Optimizing Operational Efficiency in SAG Grinding: Exploring Factors Affecting Power Usage and Tonnage

The video discusses the empirical modeling approach in SAG milling and the challenge of processing large amounts of information.

The presenter emphasizes the need to learn and understand how to program neural networks before relying on them for data processing.

The main perturbations in SAG milling are related to particle size and hardness, and operators have limited control over these factors.

🔑 The optimization of SAG milling involves factors such as airflow, rotation speed, and ore percentage.

⚙️ The main outcomes of optimizing SAG milling are the feed tonnage and mill power.

🔄 Understanding and controlling energy specific to the process is crucial for optimization.

💡 The speaker discusses the concept of specific energy and its dependence on factors such as feed size and hardness.

📊 The speaker proposes a correlation equation to adjust the specific energy based on factors like solid percentage and filling ratio.

🔬 The speaker emphasizes the importance of studying large databases to understand the factors that determine mill throughput.

🔑 The video discusses the optimization of SAG grinding operations, focusing on a second-order polynomial equation that accounts for deviations from the average.

💡 By using Excel, the best values for the coefficients of the equation can be determined, resulting in an adjusted specific energy model that closely matches the real energy.

⚙️ The video also introduces a torque model that depends on the angle of inclination of the center of gravity, allowing for the calculation of mill power.

👉 Increasing the percentage of solids in the grinding process leads to greater energy efficiency and increased tonnage.

📈 The specific energy decreases as the percentage of solids increases, indicating improved efficiency at higher densities.

💡 Simulations can be used to explore the effects of changing variables such as solid percentage, rotation speed, and filling level on grinding efficiency.

⚙️ The video discusses the impact of speed on efficiency and power in SAG milling, highlighting that increasing speed increases power but only slightly improves efficiency.

📊 The density of the load and the ratio of rock to balls also play a crucial role in optimizing SAG milling, with higher apparent density and more balls leading to increased power and capacity.

💡 The video concludes by emphasizing that while the discussed optimization methods apply specifically to SAG milling, other operations may have different responses, highlighting the importance of case-specific analysis.

🔑 Higher apparent densities result in lower specific energy consumption and increased processing capacity.

🧩 Operational optimization in SAG grinding mills involves finding the right density levels.

📊 Empirical correlations can be used to identify optimal conditions and set control reference points.