Optimization of Coagulation Process for Landfill Leachate Pre-Treatment Using Response Surface Methodology (RSM)
Bibliographic record
Abstract
The effect of a coagulation process with ferrous sulfate as a coagulant on COD, apparent color and turbidity was evaluated using response surface methodology (RSM). A jar test method was used for the pre-treatment of mature landfill leachate of the Pulau Burung Sanitary Landfill, Malaysia. The results of response surface methodology (RSM) showed that ferrous sulfate was most efficient under alkaline conditions and with a coagulant dosage 10 g/l. At the optimum setting for coagulant dosage was 10 g/l and pH 11.7, resulted in maximum of (22%) in COD, (42%) in apparent color, and (31%) in turbidity. It was observed that the COD, apparent color and turbidity reductions decreased with pH and FeSO4 levels beyond the optimum conditions. This paper illustrates the usefulness of experimental design in running experiments and optimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".