Near-Critical CO2-Assisted Liquefaction-Extraction of Biomass and Wastes to Fuels and Value-Added Products
Bibliographic record
Abstract
Abstract With the growing need for sustainable carbon-neutral liquid fuels, low-grade feedstocks, such as lignocellulosic biomass, and municipal solid wastes offer sufficient potential via thermochemical conversion. But the existing thermochemical means are limited in feed flexibility and scalability and require significant processing (energy and costs) of the intermediates. Bio-oil/biocrude intermediate from fast pyrolysis and hydrothermal techniques is impeded by issues of stability and oxygen content, along with hydrotreating viability. To address these issues, we investigated a novel pathway of near-critical CO2-assisted integrated liquefaction-extraction (NILE) technology in conceptual aspects for conversion of various biomass and municipal solid wastes into high-quality biocrude with high compatibility for co-hydrotreating with traditional fossil crude for liquid fuel needs in power and transportation sectors. Using supercritical CO2 for dewatering wet feedstocks, for liquefaction, and extraction for lighter biocrude has produced biocrude with lower oxygen content by 50%, lowered metal content by 90%, stable viscosity, low acidity, and good aging stability compared to that produced from hydrothermal liquefaction along with higher hydrotreating and co-hydrotreating compatibility. Hydrotreating of the biocrude extract from supercritical CO2 extraction also was feasible with no detected coke deposition, an oxygen content of 1%, and catalyst deactivation. The validation and capabilities of the NILE concept urge for its further development to obtain sustainable liquid fuels with lower greenhouse gas emissions and costs.
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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".