Phenol biodegradation in a bioreactor considering different geometries and process parameters
Bibliographic record
Abstract
Abstract In this study, a three‐dimensional coupled lattice Boltzmann model and cellular automata platform was developed to simulate biofilm growth and phenol biodegradation as an effective and sustainable way to remove phenolic contaminants in aquatic systems. Two three‐dimensional bioreactors with cubic or spherical obstacles at varying inlet phenol concentrations and flow velocities were examined. The results showed that the cubic‐bioreactor had higher phenol reduction than the spheric‐bioreactor due to the greater effects of cubic obstacles on flow patterns. The biofilm concentration in bioreactors decreased up to 36.4% as inlet velocity increased and in the spheric‐bioreactor at the same inlet phenol concentration. The cubic‐bioreactor had the highest normalized reduction rate of 1.194 at the lowest inlet phenol concentration, while the spheric‐bioreactor had the lowest one of 0.871 at the highest inlet phenol concentration. The results proved the accuracy of the model to assess the performance of wastewater treatment bioreactors under different conditions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".