A simultaneous study on desulphurization and denitrogenation using acid‐treated activated alumina: Multi‐component adsorption systems
Bibliographic record
Abstract
Abstract In this study, sulphuric acid‐treated activated alumina (AA) was used for sulphur and nitrogen removal from model fuel in a batch adsorption study. Dibenzothiophene (DBT), a sulphur compound, and quinoline, a nitrogen compound dissolved in isooctane, were taken as a model liquid fuel. Detailed characterization of the adsorbent, such as scanning electron microscopy (SEM), thermogravimetric analysis‐differential thermal analysis (TGA‐DTA), Fourier transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET), and X‐ray diffraction (XRD), was performed to understand the DBT and quinoline adsorption mechanism onto AA adsorbent. Sulphur and nitrogen removal efficiencies were found to be 64% and 91%, respectively. Mono‐component adsorption isotherm was studied by using different models such as Langmuir, Freundlich, and Redlich‐Peterson (R‐P) isotherms. The R‐P isotherm model well‐predicted the individual equilibrium data for both nitrogen and sulphur from the liquid fuel. Binary‐component adsorption studies were performed by mixing both DBT and quinoline in isooctane. Binary‐equilibrium data were modelled by multi‐component models such as modified Langmuir isotherm, non‐modified Langmuir, extended Langmuir, extended Freundlich isotherm, Sheindorf‐Rebuhn‐Sheintuch (SRS), non‐modified R‐P model, and modified R‐P model. The extended Freundlich (E‐F) adsorption isotherm model was found to best fit the binary equilibrium system.
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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) | 0.000 | 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".