Modelling COD Removal from Slaughterhouse Wastewater by Electrocoagulation Using Response Surface Methodology
Bibliographic record
Abstract
Modeling is an indispensable tool for a better wastewater treatment strategy. However, the modelling of slaughterhouse wastewater treatment by electrocoagulation can be difficult to achieve because of the various physico-chemical mechanisms involved. It is in this context that the objective of this study was to model and optimize COD removal and electrical energy consumption by response surface methodology (RSM) during the treatment of slaughterhouse wastewater by electrocoagulation (EC). For this purpose, a full factorial design (FD) was first used to observe the effect of experimental parameters (stirring speed, pH, time and current intensity) on COD removal and energy consumption. Then, a central composite design (CCD) was performed to optimize COD removal and electrical energy consumption. The optimum conditions are obtained at the stirring speed of 871 rpm, pH = 6.83; time of 80 min and current intensity of 1.85 A. By applying these optimal conditions for the treatment, reductions of 84 ± 1.08% of COD; 93.86 ± 0.91% of BOD; 97.80 ± 0.86% of turbidity and 99.62 ± 0.12% of PO43- and an energy consumption of 9 KWh.m-3 were obtained. Thus, this study reveals that RSM is an effective tool for the modeling and optimization of electrocoagulation.
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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 | 0.000 | 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 | 0.000 |
| 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".