Coprocessing Hydrothermal Liquefaction Biocrude from Algae with Vacuum Gas Oil Using Hydrotreating
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In this study, we investigate the effects of coprocessing algae biocrude with petroleum in hydrotreating units. Algae biocrudes are characterized by being rich in oxygen and nitrogen components, which may pose challenges for refinery catalysts and operations. The studied biocrude was produced from a marine eustigmatophyte microalga in a hydrothermal liquefaction pilot plant and afterward it was distilled to remove excess water and high-boiling material. Coprocessing experiments were carried out in a continuous hydroprocessing pilot plant using vacuum gas oil (VGO) as the petroleum feed. The tests sought to examine different coprocessing ratios (2.5, 5, and 10 vol % biocrude in VGO) relative to baseline operation with pure VGO, as well as to determine the optimum temperature to achieve certain levels of sulfur (<300 wppm) and nitrogen (<100 wppm) removal for a given feed blend. There was a gradual decline in desulfurization activity relative to the baseline level as the coprocessing ratio increased. This unwanted effect was countered by raising reactor temperature between 2 and 6 °C over the 380 °C baseline temperature for pure VGO. Denitrogenation of the biocrude blends was achieved with ease, indicating that the nitrogen components in the biocrude distillate were mostly nonrefractory. There was no evidence to suggest that coprocessing the biocrude blends accelerated catalyst deactivation. Hydrogen consumption grew to some extent, particularly at the highest coprocessing ratio. The coprocessed products were in general less dense, had higher hydrogen content, and were richer in n -paraffins compared to the one from VGO. Biogenic carbon was found to be preserved in the liquid products at coprocessing ratios of 5% and below, whereas at a 10% ratio apparently, there were losses to gas products.
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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".