Effect of micro-nutrients in anaerobic degradation of sulfate laden organics
Bibliographic record
Abstract
The effect of micro-nutrients, such as Fe, Ni, Zn, Co, and Mo, on anaerobic degradation of sulfate laden organics was investigated using bench-scale models of upflow anaerobic sludge blanket (UASB) reactor, anaerobic baffled reactor (ABR), and hybrid anaerobic baffled reactor (HABR), operating in varying conditions in ten phases (organic loading of 1.9–5.75 kg COD/(m3·d), sulfate loading of 0.54–1.88 kg SO42–/(m3·d), chemical oxygen demand (COD):SO42–ratio of 2.0–8.6). In the initial phase, no nutrient limitation was observed with COD removal of more than 94% in all three systems. Subsequently, increase in sulfate loading resulted in Ni and Co limitation and their supplementation restored COD removal in UASB system. However, baffled systems did not recover because of severe inhibition by sulfide. Results indicate that precipitation of nutrients could seriously deteriorate process performance, leading to failure even before sulfide concentration attains toxic level. The limitation of Fe coupled with high sulfate loading (1.88 kg SO42–/(m3·d)) resulted in growth of low-density, fragile, hollow, and granular biomass in UASB that washed out and caused process instability. Supplementation of Fe with other nutrients stabilized UASB process and also improved COD removal.Key words: anaerobic degradation, nutrients, UASB, ABR, HABR, sulfide toxicity, sulfate laden organics.
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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) | 0.000 | 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 | 0.000 | 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".