Development of a Nitrifying Bioreactor for the Treatment of Wastewater in Long-Term Space Applications
Bibliographic record
Abstract
The objective of this study was to evaluate the potential use of hollow fiber membrane-aerated bioreactors for the nitrification of wastewater during long-term space missions. To define the minimum acceptable treatment efficiency for the experiment, the water recovery system (WRS) at Texas Tech University (TTU) was used, which is a small-scale replica (1/20th) of the WRS at NASA's Johnson Space Center (JSC). The WRS consists of a biological system designed for nitrogen and organic carbon removal, and a post-processing system consisting of reverse osmosis (RO), ion exchange (IE) and UV treatment. The biological portion of the WRS was the focus of this research and contains an anaerobic packed bed (PB) promoting denitrification, and a tubular reactor (TR) promoting nitrification. Biological systems have been deemed promising by NASA for long-term space applications because of their low mass and high energy efficiency. Both the biological portion of the WRS and the AMR utilized an internal recycle; this study analyzed the data from a recycle ratio (RR) of 1:10 (expressed as mL/min of feed to mL/min of recycled effluent). The AMR was operated at RR 10 for a period of 120 days, while the WRS was operated at RR10 for 109 days. The percent nitrification for the AMR (60.7%) was higher than the WRS (52.3%), indicating the possibility of the AMR as a replacement for the TR. During analysis of the two systems, it was noted that the AMR contained denitrifying organisms, supported by the removal of nitrogen and organic carbon (44.7% and 91.9%, respectively). The AMR was not originally designed to operate as a denitrifying reactor, but the data indicated the potential of the AMR operating as a combined carbon-nitrogen removal system. Following the analysis of this data, two more AMR's were built and are currently being evaluated at TTU.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".