Biological nitrification and denitrification of a simulated high ammonia landfill leachate using 4-stage Bardenpho systems: system startup and acclimation
Bibliographic record
Abstract
This research investigated the nitrogen removal capability of two biological nitrification systems, with pre- and post-denitrification, when treating a landfill leachate characterized by high ammonia concentrations and low levels of biodegradable organics. The recycle ratios of the systems were set so that, at an average influent flow of 10 L/d, the actual hydraulic retention time of the first anoxic reactor was about 1.5 h for one system and 1.7 h for the other system. The systems also operated at a first aerobic reactor actual hydraulic retention time of 3 and 3.4 h, respectively. Methanol was used as a supplementary organic carbon source for denitrification. High leachate ammonia concentrations were simulated by artificially increasing influent ammonia to about 2200 mg N/L. This paper presents an overview of initial startup and acclimation, as well as some of the direct and indirect effects of methanol addition on process performance. The reported data were collected during two runs at incrementally increasing influent ammonia concentrations. During the first run to reach 2200 mg N/L, methanol loading rates were increased concomitantly with ammonia loading rates, to match expected aerobic NOx production, using a CH3OH:NOx of about 20:1. This resulted in methanol carry-over into the first aerobic zone, enhanced aerobic heterotrophic growth, and further inhibition of the nitrifying population, already inhibited by recycling through the elevated "free" ammonia levels of the first anoxic zone. When these systems were allowed to adapt up to 14 days, rather than 7 days, initially, to each incremental ammonia increase, and with methanol loading rates subsequently changed to yield CH3OH:NOx of only 5:1, the influent ammonia concentration was increased to approximately 2200 mg N/L within 88 days from the start of the second run, without any inhibitory problems. The timing and levels of ammonia and methanol loading rate increases, with respect to each other and to the corresponding previous loading rate increase, played an important role in system stability and the onset of nitrification failure.Key words: biological treatment, high ammonia leachate treatment, denitrification, methanol, nitrification.
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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 | 0.000 |
| 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 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".