Parametric Study of Methyl Orange Removal Using Metal–Organic Frameworks Based on Factorial Experimental Design Analysis
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) are one of the most energy-intensive industries. Every stage of wastewater treatment consumes energy, which is the primary contributor to WWTP costs. Adsorbents and process optimization are critical for energy savings. The removal of dyes from industrial wastewater by adsorption using commercially available adsorbents is inefficient. Metal–organic frameworks (MOFs) have outstanding properties that can improve separation performance over current commercial adsorbents, and thus, these materials represent a milestone in improving dye removal in water treatment methods. In this work, three types of metal–organic frameworks (Fe-BTC, Cu-BTC, and ZIF-8) have been investigated as prospective adsorbents for methyl orange removal from water in batch setups. The results showed that at 15 mg/L MO initial concentration and 100 mg dosage, Fe-BTC had the highest removal efficiency of 91%, followed by ZIF-8 (63%), and finally Cu-BTC (35%), which exhibited structural damage due to its instability in water. Fe-BTC maintained consistent adsorption capacity over a wide range of pH values. Furthermore, a 23 full factorial design analysis was implemented to evaluate the conditions for maximum MO-removal efficiency. The main effects, interaction effects, analysis of variance (ANOVA), and the Pareto chart were reported. The statistical analysis demonstrated that the MOF type was the most significant factor, followed by dosage and initial concentration. The analysis indicated that the type of MOF and dosage had a positive effect on the removal efficiency, while the initial concentration had a negative effect. The two-way and three-way interactions were also found to be significant.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".