Organic matter capture by a high-rate inoculum-chemostat and MBBR system
Bibliographic record
Abstract
The main objective of this study was to develop an innovative process to maximize the bio-transformation of colloidal and soluble biodegradable matter (CSB) into particulate matter (XB) for energy recovery via methane production. Two configurations were studied: (1) high-rate moving bed bioreactor (HR-MBBR) and (2) inoculum-chemostat (IC) system consisting of a very HR-MBBR inoculating a continuous flow stirred-tank reactor. The effect of hydraulic retention time (HRT), specific organic loading rate (SOLR), and dissolved oxygen (DO) level were determined using real wastewater at pilot scale. Results showed that in the HR-MBBR process, a very high CSB bio-transformation efficiency (90%) was obtained in a wide range of SOLRs (2.0 to 5.5 g CSB m−2 d−1) corresponding to an optimum HRT of 36 minutes. The IC process reached a maximum CSB bio-transformation efficiency of 77%, at SOLRs ranging from 22 to 30 g CSB m−2 d−1 at an HRT of 3.7 hours. The DO concentration in the HR-MBBR influenced the CSB bio-transformation ratio, while the HRT and the SOLR were the dominant factors influencing this ratio in the IC process. Based on these results, the IC process could be an interesting alternative to high-rate systems towards obtaining energy positive/efficient from water resource recovery facilities.
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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 | 0.001 |
| 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".