Optimizing HY Wet Ball Mill Efficiency Using Response Surface Methodology

Abstract This study uses response surface methodology (RSM) with a central composite design to optimize the HY wet ball mill work index. Key operational parameters—ore quantity, water addition, and media proportion—were systematically investigated. Variance analysis identified these factors as statistically significant ( p < 0.0001), with ore quantity exhibiting the strongest influence ( F value = 82.67). Optimal conditions (water addition: 1083.880 mL, media filling rate: 40.362%, ore quantity: 1827.130 g) achieved a minimum work index of 10.195 kW h t −1 . Validation tests confirmed the model’s accuracy, yielding an average work index of 10.168 kW h t −1 (deviation: 0.027 kW h t −1 ). Compared to single-factor tests, RSM optimization reduced energy consumption by 3.76% (0.3975 kW h t −1 ), translating to annual savings of $214,000 for a mid-sized processing plant. This demonstrates RSM’s efficacy in bridging the gap between laboratory-scale experiments and industrial grinding optimization.