Moisture Content Impact on Properties of Briquette Produced from Rice Husk Waste
Bibliographic record
Abstract
An agricultural waste-based source of energy in the form of briquettes from rice husk has emerged as an alternative energy source. However, rice husk-based briquette has a low bulk density and moisture content, resulting in low durability. This study investigated the effect of initial moisture contents of 12%, 14%, and 16% of rice husk-based briquettes blended with 10 wt% of kraft lignin on their chemical and physical characteristics. The briquetting was done using a hand push manual die compressor. The briquette properties were evaluated by performing chemical (ultimate and proximate analysis, thermogravimetric analysis), physical (density, durability, compressive strength, and surface morphology) analyses. The durability values of all briquette samples were above 95%, meeting the standard with good compressive strength, surface morphology, and acceptable density range. The briquette made from the blend with 14% moisture content showed the highest calorific value of 17.688 MJ kg−1, thanks to its desirable morphology and good porosity range, which facilitates the transport of air for combustion. Overall, this study proved the approach of enhancing the quality of briquettes from rice husk by controlling the moisture content.
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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".