Abstract
This paper presents a novel approach to enhancing energy efficiency in grinding circuits through an advanced process control (APC) strategy that focuses on reducing specific energy consumption while maximizing throughput. As energy costs and environmental concerns continue to grow, lowering energy usage is critical for sustainable and cost-effective mining operations. Our solution integrates model predictive control (MPC) with optimization and supervisory layers to optimize key variables in semi-autogenous grinding (SAG) mills, such as fill volume and energy consumption, while maintaining operational stability and safety. Implemented at a copper concentrator in Arizona, USA, this APC approach enabled a 7.3% decrease in specific energy consumption, thus significantly lowering operational costs and minimizing the facility's carbon footprint. By optimizing energy usage in real-time and maximizing throughput beyond operator’s capability, the APC system establishes a benchmark in sustainable grinding operations, supporting profitability while contributing to environmental sustainability.