Comparison of association theory and Freundlich isotherm for describing granular activated carbon adsorption of secondary sewage effluent
Bibliographic record
Abstract
Adsorption equilibria and kinetics are important to evaluate the effectiveness of an adsorption system. In this study, the adsorption behavior of organics in secondary effluent from a sewage treatment plant was investigated, using granular activated carbon (GAC) as an adsorbent. This paper provides details on the adsorption experiments conducted in a sewage treatment plant, and emphasizes the suitability of the association theory for describing adsorption characteristics of secondary sewage. The association theory was found to describe the overall adsorption equilibrium of the sewage system more precisely (with 0.33% error) than the more commonly-used Freundlich isotherm (with 9.9% error). The linear driving force approximation (LDFA) model and the fixed bed dynamics were used to predict the batch kinetics and the fixed bed adsorption experimental results respectively. The prediction was only slightly better when the isotherm parameters estimated from the association theory were used. Average errors in predicting batch and fixed bed experimental results were 0.25 and 0.55% with the association theory, and 0.34 and 0.75% with the Freundlich isotherm, respectively. Key words: adsorption, association theory, granular activated carbon, total organic carbon, wastewater.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".