High pressure acid leaching: a newly introduced technology in Indonesia
Bibliographic record
Abstract
Abstract PT Halmahera Persada Lygend (PTHPL) plans to construct new technology for nickel laterite ore processing in Obi Island, North Maluku Province. The facility is expected to produce 247,000 tonnes of NiSO4.7H2O and 32,000 tonnes of CoSO4.7H2O to be sold for the raw material of electric vehicle battery. Indonesia is one of world largest nickel laterite resources and currently only nickel ore saprolite has been exploited while nickel limonite is abandoned as waste due to the lack of technology. In the last decade, nickel smelter is booming in Indonesia to process nickel saprolite to become Ferro Nickel, Nickel Pig Iron, Nickel Matte. This study is aimed to assess the potential implementation of High-Pressure Acid Leaching (HPAL). HPAL is a proven technology but not utilized in Indonesia due to the high investment, and it requires a large media for waste disposal. Nickel limonite ore with grade 1.1 – 1.4 % can be processed using HPAL technology to produce more than 37% nickel and another beneficial product, which is cobalt. Nickel world demand shifts to support electric vehicle battery even though stainless steel demand is still high. The Government of Indonesia is currently starting to support the electric vehicle market program. Therefore HPAL project in Indonesia is a high opportunity and a good investment for investors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".