Production of Biomass Briquettes Using Coconut Husk and Male Inflorescence of Elaeis guineensis
Bibliographic record
Abstract
The decreasing availability of fuel wood coupled with the increasing prices of kerosene and cooking gas in Nigeria has drawn attention on the need to consider alternative sources of energy for domestic and industrial use in the country. The study was undertaken to evaluate the combustion properties (percentage volatile matter, percentage ash content, percentage fixed carbon, heating value) of briquette produced from coconut husk and male inflorescence of Elaeis guineensis. The experiment was laid down using the Randomized Complete Block Design (RCBD). The study involves three particle sizes (2 mm each) of coconut husk, male inflorescence of oil palm tree and cassava starch used as binder. The coconut husk and male inflorescence of Elaeis guineensis were varied into (25:30:40:50:60) respectively and bound together with starch at same ratio. Proximate analysis was carried out to determine the constituent of the briquettes which include ash content, percentage fixed carbon, percentage volatile matter and experimental test to determine the heating value was also determined. All processing variables in this study were significantly different except for heating value at P>0.05. From the result of the percentage ash content, briquette produced from coconut husk, male inflorescence and starch at (20:20:60) has the least fixed carbon (6.5%) with better performance. The highest percentage volatile matter 74.6% was obtained from coconut husk, male inflorescence and starch at (20:20:60) while low fixed carbon (18.8%) was obtained from male inflorescence and starch at (60:40). In conclusion, large quantities of wastes generated in terms of coconut husk and male inflorescence which are disposed indiscriminately can be utilized to produce briquette with enhanced performance.
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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".