Challenges and Future in Ni Laterite Ore Enrichment: A Critical Review

ABSTRACT

Nickel is a strategic element whose production and consumption in over a decade has increased to over 39% and 71%, respectively. Generally, due to their complex mineralogy, laterites, the current attractive source of Ni, are subjected to hydrometallurgy and pyrometallurgy techniques without enrichment in order to extract Ni. Therefore, numerous investigations in vogue on laterite enrichment performed spanning from 1964 to date were reviewed in this article, and the future directions were proposed through critical synthesis. The possibilities of obtaining both higher recoveries and increase in grade for Ni were noticed using selective comminution-classification method followed by flocculation-flotation strategy. Hence, in order to improve on the reported Ni yields from selective comminution-classification process, one of the ways could be to subject the ore to a multi-stage selective comminution-classification method followed by subjecting the obtained concentrate to flocculation-flotation technique. Therefore, the findings of this investigation would aid in upscaling the production to meet the demand.

HIGHLIGHTS

• Properties of goethite and serpentine were explored.

• Physical and flotation methods for laterite enrichment were reviewed.

• Future directions for laterite beneficiation were proposed.

KEYWORDS:

• Lateritic nickel
• beneficiation strategy
• flocculation-flotation
• mineralogy
• mineral processing

Disclosure statement

We do not have conflicts of interest to declare.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/08827508.2023.2214668.

Additional information

Funding

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea [No. 20227A10100010], the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)) and the research fund of Hanyang University [HY- 202200000002860].