Bio-pellets Manufacture from Palm Fruit Skin as Renewable Alternative Fuels in Updraft Type Gasification Furnaces
Bibliographic record
Abstract
Biomass is a suitable alternative to be used as fuel, but the use of biomass without prior processing can cause respiratory disease. In Indragiri Hilir Regency, the utilization of palm fruit skin waste is still minimal. Bio-pellet is a fuel derived from biomass waste that has gone through a densification process. The process of making bio-pellets is carried out by refining the raw materials, mixing the adhesives, printing the raw materials that are pressed under high pressure, and drying. The results of the research on the bio-pellet characteristic test of Nipah fruit peel waste according to its parameters obtained an average value of 1.28% water content, 0.51% ash content, 21.3% flying substance content, 76.88% bound carbon content and weight. type 1.41 gr / cm3. For the combustion test in the updraft type gasification furnace with the addition of 0.5kg, 0.3kg and 0.2kg of fuel, the results obtained from the rate of fuel consumption are 0.041kg / minute, 0.033kg / minute and 0.033kg respectively. /minute. In the results of combustion efficiency, the value according to SNI 7926: 2013 is the addition of 0.5kg of fuel at the beginning of ignition and 0.2 kg at the end of ignition, which is 0.04.
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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 | 0.001 |
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".