Peluang dan Tantangan Proses HPAL/PAL (High Pressure Acid Leaching) untuk Mengolah Bijih Nikel Laterit Kadar Rendah Sangaji Halmahera
Bibliographic record
Abstract
Indonesia berlimpah dengan SDA (sumber daya alam) bijih nikel oksida yang lazim disebut laterit. Laterit tersebut berada di Kawasan Timur Indonesia terutama di Sulawesi Tenggara, pulau Halmahera Maluku Utara, dan pulau Gag Papua. Laterit kadar tinggi jenis saprolit dengan kandungan Ni ≥ 1,8 % sudah diolah di Sulawesi Tenggara dengan jalur proses pirometalurgi. Proses pirometalurgi digunakan untuk memproduksi FeNi (ferronikel) seperti yang dilakukan oleh BUMN PT Aneka Tambang (AnTam) di Pomalaa. Atau untuk memproduksi Ni matte seperti yang dilakukan oleh PT Vale Indonesia (sebelumnya PT INCO Canada sampai 2011) di Sorowako. Sedangkan laterit kadar rendah yang terdiri dari limonit dan saprolit dengan kandungan Ni < 1,8 %, belum diolah di tanah air. Untuk mengolah laterit kadar rendah digunakan jalur proses hidrometalurgi, yaitu proses Caron (ammonia leaching) untuk memproduksi NiO dari serpentin dan proses HPAL/PAL (high pressure acid leaching) untuk memproduksi NiS dari limonit. Sehubungan dengan UU Minerba No 4 tahun 2009 yang melarang ekspor bahan baku mineral dan diwajibkan untuk mengolah bahan baku di dalam negeri. Dalam tulisan ini akan dikaji bagimana prospeknya apabila proses HPAL/PAL akan digunakan untuk mengolah laterit kadar rendah di Indonesia, khususnya untuk limonit dari Sangaji blok C Halmahera. Prosiding SMM 2012. Hal. 87-96
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".