Modelling clogging and biofilm detachment in sponge carrier media
Bibliographic record
Abstract
Sponge carrier media provide a large surface area for biofilm support; however, little information is known about how to model their dual nature as a moving bed and as porous media. To investigate the interaction of mass transfer and detachment with bio-clogging, a novel biofilm model framework was built based on individual-based modelling, and hydrodynamics were modelled using the lattice Boltzmann method. The combined model structure enabled the simulation of oxygen and biomass distribution inside the porous network as well as inside the biofilm. In order to apply the model to moving bed biofilm reactors (MBBR), biofilm detachment due to abrasion (carrier collisions) was modelled to be dependent on intracarrier distance. In the initial growth stage, biofilm grew homogeneously on the internal skeleton after which a more discontinuous growth developed which significantly increased permeability. Low detachment rates caused clogging in the outer pores which limited growth of biofilm to the surface region of the sponge. High detachment rates on the surface enabled deeper oxygen penetration with higher internal biomass activity. The degree of clogging was also sensitive to the presence of extracellular polymeric substances because of its large spatial occupancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".