Long-term operation of membrane biofilm reactors for nitrogen removal with autotrophic bacteria
Bibliographic record
Abstract
Efficient gas delivery and biofilm development on membrane fibers in a membrane biofilm reactor (MBfR) would be well suited to autotrophic nitrification and denitrification using hydrogen. Total nitrogen removal in a two-step MBfR system incorporating sequential nitrification and hydrogen-driven autotrophic denitrification was investigated in order to achieve nitrogen removal by autotrophic bacteria alone. This study also aimed at the long-term stable operation, which proved difficult in previous studies due to excessive biofilm accumulation in autotrophic denitrification systems. Consecutive operation of nitrification and autotrophic denitrification lasted 230 days. Average specific nitrification rate of 1.87 g N/m(2) d was achieved and the performance was very stable throughout the experimental periods. Nitrification performance from this study showed comparable rates to previous studies although this work was conducted at slightly lower temperature. Batch tests confirmed the presence of nitrifiers from the effluent of the nitrification reactor, which reattached to the biofilm in the denitrification reactor leading to further nitrification. Performance of autotrophic denitrification was maintained stably throughout the experimental periods, however biofilm control by nitrogen sparging was required for process stability. Average specific denitrification rate of 1.41 g N/m(2) d and a maximum specific denitrification rate of 2.04 g N/m(2) d was maintained. This study showed that, with an appropriate biofilm control plan, stable long-term operation of a fully autotrophic MBfR system for total nitrogen removal was possible without major membrane cleaning procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".