Conversion of Co-hydrothermal liquefaction biocrude into high-quality biofuels via hydrodeoxygenation: Process optimization and analysis
Bibliographic record
Abstract
The growing demand for renewable energy has driven significant research into biofuels as sustainable alternatives to fossil fuels. Hydrothermal liquefaction (HTL) is an effective thermochemical process that converts biomass into biocrude; however, the resulting biocrude contains a high oxygen content, leading to undesirable properties such as high acidity, low stability, and poor compatibility with petroleum-based fuels. To improve its fuel quality, hydrodeoxygenation (HDO) is employed to reduce the oxygen content and enhance its properties. In this study, biocrude derived from the co-HTL of wheat straw and waste cooking oil was upgraded using HDO over a commercial sulfided NiMo/γ-Al₂O₃ catalyst. An initial reaction time study was conducted, where biocrude samples were collected every two hours up to 12 hours. The lowest oxygen content was achieved at 8 hours, beyond which minimal changes in oxygen content were observed. Based on these findings, a Design of Experiments approach was implemented to optimize temperature, pressure, and catalyst loading while keeping the reaction time constant at 8 hours. The upgraded biocrude obtained under optimal conditions was characterized using elemental analysis, higher heating value, pH, density, viscosity, nuclear magnetic resonance, gas chromatography-mass spectrometry, Fourier transform infrared spectroscopy, simulated distillation analysis. The fuel properties of the optimized product were compared with those of commercial biodiesel to assess its viability as a transportation fuel. The results demonstrate that optimizing HDO conditions significantly improves the quality of biocrude, reducing its oxygen content and enhancing its stability, making it a promising alternative to conventional biofuels.
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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".