Data analytics and machine learning for digital transformation in mineral processing: A conceptual framework for copper-molybdenum concentration
The mineral processing industry faces mounting pressure to improve operational efficiency, reduce energy consumption, and enhance equipment reliability through digital transformation. This study presents a conceptual framework for data analytics and machine learning implementation across three operational domains of a hypothetical copper-molybdenum concentrator: grinding optimization, flotation performance prediction, and predictive maintenance. All performance figures represent projected outcomes grounded in published benchmarks rather than measured results from an operating plant. Four algorithms were developed and evaluated, each matched to the characteristics of its process domain. A Random Forest model was developed for P80 particle size prediction, achieving R² = 0.92 on the validation dataset, with projected reductions in specific energy consumption of 9.5%. An Artificial Neural Network was implemented for copper recovery soft sensing, achieving 94% prediction accuracy within ±2% of assay results, with projected reductions in recovery excursion duration through earlier intervention. A Convolutional Neural Network based on MobileNetV2 transfer learning achieved 91.3% accuracy in froth image classification across four operational states. A Long Short-Term Memory network for SAG mill bearing health monitoring achieved 87% probability of detection for degradation events 24-48 hours in advance, with projected reductions in unplanned downtime of ~40% and emergency maintenance events of ~70% relative to reactive baselines in the literature. Cross-domain synergies between the integrated models are expected to generate additional value beyond individual contributions. This paper discusses key implementation challenges, including data quality, model retraining, operator acceptance, and edge computing. The projected improvements provide a literature-grounded roadmap for digital transformation in mineral processing.