Ammonia recovery from simulated food liquid digestate using bipolar membrane electrodialysis
Bibliographic record
Abstract
Contamination of natural waters due to nitrogenous wastes has become a crucial environmental problem due to deterioration of water quality and eutrophication in aquatic eco-systems. Thus, the reduction of nitrogen accumulation in the natural environment is vital to maintain a healthy eco-system. Bipolar membrane electrodialysis (BMED) is a promising technology for selective ammonia separation from high-strength wastewater, such as liquid digestates of food waste or wastewater sludge. This technology was recently studied for reducing membrane scaling problems associated with conventional electrodialysis (ED) systems due to the water splitting mechanism in the BPM interface. A bench-scale BMED stack was built using 5 pairs of cation exchange membranes (CEMs) and bipolar membranes (BPMs). Using the BMED stack, a simulated food liquid digestate solution was examined to separate ammonia with different voltage applications and inter-membrane distances. The highest ammonia recovery was obtained at a cell pair voltage of 5.83 V (81% separation). Experiments on investigation of optimal inter-membrane distance of BMED operation suggested that the inter-membrane distance could be increased up to 2.46 mm without a significant decrease in nitrogen recovery. The residual Ca2+ and Mg2+ in the CIP (clean-in-place) solution which explains the degree of the scaling problem in the BMED was observed consistently below 2% of the initial mass introduced to the system, indicating that BMED design and regular CIP were effective in scaling control. The ammonia loss through CEMs to the feed cell by back diffusion was minimized due to high pH in the base cell since uncharged free ammonia was dominant over ammonium cation in the base cell. The energy required for BMED operation was comparatively low; 1.93-6.93 kWh/kg-N within 90 mins. Therefore, BMED can be considered as a sustainable candidate for selective ammonia recovery at high energy efficiency with successful scaling control.
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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".