Adsorption of Methylene Blue on Fix Bed Column Using Adsorbent from Tea Waste
Bibliographic record
Abstract
Printing and dyeing wastewater with a high salt content makes it difficult to process industrial wastewater, one of them is Methylene Blue (MB).MB waste is a dye commonly used in the textile industry which is harmful to the environment and threatens public health.MB removal can be done by adsorption method using tea waste adsorbent.This study aims to characterize and analyze the efficiency of tea waste in absorbing methylene blue using an adsorption column.The adsorption process was carried out with various concentrations of adsorbate (15 and 30 ppm), various bed heights (8, 12, and 16 cm), and various operating times (15, 30, 45, 60, 75, and 90 minutes), with the flow rate of 6 L.min -1 in the column.The kinetic model data were analyzed using simple first order and pseudo second order.The results showed that the highest absorption efficiency of 99.48% was obtained at adsorbate concentration of 30 mg.L -1 , contact time of 75 minutes and bed height of 16 cm.The appropriate adsorption equilibrium mechanism is the Langmuir isotherm as evidenced by the correlation coefficient (R 2 ) of 0.998, while the resulting kinetic model is Pseudo Second Order, with R 2 of 0.999, qe of 1.933 mg.g -1 , and k2 of 19,947 mg -1 min -1 .This can be seen from the correlation value (R 2 ) which is close to 1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".