Real time optimization based on a serial hybrid model for gold cyanidation leaching process

Abstract

To implement optimization and control on gold cyanidation leaching process (GCLP), it is an important prerequisite to establish an accurate process model. In this paper, a hybrid model in serial structure was proposed, where a first-principle model based on mass conservation equations was presented to describe the basic process behavior and its unknown kinetic reaction rates were predicted using BP ANN models without any structures considered. The proposed serial hybrid model had been applied to the prediction of gold recovery of the GCLP in a gold treatment plant. The results indicate that the proposed serial hybrid model has better prediction performance and generalization ability than the pure mechanistic model. To further reduce the effect of prediction error (plant-model mismatch) on real time optimization (RTO), modifier adaptation approach had been investigated and implemented to the GCLP. The result shows that when model mismatches with the actual plant or larger process disturbance occurs, significant reduction of production cost can be actualized iteratively by implementing the proposed adaptive RTO strategy.