Agent-Based Modeling for By-Product Metal Supply—A Case Study on Indium

Abstract

With rapid development and deployment of clean energy technology, demand for certain minor metals has increased significantly. However, many such metals are by-products of various host metals and are economically infeasible to extract independently. Meanwhile, by-product metals present in the mined ores may not be extracted even if they are sent to smelters along with host metal concentrates if it is not economically favorable for the producers. This dependency poses potential supply risks to by-product metals. Indium is a typical by-product metal, mainly from zinc mining and refining, and is important for flat panel displays, high efficiency lighting, and emerging thin-film solar panel production. Current indium supply–demand forecast models tend to overlook the volatile and competitive nature of minor metal market and are mostly based on top-down approaches. Therefore, a bottom-up agent-based model can shed new light on the market dynamics and possible outcome of future indium supply–demand relationship. A multi-layered model would also be helpful for identifying possible bottlenecks of indium supply and finding solutions. This work takes indium as an example of minor metal market and sets up an agent-based model to predict future market situation and supply–demand balance. The market is modeled as a Cournot competition oligopolistic market by refineries with capacity restriction based on host metal production. The model maintains active Nash equilibrium each year to simulate competitions between suppliers. The model is validated and verified by historical data and sensitivity analysis. Several scenarios are also explored to illustrate possible uncertainties of the market.

Quantifying the drivers of long‐term prices in materials supply chains

“…Given we have already argued for the importance of price feedbacks, we will focus on approaches that incorporate market clearing of both supply and demand (for common supply and demand modeling approaches, see the review by Watari et al., 2021). Broadly speaking, metals markets are studied using (i) econometric models (Fisher et al., 1972; Fu et al., 2017; Watkins & McAleer, 2004), (ii) agent‐based models (Bollinger et al., 2012; Cao et al., 2021; Riddle et al., 2015), and (iii) system dynamics models (Elshkaki, 2013; Sprecher et al., 2015a; Sverdrup et al., 2017) or a combination of the three (Ryter et al., 2022). Econometric models often assume a partial equilibrium between demand and supply which are both estimated via statistical relationships with variables such as economic growth and population (Zink et al., 2016, 2018).…”

Solution Processing Cu3BiS3 Absorber Layers with a Thiol–Amine Solvent Mixture

“…DFT also suggests that the valence band maximum (VBM) consists mostly of Cu d and S p states with little impact from Bi s states, while the conduction band minimum (CBM) consists of mostly Bi p and S p antibonding states . Wittichenite contains bismuth, which is a green heavy element due to its low toxicity/environmental impact and relative Earth abundance, where Bi reserves total 370 000 tons whereas In reserves total 15 000 tons (>300 000 tons when considering Zn codeposits or lower quality ores that are more difficult and expensive to extract). − …”