Key Takeaways Sensor-based ore sorting is a mature, high-adoption category — TOMRA and Steinert together hold over 40% of the market. The “AI” is in the real-time classification logic that decides, particle-by-particle, whether a rock is ore or waste based on sensor signal — this has …
Key Takeaways The competitive frontier in ore sorting isn’t a single sensor — it’s sensor fusion: combining XRT, optical/machine vision, laser, and induction/NIR signals on one platform. Steinert’s KSS is explicitly built as a combined sensor system, letting you pair XRT, XRF, or …
Key Takeaways Environmental and social permitting is one of the slowest, most document-heavy parts of standing up a copper project — and it’s only now starting to see AI tooling. The clearest fit is AI-assisted ESG/compliance reporting platforms (e.g. Ecodrisil ESG Xpress), which automate data …
Key Takeaways Petrographic thin-section analysis (identifying minerals and textures under a microscope) is still overwhelmingly analyst-driven — there’s no single dominant commercial AI product yet. Published CNN research is real and promising: concatenated convolutional neural network …
Key Takeaways BWI (Bond Work Index), DWI (Drop Weight Index), and the Abrasion Index all still start from physical rock-breakage tests, run through JKTech’s JK Drop Weight Test / JKSimMet — the industry-standard equipment and simulation software. 2025 …
Key Takeaways RQD, fracture frequency, point-load strength, clay content, comminution hardness (BWI/DWI), and metal recovery (Cu, Mo, Au, Ag) can all be estimated with machine learning instead of relying purely on sparse …
Key Takeaways Both calculated mineralogical models and geometallurgical (geomet) models are, in practice, regression/classification problems trained on automated-mineralogy data — not distinct disciplines requiring separate tooling. QEMSCAN, MLA, and TIMA …
Key Takeaways Uniaxial Compressive Strength (UCS) is the standard rock-strength measurement, but it’s a destructive, slow, sample-hungry lab test. 2025-2026 peer-reviewed studies (Nature Scientific Reports, Acta Geophysica) show ML models — SVM, Gaussian …
Key Takeaways A proxy model is a classic supervised-regression problem: predict an expensive/slow-to-measure variable from cheap/fast-to-measure ones. Cancha, built specifically for geometallurgy, and general-purpose Python (scikit-learn, XGBoost) are the two realistic paths — commercial turnkey vs. …
Key Takeaways QA/QC monitoring — tracking lab standards, blanks, and duplicates for drift or contamination — is a natural home for statistical anomaly detection. ALS QCPro and ioGAS’s QAQC modules are the standard tools, both now adding statistical/ML …