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)

    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 (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
    Volume16
    Issue number6
    Publication statusPublished - 2006 Jun

    Keywords

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

    ASJC Scopus subject areas

    • Biotechnology
    • Applied Microbiology and Biotechnology

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