Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Bayesian Calibration for Optimizing Biophysicochemical Reaction Kinetics Models in Water Environments and Treatment Systems: Case Studies in the Microbial Growth-decay and Flocculation Processes

베이지안 보정 기법을 활용한 생물-물리-화학적 반응 동역학 모델 최적화: 미생물 성장-사멸과 응집 동역학에 대한 사례 연구

  • Byung Joon Lee (Energy and Environment Institute, Kyungpook National University)
  • 이병준 (경북대학교 에너지환경연구소)
  • Received : 2024.03.14
  • Accepted : 2024.06.04
  • Published : 2024.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

Biophysicochemical processes in water environments and treatment systems have been great concerns of engineers and scientists for controlling the fate and transport of contaminants. These processes are practically formulated as mathematical models written in coupled differential equations. However, because these process-based mathematical models consist of a large number of model parameters, they are complicated in analytical or numerical computation. Users need to perform substantial trials and errors to achieve the best-fit simulation to measurements, relying on arbitrary selection of fitting parameters. Therefore, this study adopted a Bayesian calibration method to estimate best-fit model parameters in a systematic way and evaluated the applicability of the calibration method to biophysicochemical processes of water environments and treatment systems. The Bayesian calibration method was applied to the microbial growth-decay kinetics and flocculation kinetics, of which experimental data were obtained with batch kinetic experiments. The Bayesian calibration method was proven to be a reasonable, effective way for best-fit parameter estimation, demonstrating not only high-quality fitness, but also sensitivity of each parameter and correlation between different parameters. This state-of-the-art method will eventually help scientists and engineers to use complex process-based mathematical models consisting of various biophysicochemical processes.

Keywords

Acknowledgement

이 논문은 2021년도 한국연구재단 이공분야 기초연구사업(NRF-2020R1I1A3A04036895)의 지원을 받아 수행됨.

References

  1. Beckers, F., Heredia, A., Noack, M., Nowak, W., Wieprecht, S., and Oladyshkin, S. (2020). Bayesian calibration and validation of a large-scale and time-demanding sediment transport model, Water Resources Research, 56(7), e2019WR026966. https://doi.org/10.1029/2019WR026966 
  2. Burd, A. B. and Jackson G. A. (2002). Modeling steady-state particle size spectra, Environmental Science and Technology, 36(3), 323-327. https://doi.org/10.1021/es010982n 
  3. Choi, Y., Baek, S., Kim, J., Choi, J., Hur, J., Lee, T., Park, C., and Lee B. J. (2017). Characteristics and biodegradability of wastewater organic matter in municipal wastewater treatment plants collecting domestic wastewater and industrial discharge, Water, 9(6), 409. https://doi.org/10.3390/w9060409 
  4. Duarte, M. S., Martins, G., Oliveira, P., Fernandes, B., Ferreira, E. C., Alves, M., Lopes, F., Pereira, M. A., and Novais, P. (2024). A review of computational modeling in wastewater treatment processes, ACS ES&T Water, 4(3), 784-804. https://doi.org/10.1021/acsestwater.3c00117 
  5. Gelman, A. G. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences, Statistical Science, 7(4), 457-472. https://doi.org/10.1214/ss/1177011136 
  6. Gujer, W., Henze, M., Mino, T., and van Loosdrecht, M. (1999). Activated sludge model No. 3, Water Science and Technology, 39(1), 183-193. https://doi.org/10.1016/S0273-1223(98)00785-9 
  7. Henze, M., Grady, C. P. L., Gujer, W., Marais, G. v. R., and Matsuo, T. (1987). Activated sludge model No. 1, IAWPRC, London, England. 
  8. Jeppsson, U. (1996). Modelling aspects of wastewater treatment processes, PhD Dissertation, Lund Institute of Technology, Lund, Sweden. 
  9. Kim, J. I. and Lee, B. J. (2020). Investigation on flocculi-floc interaction and flocculation in extracellular polymeric substances, ionic species and clay-containing suspension, Journal of Korean Society on Water Environment, 36(3), 185-193. https://doi.org/10.15681/KSWE.2020.36.3.185 
  10. Lee, B. J., Fettweis, M., and Toorman, E., and Molz, F. J. (2012). Multimodality of a particle size distribution of cohesive suspended particulate matters in a coastal zone, Journal of Geophysical Research: Oceans, 117, C03014. https://doi.org/10.1029/2011JC007552 
  11. Lee, B. J., Toorman, E., Molz, F. J., and Wang, J. (2011). A two-class population balance equation yielding bimodal flocculation of marine or estuarine sediments, Water Research, 45(5), 2131-2145. https://doi.org/10.1016/j.watres.2010.12.028 
  12. Lee, B. J., Wentzel, M. C., and Ekama G. A. (2006). Measurement and modelling of ordinary heterotrophic organism active biomass concentrations in anoxic/aerobic activated sludge mixed liquor, Water Science and Technology, 54(1), 1-10. https://doi.org/10.2166/wst.2006.365 
  13. Lee, B. J., Wentzel, M., Ekama, G. A., Choi, Y., and Choi, J. (2014). Measurement of ordinary heterotrophic organism active biomass in activated sludge mixed liquor: Evaluation and comparison of the quantifying techniques, Environmental Engineering Research, 19(1), 91-99. DOI: https://doi.org/10.4491/eer.2014.19.1.091 
  14. Van Loosdrecht, M. C. M., Lopez-Vazquez, C. M., Meijer, S. C. F., Hooijmans, C. M., and Brdjanovic, D. (2015). Twenty-five years of ASM1: Past, present and future of wastewater treatment modelling, Journal of Hydroinformatics, 17(5), 697-718. https://doi.org/10.2166/ hydro.2015.006 
  15. van Mourik, S., ter Braak, G., Stigter, H., and Molenaar, J. (2014). Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters, PeerJ, 2, e433. https://doi.org/10.7717/peerj.433 
  16. van Turnhout, A. G., Kleerebezem, R., and Heimovaara, T. J. (2016). A toolbox to find the best mechanistic model to predict the behavior of environmental systems, Environmental Modelling & Software, 83, 344-355. https://doi.org/10.1016/j.envsoft.2016.05.002 
  17. Villacorte, L. O., Ekowati, Y., Neu, T. R., Kleijn, J. M., Winters, H., Amy, G., Schippers, J. C., and Kennedy, M. D. (2015). Characterisation of algal organic matter produced by bloom-forming marine and freshwater algae, Water Research, 73, 216-230. https://doi.org/10.1016/j.watres.2015.01.028 
  18. Vrugt, J. A. (2016). Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation, Environmental Modelling & Software, 75, 273-316. https://doi.org/10.1016/j.envsoft.2015.08.013 
  19. Wentzel, M., Mbewe, A., and Ekama, G. (1995). Batch test for measurement of readily biodegradable COD and active organism concentrations in municipal waste waters, Water SA, 21(2), 117-124. 
  20. Winterwerp, J. C. and van Kesteren, W. G. M. (2004). Introduction to the physics of cohesive sediment in the marine environment, Elsevier, Amsterdam, the Netherlands.