Modeling And Optimization Of Gold Cyanide Leaching Process In Hydrometallurgy

With the steady progress of industrialization, high grade ore is gradually reducing. It is very important to use low-grade non-ferrous mineral resources effectively and economically for sustainable development strategy of our country. Hydrometallurgy technology is one of the two extractive metallurgy technologies. The remarkable advantage of hydrometallurgy is high comprehensive recovery rates of valuable metals in raw materials. Therefore hydrometallurgy is more suitable for recovering low-grade metal resources. Leaching process is one of the central unit operations in the hydrometallurgical processes. Although leaching technic in hydrometallurgy of our country meets advanced world standards, the control of leaching production process still remains off-line analysis, experience adjustments and manual control, which lead to low efficiency, high resources consumption and unstable product quality. So establishing accurate leaching process model to predict the leaching rate and to optimize the operating variables, has become an urgent matter.This thesis aims at hydrometallurgy leaching process of a gold refinery in Shandong province. Based on deeply analyzing the characteristics of leaching production process, we establish the mechanistic model for forecasting of leaching rate, Then discuss the amount of cyanide added optimal setting for the purpose of lowest cost. The main researches are summarized as follows:1. Through analyzing the basic principle of leaching process, the leaching of gold ore which takes cyanide as the leaching agent is studied. A mechanism model for leaching process is proposed by analyzing the relationship of material balance and energy balance. The validity and generalization of the model are proved through simulation experiment. The basis of hybrid model and process optimization is established by the mechanism model.2. According to the mechanism model, this thesis builds the hybrid model of leaching process by the hybrid modeling theory. It consists of two parts:the mechanism model and data-driven model. The mechanism model describes the basic characteristics of the leaching process. The data-driven model compensates the model error by neural network based on genetic algorithm. By simulation experiment, the efficiency of the hybrid modeling methods has been verified.3 According to the research of the leaching process, the thesis establishes the optimization model for operation variables of leaching process with the aim of minimum material loss. And then it uses the GA-PSO optimization method to optimize the process operating variables. The validity of the optimization method is proved through simulation experiment.4 Taking the whole process of hydrometallurgical refinery integrated control system project in Shandong province as background, a prediction and optimization operating platform software of leaching section has been designed and developed based on the above theory studies. Prediction for leaching rate of leaching process has been achieved. And can provide the optimal operating guide for this process.