A Pre- and Post-Denitrification System Treating a Very High Ammonia Landfill Leachate: Effects of pH Change on Process Performance
Bibliographic record
Abstract
The prime objective of this research was to investigate the nitrogen removal capabilities of a pre- and post-denitrification process, when treating landfill leachate containing an ammonia concentration of over 2,200 mg N l(-1). The treatment system, also known as a 4-Stage Bardenpho process, was operated with an external recycle ratio of 3:1 and an internal recycle ratio of 4:1. The first anoxic reactor actual hydraulic retention time was 1.5 hours, while the first aerobic retention time was 3 hours. The very high ammonia concentration was simulated by pumping ammonium chloride into the first anoxic reactor of the system. Methanol was used as the organic carbon source for denitrification. At an influent ammonia concentration of about 2,200 mg N l(-1), the anoxic pH levels stabilized at about 8.6 within the first reactor, and at about 9.8 within the second reactor. These high anoxic pH levels were partially responsible for decreased denitrification and, hence, residual NO concentrations in the effluent. By decreasing the pH in the anoxic basins, the overall performance of the system immediately improved; the effluent NOx concentration decreased rapidly over a period of about six days, from an average 80 mg N l(-1) to about 60 mg N l(-1), with some samples as low as 40 mg N l(-1). Subsequently, increased leachate toxicity resulted in an unexpected system failure; although the treatment system eventually recovered and stabilized, denitrification in each anoxic reactor remained at only about 55%, with final effluent NOx concentrations of over 100 mg N l(-1). Despite this reduced level of performance, the decrease in anoxic pH resulted in enhanced nitrification performance, complete ammonia removal in the first aerobic reactor, and a more stable, overall performance in the 4-Stage Bardenpho process.
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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".