Statistical Modeling and Optimization of Greywater Treatment Using Electrocoagulation Process
Bibliographic record
Abstract
As the worldwide water situation worsens, new water supplies need to be developed, and greywater reclamation is one of the main sustainable water management systems.The efficacy of the electrocoagulation (EC) process, optimized through design of experiments (DOEs), is evaluated for the treatment of greywater in this study.The research was designed to model and optimize the removal of targeted selected key pollutants, turbidity, color, and COD, by assessing the impact of five significant operating variables: contact time (5-25 min), current density (0.5-1.5 A), NaCl amount (0-100 ppm), initial pH (3-9), and temperature (20-40℃).A statistical examination was conducted using a 13-run experimental dataset.The results indicated that NaCl dosage and electrical current were the two major factors significantly affecting removal efficiencies, with a strong correlation within the tested range.This unexpected outcome can be attributed to the use of lower currents (approximately 0.5 A), and a very small amount of electrolyte was added during the process.Hence, better results were achieved due to avoiding the detrimental side reactions and thus obtaining higher energy efficiency.Factors including treatment time, pH, and temperature showed weaker linear effects, which can be explained by the presence of non-linear relationships and process plateaus.The statistical DOE was utilized to fit the second-order polynomial models to the removal efficiencies, and these models were predicted accurately (R² > 0.90).The simulation of optimization revealed that the conditions for all contaminants are simultaneously and maximally removed at a low current, a moderate treatment time, and a near-neutral pH.This research shows that the EC process is a highly efficient and potentially cost-effective technology for the treatment of greywater.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".