Coprocessing Partially Hydrodeoxygenated Hydrothermal Liquefaction Biocrude from Forest Residue in the Vacuum Gas Oil Hydrocracking Process
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Hydrodeoxygenation of biogenic feedstocks is an option to alleviate the challenges associated with their coprocessing in petroleum refinery units. This study investigates the effect of coprocessing a partially hydrodeoxygenated biocrude with vacuum gas oil (VGO) in a hydrocracking process. The biocrude, produced by hydrothermal liquefaction (HTL) of forest residues, was hydrodeoxygenated to an oxygen level of 3.6 wt % at which it became fully miscible in the VGO feed. The coprocessing feed blend was constituted of 7.5 wt % hydrodeoxygenated biocrude in 92.5 wt % VGO. Pilot plant tests were carried out in two sequential stages: hydrotreating and hydrocracking. Hydrotreating was performed first, with the purpose of meeting the sulfur and nitrogen specifications of the hydrocracking catalyst. The hydrotreated products next underwent hydrocracking to produce enough product for physical distillation. The tests were conducted using pure VGO first to set a baseline for the study and then the coprocessing feed. During both hydrotreating and hydrocracking, coprocessing required increasing the reactor temperature by 10–15 °C over the baseline temperature for pure VGO to offset the poisoning effect of oxygen compounds. The hydrotreating stage was also affected by reactor plugging issues attributed to unstable high-boiling material in the biocrude. In spite of this, the coprocessing scheme was shown to stand on par with the VGO baseline in terms of overall product distribution and without consuming more hydrogen. Moreover, the coprocessed naphtha, diesel, and jet fuel fractions showed only subtle differences in properties and hydrocarbon composition relative to those from VGO. Biogenic carbon measurements revealed that the coprocessed naphtha, diesel, and jet fuel fractions contained 8–9 wt % biogenic carbon.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".