Investigation of electrode passivation during electrocoagulation treatment with aluminum electrodes for high silica content produced water
Bibliographic record
Abstract
Abstract One of the main challenges for the implementation of electrocoagulation (EC) in water treatment are fouling and passivation of the electrodes, especially for applications with high contaminant concentrations. For the first time, we investigated in this study the process of fouling mitigation by polarity reversal during the EC treatment of boiler blowdown water from oil-sands produced water, characterized by high silica concentrations (0.5–4 g L−1). This effluent is typically obtained from an evaporative desalination process in oil production industries. Potentiodynamic characterisation was used to study the impact of passivation on the anode dissolution. Although a charge loading of 4,800 C L−1 was found to remove about 98% of silica from a 1 L batch of 4 g L−1 Si solution, fouling reduced the performance significantly to about 40% in consecutive cycles of direct current EC (DC-EC) treatment. Periodic polarity reversal (PR) was found to reduce the amount of electrode fouling. Decreasing the polarity period from 60 to 10 s led to the formation of a soft powdery fouling layer that was easily removed from the electrodes. In contrast, with DC operation, a hard scale deposit was observed. The presence of organics in the field samples did not significantly affect the Si removal, and organics with high levels of oxygen and sulfate groups were preferentially removed. Detailed electrochemical and economic investigations suggest that the process operating at 85 °C achieves 95% silica removal (from an initial concentration of 481 mg L−1) with an electrical energy requirement of 0.52 kWh m−3, based on a charge loading of 1,200 C L−1, an inter-electrode gap of 1.8 cm and a current density of 16 mA cm−2.
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".