eCoreX – AI-methods to Link Mineralogy and Core Sawing to Comminution Efficiency

Purpose and goal

The goal is to explore a cost-effective method for predicting the mechanical properties of rock materials, such as strength and grindability, by analyzing energy consumption during core sawing combined with mineralogical data from XRF scanning. The project aims to establish a correlation between sawing energy, tensile strength, hardness, and mineralogy. Using AI models and DEM simulations, the method will be verified to enable more precise assessments of costs and feasibility in ore processing.

Expected effects and result

Expected outcomes include a method for predicting rock material strength and grindability through energy analysis during core sawing combined with XRF scanning and AI. This enables more cost-effective estimation of processing costs without the need for expensive physical tests. The method will be validated in a feasibility study, establishing the robustness of the correlation between sawing energy, mineralogy, and strength, aiming towards a large-scale technical implementation.

Planned approach and implementation

The project is divided into five work packages (WP). WP1 focus on project coordination, sample logistics, and reporting. WP2, led by Boliden, ensures sample collection and evaluates industrial relevance. WP3 includes experimental testing, such as sawing energy measurement and XRF analysis. WP4 focuses on data management and AI modeling, based on preliminary datasets and experimental results. WP5 uses the experiments for DEM calibration and simulation to evaluate crushing-grinding performance.