Biosorption of Acid Yellow by Spent Brewery Grains in a Batch System: Equilibrium and Kinetic Modelling
Bibliographic record
Abstract
Biosorption of Acid Yellow (AY17) a monoazo acid dye currently used in textile and dyeing industries was investigatedusing Spent Brewery Grains (SBG) a brewing industry waste in a batch system with respect to initial pH, temperature,initial dye concentration, biosorbent dosage, and contact time. The biomass exhibited the highest dye uptake capacity at303 K, initial pH value of 2, the initial dye concentration of 150mg/L, biosorbent dosage of 0.5 g and contact time of 40min. The extent of dye removal increased with increase in time, biosorbent dosage and decreased with increase intemperature. The equilibrium sorption capacity of the biomass increased on increasing the initial dye concentration upto 150 mg/L and then started decreasing in the studied concentration up to 600 mg/L.The experimental results hasshown that the acidic pH favours the biosorption. Langmuir and Freundlich adsorption model is used for themathematical description of the biosorption equilibrium and isotherm constants are evaluated at different temperatures.Equilibrium data fitted very well to the Freundlich model in the studied concentration (25-600 mg/L) and temperature(303-323 K) ranges. The pseudo first- and second-order kinetic models were also applied to the experimental data. Theresults indicated that the dye uptake process followed the pseudo second-order rate expression and adsorption rateconstants increased with increasing concentration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".