Flux Optimization Using Genetic Algorithms in Membrane Bioreactor

  • Kim Jung-Mo (Green Engineering Team, Environment and Energy Division) ;
  • Park Chul-Hwan (Green Engineering Team, Environment and Energy Division) ;
  • Kim Seung-Wook (Department of Chemical and Biological Engineering, Korea University) ;
  • Kim Sang-Yong (Green Engineering Team, Environment and Energy Division)
  • Published : 2006.06.01

Abstract

The behavior of submerged membrane bioreactor (SMBR) filtration systems utilizing rapid air backpulsing as a cleaning technique to remove reversible foulants was investigated using a genetic algorithm (GA). A customized genetic algorithm with suitable genetic operators was used to generate optimal time profiles. From experiments utilizing short and long periods of forward and reverse filtration, various experimental process parameters were determined. The GA indicated that the optimal values for the net flux fell between 263-270 LMH when the forward filtration time ($t_f$) was 30-37 s and the backward filtration time ($t_b$) was 0.19-0.27 s. The experimental data confirmed the optimal backpulse duration and frequency that maximized the net flux, which represented a four-fold improvement in 24-h backpulsing experiments compared with the absence of backpulsing. Consequently, the identification of a region of feasible parameters and nonlinear flux optimization were both successfully performed by the genetic algorithm, meaning the genetic algorithm-based optimization proved to be useful for solving SMBR flux optimization problems.

Keywords

References

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