ANALISIS PENGARUH PENGGUNAAN BAHAN BAKAR GAS DAN HSD (HIGH SPEED DIESEL) TERHADAP KINERJA DAN PRODUKSI GAS BUANG PEMBANGKIT PADA VARIASI BEBAN PLTGU X
Bibliographic record
Abstract
PLTGU merupakan penggabungan antara Pembangkit Listrik Tenaga Gas (PLTG) dan Pembangkit Listrik Tenaga Uap (PLTU). Sama halnya dengan PLTU, bahan bakar PLTGU bisa berwujud cair,seperti High Speed Diesel (HSD) maupun berupa gas yaitu Compress Natural Gas (CNG). Tujuan penelitian ini yaitu untuk menghitung dan mengetahui pengaruh penggunaan BBG dan HSD terhadap kinerja sistem PLTGU, serta produksi gas buang pada turbin gas. Metode yang digunakan yaitu studi literatur mengenai dasar teori, melakukan observasi ke ruang kontrol dengan mempelajari log sheet. Data juga diperoleh dari pencatatan otomatis oleh komputer ACS Historical Data Management System yang digunakan untuk menyimpan data operasi pembangkit. Hasil yang diperoleh yaitu perbandingan antara BBG dan HSD dengan beban operasi PLTGU yang sama menunjukkan bahwa efisiensi sistem PLTGU tertinggi yang didapat yaitu senilai 48,987 % pada beban operasi 228 MW menggunakan HSD, dan nilai terendahnya yaitu sebesar 36,136 % pada beban operasi 211 MW, serta perbandingan antara BBG dan HSD dengan beban operasi GTG yang sama menunjukkan bahwa produksi gas buang tertinggi yang diperoleh senilai 537,643 kW pada beban operasi GTG 75 MW dengan BBG, dan nilai terendahnya yaitu sebesar 516,113 kW pada beban operasi GTG sebesar 70 MW dengan HSD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".