An Agent-Based Modeling Perspective of Bio-Mediated Ureolysis
Publication: Geo-Congress 2024
ABSTRACT
Microbially induced carbonate precipitation (MICP) is a biogeochemical process that uses microbes to create favorable conditions in a porous medium for bio-cementation. The hydrolysis of urea and the resulting variation in pore-scale chemistry creates variations in MICP. Research is needed to characterize the spatial and temporal development of precipitation at the particle level that influences soil properties and mechanical behavior due to varying granular characteristics. Several researchers have used reactive transport modeling to predict precipitation distribution patterns over meter-length scales; however, this continuum approach is less applicable for assessing varying granular characteristics at the pore-scale. This study investigates agent-based modeling (ABM) as a new framework to simulate complex interactions at the micro-level to generate phenomena observed at the macro-scale. In this study, an ABM was developed to simulate urea hydrolysis by simplifying the complex biogeochemical interactions between the bacteria Sporosarcina pasteurii and an aqueous MICP environment, including the pH-dependent ammonium speciation. Results show that the ABM captures similar urea degradation trends compared to existing reactive transport models and experimental data. Additionally, insights from the ABM suggest that nutrient depletion zones emerge during ureolysis and are influenced by super-agent size and distribution. These findings indicate that ABM can provide additional insights into the MICP process that cannot be captured by reactive transport modeling.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Burdalski, R. J., and Gomez, M. G. (2020). “Investigating the effect of microbial activity and chemical concentrations on the mineralogy and morphology of ureolytic bio-cementation.” In Geo-Congress 2020: Biogeotechnics (pp. 83–95). Reston, VA: ASCE.
Crank, J. (1979). The Mathematics of Diffusion. Oxford University Press.
DeJong, J. T., Mortensen, B. M., Martinez, B. C., and Nelson, D. C. (2010). “Bio-mediated soil improvement.” Ecol. Eng., 36(2), 197–210.
Faeli, Z., Montoya, B. M., and Gabr, M. A. (2022). “Reactive transport modeling of microbial induced calcium carbonate precipitation utilizing various configurations of injection wells.” In: Geo-Congress 2022, 396–407.
Graddy, C. M., Gomez, M. G., Kline, L. M., Morrill, S. R., DeJong, J. T., and Nelson, D. C. (2018). “Diversity of sporosarcina-like bacterial strains obtained from meter-scale augmented and stimulated biocementation experiments.” Environ. Sci. Technol., 52(7), 3997–4005.
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., and DeAngelis, D. L. (2006). “A standard protocol for describing individual-based and agent-based models.” Ecol. Modell., 198(1-2), 115–126.
Hellweger, F. L., Kravchuk, E. S., Novotny, V., and Gladyshev, M. I. (2008). “Agent‐based modeling of the complex life cycle of a cyanobacterium (Anabaena) in a shallow reservoir.” Limnology and Oceanography, 53(4), 1227–1241.
Hellweger, F. L., and Bucci, V. (2009). “A bunch of tiny individuals—Individual-based modeling for microbes.” Ecol. Modell., 220(1), 8–22.
Lauchnor, E. G., Topp, D. M., Parker, A. E., and Gerlach, R. (2015). “Whole cell kinetics of ureolysis by Sporosarcina pasteurii.” J. Appl. Microbiol., 118(6), 1321–1332.
Miller, J. H., and Page, S. (2007). Complex Adaptive Systems. Princeton University Press.
Minto, J. M., Lunn, R. J., and El Mountassir, G. (2019). “Development of a reactive transport model for field‐Scale simulation of microbially induced carbonate precipitation.” Water Resour. Res., 55, 7229–7245.
Nasser, M. K., Gurung, D., Bastani, M., Ginn, T. R., Shafei, B., Gomez, M. G., Graddy, C. M. R., Nelson, D. C., and DeJong, J. T. (2018). “Large-scale experiments in microbially induced calcite precipitation (micp): reactive transport model development and prediction.” Water Resour. Res., 54 (1), 480–500.
Qin, C. Z., Hassanizadeh, S. M., and Ebigbo, A. (2016). “Pore‐scale network modeling of microbially induced calcium carbonate precipitation: Insight into scale dependence of biogeochemical reaction rates.” Water Resour. Res., 52(11), 8794–8810.
Scheffer, M., Baveco, J. M., DeAngelis, D. L., Rose, K. A., and van Nes, E. (1995). “Super-individuals a simple solution for modelling large populations on an individual basis.” Ecol. Modell., 80(2-3), 161–170.
Shafiee, M. E., and Berglund, E. Z. (2017). “Complex adaptive systems framework to simulate the performance of hydrant flushing rules and broadcasts during a water distribution system contamination event.” J. Water Resour., 143(4).
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Zhang, T., and Klapper, I. (2010). “Mathematical model of biofilm induced calcite precipitation.” Water Sci. Technol., 61(11), 2957–29.
Information & Authors
Information
Published In
Geo-Congress 2024
Pages: 446 - 455
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Carbonation
- Chemical processes
- Chemistry
- Climates
- Engineering materials (by type)
- Environmental engineering
- Geomechanics
- Geotechnical engineering
- Granular materials
- Material mechanics
- Material properties
- Materials engineering
- Mechanical properties
- Meteorology
- Microbes
- Organisms
- Pollution
- Porous media
- Precipitation
- Soil mechanics
- Soil pollution
- Soil properties
- Soil treatment
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.