Perspectives on Cobalt Supply through 2030 in the Face of Changing Demand.

Lithium-ion battery demand, particularly for electric vehicles, is projected to increase by over 300% throughout the next decade. With these expected increases in demand, cobalt (Co)-dependent technologies face the risk of significant impact from supply concentration and mining limitations in the short term. Increased extraction and secondary recovery form the basis of modeling scenarios that examine implications on Co supply to 2030. Demand for Co is estimated to range from 235 to 430 ktonnes in 2030. This upper bound on Co demand in 2030 corresponds to 280% of world refinery capacity in 2016. Supply from scheduled and unscheduled production as well as secondary production is estimated to range from 320 to 460 ktonnes. Our analysis suggests the following: (1) Co price will remain relatively stable in the short term, given that this range suggests even a supply surplus, (2) future Co supply will become more diversified geographically and mined more as a byproduct of nickel (Ni) over this period, and (3) for this demand to be met, attention should be paid to sustained investments in refined supply of Co and secondary recovery.

Increasing secondary and renewable material use: a chance constrained modeling approach to manage feedstock quality variation.

The increased use of secondary (i.e., recycled) and renewable resources will likely be key toward achieving sustainable materials use. Unfortunately, these strategies share a common barrier to economical implementation - increased quality variation compared to their primary and synthetic counterparts. Current deterministic process-planning models overestimate the economic impact of this increased variation. This paper shows that for a range of industries from biomaterials to inorganics, managing variation through a chance-constrained (CC) model enables increased use of such variable raw materials, or heterogeneous feedstocks (hF), over conventional, deterministic models. An abstract, analytical model and a quantitative model applied to an industrial case of aluminum recycling were used to explore the limits and benefits of the CC formulation. The results indicate that the CC solution can reduce cost and increase potential hF use across a broad range of production conditions through raw materials diversification. These benefits increase where the hFs exhibit mean quality performance close to that of the more homogeneous feedstocks (often the primary and synthetic materials) or have large quality variability. In terms of operational context, the relative performance grows as intolerance for batch error increases and as the opportunity to diversify the raw material portfolio increases.