Lagrangian modeling of hydrodynamic–kinetic interactions in (bio)chemical reactors: Practical implementation and setup guidelines. (10th January 2017)

• Record Type: Journal Article
• Authors: Haringa, Cees; Noorman, Henk J.; Mudde, Robert F.
• Is Part Of: Chemical engineering science. Volume 157(2017)
• Page Start: 159
• Page End: 168
• Publication Date: 2017-01-10
• Subjects: RD Reaction dynamics -- EL Euler–Lagrange -- MRF Multiple Reference Frame -- SM Sliding Mesh; Euler–Lagrange -- Industrial scale -- Bioreactor -- Hydrodynamics -- Metabolic modeling; 76M28; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2016.07.031
• Languages: English
• ISSNs: 0009-2509

Abstract

Abstract: Large substrate concentration gradients can exist in chemical or biochemical reactions, resulting from a large circulation time compared to the turnover time of substrates. The influence of such gradients on the microbial metabolism can significantly compromise optimal bioreactor performance.Lapin et al. (2004) proposed an Euler–Lagrange CFD method to study the impact of such gradients from the microbial point of view. The discrete representation of the biomass phase yields an advantageous perspective for studying the impact of extra-cellular variations on the metabolism, but at significant computational cost. In particular, the tracked number of particles, as well as the applied time resolution, have a large impact on both the accuracy of the simulation and the runtime of the simulation. In this work we study the influence of these parameters on both the simulation results and computation time, and provide guidelines for accurate Euler–Lagrange bioreactor simulations at minimal computational cost. Abstract : Highlights: We apply Lagrangian CFD in reactors to allow dynamic study of the catalyst phase. The required number of tracked particles is predicted from physical parameters. Reaction models have been coupled to massless particle tracking in ANSYS FLUENT. Particle tracking has successfully been combined with the MRF impeller method. Guidelines for the practical setup of Lagrangian reactor simulation are presented.