Electrocoagulation Using a Hybrid Combination of Iron and Aluminum Electrodes with Asymmetric Polarity Reversal
Bibliographic record
Abstract
This study explores the use of asymmetric polarity reversal (PR) in continuous electrocoagulation (EC) for removing silica and hardness using Fe/Al hybrid electrodes. Asymmetric PR takes advantage of aluminum’s tendency to undergo cathodic dissolution by alternating a “forward” current with cheaper iron anodes and a shorter reverse current with aluminum as the anode, as iron cathodes are not prone to dissolution. Adjusting the polarity reversal time (PRT) varied the relative dosing of the Fe and Al coagulants. Silica removal exceeded 95% across all asymmetric PRTs at a fixed charge loading of 2000 C L –1, with energy consumption between 1.44 and 1.93 kWh m –3 . Symmetric 10 min PRT achieved 90% Ca and 75% Mg removal, outperforming direct current (DC) EC. Both asymmetric PRTs of 10 min (Fe) and 30 s (Al) and a symmetric PRT of 10 min (Fe/Al) achieved high contaminant removal and relatively low treatment costs while using different amounts of Fe and Al. These configurations also lowered the cell voltage due to reduced fouling and passivation. This hybrid EC with asymmetric PR offers a novel, cost-effective method for adjusting the performance and optimizing the cost of wastewater treatment.
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) | 0.001 | 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".