Co-processing of Hydrothermal Liquefaction Biocrude with Vacuum Gas Oil through Hydrotreating and Hydrocracking to Produce Low-Carbon Fuels
Bibliographic record
Abstract
In this study, we investigate the potential of co-processing hydrothermal liquefaction (HTL) biocrude with vacuum gas oil (VGO) in a hydrocracking process with hydrotreating as the first step. Experiments were conducted in a continuous hydroprocessing pilot plant in two stages: hydrotreating and hydrocracking. Two feeds were tested: first pure VGO to establish a baseline and then a co-processing blend having 7.5 vol % HTL biocrude. In the first stage, the VGO and co-processing blend were sequentially hydrotreated to meet the quality specification of the hydrocracking catalyst. The second stage consisted of hydrocracking the two hydrotreated products from the first stage, and the resulting products were distilled into naphtha, diesel, and jet fuel fractions for characterization. The hydrotreating step achieved satisfactory sulfur and nitrogen removal levels for both feeds, but it was ineffective in converting oxygen compounds in the co-processing blend, resulting in a product with 1530 ppmw oxygen. During hydrocracking, the co-processing blend required a higher reaction temperature than the baseline VGO to achieve the same conversion level, a behavior attributed to the oxygen and nitrogen levels in the co-processing blend after hydrotreating. Despite these effects, overall product distribution and hydrogen consumption for both scenarios were quite comparable. Characterization of hydrocracked products showed only subtle differences in quality and hydrocarbon type composition, while biogenic carbon measurements revealed that the majority of biogenic carbon is transferred to the naphtha, diesel, and jet fuel fractions.
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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".