Electrochemical removal of organics and oil from sawmill and ship effluentsA paper submitted to the Journal of Environmental Engineering and Science.
Bibliographic record
Abstract
The present study investigates the electrocoagulation treatment of two different wastewaters, namely sawmill wastewater and ship waste effluent, charged with organic matter. Monopolar electrode configuration was studied for both types of effluents at current intensity of 2.0 A for a total treatment time of 90 min. Soluble chemical oxygen demand (CODs) removal was very low (12.5% to 13.6%) for sawmill effluent in comparison to 74.7% to 75.4% obtained for ship effluents. Thus, ship effluent was further examined in details for its treatment efficacy in terms of electrode configuration and type, current intensity, treatment time, and pH. It was observed that bipolar electrode configuration using the Al electrode at 0.3 A gave the highest CODsremoval of 77%. Effluent pH increased rapidly in the initial 20 min with a concomitant decrease in CODsconcentration. Electrocoagulated-flocculated ship effluent improved performance relative to simple flocculation with respective removals of 86% of turbidity, 56% of CODs; 69% of total COD (CODt), 90% of oil and grease, 94% of C10-C50hydrocarbons and 89% of biochemical oxygen demand (BOD5). Residual Al3+concentration in the solution followed a linear trend with treatment time. Meanwhile, the sludge production increased progressively during 60 min of treatment time. Total cost for treatment of ship effluent, including energy, electrodes, and sludge disposal fee is estimated between CAN$1.34 and CAN$2.40m–3.
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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) | 0.001 | 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".