Self-optimizing control of secondary grinding – coping without particle size monitoring

In secondary grinding circuits, the product particle size is a key variable influencing downstream performance. However, online particle size analyzers are often too costly or impractical to implement, limiting the ability to reach and maintain production objectives. This paper investigates self-optimizing control (SOC) of the product particle size using readily available instrumentation. The method identifies linear combinations of process variables that remain close to their target values despite disturbances, using a null space approach applied to steady-state data extracted from a dynamic model of the grinding circuit. Simulation results show that SOC can significantly reduce product size fluctuations caused by ore hardness variations, ore feed rate variations, and ore particle size variations. Compared to the baseline strategy, which leads to deviations of up to 9.2% relative to the nominal product size, the best SOC configuration limits fluctuations to less than 2%, using simple control loops.