Optimizing Hydraulic Retention Times in Denitrifying Woodchip Bioreactors Treating Recirculating Aquaculture System Wastewater
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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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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 | 0.002 | 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 | 0.001 |
| 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 it