2022/12/07 by Yassine Yaakoubi, Yaakoubi, Yassine, Hager Radi +3 · 1 citation
Engineering · #Artificial Intelligence (cs.AI) #Extraction and Separation Processes #FOS: Computer and information sciences #Machine Learning (cs.LG) #Metal Extraction and Bioleaching #Mining Techniques and Economics
paper · pdf · doi:10.48550/arxiv.2212.03865
openalex publication_date 2022/12/07 · openalex created_date 2022/12/22 · openalex updated_date 2026/07/28
Valuing mineral assets is a challenging task that is highly dependent on the supply (geological) uncertainty surrounding resources and reserves, and the uncertainty of demand (commodity prices). In this work, a graph-based reasoning, modeling and solution approach is proposed to jointly address mineral asset valuation and mine plan scheduling and optimization under supply and demand uncertainty in the "mining complex" framework. Three graph-based solutions are proposed: (i) a neural branching policy that learns a block-sampling ore body representation, (ii) a guiding policy that learns to explore a heuristic selection tree, (iii) a hyper-heuristic that manages the value/supply chain optimization and dynamics modeled as a graph structure. Results on two large-scale industrial mining complexes show a reduction of up to three orders of magnitude in primal suboptimality, execution time, and number of iterations, and an increase of up to 40% in the mineral asset value.