Abstract This study presents a preliminary techno‑economic assessment of two representative Australian heavy mineral sand (HMS) operations: a wet and a dry mining operation, each connected with a mineral separation plant (MSP). Class‑4 capital expenditure is estimated at A380 million for the wet route (mining capacity of 2, 000–4, 000 t h⁻¹) and A332 million for the dry route (mining capacity of 350–650 t h⁻¹), with both estimates inclusive of the MSP. The wet‑concentration plant and financing/owner’s costs dominate capital cost (CAPEX), followed by mining units. Annual estimated operating expenditure is A144 million for wet mining and A122 million for dry mining, driven mainly by labour, energy, and cost of capital. Both routes produce zircon‑rich, ilmenite‑rich, and leucoxene‑rich concentrates. Treating ilmenite and leucoxene as by‑products, the production cost of zircon‑rich concentrate is A987 t⁻¹ for the wet route and A834 t⁻¹ for the dry route. Discounted Cash Flow (DCF) modelling over a 20‑year life (10% discount rate, 30% company tax) yields a Net Present Value (NPV) of A296 million, Internal Rate of Return (IRR) of 23%, and eight‑year Payback Period (PBP) for the wet scenario, versus A424 million, 32%, and five years for the dry scenario. Sensitivity analysis identifies total CAPEX and zircon‑concentrate price as the strongest input items. Under the baseline TEA scenarios, operations are powered by a combination of grid electricity and diesel. Replacing grid power with on‑site renewables can significantly reduce annual emissions, about 10–46% in the electricity‑intensive wet mining and 6–31% in the dry mining, while the former favours a modest wind‑plus‑grid mix and the latter can accommodate a larger wind‑plus‑battery system. Although preliminary and subject to further data refinement, the results indicate that Australia’s HMS projects remain economically attractive and can align with 2050 net‑zero targets if future designs integrate cost control with targeted cost-effective renewable energy sources depending on the locations. Such evaluation requires project specific data inputs and customised studies.
Laghaei et al. (2026) studied this question.