Breakthrough Curve Model Evaluation for the Sorption of Perfluorooctanoic Acid in a Fixed Bed Column
Bibliographic record
Abstract
Solid-liquid adsorption has been demonstrated as an effective technology in eradicating contaminants of environmental concern; however, studies on proper model evaluation are scanty.On the other hand, proper system modeling is imperative to correlate adsorption dynamic data for effective process design, particularly in fixed-bed adsorption studies.As such, this study focuses on the sorption of perfluorooctanoic acid on chitosan-carbon nanotube hydrogel beads from aqueous solution in a fixed-bed adsorption column.The traditional and fractal-like Thomas, Bohart-Adams, and Yoon-Nelson breakthrough curve models were employed to fit adsorption experimental data.Breakthrough curve models were statistically evaluated using the Akaike information criterion (AIC) as the first case for the present system.Native model evaluation parameters, i.e., R 2 and adjusted R 2 , did not give conclusive results on the preferred model.On the other hand, the AIC explicitly indicated that the traditional Thomas, Bohart-Adams, and Yoon-Nelson models were the preferred models to navigate the system with minimal error compared to the fractal-like models.From the present study's findings, it is evident that a model with more parameters does not automatically qualify it to produce a better fit.Therefore, the AIC can be employed to evaluate competing models with minimal biasness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".