Catalytic Hydrothermal Conversion of Glucose to Light Petroleum Alkanes
Bibliographic record
Abstract
The production of carbon-neutral liquid fuels from renewable biomass has attracted worldwide interest in an age of depletion of fossil fuel reserves and pollutions caused by utilization of fossil petroleum. Currently, commercial bio-oil production technologies include bio-ethanol, bio-diesel and pyrolysis bio-oil. But, these bio-oils mainly consist of alcohols and aromatic chemicals rather than alkanes of the main components of gasoline and diesel. Direct utilization of these bio-oils can corrode car engines as well as emitting large unburned hydrocarbons particles through automotive combustion system. Therefore, in this study, catalytic hydrothermal conversion (CHTC) of glucose to alkanes in a single batch reactor was investigated with respect to effects of conversion parameters such as initial pressure of process gas H2, pH level of aqueous solution and catalysts on alkane yields and compositions. Results showed that the highest alkane yield of 21.6% (based on the mol of the input glucose) was obtained at 265 °C, with 300 psi of H2 process gas, 0.5 g catalyst of 1w%. Pt/Al2O3 and a residence time of 15 h. The alkane yield was significantly influenced by the initial pressure of H2, which increased with increasing H2 pressure. On the other hand, the alkane yields first increased and then decreased with pH levels. Also, more alkanes were produced by Pt/Al2O3 than Pd/Al2O3. Regarding alkane compositions, high initial pressure of H2 favored the production of relatively heavy C3–4 alkanes. With 300 psi of initial H2, C3H8 and C4H10 accounted for 75% of the total produced alkanes. All of the experimental data in this study lead to one conclusion that petroleum alkanes can be directly produced from glucose.
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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".