Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine (PSO-SVM) algorithm

X-ray fluorescence (XRF) sensor-based ore sorting enables efficient beneficiation of heterogeneous ores, while intraparticle heterogeneity can cause significant grade detection errors, leading to misclassifications and hindering widespread technology adoption. Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals. Previous studies mainly used linear regression (LR) algorithms including simple linear regression (SLR), multivariable linear regression (MLR), and multivariable linear regression with interaction (MLRI) but often fell short attaining satisfactory results. This study employed the particle swarm optimization support vector machine (PSO-SVM) algorithm for sorting porphyritic copper ore pebble. Lab-scale results showed PSO-SVM outperformed LR and raw data (RD) models and the significant interaction effects among input features was observed. Despite poor input data quality, PSO-SVM demonstrated exceptional capabilities. Lab-scale sorting achieved 93.0% accuracy, 0.24% grade increase, 84.94% recovery rate, 57.02% discard rate, and a remarkable 39.62 ¥/t net smelter return (NSR) increase compared to no sorting. These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality (T=10, T is XRF testing times). The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated. Input element selection and mineral association analysis elucidate element importance and influence mechanisms.