Management of fatty acid methyl ester (fame) wastewater by a combined two stage chemical recovery and coagulation process
Bibliographic record
Abstract
Abstract A two‐step treatment process for fatty acid methyl ester (FAME) wastewater was carried out at a laboratory scale and ambient temperature. In the first step, FAME was chemically recovered from the wastewater using three types of acid (H2SO4, HNO3, and HCl) at different pH values ranging from 1.0 to 8.0. Optimally, approximately 15–30 mL/L of FAME was recovered when using H2SO4 at a final wastewater pH of 1–2.5 and a reaction time of 7 min. The properties of the recovered FAME were within the acceptable ranges for both community and methyl ester standards, except for the viscosity and the quantity of methyl ester. In the second treatment step, the aqueous phase discharged from the first step was adjusted to within the favourable pH range for chemical coagulation by either Al2(SO4)3 (pH 4.5–10) or poly‐aluminum chloride (PAC; pH 2.5–7.0) by the addition of CaO, and then subjected to chemical coagulation with either Al2(SO4)3 or PAC, as appropriate dose at 0–10 g/L. Under optimum conditions, >98.3%, 97.7%, and 99.2% of COD, BOD5, and oil and grease were respectively removed using Al2(SO4)3 at 2 g/L, whilst that achieved by PAC coagulation (at 1 g/L) was slightly lower at 98.2%, 96.5%, and 98.6%, respectively. The calculated operating cost of this management system was significantly cheaper than those using conventional management procedures, but will require an additional treatment stage such as biological remediation in sedimentation ponds to reduce the pollutant levels to acceptable limits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".