Statistical Analysis and Optimization of Acid Dye Biosorption by Brewery Waste Biomass Using Response Surface Methodology
Bibliographic record
Abstract
Biosorption of Acid Yellow (AY 17) and Acid Blue (AB 25) were investigated using a biomass obtained from brewery industrial waste spent brewery grains (SBG). A 24 full factorial response surface central composite design with seven replicates at the centre point and thus a total of 31 experiments were employed for experimental design and analysis of the results. The combined effect of time, pH, adsorbent dosage and dye concentration on the dye biosorption was studied and optimized using response surface methodology. The optimum contact time, pH, adsorbent dosage and dye concentration were found to be 45min, 6, 0.5g, 75 mg/L respectively for the maximum decolorization of AY 17(97.2%) and 40 min, 2, 0.4g and 75 mg/L respectively for the maximum decolorization of AB 25(97.9%). A quadratic model was obtained for dye decolourization through this design. The experimental values were in good agreement with predicted values and the model developed was highly significant, the correlation coefficient being 0.89 and 0.905 for AY 17 and AB 25 respectively. Experimental results were analyzed by Analysis of variance (ANOVA) statistical concept.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".