Effects of bioaugmentation strategies in UASB reactors with a methanogenic consortium for removal of phenolic compounds
Bibliographic record
Abstract
The removal of phenol, ortho- (o-) and para- (p-)cresol was studied with two series of UASB reactors using unacclimatized granular sludges bioaugmented with a consortium enriched against these substances. The parameters studied were the amount of inoculum added to the sludges and the method of immobilization of the inoculum. Two methods were used, adsorption to the biomass or encapsulation within calcium alginate beads. In the bioaugmentation by adsorption experiment, and with a 10% inoculum, complete phenol removal was obtained after 36 d, while 178 d were required in the control reactor. For p-cresol, 95% removal was obtained in the bioaugmented reactor on day 48 while 60 d were required to achieve 90% removal in the control reactor. For o-cresol, the removals were only marginally better with the bioaugmented reactors. Tests performed with the reactors biomass under non-limiting substrate concentrations showed that the specific activities of the bioaugmented biomasses were larger than the original biomass for phenol, and p-cresol even after 276 of operations, showing that the inoculum bacteria successfully colonized the sludge granules. Immobilization of the inoculum by encapsulation in calcium alginate beads, was performed with 10% of the inoculum. Results showed that the best activities were obtained when the consortium was encapsulated alone and the beads added to the sludges. This reactor presented excellent activity and the highest removal of the various phenolic compounds a few days after start-up. After 90 d, a high-phenolic compounds removal was still observed, demonstrating the effectiveness of the encapsulation technique for the start-up and maintenance of high-removal activities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".