Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
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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
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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

  1. Cabassud, C., S. Laborie, and J. M. Laine. 1997. How slug flow can improve ultrafiltration flux in organic hollow fiber. J. Membr. Sci. 128: 93-101 https://doi.org/10.1016/S0376-7388(96)00316-X
  2. Chang, I, S., P. L. Clech, B. Jefferson, and S. Judd. 2002. Membrane fouling in membrane bioreactors for wastewater treatment. J. Environ. Eng. 128: 1018-1029 https://doi.org/10.1061/(ASCE)0733-9372(2002)128:11(1018)
  3. Choi, J. W., W. C. Lee, J. M. Cho, Y. K. Kim, and S. Y. Park. 2002. Control of feed rate using neurocontroller incorporated with genetic algorithm in fed-batch cultivation of Scutellaria baicalensis georgi. J. Microbiol. Biotechnol. 12: 687-691
  4. Gohel, V., D. Jiwan, P. Vyas, and H. S. Chhatpar. 2005. Statistical optimization of chitinase production by Panotoea dispersa to enhance degradation of crustacean chitin waste. J. Microbiol. Biotechnol. 15: 197-201
  5. Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Inc., NY, U.S.A
  6. Grace, H. P. 1956. Structure and performance of filter media. AIChE J. 2: 307-315 https://doi.org/10.1002/aic.690020307
  7. Jayati, R. D., K. D. Pranab, and B. Rintu. 2005. Modeling and optimization of protease production by a newly isolated Pseudomonas sp. using a genetic algorithm. Process Biochem. 40: 879-884 https://doi.org/10.1016/j.procbio.2004.02.013
  8. Kennedy, M., S. M. Kim, I. Mutenyo, L. Broens, and J. Schippers. 1998. Intermittent crossflushing of hollow fiber ultrafiltration systems. Desalination 118: 175-187 https://doi.org/10.1016/S0011-9164(98)00121-0
  9. Kuberkar, V., P. Czekaj, and R. H. Davis. 1998. Flux enhancement for membrane filtration of bacterial suspensions using high-frequency backpulsing. Biotechnol. Bioeng. 60: 77-87 https://doi.org/10.1002/(SICI)1097-0290(19981005)60:1<77::AID-BIT9>3.0.CO;2-Y
  10. Lee, S. M., J. Y. Jung, and Y. C. Chung. 2001. Novel method for enhancing permeate flux of submerged membrane system in two phase anaerobic reactor. Water Res. 35: 471-477 https://doi.org/10.1016/S0043-1354(00)00255-4
  11. Lin, J. Q., S. M. Lee, and Y. M. Koo. 2004. Model development for lactic acid fermentation and parameter optimization using genetic algorithm. J. Microbiol. Biotechnol. 14: 1163-1169
  12. Mallubhotla, H. and G. Belfort. 1996. Semiempirical modeling of cross-flow microfiltration with periodic reverse filtration. Ind. Eng. Chem. Res. 35: 2920-2928 https://doi.org/10.1021/ie950719t
  13. Mores, W. D., C. N. Bowman, and R. H. Davis. 2000. Theoretical and experimental flux maximization by optimization of backpulsing. J. Membr. Sci. 165: 225-236 https://doi.org/10.1016/S0376-7388(99)00241-0
  14. Park, B. G., W. G. Lee, W. Zhang, Y. K. Chang, and H. N. Chang. 1999. Effects of ultrasonic waves on filtration performance and fermentation in an internal membrane -filtration bioreactor. J. Microbiol. Biotechnol. 9: 243-248
  15. Redkar, S. G. and D. H. Davis. 1995. Crossflow microfiltration with high-frequency reverse filtration. AIChE J. 41: 501-508 https://doi.org/10.1002/aic.690410308
  16. Redkar, S. G., V. Kuberkar, and R. H. Davis. 1996. Modeling of concentration polarization and depolarization with high-frequency backpulsing. J. Membr. Sci. 121: 229-242 https://doi.org/10.1016/S0376-7388(96)00179-2
  17. Roubos, J. A., G. Van Straten, and A. J. B. van Boxtel. 1999. An evolutionary strategy for fed batch bioreactor optimization concepts and performance. J. Biotechnol. 67: 173-187 https://doi.org/10.1016/S0168-1656(98)00174-6
  18. Serra, C., L. Durand-Bourlier, M. J. Clifton, P. Moulin, J. C. Rouch, and P. Aptel. 1999. Use of air sparging to improve backwash efficiency in hollow fiber modules. J. Membr. Sci. 161: 95-113 https://doi.org/10.1016/S0376-7388(99)00106-4
  19. Silva, C. M., D. W. Reeve, H. Husain, H. R. Rabie, and K. A. Woodhouse. 2000. Model for flux prediction in highshear microfiltration systems. J. Membr. Sci. 173: 87-98 https://doi.org/10.1016/S0376-7388(00)00355-0
  20. Sondhi, R. and R. Bhave. 2001. Role of backpulsing in fouling minimization in crossflow filtration with ceramic membranes. J. Membr. Sci. 186: 41-52 https://doi.org/10.1016/S0376-7388(00)00663-3
  21. Tardieu, E., A. Grasmick, V. Geaugey, and J. Manem. 1999. Influence of hydrodynamics on fouling velocity in a recirculated MBR for wastewater treatment. J. Membr. Sci. 156: 131-140 https://doi.org/10.1016/S0376-7388(98)00343-3
  22. Ueda, T., K. Hata, Y. Kikuoka, and O. Seino. 1997. Effects of aeration on suction pressure in a submerged membrane bioreactor. Wat. Res. 31: 489-494 https://doi.org/10.1016/S0043-1354(96)00292-8