PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 7, 2026Water Research2 citationsOpen Access

A modeling perspective on biofilm formation in granular activated carbon filters – local promotion of autotrophic microorganisms due to the effects of adsorption processes on the availability of organic substrates

View Full Paper
TKTobias KaiserCPCristian PicioreanuSLSusanne Lackner

Key Points

  • This study aims to understand how adsorption processes in granular activated carbon (GAC) filters affect biofilm dynamics and microorganism growth.
  • Simulated biofilm dynamics using a one-dimensional grain approach
  • Accounted for spatial gradients along the filter bed length
  • Tested the model with operational data from pilot-scale GAC filters
  • Assessed the effects of backwash events on microbial growth
  • Heterotrophic microorganisms grew closer to the filter influent, while autotrophs thrived in intermediate regions
  • Adsorption led to a stricter separation of microorganisms in the filter compared to non-adsorbing systems
  • Backwash events temporarily enhanced autotrophic growth due to organic retention and release

Abstract

• Simulation of biofilm dynamics in a granular activated carbon filter bed • Distributed 1D grain approach for continuum representation of longitudinal gradients • Adsorption of organic substrates locally promotes autotrophic growth • Temporary storage of organics directly after backwash events reinforces this effect • Overall higher activity of ammonia oxidizers in granular activated carbon filters Various studies demonstrated that biofilm formation occurs in granular activated carbon (GAC) filters for (waste-)water treatment. However, little is known about how transient adsorptive interactions between organic solutes and the GAC within the filter bed influence biofilm development on the macroscale. This study proposes a numerical approach to simulate biofilm development in a GAC filter bed. For this purpose, a model approach for simulations at the single grain scale was extended to additionally account for spatial gradients along the filter bed length. The model was successfully tested with operational data from pilot-scale GAC filters. The subsequently simulated scenarios aimed at conceptually identifying key interactions between the GAC and biofilm formation, including spatial gradients in its composition. The simulation results showed that both heterotrophic and autotrophic microorganisms grew in GAC filters under typical operating conditions. The heterotrophs grew closer to the filter influent, consistent with the system’s plug-flow-like behavior. Adsorption of organic solutes onto the GAC resulted in a stricter longitudinal separation of the two general types of microorganisms in the filter bed compared to a non-adsorbing reference filter bed by decreasing the downstream concentrations of organic substrate. Considering explicit backwash events further consolidated this separation for the GAC case. Together with the periodic adsorptive retention and release of organic solutes in the upper filter bed section, depending on the current biological activity, backwash events created even more favorable conditions for autotrophic growth in intermediate regions of the filter bed. Overall, the simulation results showed that autotrophic activity was locally enhanced by adsorptive effects of the GAC and that its extent was directly influenced by the simulated backwash regime. Considering the link between autotrophic activity and co-metabolic biotransformation of organic micropollutants discussed in literature, the results further highlight the potential biological contributions to the overall removal of certain micropollutants in GAC filters, but also the necessity to adequately represent longitudinal gradients and biofilm thickness control mechanisms in mathematical models.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Kaiser et al. (2026) studied this question.

synapsesocial.com/papers/69abc1015af8044f7a4e99d1https://doi.org/10.1016/j.watres.2026.125693
Ask AI
Helpful
Bookmark
Share
View Full Paper