Binxin Wu1, Yan Liu2, Wei Liao2, and Shulin Chen3. (1) Philadelphia Mixing Solutions Ltd, 1221 East Main Street, Palmyra, PA 17078, (2) Department of Biosystems & Agricultural Engineering, Michigan State University, 203 Farrall Hall, East Lansing, MI 48824, (3) Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164
Aeration and mixing are two main energy consumption and costly parts in most of aerobic fermentation processes. A numerical model coupling fluid flow and mass transfer to predict the energy cost of pelletized fungal fermentation is presented. The fluid flow model involving oxygen dissolved in fermentation broth will be developed based upon the theories of gas-solid-liquid three-phase and turbulence, in which the governing equations are composed of continuity, momentum, slip and drift velocity, volume fraction for each phase, and k-e standard turbulence equations. The mass transfer model will be developed by diffusion-reaction in a fungal pellet with zero-order kinetics, in which the external and internal mass transfers are taken into account. SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm will be used to solve the flow fields with mechanical agitation, and finite volume method will be applied to solve pellet diffusion-reaction process. Then the fluid flow and mass transfer will be coupled by Sherwood Number which is calculated from particle Reynolds number and Schmidt number. Model parameters such as reaction kinetic coefficients, diffusivity are obtained from experiments and literatures. Simulations will be performed to determine the minimum power input for the aerobic fermentation processes. Lactic acid production using pelletized Rhizopus oryzae fermentation will be conducted in 1L and 5L reactor to validate this theoretical model. Finally, an optimum mixing strategy will be proposed. A general computer program will be developed to help engineers to improve the fermentation performance for existing bioreactors and design new bioreactors as well as provide more information for bioprocessing economic evaluation.