Investigation of process changes and microbial community dynamics to improve Ammonia removal in rotating biological contactors treating domestic and industrial wastewater
Bibliographic record
Abstract
Process changes, including installation of a flow splitter to equalize flow into two rotating biological contactors (RBCs) and an influent water pH adjustment, were implemented at a full-scale WWTF to improve ammonia removal. 16S rRNA amplicon sequencing and novel statistical methods were used to provide unique insight into the structure of microbial communities within the RBCs and their impact on ammonia removal before and after the process changes. RBC A met effluent ammonia targets throughout the study, while ammonia removal in RBC B improved after the process changes were initiated. The flow splitter was installed shortly before pH adjustments were made, and multiple linear regression analysis was used to isolate each process change to determine if the individual or the combined impact of these changes contributed to variations in ammonia removal. The flow splitter was primarily responsible for the improved ammonia removal, likely due to a decrease in flow into RBC B. The microbial communities were distinct between RBC A and RBC B, and while shifts in the microbial community occurred after flow equalization, these changes were minor and likely not the primary cause of increased ammonia removal. When treatment objectives are not met, utilities must respond quickly and often implement multiple process changes within a short time. Once treatment is optimized, it can be difficult to determine which process was primarily responsible for achieving effluent targets. This study highlights the role of advanced molecular tools and statistical techniques in identifying optimization strategies critical for improved treatment performance.
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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 | 0.000 | 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 | 0.000 |
| 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".