Biomass Briquettes as an Alternative Fuel: A Comprehensive Review
Bibliographic record
Abstract
Effectively utilizing agro residues, when abundantly available, can help energy conservation efforts and increase farmers’ incomes. This article highlights the effective utilization of agro and industrial biomass residues in the form of briquettes. The various types of feedstock used and the technologies adopted in the briquetting process are discussed. Process parameters and feedstock variables influencing briquetting are described. Combustion studies undertaken using briquettes are comprehensively detailed. This literature review reveals that the combustion characteristics of briquettes not only depend on the type of the feedstock but also on the density, moisture content, binder percentage, and method used for briquetting. Higher ash content lowers the calorific value. The ash formed during combustion causes slagging and fouling which in turn lead to corrosion. It is concluded that biomass briquettes can meet the energy demands for cooking and heating needs, especially in rural areas where abundant biomass feedstock is available. It is recommended that research focuses more on investigating emissions along with the combustion of briquettes manufactured from different origins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".