Partial Upgrading of Lignocellulosic Bio-Oil via Deep Catalytic Hydrotreating
Bibliographic record
Abstract
According to the International Energy Agency, bioenergy accounts for one-tenth of the world's total primary energy supply and biofuel production is forecasted to increase 25% by 2024. The bio-oil is composed of a complex mixture of oxygenated compounds with a relatively high concentration of water. Compared to crude oil, bio-oil has high oxygen content, lower energy density, high acidity, high viscosity and water content. Submitting the bio-oil to atmospheric distillation at the refinery is not possible at this stage since the highly reactive compounds in the bio-oil will plug the distillation column at high temperatures, and moreover, high temperatures will accelerate the corrosion effect of the bio-oil in the refinery lines. Therefore, an upgrading step is necessary to make the bio-oil admissible to the refinery. The ultimate goal of this research is to produce biofuel equivalent to conventional fuels from lignocellulose-derived bio-oil. The main objective of this study is to reduce the oxygen content of the bio-oil via the catalytic hydrotreating process and to improve the quality of the upgraded oil. The effect of the process variables such as operating pressure, temperature and space velocity on the product quality was studied. The best results were obtained using CAT-M5, at 1750 psig, 370 C and 0.4 h-1 space velocity resulting in 77% reduction in oxygen content, 99.5% reduction in viscosity, 100% reduction in total acid number, 55% phenol conversion and 91% residue conversion. It was found that increasing pressure, unlike temperature, does not have a noticeable effect on microcarbon residue reduction, viscosity, and boiling point distribution of the product, but it improves the degree of deoxygenation and molar H/C ratio. Moreover, at temperatures higher than 350 C, hydrocracking along with hydrogenation notably improved the residue conversion, viscosity and MCR reduction. Comparing the performance of catalysts showed that CAT-M5, unlike CAT-M3, eliminated the solid precipitation in the products thanks to its large pore size. Furthermore, CAT-M5 had a higher residue conversion rate, MCR and viscosity reduction rate. In contrast, CAT-M3 had a better performance in deoxygenation and phenol conversion.
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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".