation") or settling solids to remove them from a liquid suspension ("setting kinetics"), as shown in Figure . It is key to understand the sensitivity of mixing performance on the process to assure successful transfer across different scale-up vessels. Assessing solid ̶ liquid mixing through experimental means, such as measuring cloud height, 39 axial concentration gradient, or particle velocity, 41 can be time-consuming, costly, and impractical for large reactors. In industrial settings, computational tools like VisiMix and Dynochem are utilized, employing mathematical equations derived from reactor databases to evaluate mixing performance at different scales. However, these simulations require extensive input data, including geometry details, boundary conditions, and material properties, making data collection a time-consuming and resource-intensive process. For specific mixing challenges, Computational Fluid Dynamics (CFD) modeling offers a more in-depth understanding of the process. Given the inherently visual nature of determining solution homogeneity, we sought to demonstrate how HeinSight2.0 could readily inform mechanical mixing studies.