Hydroprocessing of Oleic Acid for Production of Jet-Fuel Range Hydrocarbons over Cu and FeCu Catalysts
Bibliographic record
Abstract
In the present study, a series of monometallic Cu/SiO2-Al2O3 catalysts exhibited immense potential in the hydroprocessing of oleic acid to produce jet-fuel range hydrocarbons. The synergistic effect of Fe on the monometallic Cu/SiO2-Al2O3 catalysts of variable Cu loadings (5–15 wt%) was ascertained by varying Fe contents in the range of 1–5 wt% on the optimized 13% Cu/SiO2-Al2O3 catalyst. At 340 °C and 2.07 MPa H2 pressure, the jet-fuel range hydrocarbons yield and selectivities of 51.8% and 53.8%, respectively, were recorded for the Fe(3)-Cu(13)/SiO2-Al2O3 catalyst. To investigate the influence of acidity of support on the cracking of oleic acid, ZSM-5 (Zeolite Socony Mobil–5) and HZSM-5(Protonated Zeolite Socony Mobil–5)-supported 3% Fe-13% Cu were also evaluated at 300–340 °C and 2.07 MPa H2 pressure. Extensive techniques including N2 sorption analysis, pyridine- Fourier Transform Infrared Spectroscopy (Pyridine-FTIR), X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), and H2-Temperature Programmed Reduction (H2-TPR) analyses were used to characterize the materials. XPS analysis revealed the existence of Cu1+ phase in the Fe(3)-Cu(13)/SiO2-Al2O3 catalyst, while Cu metal was predominant in both the ZSM-5 and HZSM-5-supported FeCu catalysts. The lowest crystallite size of Fe(3)-Cu(13)/SiO2-Al2O3 was confirmed by XRD, indicating high metal dispersion and corroborated by the weakest metal–support interaction revealed from the TPR profile of this catalyst. CO chemisorption also confirmed high metal dispersion (8.4%) for the Fe(3)-Cu(13)/SiO2-Al2O3 catalyst. The lowest and mildest Brønsted/Lewis acid sites ratio was recorded from the pyridine–FTIR analysis for this catalyst. The highest jet-fuel range hydrocarbons yield of 59.5% and 73.6% selectivity were recorded for the Fe(3)-Cu(13)/SiO2-Al2O3 catalyst evaluated at 300 °C and 2.07 MPa H2 pressure, which can be attributed to its desirable textural properties, high oxophilic iron content, high metal dispersion and mild Brønsted acid sites present in this catalyst.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".