Effect of acetate and methanol on the kinetics of total dissolved selenium removal in a chemostat
Bibliographic record
Abstract
Abstract BACKGROUND Carbon (C) source consumption in industrial bioreactors removing soluble selenium (Se) contributes to the operating cost and can influence the rate of removal. This study used a laboratory chemostat to investigate the dependency of the rate of dissolved Se removal on the concentration of acetate and methanol, two C sources used in treatment of Se containing mine‐influenced water. It was hypothesized that the rate of dissolved Se removal follows a Monod kinetic model with C source as the limiting substrate. RESULTS A chemostat fed with 25 mg‐selenate‐Se L−1 and acetate as C source at different concentrations operated over a range of hydraulic retention times (HRTs) achieved maximal removal of 99.7% dissolved Se at an HRT of 1.5 days [rate 16.7 mg‐Se (L day)−1]. When methanol was fed into the chemostat as the C source instead of acetate, the extent and rate of removal were much less [65% at an HRT of 6 days, rate 2.7 mg‐Se (L day)−1]. Carbon source consumption per mole of dissolved Se removed was not constant and exceeded stoichiometric estimates. Monod kinetic parameters for the Dechloromonas/Ralstonia consortium growing on acetate were estimated as = 3.36 mg‐C L−1 and = 0.70 L day−1. The Methylophilaceae consortium growing on methanol did not exhibit Monod kinetic growth. CONCLUSIONS Using acetate as a C source achieved greater efficiency of dissolved Se removal than when methanol was used. Total dissolved Se removal rate was dependent on acetate only at low concentrations and when the HRT was close to washout. However, total dissolved Se removal rate was strongly dependent on HRT when methanol was used. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".