Utilization of PT INALUM Baking Filter Dust Waste and Coconut Shell Charcoal in Making Hybrid Briquettes
Bibliographic record
Abstract
The global energy crisis and dependence on fossil fuels require the search for renewable and environmentally friendly energy sources. The smelter industry of PT INALUM produces Baking Filter Dust (BFD) as waste, which can pollute theenvironment if not properly managed. Meanwhile, coconut shells, an abundant agricultural waste, hold potential as an alternative fuel. This research aims to develop hybrid briquettes from BFD and coconut shell charcoal, according to SNINo.01-6235-2000 standards. The study involved collecting and preparing BFD from PT INALUM and coconut shell charcoal, producing briquettes with varying ratios from 20% BFD: 80% charcoal to 80% BFD: 20% charcoal. The quality of the briquettes was tested through proximate and ultimate analyses, with characterization using XRF, FTIR, SEM-EDS, and TGA to determine elemental content, functional groups, structure, chemical composition, and thermal stability. The results indicated that briquettes with 60% BFD and 40% coconut shell charcoal exhibited the highest quality, with a calorific value of 6557.3 cal/g, fixed carbon content of 86.2%, ash content of 3.38%, moisture content of 3.22%, volatile matter content of 7.2%, and sulfur content of 0.47%. These briquettes meet SNI No.01-6235-2000 standards and provide a viable solution for managing industrial waste and supplying sustainable alternative fuel.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".