Inorganic precipitation during autotrophic denitrification under various operating conditions
Bibliographic record
Abstract
The effects of hydraulic retention time (HRT, 7 h, 13.5 h and 18 h) and pH (7.5 and 8.0) on the denitrification performance and the extent of inorganic precipitation, in a H2-driven non-porous membrane biofilm reactor for autotrophic denitrification were investigated. The membrane was cleaned periodically to evaluate the solids contents generated in the system and identify the precipitants. The reactor that was operated under the longer HRT contained more inert solids than when operated under shorter HRT, and showed lower specific nitrate removal rates, even though the generation of volatile solids was almost identical in all tested cases. A lower pH level at shorter HRT, due to higher influent dilution rates, resulted in improved denitrification rates. However, aggressive pH control, by adding additional phosphate buffer, was not beneficial because of extensive precipitation of up to 76% of total solids. Inorganic precipitation was strongly and linearly related to the denitrification performance, indicating the inhibitory effect of inorganic precipitants on the transfer of gases or substrates in a membrane biofilm reactor. The sloughed-off solids from the reactors were characterized by ICP analysis and revealed an abundance of various calcium-related precipitants, which were found to be responsible for the increase in the inert solids content. Calcium phosphate precipitation was identified as the major reason for the inhibition of system performance in this study. Operation under long HRT increases the potential to generate substantial amounts of diverse, inhibitory inorganic precipitants.
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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) | 0.001 | 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 | 0.000 |
| 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".