Bioprocess and Biosystems Engineering

, Volume 27, Issue 2, pp 81–89 | Cite as

Analysis and application of ADM1 for anaerobic methane production

  • Hyeong-Seok Jeong
  • Chang-Won Suh
  • Jae-Lim Lim
  • Sang-Hyung Lee
  • Hang-Sik Shin
Original papers


An anaerobic model for the serum bottle test was developed and analyzed with sensitivities of stoichiometric and kinetic parameters to the components in order to establish a basis for appropriate application of the model. Anaerobic glucose degradation in a serum bottle was selected as an example. The anaerobic model was developed based on the anaerobic digestion model no. 1 (ADM1), which had five processes with 17 kinetic and stoichiometric parameters. Sensitivity analysis showed that the yield of product on the substrate (f) has high sensitivities to model components, and that the methane concentration was the most sensitive component. Important parameters including yield of product on the substrate (f), yield of biomass on the substrate (Y), and half-saturation values (K) were estimated using genetic algorithms, which optimized the parameters with experimental results. The Monod maximum specific uptake rate (k) was, however, so strongly associated with the concentration of biomass, that values could not be estimated individually. Simulation with estimated parameters showed good agreement with experimental results in the case of methane production. However, there were some differences in acetate and propionate concentrations.


ADM1 Sensitivity analysis Genetic algorithms Parameter estimation 


  1. 1.
    Bastone DJ, Keller J, Angelidaki I, Kalyuzhnyi SV, Pavlostathis SG, Rozzi A, Sanders WTM, Siegrist H, Vavilin VA (2002) Anaerobic digestion model no. 1 (ADM1). IWA scientific and technical report no. 13. IWA publishing, LondonGoogle Scholar
  2. 2.
    Henze M, Gujer W, Mino T, Loosdrecht M (2000) Activated sludge models ASM1, ASM2, ASM2d and ASM3. IWA scientific and technical report no. 9. IWA publishing, LondonGoogle Scholar
  3. 3.
    Vanrolleghem PA, Spanjers H, Britta P, Ginestet P, Takacs I (1999) Estimating (combination of) activated sludge model no. 1 parameters and components by respirometry. Water Sci Technol 39(1):195–214CrossRefGoogle Scholar
  4. 4.
    Choi DJ (2000) Modeling for optimization of activated sludge process and parameter estimation using artificial intelligence. PhD thesis, Korea Advanced Institute of Science and Technology, Republic of KoreaGoogle Scholar
  5. 5.
    Mussati M, Gernaey K, Gani R, Jørgensen SB (2002) Computer aided model analysis and dynamic simulation of a wastewater treatment plant. Clean Tech Environ Policy 4:100–114Google Scholar
  6. 6.
    Veldhuizen HM, Loosdrecht MCM, Jeijnen JJ (1999) Modelling biological phosphorus and nitrogen removal in a full scale activated sludge process. Water Res 33(16):3459–3468Google Scholar
  7. 7.
    Krühne U (2000) Stabilisation of biological phosphorus removal from municipal wastewater. PhD thesis, Technical University of Denmark, DenmarkGoogle Scholar
  8. 8.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MichiganGoogle Scholar
  9. 9.
    Kim S, Lee H, Kim J, Ko J, Woo H, Kim S (2002) Genetic algorithms for the application of activated sludge model no. 1. Water Sci Technol 45(4–5):405–411Google Scholar
  10. 10.
    Wang QJ (1997) Using genetic algorithms to optimise model parameters. Environ Model Software 12(1):27–34Google Scholar
  11. 11.
    Wang PP, Zheng C (1998) An efficient approach for successively perturbed groundwater models. Adv Water Resources 21:499–508CrossRefGoogle Scholar
  12. 12.
    Gupta I, Khanna A, Gupta P (1999) Genetic algorithm for optimization of water distribution systems. Environ Model Software 14:437–466Google Scholar
  13. 13.
    Park LJ, Park CH, Park C, Lee T (1997) Application of genetic algorithms to parameter estimation of bioprocesses. Med Eng Comput 35(1):47–49Google Scholar
  14. 14.
    Gujer W, Henze M, Mino T, Loosdrecht M (1999) Activated sludge model no. 3. Water Sci Technol 39(1):183–193CrossRefGoogle Scholar
  15. 15.
    Owen WF, Stuckey DC, Healy JB, Young LY Jr, McCarty PL (1979) Bioassay for monitoring biochemical methane potential and anaerobic toxicity. Water Res 13:485–492CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Hyeong-Seok Jeong
    • 1
  • Chang-Won Suh
    • 1
  • Jae-Lim Lim
    • 2
  • Sang-Hyung Lee
    • 1
  • Hang-Sik Shin
    • 1
  1. 1.Department of Civil and Environmental EngineeringKorea Advanced Institute of ScienceDaejeonRepublic of Korea
  2. 2.Korea Institute of Water and Environment (KIWE)Korea Water Resources CorporationDaejeonRepublic of Korea

Personalised recommendations