Abstract

In the food and beverage industry, one of the effective aseptic measures to eliminate microorganisms enter the product from the package is by spray rinsing, which refers to flushing the package using liquid. New packages and processing lines typically undergo exhaustive bio-validations to meet residual targets and decide if a rinsing setting for a specific package is effective. Due to the physical limitations and insufficient understanding for identifying the exact cold spots, bio-validation's effectiveness is dependent on estimating the worst spot for testing as only a few locations in a package can be tested. A tool that can pinpoint the cold spots before physical validation can guide the rinsing setting to maximize cleaning effectiveness while minimizing water usage. This study developed an aseptic model to enhance the understanding of the spray rinsing process, including the package (bottle) and spray nozzle using the Computational Fluid Dynamics (CFD) tool. This model's target application is to provide better insights for improved package and equipment designs and eventually contribute to the optimization of the entire packaging sanitization process. An unsteady three-dimensional two-phase (liquid/gas) turbulent flow model is developed. The Volume of Fluid (VOF) method is applied to capture the interphase physics. CFD results from the model allow the instantaneous tracking of the liquid coverage on the package surface by various rinsing settings. Qualitatively, flow shielding and/or channeling due to specific package feature designs can be visualized. These designs induce special flow patterns on the bottle surface during the rinsing process and affect the cold spots. The mechanical and chemical aspects of this spray rinsing can be approximately quantified by extracting the wall shear stress and the cumulative volume fraction of liquid during the rinsing process, respectively. The time-dependent liquid volume in the package can also be monitored to track the draining process after rinsing. Accompanied with measurement data from the physical spray rinsing process, this study lays the foundation for an end-to-end simulation-driven process optimization.