Multi-objective decision-making for flexible design and planning of oxygen production facilities in an uncertain lunar environment

In-Situ Resource Utilization (ISRU), the act of utilizing local extraterrestrial resources, such as the lunar regolith, has been proposed and researched extensively to establish a self-sustained human presence in space. Oxygen production from lunar resources has gained significant attention, especially with growing interest in lunar exploration. Previous studies have recognized the uncertainties associated with the lunar environment and operations of ISRU plants, such as resource content, and their effects on ISRU plant performance. These studies, however, often overlook the fact that knowledge about uncertainty can be continuously updated through information-gathering. This paper analyses the design and planning of ISRU facilities as a multi-objective sequential decision-making problem under uncertainty. Inspired by the value of information analyses commonly used in the terrestrial resource extraction industry, this research employs multi-objective decision analysis to examine different lunar ISRU plant designs and deployment strategies, identifying potential risk management trade-offs in oxygen production. Monte Carlo simulations reveal the effects of various uncertain parameters on the performance of each ISRU plant, while Bayesian inference updates these uncertain parameters. A case study of the deployment of pilot and full-scale ISRU plants in the lunar southern polar region reveals trade-offs between design and planning decisions. Four different oxygen production architectures are considered in this case study: carbothermal reduction of dry regolith, water extraction from icy regolith, and hybrid of these technologies connecting them in parallel and series. The results suggest well-balanced performances of deploying a parallel or series hybrid pilot plant regarding all three metrics: oxygen yield, mass payback, and mass-energy efficiency. On the other hand, deploying water extraction architecture for both pilot and full-scale plants may work best for mass payback and mass-energy efficiency, while it also shows significantly smaller oxygen yield. The study also highlights the flexibility added by deploying a hybrid lunar ISRU pilot plant, which allows decision-makers to adjust their strategies as needed. • Lunar ISRU deployment is solved as a sequential decision problem. • Multi-objective decision tree analysis is used for its simplicity. • Bayesian inference models constant information updates. • Case study deploys lunar ISRU pilot and full-scale plants. • Deploying a hybrid pilot plant offers flexibility for full-scale planning.