Flux optimization using genetic algorithms in membrane bioreactor

Jungmo Kim, Chulhwan Park, Seung Wook Kim, Sangyong Kim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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 (tf) was 30-37 s and the backward filtration time (tb) 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.

Original languageEnglish
Pages (from-to)863-869
Number of pages7
JournalJournal of microbiology and biotechnology
Issue number6
Publication statusPublished - 2006 Jun


  • Backpulse frequency
  • Flux optimization
  • Genetic algorithm
  • Membrane bioreactor

ASJC Scopus subject areas

  • Biotechnology
  • Applied Microbiology and Biotechnology


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