Evaluation of heterotrophic nitrite removal by a sulphide and acetate oxidizing mixed culture originated from an oil reservoir
Bibliographic record
Abstract
Abstract BACKGROUND: Shortcut biological nitrogen removal (SBNR) has attracted much attention in recent years due to lower aeration and chemical oxygen demand (COD) requirements, shorter residence time and smaller biomass production. In this work an oil reservoir denitrifying culture, with the ability to function under autotrophic and heterotrophic conditions was used for heterotrophic denitritation. Using freely suspended cells, effects of nitrite concentration (10–50 mmol L −1 ) and temperature (15–35 °C) on the kinetics of denitritation were investigated and a kinetic model was developed. Potential for enhancement of nitrite removal rate, and impacts of nitrite concentration and loading rate were investigated in a continuous biofilm reactor. RESULTS: Nitrite did not impose any inhibitory effect, even at the highest applied concentration of 50 mmol L −1 . Increase of temperature in the range 15–35 °C enhanced the reduction rate significantly. Fitting the experimental data into the model developed, values of biokinetic coefficients (µ max−NO2 , K S−NO2 , Y X−NO2 , Y X−Ace−NO2 and Eµ‐ NO2 ) were determined. In the biofilm reactor increases in nitrite loading rate (through flow rate or feed nitrite concentration) led to a linear increase of nitrite removal rate, with the highest removal rate of 140.6 mmol L −1 h −1 achieved with a residence time of 0.19 h. CONCLUSION: The enrichment culture used in this study is not only a superior biocatalyst for simultaneous removal of sulphide, nitrate and BOD, it could also be used effectively in the denitritation step of an SBNR process. The kinetic model developed would certainly have beneficial applications in the design, operation and control of the SBNR process. Copyright © 2011 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".