Efficiency of Industrial-Scale Biogas Application from Palm Oil Mill Effluent (POME) as a Renewable Energy Source: A Case Study at PT AMP
Bibliographic record
Abstract
The palm oil industry is a large contributor to greenhouse gases in Indonesia. The application of biogas from the palm oil mill effluent (POME) treatment plant has become one of the solutions so it has been implemented at PT Agro Masang Perkasa (AMP). The catch of methane gasses is carried out by covering the waste pond with High-density Polyethylene (HDPE) material to create anaerobic conditions in the waste pool. The research results show that the methane gas obtained has been utilized as a renewable energy source such as biogas in factories with a total gas flow supplied to the engine of 135957, 121655, and 133736 Nm 3 respectively, and an average power produced of 159.530, 153.168, and 160.161 MWh per month during observations in January, February, and March 2022. Although not all of the captured biogas is used for electrical energy, the benefits of implementing this technology mean that PT AMP has an average electricity efficiency in January, February, and March 2022 of 15.20 %, 22.49%, and 20.96%. Based on calculations, it is found that the use of methane capture or biogas technology at PT AMP can provide cost efficiency in a year of IDR. 5.21 billion and can return the installation capital within 3.5 years.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".