The microbiome of two strategies for ammonia removal with the sequencing batch moving bed biofilm reactor treating cheese production wastewater
Bibliographic record
Abstract
ABSTRACT The moving bed biofilm reactor is a compact technology established for treating total ammonia nitrogen (TAN) from municipal wastewater via nitrification or denitrification. The sequencing batch moving bed biofilm reactor (SB-MBBR) has been applied for on-site biological treatment of carbon and phosphorous from cheese production wastewater; however, nitrification is limited by the competition between nitrifiers and heterotrophs. Two strategies are compared to circumvent heterotrophic competition and achieve TAN oxidation in an SB-MBBR system already achieving carbon and phosphorous removal: extended aerobic operation of a single SB-MBBR and two SB-MBBRs in series. TAN oxidation occurred after 810 hours with the extended aerobic operation, where a microbiome shift occurred to support an ammonia-oxidizing bacterial population. Thus, a single SB-MBBR is not feasible for achieving nitrification simultaneously with carbon and phosphorous removal when treating cheese production wastewater. After 30 hours of operation, two SB-MBBRs in series achieve TAN removal, possibly through partial nitritation, with a TAN surface area removal rate of 1.07 ± 0.05 g-N·m −2 d −1 and an enriched abundance of ammonia-oxidizing bacteria. To the best of our knowledge, this is the first study to analyze the microbiome of the SB-MBBR achieving TAN removal from cheese production wastewater and present an operational strategy to achieve TAN while treating cheese production wastewater with SB-MBBRs. Also, this is the first study to show evidence that partial nitrification can be achieved in an SB-MBBR system that is also treating carbon and phosphorous from cheese production wastewater and demonstrates the potential for the SB-MBBR to be incorporated in a deammonification system. IMPORTANCE Cheese production facilities must abide by sewage discharge bylaws that prevent overloading municipal water resource recovery facilities, eutrophication, and toxicity to aquatic life. Compact treatment systems can permit on-site treatment of cheese production wastewater; however, competition between heterotrophs and nitrifiers impedes the implementation of the sequencing batch moving bed biofilm reactor (SB-MBBR) for nitrification from high-carbon wastewaters. This study demonstrates that a single SB-MBBR is not feasible for nitrification when operated with anerobic and aerobic cycling for carbon and phosphorous removal from cheese production wastewater, as nitrification does not occur in a single reactor. Thus, two reactors in series are recommended to achieve nitrification from cheese production wastewater in SB-MBBRs. These findings can be applied to pilot and full-scale SB-MBBR operations. By demonstrating the potential to implement partial nitrification in the SB-MBBR system, this study presents the possibility of implementing partial nitrification in the SB-MBBR, resulting in the potential for more sustainable treatment of nitrogen from cheese production wastewater.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".