What question did this study set out to answer?

To develop a framework that integrates cost considerations into AI models for mining operations.

January 14, 2026Minerals0 citationsOpen Access

Cost-Integrated AI Meta-Models for Mine-to-Mill Optimisation: Linking Fragmentation, Throughput, and Operating Costs Across the Value Chain

PNPouya NobaharAustralian Research Council CXChaoshui XuUniversity of Leeds PDPeter DowdAustralian Research Council

Key Points

To develop a framework that integrates cost considerations into AI models for mining operations.
Developed a cost-aware AI meta-modelling framework
Used over three million scenarios from the Integrated Extraction Simulator (IES)
Formulated stage-wise cost models for various processes
Achieved predictive accuracies above 90% with Random Forest models
Finer fragmentation lowers total comminution costs despite increased explosive costs
SAG mill load and speed display U-shaped cost relationships
Transformed traditional optimisation into a financially informed decision-support system

Abstract

This study presents an integrated, cost-aware artificial intelligence (AI) meta-modelling framework for mine-to-mill optimisation that couples high-fidelity simulation with data-driven predictive modelling. Using over three million scenarios generated in the Integrated Extraction Simulator (IES), the framework quantifies how upstream design parameters such as burden, spacing, hole diameter, and explosive density propagate through screening, crushing, stockpiling, and grinding to affect downstream costs and throughput. Random Forest-based meta-models achieved predictive accuracies above 90%, enabling the rapid evaluation of technical and financial trade-offs across the mining value chain. Stage-wise cost models were formulated for drilling, blasting, comminution, and material handling to link process variables to costs per tonne. The results reveal clear non-linear cost responses: finer fragmentation reduces the total comminution cost despite higher explosive expenditure, while SAG mill load and speed exhibit U-shaped cost relationships with distinct optimal operating windows. By combining physics-based simulations, machine learning, and cost integration, the framework transforms traditional stage-wise optimisation into a holistic, financially informed decision-support system. The proposed methodology supports real-time, AI-enabled digital twins capable of adaptive mine-to-mill optimisation, paving the way for more efficient and sustainable resource extraction.

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