Analisis Dampak Lingkungan Pengolahan Limbah Fly Ash dan Bottom Ash dengan Metode Siklus Daur Hidup (Life Cycle Assessment/LCA) di Industri Pembangkit Listrik Tenaga Uap
Bibliographic record
Abstract
PLTU Teluk Balikpapan dengan kapasitas mencapai 2 x 110 MW menghasilkan limbah fly ash dan bottom ash. Limbah tersebut diolah dengan cara ditimbun pada lahan terbuka. Penimbunan tersebut kurang efektif karena membutuhkan banyak lahan untuk menampung limbah yang dihasilkan. Maka diusulkan tiga skenario pengolahan limbah fly ash dan bottom ash yaitu skenario 1 penimbunan di landfill, skenario 2 pemanfaatan menjadi paving block dan skenario 3 pemanfaatan menjadi kompos. Penelitian ini bertujuan untuk menganalisis dampak lingkungan dari pengolahan limbah fly ash dan bottom ash eksisting dan untuk mengetahui skenario terbaik pengolahan limbah fly ash dan bottom ash dengan metode Life Cycle Assessment (LCA). Tahapan LCA mengacu pada ISO 14040 tahun 2006 yang terdiri dari tujuan dan ruang lingkup, analisis inventori, analisis dampak, dan interpretasi. Hasil analisis kontribusi dampak terhadap lingkungan dengan skenario 1 diperoleh tiga dampak dengan nilai tertinggi yakni natural land transformation dengan nilai 15,8 lalu climate change dengan nilai 9,5 dan particulate matter formation dengan nilai 6,8. Selanjutnya, hasil perhitungan menunjukan bahwa skenario terbaik pengolahan limbah fly ash dan bottom ash adalah skenario 3 yaitu pengolahan menjadi kompos.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".