Design Optimization for a Microfluidic Crossflow Filtration System Incorporating a Micromixer.

Micromachines (Basel)

School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Korea.

Published: November 2019

AI Article Synopsis

  • This study focuses on optimizing a microfluidic crossflow filtration system by integrating a staggered herringbone micromixer (SHM).
  • Computational fluid dynamics (CFD) and the Taguchi method were used to identify optimal design parameters that reduce fouling, investigating how flow and mass transfer differ between the SHM model and a plain rectangular microchannel.
  • Key design parameters evaluated include the number of grooves, groove depth, inter-groove spacing, and half mixing period spacing, with Analysis of Variance (ANOVA) helping to pinpoint their contributions, ultimately demonstrating that the optimized SHM model minimizes wall concentration growth the most effectively.

Article Abstract

In this study, we report on a numerical study on design optimization for a microfluidic crossflow filtration system incorporated with the staggered herringbone micromixer (SHM). Computational fluid dynamics (CFD) and the Taguchi method were employed to find out an optimal set of design parameters, mitigating fouling in the filtration system. The flow and the mass transfer characteristics in a reference SHM model and a plain rectangular microchannel were numerically investigated in detail. Downwelling flows in the SHM model lead to backtransport of foulants from the permeable wall, which slows down the development of the concentration boundary layer in the filtration system. Four design parameters - the number of grooves, the groove depth, the interspace between two neighboring grooves, and the interspace between half mixing periods - were chosen to construct a set of numerical experiments using an orthogonal array from the Taguchi method. The Analysis of Variance (ANOVA) using the evaluated signal-to-noise (SN) ratios enabled us to identify the contribution of each design parameter on the performance. The proposed optimal SHM model indeed showed the lowest growth rate of the wall concentration compared to other SHM models.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952986PMC
http://dx.doi.org/10.3390/mi10120836DOI Listing

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