Comparative study of the thermo‐catalytic reforming of agricultural and forest residue and advanced characterization of final products in a cold climate
Bibliographic record
Abstract
Abstract Global agricultural and forest residues hold promise for renewable fuel production through thermo‐catalytic reforming (TCR). Limited data exists on TCR outcomes for regions known for cold conditions like Canada. This study used a 2 kg h −1 TCR unit for the intermediate pyrolysis/reforming of agricultural (wheat straw pellet, WSP) and forest (softwood pellet, SWP) residues. Maximum bio‐oil yields were 8.43% for wheat straw pellets and 7.99% for softwood pellets at 400 and 500°C reactor and reformer temperatures. Feedstock, bio‐oil, and biochar properties were analyzed through proximate and ultimate analysis. At 550°C reactor and 700°C reforming temperatures, 70.73% of the wheat straw pellet‐based gas yield contained 36.11 vol.% H 2 and 11.08 vol.% CH 4 , giving a higher heating value (HHV) of 12.54 MJ kg −1 . A high concentration of CH 4 (22.02 vol.%) in the softwood pellet‐based gas gave an HHV of 17.94 MJ kg −1 . The low viscosity (3.9 mPa · s −1 ) and total acid number (7.3 mg KOH g −1 ) wheat straw pellet‐based bio‐oil had an O/C molar ratio of 0.09 and an HHV of 35.80 MJ kg −1 . The 400/600°C reactor/reformer temperatures gave the lowest area percentage of mono‐aromatic (16 vol.%) and polycyclic aromatic (11.20 vol.%) compounds in the softwood pellet bio‐oil. The O/C molar ratio (0.5–0.6) in softwood pellet biochar elevated the higher heating value from 32.37 to 34.57 MJ kg −1 . The study results guide optimal TCR unit operation in cold climates like Canada with local feedstocks, emphasizing its notable hydrogen production over bio‐oil and biochar.
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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".