Torrefaction of Palm Kernel Shell and Petcoke Blends for Various Mixing Ratios and Temperatures
Bibliographic record
Abstract
In the present study, torrefaction of palm kernel shell (PKS) and petcoke blends was performed for the production of solid biofuels with high energy density. The torrefaction process was performed for mixtures with various mixing ratios (by weight) from 90:10 to 60:40 (PKS:petcoke). For torrefaction under various temperatures of 250℃ to 300℃, the mixing ratio of 60:40 was used. Meanwhile, residence time and nitrogen flow rate were fixed at 30 minutes and 1 l/min, respectively. In general, the fixed carbon and ash contents increased, while the moisture and volatile matter contents decreased after torrefaction. It has been elucidated that mass yield is a dominant factor that affects the energy yield of torrefied mixtures rather than the higher heating value (HHV) ratio. Based on the energy yield and ultimate analysis, it was found that a higher amount of petcoke and higher temperature give better performance, thus causing the torrefied mixture to become very close to coals region in Van Krevelen diagram. In this case, the mixture with a mixing ratio of 60:40 torrefied under the temperature of 300℃ gives the best performance. It was also found that this mixture is thermally stable than the mixture torrefied at 250℃.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".