Hydrogen limitation—a method for controlling the performance of membrane biofilm reactor for autotrophic denitrification of wastewater
Bibliographic record
Abstract
Hydrogen-driven denitrification using the fiber membrane biofilm reactor (MBfR) was evaluated for consistent operation in tertiary wastewater treatment. The possibility of controlling the process rates, as well as biofilm parameters by supplying limited amounts of electron donor (hydrogen), was tested. Limiting the hydrogen supply proved to be efficient in controlling the biofilm growth and performance of the MBfR. Denitrification rates remained unchanged for both synthetic wastewater (SWW) and real municipal wastewater (MWW) effluent as well through the fluctuations in the substrate (NO3-N) concentration. The average denitrification rates were 0.50 (+/- 0.02) g NO3-N per day per m2 for SWW and 0.59 (+/- 0.04) g NO3-N per day per m2 for MWW. Biofilm density rather than thickness was the determining factor in substrate diffusion and biofilm sloughing, ultimately determining operating stability. Limited hydrogen supply assured constant volatile solids (VS) concentration in the biofilm. It was determined that VS/TS ratio higher than 0.25 assured stable biofilm operation. Decrease of VS/TS ratio below 0.25 led to shearing of the nonbiological outer layers of the biofilm. The values of chemical oxygen demand (COD), volatile suspended solids (VSS) and total suspended solids (TSS) in the final effluent were stable and well below wastewater effluent guidelines. Substitutions of bicarbonate with gaseous carbon dioxide as the carbon source did not affect denitrification rates despite lower than optimum pH conditions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".