Integrated vertical mining and processing for Critical Mineral: A case study in Obi Island, North Maluku Province
Bibliographic record
Abstract
Abstract Harita Nickel Group in Obi Island, Indonesia pioneered the utilization of limonite nickel ore (Ni <1.5%) for electric car batteries in the country. Nickel mining in Obi Island transitioned from nickel mining in 2010 to integrated mining by establishing downstream processing of raw materials for stainless steel in 2016 and electric car battery in 2018. Pyrometallurgical and hydrometallurgical technologies are employed in factories within Harita Nickel’s mining concession area, notably PT Trimegah Bangun Persada Tbk (PTTBP). This vertically integrated mining and downstream processing in Obi Island, South Halmahera Regency, North Maluku Province, marks a significant advancement in Indonesia’s mining industry. The close proximity (<3km) of mining and factory locations ensures efficiency and low costs. In vertically integrated mining, companies seek to maximize the use of all materials involved in production processes, minimizing waste and optimizing resource utilization. This research focusing on the waste generated during nickel processing of RKEF can be repurposing into new products, reducing environmental impact and increasing sustainability. Nickel, a critical mineral, has garnered global attention, particularly since Indonesia emerged as the world’s largest nickel ore reserve. The extractive industry, employing pyrometallurgy technology, has rapidly produced stainless steel raw materials from nickel mining since 2016. Indonesia’s nickel ore, known as saprolite nickel with high nickel content (>1.7%), has been exploited since the 1970s, while the low-grade limonite nickel layer gained traction only around mid-2018. In the future, Harita Nickel project in western Obi Island will be developed as an Eco Industrial Park for a greater integration, highlighting the comprehensive benefits of vertically integrated mining and providing a more thorough and insightful evaluation of its potential.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".