Blending Characterization for Effective Management in Mining Operations

Abstract

Ore blending plays a critical role in ensuring feed consistency and optimizing downstream processes in the mining industry. Despite its importance, effective blending remains challenging due to ore variability and operational constraints. This review focuses exclusively on modern, data-driven blending methodologies, with particular emphasis on the application of data science and machine learning (ML) in predicting key process variables and supporting real-time decision-making. It discusses core challenges such as data quality, feature engineering, and model generalization, alongside enabling technologies including sensor integration, automation platforms, and real-time data acquisition systems. By consolidating the recent literature and highlighting emerging trends, this work outlines future directions for advancing intelligent blending systems and underscores the importance of standardized, high-quality data in the development of robust digital solutions for mineral processing.