(451g) Transient Mean Age Analysis in Continuous Processing Systems

Transient mean age analysis is an approach for predicting spatial variations in local age and local mean residence time across an open system. Like tracer particle-based approaches, transient mean age can be used to predict the total tank mean residence time and quantify non-idealities in the total tank residence time distribution (RTD). Beyond tracer particle-based approaches, however, transient mean age illuminates the specific regions and pathways inside the tank driving the exit RTD. In this way, transient mean age analysis provides quantified and actionable insights into the internal flow field driving the total tank properties measured at the exit.

This work, we show how transient mean age can be combined with large eddy simulations to perform real-time, transient mean-age analyses. After a brief review of the transient mean age theory, we perform self-consistent simulations to predict a total tank residence time using both mean-age and tracer particles approaches. We compare results and discuss the strengths/convergence behavior of each methodology. We then validate these predictions to experimental data, obtained for system with the same geometry and flow configurations used in the models.