Biofules 2018: Hydroprocessing of Lignin for the Production of Liquid Fuels andChemicals-Charles Xu-Western University, Canada
Bibliographic record
Abstract
Forestry biomass and residues (such as harvesting residues, sawdust, and bark) and lignin a by-product from pulping processes can be promising bio-renewable sources for the production of liquid fuels and chemicals. The lignin component of woody biomass is of particular interest as it comprises the world's largest natural source of aromatic compounds and is produced in large quantities as a byproduct of pulp and paper processing. The main challenge in lignin utilization for fuels and chemicals is that it is composed of very large molecules with low heating values (due to high oxygen content) and low reactivity. Accordingly, the overall objective of this work is the production of chemicals and fuels by the catalytic hydro processing of lignin and lignin-derived bio-oils aiming to reduce their molecular weights and oxygen contents. This work investigated the catalytic hydro processing of a number of different lignin?s for the production of fuels and chemicals. Several supported metal hydrogenation catalysts were investigated for the depolymerisation, deoxygenation, and desulfurization of Kraft lignin (KL) organosolv lignin (OL) and hydrolysis lignin (HL) under hydrogen atmospheres to produce depolymerized lignins or lignin-derived bio-oils. Screening of catalysts was performed using guaiacol as a model compound. The most effective catalyst under the conditions tested was found to be 1wt.% Mo-doped 5wt.% Ru supported on activated charcoal (MoRu/AC). The selected catalyst proved to be very effective for hydro processing of organosolv lignin, hydrolysis lignin (HL) and depolymerized hydrolysis lignin (DHL).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.018 |
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".