Introduction
Ore blending is the process of mixing different types of ores to achieve a desired grade and quality for processing ((1) ORE BLENDING | LinkedIn, n.d.). In mining operations, it is common to have different types of ore with different grades and characteristics. Blending multiple ore types significantly impacts crushing performance by altering energy consumption, particle size distribution, and equipment wear, while also introducing complexities related to material interactions. In this article, we are going to highlights the various impact or blending multiple ore types on crushing performance.
Energy consumption
When blending ores of varying hardness, the energy required for crushing tends to align with the hardest component. For example, Mertainen ore (a harder ore) was found to disproportionately increase energy use in primary rod milling compared to softer ores like Kirunavaara and Leveäniemi (Yan & Eaton, 1994). Blending can lead to non-linear energy utilization due to interactions between ore types. Harder ores may shield softer ones from breakage, forcing prolonged grinding to achieve target particle sizes ((1) The Strategic Role of Blending in the Mining Industry: Comprehensive Management Strategies and Applications | LinkedIn, n.d.).
Particle size distribution
Wider size variability: Mixing ores with contrasting hardness (e.g., hard Mertainen and soft Leveäniemi) often results in uneven breakage. Softer ores may become over-crushed (“slimes”), while harder ores resist fragmentation, leading to a broader particle size distribution (Mkurazhizha, 2018). Achieving a uniform target grind (e.g., P80) becomes harder, as softer components may require excessive grinding to accommodate harder ones, wasting energy and reducing efficiency ((1) The Strategic Role of Blending in the Mining Industry: Comprehensive Management Strategies and Applications | LinkedIn, n.d.).
Feed consistency and process stability
Effective blending stabilizes feed chemistry and physical properties, enabling consistent crusher operation and minimizing unplanned adjustments. For example, blending copper ores with differing silica/iron content can create a uniform feed that meets plant specifications. Advanced technologies, such as real-time sensors and AI-driven scheduling, optimize blend ratios to maintain feed quality and crushing efficiency (Feng et al., 2023).
Conclusion
Ore blending introduces both opportunities (e.g., resource optimization, consistent feed) and challenges (e.g., energy spikes, equipment wear). Success hinges on rigorous geometallurgical analysis and dynamic blending strategies to align crushing performance with operational goals.
Reference
(1) ORE BLENDING | LinkedIn. (n.d.). Retrieved April 24, 2025, from https://www.linkedin.com/pulse/ore-blending-bishnu-mishra/
(1) The Strategic Role of Blending in the Mining Industry: Comprehensive Management Strategies and Applications | LinkedIn. (n.d.). Retrieved April 24, 2025, from https://www.linkedin.com/pulse/strategic-role-blending-mining-industry-comprehensive-jivtode-al0wc/
Feng, Z., Liu, G., Wang, L., Gu, Q., & Chen, L. (2023). Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning. Sustainability, 15(6), Article 6. https://doi.org/10.3390/su15065279
Mkurazhizha, H. (2018). The effects of ore blending on comminution behaviour and product quality in a grinding circuit- Svappavaara (LKAB) Case Study. https://www.semanticscholar.org/paper/The-effects-of-ore-blending-on-comminution-and-in-a-Mkurazhizha/b95fc0c5d4ab6424272c8f28076913133f7c7bf9
Yan, D., & Eaton, R. (1994). Breakage properties of ore blends. Minerals Engineering, 7(2), 185–199. https://doi.org/10.1016/0892-6875(94)90063-9
