Circular Economy Approach on Decarbonization by Repurposing Used Cooking Oil for Nickel Smelter – A Case Study of PT Megah Surya Pertiwi, Obi Island, North Maluku, Indonesia
Bibliographic record
Abstract
Abstract This research was instigated by the world’s endeavours to fight dependency on fossil energy sources. While coal prevails as a beneficial energy source, countries worldwide have strived to reduce coal usage in the future under the Net Zero Emission Pledge. Questions linger on how to minimize coal usage, play down its impact on the environment, and maintain economic advantages. This quantitative study focused on the repurposing of used cooking oil for alternative energy sources at PT Megah Surya Pertiwi (PT MSP), the first downstream company of Harita Nickel that processes nickel saprolite ore to Ferronickel using the Rotary Kiln Electric Furnace (RKEF) technology in Obi Island, North Maluku, Indonesia. As the company’s employment grows, wastes, including used cooking oil (UCO), accumulate. The result showed that repurposing UCO in PT MSP has lowered the consumption of coal by 2,206 tons (7.53%), diverted 2,980 tCO2e indirect GHG emission (Scope 1), and saved the total cost up to IDR 1,408,231,332.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".