Sequential removal of selenate, nitrate and sulfate and recovery of elemental selenium in a multi-stage bioreactor process with redox potential feedback control
Bibliographic record
Abstract
Bioreduction can facilitate oxyanions removal from wastewater. However, simultaneously removing selenate, nitrate and sulfate and recovering high-purity elemental selenium (Se0) from wastewater by a single system is difficult and may lead to carcinogenic selenium monosulfide (SeS) formation. To solve this issue, a two-stage biological fluidized bed (FBR) process with ethanol dosing based on oxidation-reduction potential (ORP) feedback control was developed in this study. FBR1 performance was first evaluated at various ORP setpoints (between −520 and −360 mV vs. Ag/AgCl) and elevated sulfate concentration. Subsequently, ethanol-fed FBR2 was used to reduce sulfate from FBR1 effluent, followed by an aerated sulfide oxidation reactor (SOR). At − 520 mV ≤ ORPs ≤ − 480 mV, FBR1 removed 100 ± 0.1% nitrate and 99.7 ± 0.3% selenate without sulfate reduction. At ORPs ≥ − 440 mV, selenate reduction was incomplete, whereas nitrate removal remained stable. Se0 recovery efficiency from FBR1 effluent was 37.5% with 71% Se purity. FBR2 converted 86% of the remaining sulfate in FBR1 effluent to hydrogen sulfide, but the over-oxidation of dissolved sulfide in SOR decreased the overall sulfate removal efficiency to ~46.3%. Overall, the two-stage FBR process with ORP feedback dosing of ethanol was effective for sequentially removing selenate, nitrate and sulfate and recovering Se0 from wastewater.
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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) | 0.001 | 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 | 0.001 |
| 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".