Hydroprocessing of oleic acid for production of jet fuel range hydrocarbons over Sn(1)‐Fe(3)‐Cu(13)/SiO<sub>2</sub>‐Al<sub>2</sub>O<sub>3</sub> catalyst: Process parameters optimization, kinetics, and thermodynamic study
Bibliographic record
Abstract
Abstract Hydroprocessing of vegetable oil to high‐quality jet fuel range hydrocarbons (HRJ) plays a significant role in the development of completely interchangeable substitute for conventional petroleum‐derived jet fuel and has drawn the attention of aviation experts due to its capacity to mitigate greenhouse gas emissions associated with the aviation industry. The limited performance of 1 wt. % Sn promoted Fe(3)‐Cu(13)/SiO2‐Al2O3 catalyst in our previous study has been attributed to the successive consideration of one variable at a time in its evaluation. Maximization of oleic acid conversion and selectivity of jet fuel range hydrocarbons from hydroprocessing of oleic acid over 1 wt. % Sn promoted Fe(3)‐Cu(13)/SiO2‐Al2O3 catalyst with the best combination of the process parameter involved via multivariate approach, and evaluation of kinetic and thermodynamic activation parameters is the focus of this study. Reduced cubic oleic acid conversion model and reduced quadratic jet fuel range hydrocarbons selectivity model of high significance levels, adequate precision, and high correlation coefficient were developed. Reaction temperature of 339.5°C, 1.6 MPa H2 pressure, 6.2 wt.% catalyst concentration, and 8.0 h reaction time were optimum process parameters that can maximize oleic acid conversion and selectivity of jet fuel range hydrocarbons at 98.2% and 82.2%, respectively. This process was found to be endothermic, irreversible, and nonspontaneous with 45.8 KJ/mol activation enthalpy of reaction, 0.25 KJ/mol entropy of reaction, and the reaction's Gibb's free energy of 198.8 KJ/mol at 340°C. The minimum energy required for the reaction to take place was evaluated as 50.7 KJ/mol.
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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".