Data-based Compensation Method for Optimal Operation Setting of Gold Cyanide Leaching Process

reaction mechanism of gold cyanide leaching process is complex, and there are many factors affecting leaching process. These will lead to some errors between process model and actual process. Therefore, the operation setting point acquired through model-based optimization is not the actual optimal one. As a result, it is difficult for the process to operate under the minimum material consumption required by the hydrometallurgy process. Therefore, a data-based compensation method for optimal operation setting of gold cyanide leaching process is proposed. Firstly, the optimal operation setting point is obtained through model-based optimization. Then, near the setting point, using the idea of Just-In-Time Learning (JITL), the model is established to describe the relationship between setting point deviation and material consumption reduction. On the basis of this model, the deviation of the setting point from the actual optimal one can be obtained by optimization. Furthermore, through iterative compensation, the setting point gradually converges to the actual optimal one, and the material consumption is further reduced. Finally, the gold cyanide leaching process in a smelter is taken as study object, the results prove this method is effective.