Mild hydrotreatment of biocrude derived from <scp>hydrothermal liquefaction</scp> of agriculture waste: improving biocrude miscibility with vacuum gas oil to aid co‐processing
Bibliographic record
Abstract
Abstract The co‐processing of biocrude in petroleum refineries is viewed as an economical and practical pathway to produce low‐carbon fuels. A major challenge to co‐processing is the poor miscibility of biocrudes with petroleum due to their high levels of oxygen. This study investigated the mild hydrodeoxygenation of biocrude derived from hydrothermal liquefaction (HTL) of agriculture waste as a means to enhance its miscibility with petroleum vacuum gas oil (VGO). Blending compatibility tests were performed to identify the extent of deoxygenation required to achieve blending of 10 wt% treated biocrude in VGO. The highest oxygen removal (~72%) was achieved by increasing the temperature in three steps (240, 280, and 300 °C), with pressure kept at 1400 psi and a catalyst‐to‐feed ratio of 0.19 g g−1. Under such conditions, the hydrotreated biocrude was found to be miscible in VGO and the resulting blend was stable over 7 days. The hydrotreated biocrude products were characterized using nuclear magnetic resonance to identify changes in oxygenated compound classes, such as carboxylic acids, alcohols, ethers, carbohydrates, carbonyls, and phenolics. Carboxylics and phenolics were identified as important contributors to the miscibility of the biocrude. In further testing, two 10 wt% blends of hydrotreated and raw biocrude in VGO were co‐processed through hydrotreating. These supplementary tests confirmed that the hydrotreated biocrude performed better in terms of catalytic activity than the raw biocrude. © 2021 Her Majesty the Queen in Right of Canada. Biofuels, Bioproducts and Biorefining © 2021 Society of Industrial Chemistry and John Wiley & Sons Ltd. Reproduced with the permission of the Minister of Natural Resources Canada.
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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".