The adaptive real-time optimization (RTO) of gold cyanidation leaching process in a hydrometallurgy plant was investigated. To solve plant-model mismatch

an adaptive real-time optimization strategy based on the modifier adaptation method was proposed

and the real plant data and gradient information were used to correct the original optimization problem iteratively to drive its solution to converge to the optimal set point for the plant. The simulation results showed that in the presence of moderate measurement noise and model uncertainty

the iterates based on the proposed adaptive strategy could converge to the optimal set point for the plant after several iterations and moreover the step of parameter estimation was not necessary

laying an important foundation for the successful implementation of the plant-wide optimization and control for hydrometallurgy process.