Hydrothermal liquefaction technology: potential, challenges, and research at NRC
Bibliographic record
Abstract
Hydrothermal liquefaction is a promising technology for niche feedstocks like sewage sludge, food-wastes, bio-solids, organic waste streams etc. Availability estimates of these low or negative value waste feedstocks in the United States include: 15.2 million ton per year of food wastes, 5.9 million ton per year of waste fats and oils, 41.5 million ton per year of animal manure, and 13.8 million ton per year of sewage sludge. Collectively, these wastes have a potential of replacing a significant portion (up-to 25 % in some estimates) of US aviation kerosene demand. Bio-crude produced from HTL is generally heavier than conventional crude, with a high heating value as high as 35-40 MJ Kg-1 , heteroatom content around 10-12 weight percent, mostly oxygen and/or nitrogen depending on feedstock. Akin to crude oil upgrading in petroleum refineries, following various complex and interdependent hydro-processing, separations, and finishing steps, HTL bio-crude could be transformed to fungible biofuels (green diesel, sustainable aviation fuel, etc.) in standalone bio-refineries or co-processed in existing petroleum refineries thus eliminating significant capital investments and exploiting economies of scale offered by large existing upgrading facilities. There is also a possibility to use HTL bio-crude or its fraction directly in turbines and engines with minimum upgrading. Despite great potential and opportunities, HTL technology is still at a low readiness level and significant challenges exist that have hindered commercialization: lack of understanding of feedstock specific characteristics of complex bio-crudes and how these influence upgrading and end-use applications; its compatibility with petroleum crudes; challenges around upgrading catalyst performance; potential improvements in yields and quality of bio-crude to improve economics, and make it more amenable for upgrading/end-use; and ultimately its demonstration for end-use. The presentation will focus on some of these challenges with examples of recent research at NRC to bridge these gaps.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".