Adsorption diffusion behaviour of 4,6‐DMDBT from diesel fuel
Bibliographic record
Abstract
The equilibrium and kinetics of adsorption desulphurization of model diesel fuel under different operating conditions were investigated. The homogeneous surface diffusion model (HSDM) and pore diffusion model (PDM) taking into account external resistance and intraparticle resistance were employed to represent the adsorption kinetics of 4,6‐dimethyldibenzothiophene (4,6‐DMDBT). The equilibrium isotherm analysis revealed that the equilibrium can be represented well by the Freundlich model. The proposed HSDM model and PDM model can successfully describe the adsorption of 4,6‐DMDBT for different operating conditions studied. The adsorbate was absorbed rapidly initially because the adsorption rate at the beginning was dominated only by external diffusion. The Biot numbers proved that the predominant rate‐controlling step was intraparticle diffusion. Simultaneously, the adsorption rate was mainly controlled by surface diffusion at lower initial bulk phase concentration and less adsorbent dosage, while pore diffusion played a significant role at higher initial bulk phase concentration and more adsorbent dosage. The temperature had little effect on the diffusion of 4,6‐DMDBT from diesel fuel. The estimated values of pore diffusion coefficient and surface diffusion coefficient were 1.55 × 10−12 m2/s and 5.855 × 10−14 m2/s, respectively. They were independent of adsorbent dosage, initial 4,6‐DMDBT concentration, and temperature, which can provide guidance for fixed‐bed adsorption columns design and process operation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".