Hydroprocessing of palm oil using Rh/<scp>HZSM</scp>‐5 for the production of biojet fuel in a fixed bed reactor
Bibliographic record
Abstract
Abstract The hydroprocessing of palm oil was performed in a continuous fixed bed reactor to produce biojet fuel. Rh/HZSM‐5 was used as catalyst and was characterized by N2 adsorption‐desorption, x‐ray diffraction (XRD), Scanning electron microscopy (SEM), NH3‐temperature programmed desorption (NH3‐TPD), and H2‐temperature programmed reduction (H2‐TPR). The effect of operating parameters including reaction temperature, molar ratio, and solvent on the yield of biojet and biodiesel was investigated. The catalyst deactivated rapidly in the solvent‐free system due to carbon deposition. The application of 30% heptane in the feed maintained the conversion of palm oil above 90% for 8 hours. At 330°C, the yield of liquid fuel product was slightly less than that obtained at 300°C as more gaseous product was formed. The H2‐to‐oil molar ratio of 158 at 300°C was insufficient to maintain the reaction at high conversion. At 300°C and a H2‐to‐oil molar ratio of 316, the system was stable for the entire experimental period of 8 hours providing the biojet yield and selectivity of 15.3% and 28.5%, respectively. Although, this system was operated at a relatively low temperature for hydroprocessing, the productivity parameter of 0.60 kg product/Kg cat‐h was significantly higher compared to the data provided in the literature.
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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".