Adsorption of <scp>5G</scp> blue reactive dye using passion fruit pomace: Kinetics, <scp>ANN</scp> modelling, and process optimization
Bibliographic record
Abstract
Abstract The present work reports on the use of artificial neural networks to predict the adsorption of 5G blue reactive dye (5GBRD) on yellow passion fruit pomace in a fixed‐bed process and the % dye removal optimization. The samples were characterized using a thermogravimetric analyzer and scanning electron microscopy. Batch adsorption experiments were conducted to analyze the impact of the initial concentration of 5GBRD, contact time, and solution pH and temperature. For the fixed‐bed adsorption experiments, the processing time (0–55 h), inlet flow rate (1–4 mL min −1 ), initial dye concentration (35–70 mg L −1 ), and bed height (15–23 cm) were evaluated. The predictive model was built using a multilayer perceptron machine learning (artificial neural network [ANN]) model, and the process optimization used the dividing rectangles (DIRECT) algorithm. The best ANN model architecture was 4–4–1 and the accuracy of testing data were as follows: coefficient of determination ~0.97, mean squared error ~0.004, mean average error ~0.04, and root mean square error ~0.06. The DIRECT optimization algorithm indicated that the maximum % dye removal is achieved at 43.8 h, 3.7 mL min −1 , 66 mg L −1 , and 19.3 cm. The ANN model and DIRECT optimization algorithm are valuable tools for practical applications in adsorption process modelling and optimization.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".