Membrane Scaling in Electrodialysis Fed with High-Strength Wastewater
Bibliographic record
Abstract
Membrane scaling problems can limit broad applications of electrodialysis (ED) for nutrients recovery from wastewater. In this study, we investigated the calcium- and magnesium-scale precipitation on ion-exchange membranes (IEMs) using a laboratory-scale ED reactor. Two high-strength wastewater streams, including municipal waste (MW) liquid digestate and food waste (FW) liquid digestate, were fed into the ED reactor. For the operation with MW liquid digestate, the cumulative Ca2+ loss increased with the increasing electric current, while the electric current conditions did not affect the cumulative Mg2+ loss. After 8-h operation, 60.1% of Ca2+ and 39.0% of Mg2+ in the MW liquid digestate were lost in the form of precipitates. Observed scalants on cation-exchange membranes were vaterite, amorphous calcium carbonate (ACC), and struvite, while ACC was not found on anion-exchange membranes. Observed scalants of calcium carbonate with MW liquid digestate (vaterite and ACC) were different from scalants (calcite) found with synthetic solutions. Among these scalants, struvite was formed as sharp (needle-shaped) crystals that can potentially damage the IEM. The gradual loss of Mg2+ was observed with FW liquid digestate because of high PO43− concentration, indicating the formation of struvite. The membrane with high selectivity for divalent ions resulted in the rapid decrease in electric current, implying serious membrane scaling on IEMs. These findings demonstrated that the membrane scaling problems by calcium and magnesium precipitation are ubiquitous in ED for nutrients recovery from wastewater.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".