Ammonia separation from wastewater using bipolar membrane electrodialysis
Bibliographic record
Abstract
Abstract Nitrogen pollution is a serious environmental challenge in natural water and thus selective ammonia separation in wastewater treatment is of great importance to decrease the nitrogen load to natural water systems. Bipolar membrane electrodialysis (BMED) is a relatively new ion exchange membrane technology that can be used for ammonia recovery from wastewater as a beneficial substance. A bench‐scale BMED stack with seven pairs of bipolar membrane (BPM) and a cation exchange membrane (CEM) was operated under various voltage applications to separate ammonia from dewatering centrate (liquid downstream from dewatering of anaerobically digested wastewater sludge). Ammonia in the wastewater was rapidly separated (up to 87% in 30 min) and recovered as ammonium hydroxide solution using the BMED stack. We found that the maximum rate of ammonium separation was governed by the concentration polarization near CEMs rather than water transport into BPMs. In addition, even with the significantly high organic level in dewatering centrate (408 mg/L as total suspended solids), high efficient ammonia separation was maintained over 8 repeated BMED operations without any pretreatment of the feed wastewater, indicating effective organic fouling control with regular chemical cleaning. Furthermore, BMED operation for 30 minutes at 5.0 V per cell pair was found to be ideal for high purity ammonium hydroxide production and low electrical energy consumption. Based on the high separation efficiency and low energy consumption, we suggest that BMED be further investigated as an attractive option for ammonia separation and recovery from wastewater.
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 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".