Biological denitrification of reverse osmosis brine concentrates: II. Fluidized bed adsorber reactor studies
Bibliographic record
Abstract
This phase of the study (Part II) investigates the application of a high-rate fluidized bed adsorber reactor (FBAR) process for biological denitrification of reverse osmosis (RO) brine concentrate. The companion paper (Part I) reported the first phase of the project, describing the batch and chemostat biokinetic studies employed for evaluating the process feasibility, and for optimizing the reaction conditions including the carbon and energy source, pH, temperature, and the carbon-to-nitrogen ratio. This paper describes the next stage of the study involving FBAR experiments using granular activated carbon (GAC) for denitrification and sulfate reduction conducted at different hydraulic retention times, and nitrate concentrations. These experiments showed that nitrate removal efficiencies in excess of 99% were achieved. Similar FBAR experiments conducted with sand showed that initially less nitrate removal was experienced, but under steady-state conditions over 99% removal was achieved. Additionally, this study examined the simultaneous denitrification and sulfate removal in a similar FBAR process. A second FBAR, employed to remove the remaining sulfate from the first FBAR, achieved over 99% removal. A biofiltration process was designed and operated to effectively eliminate hydrogen sulfide in the sulfate-reducing FBAR process. The sulfate reduction FBAR was also responsible for the removal of toxic metals and metalloids present in the brine concentrate by a combination of sulfide precipitation and sorption onto iron sulfides.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".