Removal of Sulfonated Humic Acid through a Hybrid Electrocoagulation–Ultrafiltration Process
Bibliographic record
Abstract
This study investigated the removal of sulfonated humic acid (SHA) from water through a hybrid electrocoagulation–ultrafiltration treatment process. The effects of major operating parameters including electrocoagulation time, current density, and initial pH on the electrocoagulation performance were evaluated. The increase in current density and operating time as well as decrease of pH improved the SHA removal efficiency. The operating conditions of electrocoagulation process were optimized through Box–Behnken design to maximize SHA removal. The optimum conditions for electrocoagulation included time of 7 min, current density of 10 mA/cm 2, and pH of 5. Effective SHA removal was further achieved in the hybrid electrocoagulation–ultrafiltration treatment process. The performances of three molecular weight cutoff membranes were examined. The results showed that the SHA removal efficiency increased with the increasing initial concentration of SHA and decreased with the increasing transmembrane pressure. The SHA removal efficiency was more than 95% by 5 kDa membrane. The SHA removal efficiency by different membranes from high to low in turn was: 5 kDa > 8 kDa > 10 kDa. The results will have significant implications for the treatment of complex drilling and hydraulic fracturing wastewater through electrocoagulation–ultrafiltration process.
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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) | 0.001 | 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".