Optimizing Hydraulic Retention Times in Denitrifying Woodchip Bioreactors Treating Recirculating Aquaculture System Wastewater
Bibliographic record
Abstract
The performance of wood-based denitrifying bioreactors to treat high-nitrate wastewaters from aquaculture systems has not previously been demonstrated. Four pilot-scale woodchip bioreactors (approximately 1:10 scale) were constructed and operated for 268 d to determine the optimal range of design hydraulic retention times (HRTs) for nitrate removal. The bioreactors were operated under HRTs ranging from 6.6 to 55 h with influent nitrate concentrations generally between 20 and 80 mg NO3−–N L−1. These combinations resulted in N removal rates >39 g N m−3 d−1, which is greater than previously reported. These high removal rates were due in large part to the relatively high chemical oxygen demand and warm temperature (∼19°C) of the wastewater. An optimized design HRT may not be the same based on metrics of N removal rate versus N removal efficiency; longer HRTs demonstrated higher removal efficiencies, and shorter HRTs had higher removal rates. When nitrate influent concentrations were approximately 75 mg NO3–N L−1 (n = 6 sample events), the shortest HRT (12 h) had the lowest removal efficiency (45%) but a significantly greater removal rate than the two longest HRTs (42 and 55 h), which were N limited. Sulfate reduction was also observed under highly reduced conditions and was exacerbated under prolonged N-limited environments. Balancing the removal rate and removal efficiency for this water chemistry with a design HRT of approximately 24 h would result in a 65% removal efficiency and removal rates of at least 18 g N m−3 d−1. Core Ideas Woodchip bioreactor design parameters for aquaculture wastewater were developed. This application resulted in the highest N removal rates reported (39 g N m−3 d−1). Retention times differ for optimized removal efficiency versus removal rate. Sulfate reduction intensified under prolonged N-limited environments.
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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 | 0.001 |
| 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 | 0.001 | 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".