Abstract

The gold cyanidation leaching process (GCLP) has been prevalent in the hydrometallurgical industry. To better control and optimize the plant operation, mechanistic models have been established which capture the physical behaviors of the GCLP. However, due to various disturbances and uncertainties, an optimized operation based on the nominal model is practically suboptimal. To perform real-time optimization (RTO) of the GCLP, this paper proposes a two-layer control architecture integrating the self-optimizing control (SOC) and modifier adaptation (MA), both of which are useful RTO approaches with distinct advantages. In the lower layer of the proposed control system, the SOC is implemented with measurement combinations as the controlled variables that are tracked at optimally insensitive setpoints to account for parametric disturbances. In the upper layer, the setpoints of self-optimizing controlled variables are further optimized in a tailored MA framework, such that the structural plant–model mismatch is...

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