Effect of Fuel Additives on Agricultural Straw Pellet Quality
Bibliographic record
Abstract
An investigation was conducted to determine the effect of addition of different levels of AK2, a fuel additive that reduces ash fusion for agricultural biomass, on the physico-chemical properties of biomass pellets. Three different biomass straws, barley, oat, and wheat were ground at two hammer mill screen sizes of 0.8 mm and 1.6 mm. Each ground biomass sample was mixed with three levels of AK2, 0.05%, 0.10%, and 0.15% by mass and also a blank (no AK2) was set aside for comparison. Pellets were made using single-pelleting unit at a pre-set load of 4 400 N corresponding to a pressure of 138.9 MPa. Physical quality of pellets were determined by measuring pellet density, relaxed density, durability, and the specific energy required to make a pellet. Pellets having higher durability values (74%-88%) were obtained from ground straw at hammer screen size of 0.8 mm and AK2 level of 0.15% compared to other treatments. Carbon, hydrogen, nitrogen, and sulfur content of blank pellets and those with 0.15% AK2 at hammer screen size of 0.8 mm were determined. Pellets made with 0.15% AK2 at hammer screen size of 0.8 mm, manufactured by pilot-scale pellet mill, were gasified and the tar content was determined. The tar content of pellets with 0.15% AK2 was significantly lower than blank pellets. Keywords: biomass, biofuels, slag, foul, pelleting, fuel additive DOI: 10.3965/j.ijabe.20140702.011 Citation: Emami S, Tabil L G, Adapa P, George E, Tilay A, Dalai A, et al. Effect of fuel additives on agricultural straw pellet quality. Int J Agric & Biol Eng, 2014; 7(2): 92－100.
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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".