Coal Switching Evaluation on Coal-Fired Power Plants: Case Study in Indonesia
Bibliographic record
Abstract
Indonesia’s energy mix is still dominated by coal, reaching 67.21% in 2022. On the other hand, the coal crisis is a significant challenge to providing electricity needs. Hence, the coal specifications consumed are commonly lower than the coal-fired power plant’s design. In addition, efficiency is essential to reduce production costs, and coal switching is an alternative resolution to keep electricity production. This study compares two coal with a calorific value of 4,500 kcal/kg and 4,200 kcal/kg by considering the equivalent availability factor (EAF), equivalent forced outage rate (EFOR), production cost, and environmental aspect in six months period. The result shows EAF are 89.65% and 89.36%, EFOR 2.98% and 4.02%, cost savings on fuel components is 9.07-32.80 Rp/kWh, and emission concentration is below the standard. It was found that the coal switching program did not have a negative impact on operational, financial, and environmental aspects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".