Biological denitrification of reverse osmosis brine concentrates: I. Batch reactor and chemostat studies
Bibliographic record
Abstract
A major technological concern with reverse osmosis in water purification, wastewater treatment, and water reclamation or recycling is the production of brine concentrates high in ammonia or nitrogen. This project addresses biological denitrification of reverse osmosis brine concentrates in a bioactive fluidized bed adsorber reactor (FBAR), accomplished in four stages. The first three stages are described in this paper, while the final stage is addressed in the companion paper (Ersever et al. 2007). The first stage optimized an FBAR to produce nitrified brine for subsequent denitrification studies. The second stage employed batch reactors to evaluate denitrification parameters such as temperature, pH, total dissolved solids, and carbon-to-nitrogen ratio. The specific denitrification rate was maximum at a temperature of 35 °C, pH of 8.0, and carbon-to-nitrogen ratio of 1.8. The third stage involved chemostats to determine Monod parameters under nitrate-, nitrite-, and carbon-limiting conditions. A biokinetic model was employed to simulate chemostat dynamics and to estimate the biological parameters. The final stage entailed FBAR denitrification experiments under different hydraulic retention times, nitrate concentrations, and packing media; simultaneous denitrification and sulfate reduction were addressed. A second FBAR used in series with the first achieved an overall sulfate reduction of 99%, and a biofilter effectively removed the hydrogen sulfide generated.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".