Application of isotherms models and error functions in activated carbon CO2 sorption processes
Bibliographic record
Abstract
This work is concerned with the calculations using eight different isotherm models (Langmuir, Freundlich, Halsey, Temkin, Toth, Sips, Radke-Prausnitz, and Redlich-Peterson) to fit the experimental isotherm data of CO2 on activated carbon (AC). Moreover, systematic and comprehensive modeling of non-linearized isotherms was performed by developing an algorithm for determining their parameters and analyzing seven error functions. To determine the best-fitted isotherm model and error function, we used the sum of normalized errors (SNE) procedure. The modeling results obtained showed that the Redlich-Peterson, Radke-Prausnitz, and Toth isotherm models are best suited to the empirical data, with relatively high R2 determination coefficients. Finally, the SNE method allowed the selection of the chi-square test (χ2) and the HYBRID error as universal indicators in nonlinear regression to select the set of optimized isotherm parameters. The interpretation of the assumptions of the isotherm models, which featured a strong correlation with the experimental data, allowed a conclusion to be drawn about the sub-monolayer adsorption mechanism on the heterogeneous surface of the AC. The acquired modeling findings are expected to establish a certain theoretical foundation for the characterization of CO2 adsorption equilibrium studies at the interface between porous solid materials and gases.
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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 | 0.005 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".